Hasil untuk "Ocean engineering"

Jeong-Hwan Kim, Daehyun Kang, Young-Min Yang et al.

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)-based ocean-atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model using a U-shaped visual attention adversarial network architecture. KIST-Ocean integrates partial convolution, adversarial training, and transfer learning to address coastal complexity and predictive distribution drift in auto-regressive models. Comprehensive evaluations confirmed the model's robust ocean predictive skill and efficiency. Moreover, it accurately captures realistic ocean response, such as Kelvin and Rossby wave propagation in the tropical Pacific, and vertical motions induced by cyclonic and anticyclonic wind stress, demonstrating its ability to represent key ocean-atmosphere coupling mechanisms underlying climate phenomena, including the El Nino-Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models and their extending DL-based approaches to broader Earth system modeling, offering potential for enhancing climate prediction capabilities.

Yuqing Liu, Zhubin Zheng, Jianzhong Li et al.

Overexploitation of rare Earth mining areas in southern Jiangxi Province has caused severe vegetation degradation. However, the impact of vegetation restoration on ecosystem services (ESs) and their interactions in rare Earth mining areas remains underexplored. This study uses vegetation coverage (FVC) as an indicator to assess vegetation changes in rare Earth mining areas from 1986 to 2020. The integrated assessment of ESs and tradeoffs (InVEST) and the Carnegie-arms-stanford method model were applied to assess soil conservation, carbon storage, water retention, and purification services in the study area from 1990 to 2020, while analyzing the spatiotemporal evolution of ESs. Finally, the eXtreme Gradient Boosting model was used to construct the regional total ecosystem services (RTES) index, analyzing the threshold effect between ESs and FVC. The results reveal that: 1) From 1986 to 2020, vegetation coverage in rare Earth mining areas exhibited a fluctuating upward trend, with significant increases occurring in 40.14% of the study area; 2) ESs declined significantly overall; 3) Increased vegetation coverage improved the regional ecological environment to some extent, though this improvement was constrained by a threshold effect. To optimize RTES, vegetation coverage in the Gannan rare Earth mining areas should range between 0.6 and 0.7. This study offers a theoretical foundation for large-scale ecological management and moderate restoration of rare Earth mining areas, supporting regional sustainable development. It also underscores the need for the public and managers to recognize the impact of vegetation restoration on ecosystem functions in rare Earth mining areas.

Changsheng OuYang, Hui Li

As is known, the accurate extraction of buildings from high-resolution remote sensing images has become a pivotal objective. In some complex scenes (e.g., there will be objects with a similar spectral texture to buildings in the image, trees and shadows will obscure the buildings, etc.), the existing models cannot accurately recognize the buildings. To address this series of challenges, we propose a new method, BuildNext-Net, which consists of TransNext-EMAM blocks, upsampling convolution modules, context feature enhancement blocks (CFEBs), and multiscale depthwise convolution blocks (MSDWCBs). The encoder consisting of TransNext-EMAM blocks is used for feature extraction and outputs the generated feature maps of each layer to CFEB through skip connections. In the feature reconstruction stage, CFEB can receive the jump-connected feature maps and the feature maps obtained from upsampling, which improves the network’s capacity to comprehend and localize the target objects and image details. MSDWCB can further enhance the multiscale feature extraction capability to achieve the effect of suppressing irrelevant regions to capture multiscale salient features. It effectively solves the challenge of combining local and global information in complex scenes. It also enhances the robustness of the network in extracting buildings in complex scenes. Our method has been extensively experimented on the WHU building dataset, the Massachusetts building dataset, and the Inria building dataset. The intersection over union metrics on these three datasets are 91.21%, 76.12%, and 81.42, improving 1.06%, 1.55%, and 2.60%, respectively, compared with other state-of-the-art methods.

Zhenyu Liang, Senliang Bao, Weimin Zhang et al.

Satellite sea surface salinity (SSS) observations play a critical role in the study of ocean circulation and climate regulation. However, mesoscale salinity dynamics (e.g., eddies, fronts) remain poorly resolved by current salinity satellites, such as soil moisture and ocean salinity (SMOS), due to their low effective resolution (>100 km). To address this, we proposed the SMOS SSS super-resolution reconstruction (S5R2) network. This deep learning framework achieved super-resolution (SR) reconstruction of the SMOS L3 SSS product from 1/4° to 1/12° by fusing multivariate satellite observations. Our approach integrated a land filtering mechanism into a hybrid transformer-CNN architecture, enhancing both global and local attention to ocean dynamics while suppressing interference from land-based information. Meanwhile, we improved the search efficiency of the optimal subset of input variables by guiding the search direction and step size using prior knowledge. The results demonstrated that S5R2 outperformed existing L3 and L4 satellite SSS products and mainstream SR algorithms. Compared to the input SMOS L3 SSS product, S5R2 achieved a 20% and 60% reduction in root mean square error in the Kuroshio Extension and Gulf Stream regions, respectively. In addition, it improved the effective resolution from 100 km to 20–30 km, enabling the dynamic tracking of mesoscale eddies. This advance provides a near-real-time solution for monitoring fine-scale ocean salinity processes, with practical implications for ocean dynamics research and the operational application of salinity satellite products.

Nan Yang, Chong Wang, Meihua Zhao et al.

Ocean forecasting is crucial for both scientific research and societal benefits. Currently, the most accurate forecasting systems are global ocean forecasting systems (GOFSs), which represent the ocean state variables (OSVs) as discrete grids and solve partial differential equations (PDEs) governing the transitions of oceanic state variables using numerical methods. However, GOFSs processes are computationally expensive and prone to cumulative errors. Recently, large artificial intelligence (AI)-based models significantly boosted forecasting speed and accuracy. Unfortunately, building a large AI ocean forecasting system that can be considered cross-spatiotemporal and air-sea coupled forecasts remains a significant challenge. Here, we introduce LangYa, a cross-spatiotemporal and air-sea coupled ocean forecasting system. Results demonstrate that the time embedding module in LangYa enables a single model to make forecasts with lead times ranging from 1 to 7 days. The air-sea coupled module effectively simulates air-sea interactions. The ocean self-attention module improves network stability and accelerates convergence during training, and the adaptive thermocline loss function improves the accuracy of thermocline forecasting. Compared to existing numerical and AI-based ocean forecasting systems, LangYa uses 27 years of global ocean data from the Global Ocean Reanalysis and Simulation version 12 (GLORYS12) for training and achieves more reliable deterministic forecasting results for OSVs. LangYa forecasting system provides global ocean researchers with access to a powerful software tool for accurate ocean forecasting and opens a new paradigm for ocean science.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

Surya Dheeshjith, Adam Subel, Alistair Adcroft et al.

AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work.

WU Yundi, LIU Xieyuan, LI Lijun, NI Hui,

In this study, the effect of catalase on the transformation of aroma components in Baiyaqilan tea, a new type of oolong tea was investigated by gas chromatography-mass spectrometry (GC-MS) and odor activity value (OAV). The results showed that the types of volatile aroma components in Baiyaqilan tea changed after catalase treatment. A total of 17 and 23 components with OAV ≥ 1 were identified in Baiyaqilan tea before and after catalase treatment, respectively. Principal component analysis (PCA) performed on the aroma composition data showed a good discrimination between Baiyaqilan tea before and after catalase treatment. The OAV of aroma components such as trans-2-nonenal, linalool, trans-geranylacetone, and β-cyclocitral increased significantly after catalase treatment, while those of cis-linalool oxides decreased significantly. The changes of these components indicated that the effect of catalase on the aroma of Baiyaqilan tea was related to transformation reactions such as the oxidative decomposition of catechins, the hydrolysis of terpene alcohols, the hydrolysis of glycosidic aroma precursors, lipid oxidation, oxidative degradation of carotenoids, and oxidative deamination of amino acids. This study helps to enrich the understanding of the aroma formation of tea and provides technical references for enhancing the aroma of tea products.

Hoese Michel Tornyeviadzi, Emmauel Owusu-Ansah, Hadi Mohammed et al.

This study presents a methodological framework for optimal placement of pressure sensors in Water Distribution Networks (WDNs) for leakage monitoring under uncertainty. Monte Carlo simulation is utilized to simulate leakages of different magnitudes at various nodes in the WDN taking into consideration background noise and minimum resolution of pressure sensors. A novel sensor preselection algorithm based on community detection and maximum entropy computation to reduce the search space of the pressure Sensor Placement Problem (SPP) is presented. The pressure SPP is formulated as a multi-objective optimization problem that seeks to maximize Joint Entropy, Coverage, and minimize Total Correlation. NSGA-II is used to solve the SPP and the solutions in the optimal Pareto front are ranked using a hybrid Entropy TOPSIS to eliminate potential bias and subjective human judgement in optimal sensor configuration implementation. The sensor preselection algorithm achieved a 67% reduction in the search space (possible sensor positions) of the case study, C-TOWN WDN, with only 2.78% reduction in coverage. The result of the pressure SPP indicates only 21 pressure sensors are needed to cover 95.45% of the WDN under study. Finally, the overall performance of the proposed methodological framework is presented and compared with other related works.

Guangjun Cui, Chunhui Lan, Cuiying Zhou et al.

Micron-scale crack propagation in red-bed soft rocks under hydraulic action is a common cause of engineering disasters due to damage to the hard rock–soft rock–water interface. Previous studies have not provided a theoretical analysis of the length, inclination angle, and propagation angle of micron-scale cracks, nor have they established appropriate criteria to describe the crack propagation process. The propagation mechanism of micron-scale cracks in red-bed soft rocks under hydraulic action is not yet fully understood, which makes it challenging to prevent engineering disasters in these types of rocks. To address this issue, we have used the existing generalized maximum tangential stress (GMTS) and generalized maximum energy release rate (GMERR) criteria as the basis and introduced parameters related to micron-scale crack propagation and water action. The GMTS and GMERR criteria for micron-scale crack propagation in red-bed soft rocks under hydraulic action (abbreviated as the Wmic-GMTS and Wmic-GMERR criteria, respectively) were established to evaluate micron-scale crack propagation in red-bed soft rocks under hydraulic action. The influence of the parameters was also described. The process of micron-scale crack propagation under hydraulic action was monitored using uniaxial compression tests (UCTs) based on digital image correlation (DIC) technology. The study analyzed the length, propagation and inclination angles, and mechanical parameters of micron-scale crack propagation to confirm the reliability of the established criteria. The findings suggest that the Wmic-GMTS and Wmic-GMERR criteria are effective in describing the micron-scale crack propagation in red-bed soft rocks under hydraulic action. This study discusses the mechanism of micron-scale crack propagation and its effect on engineering disasters under hydraulic action. It covers topics such as the internal-external weakening of nano-scale particles, lateral propagation of micron-scale cracks, weakening of the mechanical properties of millimeter-scale soft rocks, and resulting interface damage at the engineering scale. The study provides a theoretical basis for the mechanism of disasters in red-bed soft-rock engineering under hydraulic action.

S. Mohsan, Muhammad Asghar Khan, Alireza Mazinani et al.

In recent decades, wireless power transfer (WPT) has gained significant interest from both academic and industrial experts. It possesses natural electrical isolation between transmitter and receiver components, ensuring a secure charging mechanism in an underwater scenario. This ground-breaking technology has also enabled power transmission in the deep-sea environment. However, the stochastic nature of the ocean highly influences underwater wireless power transmission and transfer efficiency is not up to that of terrestrial WPT systems. Recently, the research fraternity has focused on WPT in the air medium, while underwater wireless power transfer (UWPT) is challenging and yet to be explored. The major concerns are ocean current disturbance, bio-fouling, extreme pressure and temperature, seawater conductivity and attenuation. Thus, it is essential to address these challenges, which cause a substantial energy loss in UWPT. This study presents a comparison between various WPT techniques and highlights the research contributions in UWPT in recent years. Research and engineering challenges, practical considerations, and applications are analyzed in this review. We have also addressed influencing factors such as coil orientation, coil misalignment and seawater effects in order to realize an efficient and flexible UWPT system. In addition, this study proposes multiple-input and multiple-output (MIMO) wireless power transmission, which can significantly improve the endurance of autonomous underwater vehicles (AUVS). This idea can be applied to the design of an underwater wireless power station for self-charging of AUVs.

Joel Jose, Md Shadab Alam, Abhilash Sharma Somayajula

Human error is a substantial factor in marine accidents, accounting for 85% of all reported incidents. By reducing the need for human intervention in vessel navigation, AI-based methods can potentially reduce the risk of accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the potential to improve vessel navigation in challenging conditions, such as in restricted waterways and in the presence of obstacles. This is because DRL algorithms can optimize multiple objectives, such as path following and collision avoidance, while being more efficient to implement compared to traditional methods. In this study, a DRL agent is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint tracking. Furthermore, the trained agent is evaluated against a traditional PD controller with an Integral Line of Sight (ILOS) guidance system for the same. This study uses the Kriso Container Ship (KCS) as a test case for evaluating the performance of different controllers. The ship's dynamics are modeled using the maneuvering Modelling Group (MMG) model. This mathematical simulation is used to train a DRL-based controller and to tune the gains of a traditional PD controller. The simulation environment is also used to assess the controller's effectiveness in the presence of wind.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Zhen Bi, Ningyu Zhang, Yida Xue et al.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reasons are the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever large language model in the ocean domain, which is expert in various ocean science tasks. We also propose OceanGPT, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.

Audrey Delpech, Roy Barkan, Lionel Renault et al.

Abstract Internal waves contain a large amount of energy in the ocean and are an important source of turbulent mixing. Ocean mixing is relevant for climate because it drives vertical transport of water, heat, carbon and other tracers. Understanding the life cycle of internal waves, from generation to dissipation, is therefore important for improving the representation of ocean mixing in climate models. Here, we provide evidence from a regional realistic numerical simulation in the northeastern Pacific that the wind can play an important role in damping internal waves through current feedback. This results in a reduction of 67% of wind power input at near-inertial frequencies in the region of study. Wind-current feedback also provides a net energy sink for internal tides, removing energy at a rate of 0.2 mW/m $$^2$$ 2 on average, corresponding to 8% of the local internal tide generation at the Mendocino ridge. The temporal variability and modal distribution of this energy sink are also investigated.

Yu-Ming Yang, Abi Komanduru, James Garrison

Acoustic-gravity waves in the neutral atmosphere induce disturbances in the distribution of electrons in the ionosphere that can be observed in the total electron content (TEC) of global navigation satellite system (GNSS) signals. Large GNSS networks, such as Japan's GEONET, provide spatially dense samples of TEC time series, which can be cross-correlated to detect the coherent structure of these disturbances. Identifying the atmospheric wave-induced perturbations from the short-term background fluctuations can help further our understanding of geophysical processes. In this article, we introduce a method based on wavelet coherence and cross-correlation for detecting and analyzing earthquake/tsunami-induced ionospheric disturbances. We apply this method to detect and isolate the potential disturbances from the data of large GPS networks in Japan, USA, and New Zealand for the 2011 Tohoku-Oki earthquake and the 2015 Illapel earthquake and their resulting tsunamis. We then filter the regions corresponding to each strong coherence structure in time–frequency space to extract the separately identified ionospheric disturbances. The speeds and directions of arrival of these disturbances are found to be compatible with the acoustic-gravity waves from main-shock and after-shock epicenters of each event, offshore tsunami propagation, and seismic Rayleigh waves. Furthermore, we introduce a method to determine the observing heights for the far-field observations.

Kong Debin, Hadi Rezazadeh, Najib Ullah et al.

In this work, we studied a (2 + 1)-dimensional Sawada-Kotera equation (SKE). This model depicts nonlinear wave processes in shallow water, fluid dynamics, ion-acoustic waves in plasmas and other phenomena. A couple of well-established techniques, the Bäcklund transformation based on the modified Kudryashov method, and the Sardar-sub equation method are applied to obtain the soliton wave solution to the (2 + 1)-dimensional structure. To illustrate the behavior of the nonlinear model in an appealing fashion, a variety of travelling wave solutions are formed, such as contour, 2D, and 3D plots. The proposed approaches are proved more convenient and dominant for getting analytical solutions and can also be implemented to a variety of physical models representing nonlinear wave phenomena.

Wang-Yu Zhu, Yu-Mei Wang, Ming-Xue Ge et al.

BackgroundExceeding 50% tuna catches are regarded as byproducts in the production of cans. Given the high amount of tuna byproducts and their environmental effects induced by disposal and elimination, the valorization of nutritional ingredients from these by-products receives increasing attention.ObjectiveThis study was to identify the angiotensin-I-converting enzyme (ACE) inhibitory (ACEi) peptides from roe hydrolysate of Skipjack tuna (Katsuwonus pelamis) and evaluate their protection functions on H2O2-induced human umbilical vein endothelial cells (HUVECs).MethodsProtein hydrolysate of tuna roes with high ACEi activity was prepared using flavourzyme, and ACEi peptides were isolated from the roe hydrolysate using ultrafiltration and chromatography methods and identified by ESI/MS and Procise Protein/Peptide Sequencer for the N-terminal amino acid sequence. The activity and mechanism of action of isolated ACEi peptides were investigated through molecular docking and cellular experiments.ResultsFour ACEi peptides were identified as WGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12), respectively. The affinity of WGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12) with ACE was −8.590, −9.703, −9.325, and −8.036 kcal/mol, respectively. The molecular docking experiment elucidated that the significant ACEi ability of WGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12) was mostly owed to their tight bond with ACE’s active sites/pockets via hydrophobic interaction, electrostatic force and hydrogen bonding. Additionally, WGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12) could dramatically elevate the Nitric Oxide (NO) production and bring down endothelin-1 (ET-1) secretion in HUVECs, but also abolish the opposite impact of norepinephrine (0.5 μM) on the production of NO and ET-1. Moreover, WGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12) could lower the oxidative damage and apoptosis rate of H2O2-induced HUVECs, and the mechanism indicated that they could increase the content of NO and activity of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) to decrease the generation of reactive oxygen species (ROS) and malondialdehyde (MDA).ConclusionWGESF (TRP3), IKSW (TRP6), YSHM (TRP9), and WSPGF (TRP12) are beneficial ingredients for healthy products ameliorating hypertension and cardiovascular diseases.

Jui-Sheng Chou, Dinh‐Nhat Truong

Abstract This study develops a Multi-Objective Jellyfish Search (MOJS) algorithm to solve engineering problems optimally with multiple objectives. Levy flight, elite population, fixed-size archive, chaotic map, and the opposition-based jumping method are integrated into the MOJS to obtain the Pareto optimal solutions. These techniques are employed to define the motions of jellyfish in an ocean current or a swarm in multi-objective search spaces. The proposed algorithm is tested on 20 multi-objective mathematical benchmark problems, and compared with six well-known metaheuristic optimization algorithms (MOALO, MODA, MOEA/D, MOGWO, MOPSO, and NSGA-II). The results thus obtained indicate that the MOJS finds highly accurate approximations to Pareto-optimal fronts with a uniform distribution of solutions for the test functions. Three constrained structural problems (25-bar tower design, 160-bar tower design, and 942-bar tower design) of minimizing structural weight and maximum nodal deflection were solved using MOJS. The visual analytics demonstrates the merits of MOJS in solving real engineering problems with best Pareto-optimal fronts. Accordingly, the MOJS is an effective and efficient algorithm for solving multi-objective optimization problems.

Zexun Wei, Haidong Pan, Tengfei Xu et al.

As a ubiquitous movement in the ocean, tides are vital for marine life and numerous marine activities such as fishing and ocean engineering. Tidal dynamics are complicated in the East Asian marginal seas (EAMS) due to changing complex topography and coastlines related to human activities (e.g., land reclamation and channel deepening) and natural variability (e.g., seasonal variations of ocean stratification and river flow). As an important tool, numerical models are widely used because they can provide basin-scale patterns of tidal dynamics compared to point-based tide gauges. This paper aims to overview the development history of the numerical simulation of tides in the EAMS, including the Bohai Sea, the Yellow Sea, the East China Sea, the East/Japan Sea, and the South China Sea, provide comprehensive understanding of tidal dynamics, and address contemporary research challenges. The basic features of major tidal constituents obtained by tidal models are reviewed, and the progress in the inversion of spatially and temporally changing model parameters via the adjoint method are presented. We review numerical research on how a changing ocean environment induces tidal evolution and how tides and tidal mixing influence ocean environment in turn. The generation, propagation, and dissipation of internal tides in the EAMS are also reviewed. Although remarkable progresses in tidal dynamics have been made, nonstationary tidal variations are not fully explained yet, and further efforts are needed. In addition, tidal influences on ocean environment still receive limited attention, which deserves special attention.