Hasil untuk "Ocean engineering"

Vincent Hue, Bilal Benmahi, Mathieu Barthelemy et al.

Pollux is a candidate European instrument contribution to the Habitable Worlds Observatory (HWO), designed to advance our understanding of the formation and evolution of cosmic structures in the universe, and specifically search signs of life on extrasolar planets. This high-resolution spectrograph (R\,$>$\,40,000) with polarimetric capabilities offers nearly continuous and simultaneous coverage from the FUV ($\sim$100\,nm) to the NIR ($\sim$1.9\,$\micron$), making it a versatile tool for a wide range of scientific investigations from solar system studies to cosmology. Several Solar System ocean worlds have been the focal point of the scientific community to understand the conditions of their internal saline oceans, as well as the possible emergence of life beyond Earth. The ocean world science case will leverage Pollux's UV spectropolarimetric capabilities to investigate surface reflectance and composition, characterize airglow emissions in the environments of giant-planet moons, as well as constrain the microphysical properties of atmospheric aerosols.

Liu Kejing

Oil slicks are widely distributed in the ocean today, as a kind of coverage on sea surface, they became a part of ocean environment and affect their surroundings. A stochastic-dynamic theoretical model proposed in this work to illustrate how oil slicks affect global climate from micro scale relation between a piece of oil slick and sea surface temperature (SST) of its surrounding unit area, for SST is an important index of global climate. The model indicate that oil slicks make the sea surface warmer in the future, and the temperature series of the sea surface covered by oil slicks will have greater variance and fatter tails for its distribution and reduce SST predictability from a microcosmic perspective. Thus, more oil infused into the ocean makes the air-sea system more uncertain. These findings indicate that the present air-sea coupled models may lack of sufficient attention to oil slicks floating on the sea surface.

José Peixoto, Alexis Gonzalez, Janki Bhimani et al.

Programmable caching engines like CacheLib are widely used in production systems to support diverse workloads in multi-tenant environments. CacheLib's design focuses on performance, portability, and configurability, allowing applications to inherit caching improvements with minimal implementation effort. However, its behavior under dynamic and evolving workloads remains largely unexplored. This paper presents an empirical study of CacheLib with multi-tenant settings under dynamic and volatile environments. Our evaluation across multiple CacheLib configurations reveals several limitations that hinder its effectiveness under such environments, including rigid configurations, limited runtime adaptability, lack of quality-of-service support and coordination, which lead to suboptimal performance, inefficient memory usage, and tenant starvation. Based on these findings, we outline future research directions to improve the adaptability, fairness, and programmability of future caching engines.

Xiao Chen, Ying Zhang, Yujie Chen

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings.

Shimei Pang, Songze Chen, Songze Chen et al.

A pronounced nutrient gradient spans from the eutrophic Pearl River Estuary (PRE) to the oligotrophic Northern South China Sea (NSCS), yet its influence on microbial community distribution and cross-domain interactions remains poorly understood. Here, we combined rRNA amplicon sequencing, cross-domain network analysis, and null model approaches to characterize and compare the community structure, assembly processes, and interactions of archaeal, bacterial, and eukaryotic communities in particle-attached (PA) and free-living (FL) fractions along the PRE-NSCS gradient. In the PRE, microbial community assembly was predominantly governed by stochastic processes, resulting in pronounced differences in potential connectivity predicted by null models. Notably, ammonia-oxidizing archaea associated with particles likely functioned as key connectors linking nitrification modules with heterotrophic clusters. In contrast, in the NSCS, cross-domain network analysis revealed that eukaryotes play a central role in maintaining inter-domain connectivity, while FL heterotrophic bacteria formed tightly coupled core networks with their autotrophic partners. Consistent with these patterns, validated topological structures indicated that PRE communities are dominated by stochastic processes (dispersal limitation and drift), whereas NSCS FL communities are primarily shaped by homogeneous selection. Collectively, these results demonstrate that geography and particle partitioning jointly regulate microbial community assembly and network connectivity, thereby influencing distinct microbial remineralization pathways associated with particulate versus dissolved organic matter, and providing new insights into carbon-nitrogen coupling in dynamic coastal ecosystems.

Xinmei Du, Qitao Chen, Jingyun Zhuang et al.

Nucleic acids are essential dietary components with diverse physiological functions. Numerous studies have focused on the biological functions of nucleotides, nucleosides, and functional RNAs such as microRNAs. However, the nutritional value of ribosomal RNA (rRNA)-derived oligonucleotides, which are likely the predominant nucleic acid-derived components in foods, remains largely unexplored. Here, yeast was used as a food-associated eukaryotic model organism to investigate the uptake and utilization of rRNA-derived oligonucleotides. Yeast efficiently utilized short RNA oligonucleotides (approximately 5–30 nt) as nutrient sources, supporting robust cell growth. Confocal microscopy confirmed rapid uptake of Cy5-labeled RNA oligonucleotides by yeast cells. Proteomic analysis further revealed marked upregulation of proteins involved in endocytosis and autophagy in yeast cultured with RNA oligonucleotides. Collectively, these findings demonstrate that yeast can internalize and metabolize rRNA-derived oligonucleotides as efficient nutrient sources, likely through coordinated endocytic and autophagic pathways. This study highlights the nutritional potential of rRNA-derived oligonucleotides and provides a foundation for their future application in functional foods and fermentation systems.

Pierre-Emmanuel Goffi, Raphaël Tremblay, Bentley Oakes

Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineering approach that transforms a passive monitoring system into an interactive Type 2 DT with real-time control capabilities for pressurized systems operating at seven bar. We contribute details of multi-layered safety protocols, hardware-software integration strategies across Arduino controllers and Unity visualization, and real-time synchronization solutions. We document specific engineering challenges and solutions spanning interdisciplinary integration, demonstrating how our use of the constellation reporting framework facilitates cross-domain collaboration. Key findings include the critical importance of safety-first design, simulation-driven development, and progressive implementation strategies. Our work thus provides actionable guidance for practitioners developing DTs requiring bidirectional control in safety-critical applications.

Miguel Goulão, Vasco Amaral, Marjan Mernik

Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the work of researchers and practitioners in learning about the coverage and main findings of existing work as well as identifying relatively unexplored niches of research that need further attention. We performed a tertiary study on quality in MDE, in order to gain a better understanding of its most prominent findings and existing challenges, as reported in the literature. We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. Eighty out of 83 research questions in the selected secondary studies have a structure that is more often associated with mapping existing research than with answering more concrete research questions (e.g., comparing two alternative MDE approaches with respect to their impact on a specific quality attribute). We briefly outline the main contributions of each of the selected literature reviews. In the collected studies, we observed a broad coverage of software product quality, although frequently accompanied by notes on how much more empirical research is needed to further validate existing claims. Relatively, little attention seems to be devoted to the impact of MDE on the quality in use of products developed using MDE.

Daniel R. Johnston, Callum J. Shakespeare, Navid C. Constantinou

Whenever oceanic currents flow over rough topography, there is an associated stress that acts to modify the flow. In the deep ocean, this stress is predominantly a form drag due to pressure differentials across topography, caused by the formation of internal waves and other baroclinic motions: processes that act on such small scales most global ocean models cannot resolve. Despite the need to incorporate this stress into ocean models, existing parametrisations are limited in their applicability. For instance, most parametrisations are only suitable for small-scale topography and are either for periodic or steady flows, but rarely a combination thereof. Here we summarise some of the most widely used parametrisations and evaluate the accuracy of a carefully selected subset using hundreds of idealised two-dimensional and three-dimensional simulations spanning a wide parameter space. We focus on the case of an isolated Gaussian hill as an idealised representation of a seamount. In cases where the parametrisations prove to be inaccurate, we use our data to suggest improved formulations. Our results thus provide a starting point for a comprehensive parameterisation of topographic stresses in ocean models where fine scale topography is unresolved.

Saba Sanami, Amir G. Aghdam

Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.

Saeed Rajabi-Kiasari, Artu Ellmann, Nicole Delpeche-Ellmann

Changes in climate, along with increasing marine activities in coastal and offshore regions, highlight the need for effective sea level forecasting methods. In recent years, forecasting techniques, especially those utilizing machine learning/deep learning methods (ML/DL), have shown promising capabilities. However, sea level forecasting is often limited in accuracy and spatiotemporal coverage, primarily due to the challenges posed by available observational data, which complicates the assessment of existing ML/DL techniques in complex and dynamic regions like the Baltic Sea. This study addresses these challenges by utilizing a high-resolution spatiotemporal framework that integrates high-resolution hydrodynamic and marine geoid models available to Baltic countries, enabling further capabilities to be explored in terms of sea level accuracy and validation. Specifically, it examines short-term sea level forecasting in the eastern Baltic Sea and the potential of utilizing two recurrent neural network-based models such as the Long Short-Term Memory Networks (LSTMs), and the Gated Recurrent Unit (GRU) along with high-resolution input data sources. These models were specifically chosen, due to their expected capabilities with time series data and their ability to learn both short and long-term connections of the input datasets.To achieve this, a multivariate multistep-ahead (3, 6, 9, 12, and 24 h) forecasting framework was developed. The DL models' input components are high-resolution sea level data obtained from a bias-corrected hydrodynamic model, wind speed, surface pressure, and sea surface temperature. Results for various time steps (from 3 h to 24 h ahead), during the test period, revealed that the two DL models generally showed similar performance, with slightly superior results with the GRU model. For instance, GRU and LSTM showed an averaged root mean square error (RMSE) of 4.96 cm and 5.3 cm and a coefficient of determination (R²) of 0.93 and 0.92, respectively. Investigations of the time series forecasting performance at selected locations, also demonstrated the superiority of the GRU model, for all time steps, with Willmott's index (WI) values generally above 0.9 and high reliability as reflected in Prediction Interval Coverage Probability (PICP) values mostly exceeding 90 %. The results, however, weren't always perfect; both the GRU and LSTM models encountered limitations with forecasting the sea level maxima. Further examination of the spatial discrepancies also reveals some problematic areas in the eastern Gulf of Finland. This may have been influenced by the exclusion of some input components such as river discharge, salinity and meridional winds, further enhanced by complex hydrodynamics, extreme sea level variations, strong local currents, resonance-induced seiches and seasonal ice cover. In addition, an external validation of the GRU results was performed using along-track satellite altimetry from Sentinel 3A and 3B missions. For most of the satellite tracks, the discrepancy was better than 5 cm, proving the capabilities of the model generalization capabilities. These findings hold significant implications for advancing our comprehension of oceanic dynamics, enhancing maritime safety, and benefiting a wide range of applications that are dependent on accurate sea level forecasting.

Pengying YI, Junzhi LIN, Tao CHEN et al.

The Qingkou landslide in Wulong, Chongqing, has developed on an inclined thick abrupt cliff, with a large area of goaf formed due to hundreds of years of coal mining below. Based on the analysis of the geological conditions, current goaf situation, and deformation characteristics of the mountain, it is identified that the landslide exhibits both bending-tension and sideslip deformations, suggesting a bending-sideslip failure mode. The deformation history of the mountain is examined, and the deformation characteristics of the landslide are analyzed in two stages: bending deformation and sideslipping deformation. Change in the rock surface occurrence are studied accordingly. Considering the combination relationship between bedding and the free face, the mountain failure is divided into bending-tension failure, wedge failure and plane-sliding failure. Criteria for each failure stage are established, revealing the evolution rules and formation mechanism of the bending-sideslip landslide. This study provides a scientific basis for the disaster prevention and mitigation of such landslides.

Shiqiang Li, Yuwei Li, Xiaomin Ma et al.

Abstract The current research of sandwich structures under dynamic loading mainly focus on the response characteristic of structure. The micro-topology of core layers would sufficiently influence the property of sandwich structure. However, the micro deformation and topology mechanism of structural deformation and energy absorption are unclear. In this paper, based on the bi-directional evolutionary structural optimization method and periodic base cell (PBC) technology, a topology optimization frame work is proposed to optimize the core layer of sandwich beams. The objective of the present optimization problem is to maximize shear stiffness of PBC with a volume constraint. The effects of the volume fraction, filter radius, and initial PBC aspect ratio on the micro-topology of the core were discussed. The dynamic response process, core compression, and energy absorption capacity of the sandwich beams under blast impact loading were analyzed by the finite element method. The results demonstrated that the over-pressure action stage was coupled with the core compression stage. Under the same loading and mass per unit area, the sandwich beam with a 20% volume fraction core layer had the best blast resistance. The filter radius has a slight effect on the shear stiffness and blast resistances of the sandwich beams. But increasing the filter radius could slightly improve the bending stiffness. Upon changing the initial PBC aspect ratio, there are three ways for PBC evolution: The first is to change the angle between the adjacent bars, the second is to further form holes in the bars, and the third is to combine the first two ways. However, not all three ways can improve the energy absorption capacity of the structure. Changing the aspect ratio of the PBC arbitrarily may lead to worse results. More studies are necessary for further detailed optimization. This research proposes a new topology sandwich beam structure by micro-topology optimization, which has sufficient shear stiffness. The micro mechanism of structural energy absorption is clarified, it is significant for structural energy absorption design.

Dinah A. E. Ogara, Dinah A. E. Ogara, Joji Morishita et al.

Ports and neighbouring cities function as connectors between land and water and have long accommodated a substantial flow of goods and services. Port cities in the Western Indian Ocean (WIO) region and the Global South (GS) are rapidly and inevitably expanding as the demand for global trade increases. However, this expansion has numerous impacts on the surrounding marine ecosystem and the socio-economic livelihoods of local communities. We propose a framework to evaluate the sustainability of port cities in the WIO region and more broadly for cities in the GS. Through an exploratory approach, a systematic literature review (SLR) was undertaken to identify existing themes on port city and marine ecosystem sustainability indicator frameworks. The results revealed a strong bias towards sustainability publications designed for port cities in Global North. The approach developed from this study focuses on the socio-economic and environmental attributes relevant to ports in the WIO region and for GS countries. This draws from the Drivers, Pressures, States, Impacts and Responses (DPSIR) framework and includes 78 indicators. The indicators are designed to identify and report on the complex land and sea interdependencies of port cities. To test the validity of these indicators their interdependencies were examined through a Causal Network (CN) structure which identified 12 priority DPSIR CN. These were also mapped to the UNSDGs enabling the wider applicability and transferability of the framework. The resulting framework enables port cities in emerging economies to establish robust sustainable reporting systems and provides a framework that offers a unique lens for evaluating interactions embedded in the land and sea continuum.

Avijit Vinayak Pandit, Nils Dittrich, Andrea Viken Strand et al.

As wild-caught fish become scarce, feed ingredients for farming fish, such as salmon, are increasingly sourced from agricultural plants that depend on mineral fertilizers. Since these fish are naturally carnivorous, they have difficulty digesting the phosphorus in plant-based feed. So additional phosphorus supplements are added to the feed, resulting in a disproportionate increase in mineral phosphorus use and emission. Aquatic food production is increasingly relying on agriculture and mineral phosphorus resources. The feed surplus and the excreta are seldom collected and recycled, leading to a massive loss of nutrients to water bodies and the seafloor, resulting in local risk for eutrophication. Norway currently produces more than half of the world’s Atlantic salmon, and it is set to increase production from currently 1.5 to 5 Mt. in 2050. This has large implications for feed supply and emissions globally. There is a lack of studies that analyze the phosphorus system in aquatic food production at a sufficient spatial and temporal granularity to effectively inform interventions for a more circular use of phosphorus. Here, we present a multi-scale phosphorus flow analysis at monthly resolution ranging between 2005 and 2021 for aquatic food production in Norway and quantitatively discuss the effectiveness of alternative strategies for improving resource efficiency. The results indicate that P emissions from aquaculture have nearly doubled in the period between 2005 and 2021. The P use efficiency (PUE) in Norwegian aquaculture was 19% in 2021. The addition of phytase to the feed could improve the PUE by 8% by reducing P supplements and emissions by 7 kt/y. The use of Integrated Multi-Trophic Aquaculture close to fish farming sites could absorb emissions by 4 kt/y by creating new marine food products. Sludge collection systems could reduce P emissions by 4 to 11 kt/y, depending on the technology. Using the sludge in local agriculture would exacerbate the current P accumulation in soils close to the coastline, given that the animal density in this region is already high. Hence, a large and sophisticated processing infrastructure will be needed to create transportable, high-quality secondary fertilizers for effective sludge recycling in regions with a P deficit.

Yuejun Zhu, Tao Li, Dongliang Peng et al.

The existing automatic target recognition (ATR) methods for synthetic aperture radar (SAR) images mainly utilize the real-valued magnitude information while often ignoring the phase information. However, the phase information also provides important details, which can be utilized to improve the ATR performance. To address this issue, a fully complex-valued light-weight network (CVLWNet) is proposed based on complex-valued operations, such as complex-valued convolution and complex-valued batch normalization. Besides, to achieve reduced parameters and enhanced robustness of the designed network, many complex-valued blocks of operations are built, including the CMish activation function, the complex-valued residual link block (CVReLBlock), the lightweight complex-valued cross stage partial block (LC-CSPBlock). In the designed CVLWNet, the input, output, and weight parameters are all complex-valued, which makes it possible to sufficiently exploit the complex-valued characteristics of SAR data. Comparative experiments are conducted with the moving and stationary target acquisition and recognition dataset. Compared with the state-of-the-art real-valued and complex-valued models under both standard and extended operating conditions, the performance of proposed method is verified.

Liqin Qu, Mingkun Liu, Lei Guan

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms.

Taorui Zeng, Zizheng Guo, Linfeng Wang et al.

In the original publication [...]

Hassan Hage, Emmanouil Seferis, Vahid Hashemi et al.

Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model Notation (BPMN) is a widely used language to encode interactions among several participants in such PEPs. In this paper, we present SMC4PEP as a tool to convert graphical representations of a business process using the BPMN standard to an equivalent discrete-time stochastic control process called Markov Decision Process (MDP). To this aim, we first follow the approach described in an earlier investigation to generate a semantically equivalent business process which is more capable of handling the PEP complexity. In particular, the interaction between different levels of abstraction is realized by events rather than direct message flows. Afterwards, SMC4PEP converts the generated process to an MDP model described by the syntax of the probabilistic model checking tool PRISM. As such, SMC4PEP provides a framework for automatic verification and validation of business processes in particular with respect to requirements from legal standards such as Automotive SPICE. Moreover, our experimental results confirm a faster verification routine due to smaller MDP models generated from the alternative event-based BPMN models.

Julian Mak, David P. Marshall, Gurvan Madec et al.

The global ocean overturning circulation, critically dependent on the global density stratification, plays a central role in regulating climate evolution. While it is well-known that the global stratification profile exhibits a strong dependence to Southern Ocean dynamics and in particular to wind and buoyancy forcing, we demonstrate here that the stratification is also acutely sensitive to the mesoscale eddy energy dissipation time-scale. Within the context of a global ocean circulation model with an energy constrained mesoscale eddy parameterization, it is shown that modest variations in the eddy energy dissipation time-scale lead to significant variations in key metrics relating to ocean circulation, namely the Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and global ocean heat content, over long time-scales. The results highlight a need to constrain uncertainties associated with eddy energy dissipation for climate model projections over centennial time-scales, but also for paleoclimate simulations over millennial time-scales.