This paper aims to solve the spatial trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of system parameter uncertainties and complex external disturbances. To accomplish this goal, a model–data-driven learning adaptive robust control (LARC) strategy is introduced for AUVs. Firstly, a serial iterative learning control (ILC) approach is introduced as feedforward compensation, and then the corresponding trajectory tracking error dynamics model, the Feedforward Compensation–Line of Sight (FFC-LOS) guidance law, and the feedforward compensation-based kinematics controller are designed. Secondly, the dynamics controller is designed for AUVs, which consists of a linear feedback term, a nonlinear robust feedback term, an adjustable model compensation term, and a fast dynamic compensation term. In this control framework, the robust control and fast dynamic compensation parts are utilized to deal with nonlinear uncertainties and disturbances, the projection-type adaptive control part solves the influence caused by the uncertainty of system parameters, and the serial ILC part that is a data-driven learning method can further improve the trajectory tracking accuracy for repetitive tasks. Finally, comparative simulations under different scenarios and different types of disturbances are performed to verify the effectiveness of the proposed control strategy for AUVs.
Accurate biological detection is crucial for autonomous navigation of underwater robots, yet severely challenged by optical degradation and scale variation in marine environments. While image enhancement and domain adaptation methods offer some mitigation, they often operate as disjointed preprocessing steps, potentially introducing artifacts and compromising downstream detection performance. Furthermore, existing architectures struggle to balance accuracy, computational efficiency, and robustness across the extreme scale variability of marine organisms in challenging underwater conditions. To overcome these limitations, we propose UW-YOLO-Bio, a novel framework built upon the YOLOv8 architecture. Our approach integrates three dedicated modules: (1) The Global Context 3D Perception Module (GCPM), which captures long-range dependencies to mitigate occlusion and noise without the quadratic cost of self-attention; (2) The Channel-Aggregation Efficient Downsampling Block (CAEDB), which preserves critical information from low-contrast targets during spatial reduction; (3) The Regional Context Feature Pyramid Network (RCFPN), which optimizes multi-scale fusion with contextual awareness for small marine organisms. Extensive evaluations on DUO, RUOD, and URPC datasets demonstrate state-of-the-art performance, achieving an average improvement in mAP50 of up to 2.0% across benchmarks while simultaneously reducing model parameters by 8.3%. Notably, it maintains a real-time inference speed of 61.8 FPS, rendering it highly suitable for deployment on resource-constrained autonomous underwater vehicles (AUVs).
Muhammad Zubair Muis Alie, Andi Ardianti, J. Juswan
et al.
Damage to ship construction causes its structure to lose its ultimate strength. The damage could result from a collision or grounding. The ship's structural integrity following a collision or grounding must be evaluated. This is done to satisfy the ship's structural design requirements. The objective of the present study is to analyze the effect of damage implemented on single and double hull construction to the ultimate strength of ship structure. The ultimate strength of a ship's hull girder following damage from a collision or grounding was ascertained a numerical approach is used in this study. The cross-section sample of the ship is taken in this study namely single and double hull of bulk carrier. The modeling of damage to the ship's bottom and side shell sections from collisions and groundings are taken into account to know the influence of damage when it is applied to single and double hull.
The mission environment has become more and more complex, and the tempo has obviously accelerated. As a result, the traditional manual decision-making can no longer meet the requirements. There is a strong need for an advanced decision-making system to assist decision makers in carrying out on-the-spot mission command. To better carry out the research on artificial intelligent recommendation methods for mission schemes, this paper collated the research articles in this direction in China and abroad in recent years and divided the intelligent recommendation methods into three categories, namely intelligent analysis, intelligent matching, and intelligent learning. It elaborated on the core principles, technical paths, and typical applications of various methods and simultaneously analyzed the advantages and disadvantages of the three types of methods. It identified the deficiencies of the existing methods in terms of dynamic adaptability, autonomous decision-making ability, data dependence, and credibility. Finally, the future development direction was prospected, providing valuable references for subsequent research in this field.
Marine aquaculture has witnessed remarkable growth in recent decades, contributing significantly to global food security and economic development [...]
Abstract Nanodrug delivery systems (NDDS) have demonstrated broad application prospects in disease treatment, prevention, and diagnosis due to several advantages, including functionalization capability, high drug‐loading capacity, drug stability protection, and the enhanced permeability and retention (EPR) effect. However, their clinical translation still faces multiple challenges, including rapid clearance by the reticuloendothelial system (RES), poor targeting specificity, and insufficient efficiency in crossing biological barriers. To address these limitations, researchers have developed the biological carrier‐mediated nanomedicine hitchhiking strategy (BCM‐NHS), which leverages circulating cells, proteins, or bacteria as natural “mobile carriers” to enhance drug delivery. This approach enables nanocarriers to inherit the intrinsic biological properties, endowing them with immune evasion, prolonged circulation, dynamic targeting, biocompatibility, biodegradability, and naturally optimized biological interfaces. Here, a systematic overview of the BCM‐NHS is provided. First, the review delves into the methods of nanoparticles (NPs) binding and immobilization, encompassing both the surface‐attachment‐mediated “backpack” strategy and the encapsulation‐based “Trojan horse” strategy. Second, the classification of biological carriers, including both cell‐based and non‐cell‐based carriers, is elucidated. Third, the physical properties and release mechanisms of these nanomaterials are thoroughly described. Finally, the latest applications of BCM‐NHS in therapeutic and diagnostic contexts across various disease models including tumor, ischemic stroke, and pneumonia are highlighted.
ABSTRACTThis paper examines the procurement decisions of an agricultural processor that sources a commodity input from two quantity flexibility contracts, each of which is characterized by a unit reservation cost and a unit exercise cost, to produce and sell a commodity output under input and output spot price uncertainties. We identify the cost‐flexibility trade‐off faced by the processor when making procurement decisions and examine how this trade‐off in different contracts shapes the optimal procurement strategy. We also investigate how the spot price correlation affects the processor's procurement decisions, profitability, and the value of contract procurement; that is, the difference between the optimal expected profit with and without contract procurement. We find that as spot price correlation increases, the processor increases its total contract reservation volumes and the reservation volume from the contract with a lower exercise cost. We also find that while the optimal profit without contract procurement decreases in spot price correlation, the value of contract procurement increases; that is, contracts provide a hedge against increasing spot price correlation. Using a calibration based on a typical palm oil processor in Malaysia, we show that the hedging value does not eliminate the negative impact of an increasing correlation on the processor's profit; that is, the optimal expected profit with contract procurement also decreases in spot price correlation. We also observe that the value of contract procurement is substantial. Our results have important implications for the procurement strategy in agricultural processing industries.
This paper presents an optimal output-feedback tracking control problem for multiple unmanned marine vehicles (UMVs) to track a desired trajectory. To guarantee the control objective in an optimal manner, adaptive dynamic programming (ADP) with optimal compensation terms is adopted. A neural velocity observer is designed based on a neural network (NN) to estimate the unmeasured system states and the unknown system dynamics. Furthermore, a disturbance observer (DO) is proposed to go against the effect of the unknown external disturbance of the sea environment. It is proved that the proposed controller can guarantee that all signals in the closed-loop system are bounded. Simulation results are given to demonstrate the effectiveness of the proposed control algorithm.
The precise wake model is crucial for accurately estimating wind farm loads and power, playing a key role in wake control within wind farms. This study proposes a segmented dual-Gaussian wake model, which is built upon existing dual-Gaussian wake models but places greater emphasis on the influence of initial wake generation and evolution processes on the wind speed profile in the near-wake region. The enhanced model optimizes the wake speed profile in the near-wake region and improves the accuracy of wake diffusion throughout the entire flow field. Furthermore, the optimized dual-Gaussian wake model is utilized to estimate the power output and blade root vibration loads in offshore wind farms. Through comparative analysis of high-fidelity simulation results and actual measurement data, the accuracy of the optimized dual-Gaussian wake model is validated. This approach offers high computational efficiency and provides valuable insights for load fluctuations and power estimation, thereby advancing the development of wake control strategies rapidly.
A shallow water equations-based model with an improved local time-stepping (LTS) scheme is developed for modeling coastal hydrodynamics across multiple scales, from large areas to detailed local regions. To enhance the stability of the shallow water model for long-duration simulations and at larger LTS gradings, a prediction-correction method using a single-layer interface that couples coarse and fine time discretizations is adopted. The proposed scheme improves computational efficiency with an acceptable additional computational burden and ensures accurate conservation of time truncation errors in a discrete sense. The model performance is verified with respect to conservation and computational efficiency through two idealized tests: the spreading of a drop of shallow water and a tidal flat/channel system. The results of both tests demonstrate that the improved LTS scheme maintains precision as the LTS grading increases, preserves conservation properties, and significantly improves computational efficiency with a speedup ratio of up to 2.615. Furthermore, we applied the LTS scheme to simulate tides at grid scales of 40,000 m to 200 m for a portion of the Northwest Pacific. The proposed model shows promise for modeling cross-scale hydrodynamics in complex coastal and ocean engineering problems.
Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh
et al.
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. Alternatively, approaches using feature modelling or self-supervised methods, such as the ones relying on synthetically generated anomalies, do not provide out-of-the-box interpretability. In this work, we present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline (i.e., a diffusion-like pipeline which uses corruptions not based on noise) that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance. To support our pipeline we introduce a novel synthetic anomaly generation procedure, called DAG, and a novel anomaly score which ensembles restorations conditioned with different degrees of abnormality. Our method surpasses the prior state-of-the art for unsupervised anomaly detection in three different Brain MRI datasets.
The deep western boundary current (DWBC) in the Philippine Sea has been expected to play a crucial role in transporting lower circumpolar deep water and to contribute to regional and global climate regulation. Two-year-long near-bottom current measurements reveal a southward-flowing DWBC with a mean velocity of 5 cm/s and seasonal variations—weaker in summer and stronger in winter. Seasonal variability in the DWBC is hypothesized to be induced by changes in the North Equatorial Current bifurcation latitude (NECBL) and upper pycnocline depth through potential vorticity conservation. Data-assimilated reanalysis model (GLORYS12V1) outputs, which reproduce the seasonal variability of DWBC similarly to the observation, are used for further analysis. During the seasonal period, the NECBL displays significant coherence (>0.9) with the first-mode empirical orthogonal function principal component of the simulated along-slope DWBC. The upper pycnocline depth, varying seasonally within a range of approximately 27 m, induces seasonal variability in a deep anticyclonic eddy trapped by topography. In summer, the intensified deep anticyclonic eddy obstructs the adjacent southward-flowing DWBC, weakening its strength, whereas in winter, the southward flow of the DWBC is enhanced due to the weakening of the deep anticyclonic eddy.
Giovanni Coletti, Alberto Collareta, Andrea Di Cencio
et al.
In order to investigate the serendipitous find of a gastropod encrusted by the symbiont-bearing colonial coral <i>Oculina patagonica</i>, we examined several specimens of cnidarian-encrusted gastropods, ranging in age from the Pliocene to the Recent, and characterized in detail their sclerobiont cover. The results of our analysis suggest that gastropod shells can be encrusted by symbiont-bearing colonial corals at various times: (1) when the gastropod is alive; (2) when the shell is being used by a secondary inhabitant (e.g., hermit crabs or sipunculid worms); (3) when the shell is discarded but yet to be buried. The relationship between the symbiont-bearing coral and the inhabitant(s) of the encrusted shell is an example of facultative mutualism, i.e., it is non-obligate yet beneficial for both ends as the former obtains the capability to move, and the latter improves the resistance and resilience of its armor, thus obtaining extra protection from predators. Being able to move could prove particularly useful for a symbiont-bearing coral because, in addition to removing the risk of being smothered by sediment, it would also favor the photosynthetic activity of its algal endosymbionts by allowing the coral to be always clean of sedimentary particles. Although the resulting epibiotic association would be limited in size by the ability of either the gastropod or the secondary inhabitant of the shell to move at the seafloor, these small and easy-to-miss benthic islands might become the seeds that allow sessile carbonate producers such as hermatypic colonial corals to colonize unconsolidated substrates.
The best way to stop the introduction of non-indigenous species (NISs) is by preventing their transport. In the case of ship hulls, this may be accomplished by managing entrainment onto the hull. This study was designed to examine the role of hull husbandry, i.e., cleaning and grooming, in fouling community structure and to determine the effect of husbandry on the recolonization of surfaces after a transplant was performed. A series of panels were placed at two locations along the east coast of Florida (Port Canaveral and Sebastian Inlet) that are typified by distinct fouling communities. Panels were subjected to one of three treatments: groomed weekly, cleaned every two months, or freely fouling. After four months, all panels were cleaned and transplanted between sites; no further husbandry was performed. Fouling community composition and coverage was characterized at monthly intervals both before and after transplantation. Hull husbandry was found to affect coverage and composition, with groomed panels carrying a lower cover of macrofouling in general. The effect of the original location on subsequent fouling composition and recolonization by specific organisms was confirmed for encrusting bryozoans, barnacles, sponges, and tunicates. Hull husbandry also affected subsequent fouling with specific preferences shown for surfaces that had been groomed, cleaned and undisturbed.
Se Jeong Park, Seung Wook Jung, Seung Yeop Han
et al.
This study examined the seasonal and spatial changes in the marine algal community structure in the outer and inner areas of the intertidal zone of Yeongil Bay. A seasonal survey was conducted at 13 intertidal sites in Yeongil Bay between August 2021 and July 2022. Algal samples were collected using a destructive approach, and the marine algal community was analyzed based on species composition, biomass, and the important value (IV) index. To confirm the differences between the outer and inner areas, ANOSIM and SIMPER analyses were conducted based on the biomass and IV indices of the marine algal species. The marine algal communities in the outer and inner areas of Yeongil Bay differed because of the high contribution of <i>Ulva</i> spp., as determined using the IV index. Our findings suggest that there was an influx of nutrients into Yeongil Bay from the Hyeongsan River, which had remained in the inner area for an extended period because of the slow current. This allowed <i>Ulva</i> spp., an opportunistic species, to bloom quickly and dominate the marine algal communities in the inner area. These results indicate a difference in the structure of marine algal communities between the outer and inner areas owing to the proliferation of <i>Ulva</i> spp. Therefore, <i>Ulva</i> spp. can be used as a target species to monitor the marine environment of Yeongil Bay.
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos
et al.
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free of anomalies. Diffusion models learn to modify inputs $x$ to increase the probability of it belonging to a desired distribution, i.e., they model the score function $\nabla_x \log p(x)$. Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score. However, diffusion models are trained to invert a corruption process based on Gaussian noise and the learned score function is unlikely to generalize to medical anomalies. This work addresses the problem of how to learn a score function relevant for UAD and proposes DISYRE: Diffusion-Inspired SYnthetic REstoration. We retain the diffusion-like pipeline but replace the Gaussian noise corruption with a gradual, synthetic anomaly corruption so the learned score function generalizes to medical, naturally occurring anomalies. We evaluate DISYRE on three common Brain MRI UAD benchmarks and substantially outperform other methods in two out of the three tasks.
Leigh N. Fletcher, Thibault Cavalié, Davide Grassi
et al.
ESA's Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cassini, and Juno missions, alongside ground- and space-based observatories. We focus on remote sensing of the climate, meteorology, and chemistry of the atmosphere and auroras from the cloud-forming weather layer, through the upper troposphere, into the stratosphere and ionosphere. The Jupiter orbital tour provides a wealth of opportunities for atmospheric and auroral science: global perspectives with its near-equatorial and inclined phases, sampling all phase angles from dayside to nightside, and investigating phenomena evolving on timescales from minutes to months. The remote sensing payload spans far-UV spectroscopy (50-210 nm), visible imaging (340-1080 nm), visible/near-infrared spectroscopy (0.49-5.56 $μ$m), and sub-millimetre sounding (near 530-625\,GHz and 1067-1275\,GHz). This is coupled to radio, stellar, and solar occultation opportunities to explore the atmosphere at high vertical resolution; and radio and plasma wave measurements of electric discharges in the Jovian atmosphere and auroras. Cross-disciplinary scientific investigations enable JUICE to explore coupling processes in giant planet atmospheres, to show how the atmosphere is connected to (i) the deep circulation and composition of the hydrogen-dominated interior; and (ii) to the currents and charged particle environments of the external magnetosphere. JUICE will provide a comprehensive characterisation of the atmosphere and auroras of this archetypal giant planet.
Vincent Dorie, George Perrett, Jennifer L. Hill
et al.
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and heterogeneous treatment effects can be hampered when data are structured within groups if we fail to correctly model the dependence between observations. Most machine learning methods do not readily accommodate such structure. This paper introduces a new algorithm, stan4bart, that combines the flexibility of Bayesian Additive Regression Trees (BART) for fitting nonlinear response surfaces with the computational and statistical efficiencies of using Stan for the parametric components of the model. We demonstrate how stan4bart can be used to estimate average, subgroup, and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data as well as multilevel approaches that have strict parametric forms.
AbstractPay‐for‐priority system is believed to be an efficient service mechanism in congested systems since it introduces service differentiation that prioritizes those who are more delay‐sensitive. However, in practice, not all customers are aware of the provision of such auxiliary service (i.e., priority access). Does the lack of awareness or ignorance of priority service make the social welfare or customer surplus worsen off? To answer this question, we establish a queueing‐game‐theoretic model by capturing the strategic interactions between service provider and customers to examine the effect of customer awareness on the priority queues. Our main results are as follows. First, we confirm that increasing the level of customer awareness indeed improves the revenue of service provider, and it triggers a higher priority premium price. Second, perhaps surprisingly, we find that under the profit‐maximizing priority price, the social welfare as well as the customer surplus are both nonmonotone in the level of customer awareness, that is, full or no customer awareness can be suboptimal for the social welfare and customer surplus. Third, despite the common belief that priority is socially efficient in congested systems, we demonstrate that the optimal information levels for social welfare and customer surplus are decreasing in the congestion level, and the full customer awareness is optimal only when the system load is relatively low. Finally, to reach the maximal social welfare or customer surplus, some regulation policies are proposed, whereby the social planner can provide advertising subsidy to (levy tax on) the service provider (advertising agency) when the system load is low (high).