National Research Institute for Earth Science and Disaster Resilience (NIED) integrated the land observation networks established since the 1995 Kobe earthquake with the seafloor observation networks established since the 2011 Tohoku earthquake and tsunami as MOWLAS (Monitoring of Waves on Land and Seafloor) in November 2017. The purpose of MOWLAS is to provide comprehensive, accurate, and rapid observation and monitoring of earthquake, tsunami, and volcano events throughout Japan and its offshore areas. MOWLAS data are widely utilized for long-term earthquake forecasting, the monitoring of current seismic activity, seismic and tsunami hazard assessments, earthquake early warning, tsunami warning, and earthquake engineering, as well as earthquake science. Ocean bottom observations provide an extension of observations to areas where no people are living and have the advantage of increasing lead time of earthquake early warning and tsunami warning. The application of recent technology advancements to real-time observations as well as the processing of MOWLAS data has contributed to the direct disaster mitigation of ongoing earthquakes. These observations are fundamental for both science and disaster resilience, and thus it is necessary to continue ceaseless operation and maintenance.
The classical Hjulström diagram is an empirical rule that qualitatively describes the relationship between flow velocity, particle size, and erosion–transport–deposition. It has long lacked a unified theoretical foundation, which limits the quantitative interpretation of nonlinear variations in these processes. To address this, we establish a unified analytical model for the critical mean flow velocities that govern erosion, transport, and deposition. The model is based on Yang Meiqing’s formula for critical bed shear stress, applicable to the full range of particle sizes, and the Ferguson–Church unified settling velocity formula. It incorporates the logarithmic velocity distribution law and uses the Rouse number as the critical deposition criterion. Theoretically, it demonstrates the U-shaped characteristic of the erosion zone, the “wide for fine and narrow for coarse” feature of the transport zone, and the convex curve of the deposition zone in double-logarithmic coordinates, thereby theoretically revising the traditional understanding in the classical Hjulström diagram that approximates the deposition boundary as a straight line. The correspondence between the unified analytical model and the classical Shields and Rouse theory, which can be regarded as a special case of this unified model under certain conditions, is further analyzed. By explicitly introducing the factors of riverbed inclination angle and water depth, the application scope of the Hjulström diagram is expanded, providing a reliable theoretical basis for quantitatively studying the erosion–transport–deposition relationship of river particles.
Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
The Copernicus DEM is derived as a mean of TanDEM-X phase heights. It is known that there is an almost linear—but variable—relationship between phase height and above-ground biomass (AGB). This article investigates the relationship between the mean of TanDEM-X phase heights and AGB. The slope between TanDEM-X phase height and AGB varies across different acquisitions from the same site, influenced by meteorological conditions and the height of ambiguity (HoA), which is the height corresponding to a 2π phase shift. An expression based on the Interferometric Water Cloud Model (IWCM) is introduced to represent an average of meteorological variations. This approach uses an ICESat-based method to describe the relationship between area-fill (canopy cover) and forest height. The product of density and height is shown to be approximately linear with AGB under certain conditions. This enables us to express mean phase height as a function of AGB, provided that either the AGB-height allometry or reference AGB values from field data are known. The results demonstrate a strong resemblance and near-linear relationship between mean phase height, mean forest height, and AGB across a wide range of conditions. The analysis is illustrated with 32 TanDEM-X acquisitions from the Remningstorp and Krycklan sites. Forest height and area-fill are estimated from mean phase height, and AGB is also derived for these sites.
In recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph structure of HSI data. However, existing GCN-based methods heavily rely on manually selecting superpixel segmentation scales, which limits their ability to capture multiscale contextual relationships adaptively. Moreover, the scarcity of labeled samples in HSI data further degrades the model’s robustness. To address these challenges, we propose a novel semisupervised framework—multiscale feature search GCN with meta pseudolabels (MFSGCN-MPL). First, the HSIs are segmented into superpixels of different scales, transforming them into a graph structure. After that, a neural network search algorithm is used to optimize the combination of superpixel feature weights, improving the discriminability of feature expression. Finally, meta pseudolabels are generated based on a semisupervised teacher–student model that shares the same GCN, and the student network is fine-tuned to enhance its robustness. The proposed MFSGCN-MPL model is implemented on three commonly used HSI datasets and compared with some semisupervised and supervised classification methods. The results confirmed that the proposed model automatically captured features and achieved higher classification accuracies under small sample conditions.
During polar navigation, icebreakers frequently encounter ice ridges, which can significantly reduce navigation efficiency and even pose threats to structural safety. Therefore, studying the ramming of ice ridges by the icebreaker is of great importance. In this study, the ice ridge is decoupled into the consolidated layer and the keel for modeling. The consolidated layer is simplified as layered ice, and an innovative hybrid empirical–numerical method is used to determine the icebreaking loads. For the keel, a failure model is developed using the Mohr–Coulomb criterion in combination with the effective stress principle, accounting for shear failure in porous media and incorporating both cohesion and internal friction angle. The ship is restricted to surge motion only. A comparative analysis with the model test results was conducted to assess the accuracy of the method, with the predicted ice resistance showing deviation of 9.85% in the consolidated ice area and 10.48% in the keel area. Ablation studies were conducted to investigate the effects of different ice ridge shapes, varying retreat distances, and different ship drafts on the performance of ramming the ice ridge. The proposed method can quickly and accurately calculate ice ridge loads and predict their motion responses, providing a suitable tool for on-site rapid navigability assessment and for the design of icebreakers.
Aeration is a crucial method for disrupting water stratification and enhancing the living conditions of the cultured species in the process of pond aquaculture. The mechanism by which sea cucumber (Apostichopus japonicus) gut microbiota and metabolism in response to aeration are poorly understood. In the present study, we exposed the A. japonicus to aerated ponds and non-aerated ponds for 60 days. To address the aeration effect on gut microbiota and metabolism, the gut bacteria and metabolites of A. japonicus was assessed using the 16S rRNA sequencing and metabolomics technology. The results showed that the diversity and abundance of the gut microbiota of A. japonicus increased significantly after aeration, and the aeration treatment significantly changed the structural composition of the gut microbiota of A. japonicus. Among them, the relative abundance of Vibrio decreased by 12.86 %, and the relative abundance of Photobacterium and Ruegeria both increased by more than 500 %. A total of 102 differential metabolites were screened by metabolomics, of which 70 metabolites were significantly up-regulated and 32 metabolites were significantly down-regulated. Further metabolic pathway enrichment analyses revealed that aeration affects arachidonic acid metabolism, linoleic acid metabolism, and inflammatory mediator regulation of TRP channels through pathways that promote the accumulation of diglyceride and phosphatidylcholine. Through the Pearson correlation analysis found that Photobacterium, Ruegeria are positively correlated with anti-inflammatory substances such as phosphatidylcholine, l - pyroglutamic acid, Acetyl - dl – carnitine and betaine, Vibrio and Tepidibacter showed a significant negative correlation with them. The results suggested that aeration improves the physiological state of the organism by affecting the gut microbial composition and host metabolic state of A. japonicus indirectly. This study provides a theoretical basis for the healthy and enhanced cultivation of A. japonicus.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.
At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on optical image 3D reconstruction methods in the underwater environment. However, due to the wide application of sonar in underwater 3D reconstruction, this paper also introduces and summarizes the underwater 3D reconstruction based on acoustic image and optical–acoustic image fusion methods. First, this paper uses the Citespace software to visually analyze the existing literature of underwater images and intuitively analyze the hotspots and key research directions in this field. Second, the particularity of underwater environments compared with conventional systems is introduced. Two scientific problems are emphasized by engineering problems encountered in optical image reconstruction: underwater image degradation and the calibration of underwater cameras. Then, in the main part of this paper, we focus on the underwater 3D reconstruction methods based on optical images, acoustic images and optical–acoustic image fusion, reviewing the literature and classifying the existing solutions. Finally, potential advancements in this field in the future are considered.
Xiaoyong Shang, Guoqing Zhang, Hongguang Lyu
et al.
Research on unmanned surface vessels (USVs) has evolved significantly in recent decades. In particular, intelligent navigation technology has progressed from theoretical concepts to practical applications. As USV research in ocean engineering advances, there is an increasing demand for enhanced performance in intelligent guidance strategy and path-following control systems. This manuscript proposes future development directions for USVs by providing an overview of relevant standards for the intelligence level of these vessels and describing the current status of USV engineering practices. Based on practical ocean engineering requirements, safety considerations, and energy efficiency demands, this paper summarizes the current research status, future research challenges, and potential solutions for USV intelligent guidance and path-following control algorithms from the perspective of large ship intelligence. This manuscript provides a valuable reference for academic researchers and practitioners aiming to identify and position future development directions.
Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal eddies (warm anticyclonic and cold cyclonic eddies) and abnormal eddies (cold anticyclonic and warm cyclonic eddies). However, the influence of mesoscale eddies, especially abnormal eddies, on SLD remains unclear. Based on satellite altimeter and reanalysis data, we explored the influence of mesoscale eddies on seasonal variations in SLD in the South China Sea. We found that the vertical structures of temperature anomalies within the eddies had a significant impact on the sound speed field. A positive correlation between sonic layer depth anomaly (SLDA) and eddy intensity (absolute value of relative vorticity) was investigated. The SLDA showed significant seasonal variations: during summer (winter), the proportion of negative (positive) SLDA increased. Normal eddies (abnormal eddies) had a more pronounced effect during summer and autumn (spring and winter). Based on mixed-layer heat budget analysis, it was found that the seasonal variation in SLD was primarily induced by air–sea heat fluxes. However, for abnormal eddies, the horizontal advection and vertical convective terms modulated the variations in the SLDA. This study provides additional theoretical support for mesoscale eddy–acoustic coupling models and advances our understanding of the impact of mesoscale eddies on sound propagation.
ObjectiveTo address the issue of assessing structural breach damage in ships under underwater explosion, a breach prediction method based on the PCA-BOA-KNN model is established. MethodsFirst, finite element models for five-compartment and seven-compartment segments are constructed, and explosion simulation analysis is carried out for 21 sets of underwater explosion conditions. Subsequently, principal component analysis (PCA) is employed to reduce the dimensionality of the peak acceleration, peak velocity, peak displacement, peak stress and peak overpressure values, resulting in two principal features. Finally, the PCA results are integrated into a Bayesian optimization algorithm (BOA) K-Nearest Neighbors (KNN) model. The established breach prediction model is used to predict the breach conditions at different ship cross-sections under a set of conditions. ResultsThe results show that by using PCA to extract the first two factors, the cumulative contribution rate is 85.165%. Therefore, the first two factors can represent the primary information of the five features. The results obtained using the PCA-BOA-KNN breach prediction model are generally consistent with the simulation results. ConclusionThe proposed prediction model approach is effective for predicting ship structural breaches and has reference value for predicting breachs in ship structures with different principal dimensions.
Aline de Campos, Jorge Melegati, Nicolas Nascimento
et al.
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
This paper proposes a novel trajectory generation method based on Model Predictive Control (MPC) for agile landing of an Unmanned Aerial Vehicle (UAV) onto an Unmanned Surface Vehicle (USV)'s deck in harsh conditions. The trajectory generation exploits the state predictions of the USV to create periodically updated trajectories for a multirotor UAV to precisely land on the deck of a moving USV even in cases where the deck's inclination is continuously changing. We use an MPC-based scheme to create trajectories that consider both the UAV dynamics and the predicted states of the USV up to the first derivative of position and orientation. Compared to existing approaches, our method dynamically modifies the penalization matrices to precisely follow the corresponding states with respect to the flight phase. Especially during the landing maneuver, the UAV synchronizes attitude with the USV's, allowing for fast landing on a tilted deck. Simulations show the method's reliability in various sea conditions up to Rough sea (wave height 4 m), outperforming state-of-the-art methods in landing speed and accuracy, with twice the precision on average. Finally, real-world experiments validate the simulation results, demonstrating robust landings on a moving USV, while all computations are performed in real-time onboard the UAV.