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Genome-wide association study (GWAS) has identified numerous significant loci for boll number (BN) and boll weight (BW), which play an essential role in cotton (Gossypium spp.) yield. The North Carolina design II (NC II) genetic mating population exhibits a greater number of genetic variations than other populations, which may facilitate the identification of additional genes. Accordingly, the 3VmrMLM method was employed for the analysis of upland cotton (Gossypium hirsutum L.) in an incomplete NC II genetic mating population across three environments. A total of 204 quantitative trait nucleotides (QTNs) were identified, of which 25 (24.75%) BN and 30 (29.13%) BW QTNs were of small effect (

This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model’s accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems.

ObjectivesThis paper studies the hydrodynamic characteristics of submersible under the interference of seabed sand wave topography. MethodsBased on the computational fluid dynamics (CFD) method, the process of submersible passing above a seabed sand wave is simulated in the STAR-CCM+ software using the overset grid method, and the evolution of the resistance coefficient and lift coefficient during the process is calculated. ResultsThe results show that when the submersible navigates near the seabed, interaction forces act on it. When the bow moves near the wave crest, the maximum negative lift occurs, and when the stern moves near the crest, the maximum resistance occurs. The different surface pressure distribution rules of submersible at different speeds lead to different evolution rules of the lift coefficient. The smaller the H/D, the more significant the influence of the seabed sand wave on the resistance and lift force of the submersible. When H/D > 5, the influence of the seabed sand wave on the resistance and lift force of the submersible can be ignored. ConclusionsThe results of this study provide valuable references for the safety and maneuverability of submersibles.

Abstract Straw return is a beneficial agricultural practice but mechanisms and information on microbial community alterations initiated by decomposed straw and promotion of soil nutritive content and conservation is still not adequately understood. Therefore, the study applied molecular, bioinformatics, chemical and plant yield analysis to determine the effect of wheat straw return and decomposing agent on soil, fungi and crop yield characteristics under wheat biomass return with and without the inclusion of the decomposition agent at varying times. We hypothesized no difference in fungal community diversity and composition, no changes in soil characteristics as well as no changes in cotton growth characteristics between three different conditions (i) no straw return, (ii) straw return under decomposition agent and (iii) straw return under no decomposition agent. It involved analysis of changes in fungal community diversity and composition, changes in soil characteristics as well as changes in cotton growth characteristics under the different treatments. The phyla Ascomycota and Basidiomycota were in relatively in highest abundance within the straw return under decomposition agent treatment than in both the straw return treatment and straw return under no decomposition agent treatment. The pathogenic genus Aspergillus as the most dominant under no straw return treatment, while genus Trechispora, Lulwaona and Dioszegia were most dominant under straw return under no decomposition agent treatment. Indeed, there was high fungal community dissimilarity between the three treatments. Additionally, there was increased rise in fungal diversity, soil nutrients and crop yield under the straw return within the shortest possible time, and the addition of decomposing agent further enhanced the high fungal species diversity. Graphical Abstract

Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement with target detection. Different from the traditional method of reconstructing enhanced images for target detection, G-Net realizes the integration of image enhancement and target detection. In addition, our feature map learning module (FML) can effectively extract defogging features. The test results in a real underwater environment show that G-Net improves the detection accuracy of underwater targets by about 5%, but also has high detection efficiency, which ensures the reliability of underwater robots in seabed investigation and aquaculture activities.

ObjectiveThis paper proposes a fuzzy sliding mode controller based on T-S fuzzy logic for the vertical plane motion control of an autonomous underwater glider (AUG) with limited actuator capability. MethodsIn the fuzzy sliding mode controller, the fuzzy switching rate is used to replace the switching rate in the fixed time controller to effectively suppress buffeting. The fuzzy switching rate is obtained by fitting the switching rate of the fixed time controller with T-S fuzzy rules. Based on the limited capabilities of AUG actuators, a saturation auxiliary system is designed to improve the actuator saturation effect. Finally, the performance of the system is verified by Lyapunov stability analysis and numerical simulation. ResultsThe results show that the AUG under the fuzzy sliding mode controller and the saturation auxiliary system can converge in finite time. The effectiveness of the fuzzy sliding mode controller and the saturation auxiliary system are verified by numerical simulation. ConclusionsBy making comparisons with the fixed-time controller, it is verified that the two controllers have similar control performance, and the buffeting of the fuzzy sliding mode controller is lesser.

The net structure of a cage is an important component to ensure the survival environment of aquaculture. In order to ensure the safety of the net, an experimental study on practical rigid nets is conducted in this paper. The drag force characteristics of the nets with different mesh sizes and different twine thicknesses are analyzed, and the impact of 90° and 45° attack angles on the drag force characteristics of the net are further studied. The empirical formulae for calculating the drag force of the net under different attack angles are given. The calculated drag forces are compared with the literature, and the results are in good agreement. Particularly, the current velocity is taken as one of the variables in the formulae to include the influence of nonlinear factors caused by the extreme current velocity in the drag force calculation.

Autonomous underwater manipulation is very important for the robotic and intelligence operations of oceanic engineering. However, a small target often involves limited features and results in inaccurate visual matching. In order to improve visual measurement accuracy, this paper has proposed an improved unsharp masking algorithm to further enhance the weak texture region of blurred and low contrast images. Moreover, an improved ORB feature-matching method with adaptive threshold, non-maximum suppression and improved random sample consensus has also been proposed. To overcome unknown underwater disturbances and uncertain system parameters in the underwater robotic manipulations, an adaptive non-singular terminal sliding mode controller has been proposed with a quasi-barrier function to suppress the chattering problem and improve grasp accuracy for small target. Oceanic experiments have been conducted to prove the performance of the proposed method.

The demand for oceanic resource exploration and development is increasing, and autonomous underwater vehicles (AUVs) have emerged as potential tools for ocean exploration. To meet specific operational requirements, AUVs are required to operate in close proximity to the seafloor. However, complex and changeable seafloor terrains have complex spatiotemporal characteristics, which produce unsteady hydrodynamic interference on AUVs, affecting their safety and stability. In order to investigate the hydrodynamic characteristics of AUVs in turbulent flow fields near seafloor terrains, this study selects a typical dune terrain. Based on the computational fluid dynamics (CFD) method, the flow field around an AUV near the seafloor is simulated, and the flow field is solved using the large eddy simulation (LES) method, calculating the AUV’s hydrodynamic instantaneous and average characteristics, as well as flow field characteristics at different inflow velocities and distances from the seafloor. The results indicate that when the AUV is situated within the turbulent flow field caused by the terrain, its drag, lift, and pitch moment performance present significant fluctuations. Variations in total resistance are primarily caused by variations in pressure resistance. Variations in lift and pitch moment are more sensitive to variations in the flow field structure. The AUV also affects the development of the turbulent flow field near the terrain. When the AUV is in close proximity to the seafloor, it hampers the development of vortices below it. These results will offer guidance for the maneuverability and control of AUVs in practical engineering applications.

This paper aims to understand the drag coefficient discrepancy between the equivalent-twine and twisted-twine nets based on their wake and turbulence characteristics. To that end, we conduct unsteady Reynolds-averaged Navier-Stokes (URANS) and the second-moment (Reynolds stress, RSM) simulations at a Reynolds number, Re=4.5×103, based on the effective diameter of the net twine, which corresponds to the subcritical flow regime. Then, the vortex structures and the turbulence statistics are assessed at AOA=90°. The results highlight that the wake interactions for the twisted-twine net are relatively strong compared to the equivalent-twine net, due to the disturbance of the helixes on the twisted twines. In comparison to the classical Karman vortex, the overall vortex shedding of these two nets is well organized. Symmetric vortices form behind the equivalent-twine net, while single vortices form behind the twisted-twine net. Moreover, the Reynolds normal and shear stresses show symmetric and anti-symmetric profiles. The addition of helixes to smooth circular cylinders changes the flow development, leading to a decrease of turbulence kinetic energy. With this understanding, engineers need to be carefully select the net type for preliminary design of marine aquaculture cages to avoid over- or underestimation of the drag forces.

Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.

Infrared images can rely on the thermal radiation of objects for imaging, independent of lighting conditions. Furthermore, because the thermal radiation produced by targets such as people, vehicles, and boats differs greatly from the background, it is able to distinguish objects from their environment as well. These characteristics of infrared can be complemented with visible images, which are rich in color information but vulnerable to lighting conditions. Therefore, the fusion of IR and visible images can provide a better perception of the environment. In this paper, we propose a new infrared–visible fusion algorithm. It consists of three parts: feature extraction, fusion, and reconstruction. The attention mechanism is introduced into the feature extraction to better extract features and we propose a new way of describing the fusion task. The relationship between the two inputs is balanced by introducing a fused image obtained by summing the infrared and visible images. It is also optimized for sky layering and water surface ripples, which are common in water environments. The edge information is enhanced in the loss function and noise reduction is performed. Through comparison experiments, our algorithm achieves better results.

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

This paper is concerned with the consensus of a system involving multiple underactuated autonomous underwater vehicles (AUVs). Combined with a dynamic event-triggered mechanism and a fixed-time stability theorem, the backstepping average consensus controllers are designed. Firstly, the new consensus control objective on the system for multiple underactuated AUVs in a body frame (BF) spherical coordinate system is proposed, and the tracking error kinematic equations are established based on the kinematic characteristics of the underactuated AUV. The fixed-time consensus controller is designed by the backstepping method, and the average consensus theorem is proposed to improve the Lyapunov function. Furthermore, the dynamic event-triggered mechanism is adopted to reduce the communication requirements and energy consumption. This is the first solution to the problem of a consensus controller design for a system of multiple underactuated AUVs. Finally, numerical simulation results demonstrate that the proposed method has superior effectiveness over alternatives.

The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault‐tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan‐type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault‐tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.

This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments.

For about a century, plant breeding has widely exploited the heterosis phenomenon–often considered as hybrid vigor–to increase agricultural productivity. The ensuing F1 hybrids can substantially outperform their progenitors due to heterozygous combinations that mitigate deleterious mutations occurring in each genome. However, only fragmented knowledge is available concerning the underlying genes and processes that foster heterosis. Although cotton is among the highly valued crops, its improvement programs that involve the exploitation of heterosis are still limited in terms of significant accomplishments to make it broadly applicable in different agro-ecological zones. Here, F1 hybrids were derived from mating a diverse Upland Cotton germplasm with commercially valuable cultivars in the Line × Tester fashion and evaluated across multiple environments for 10 measurable traits. These traits were dissected into five different heterosis types and specific combining ability (SCA). Subsequent genome-wide predictions along-with association analyses uncovered a set of 298 highly significant key single nucleotide polymorphisms (SNPs)/Quantitative Trait Nucleotides (QTNs) and 271 heterotic Quantitative Trait Nucleotides (hQTNs) related to agronomic and fiber quality traits. The integration of a genome wide association study with RNA-sequence analysis yielded 275 candidate genes in the vicinity of key SNPs/QTNs. Fiber micronaire (MIC) and lint percentage (LP) had the maximum number of associated genes, i.e., each with 45 related to QTNs/hQTNs. A total of 54 putative candidate genes were identified in association with HETEROSIS of quoted traits. The novel players in the heterosis mechanism highlighted in this study may prove to be scientifically and biologically important for cotton biologists, and for those breeders engaged in cotton fiber and yield improvement programs.

In this paper, under-directed topology, distributed coordinated tracking control schemes are proposed for multiple Euler-Lagrange systems with time-varying communication delays, nonlinear uncertainties, and external disturbances. Concerning with different leader velocities, both constant leader velocity case and time-varying leader velocity case are addressed by designing two different distributed observers. Combining with the proposed distributed leader velocity observers, two coordinated tracking control schemes are developed by the effort of neural network approximation and sliding mode technique, which can compensate the nonlinearities and uncertainties. For the first case, tracking errors are rigorously proved to be globally asymptotically converged by using Lyapunov-Krasovskii method. To further eliminate chattering caused by the discontinuous sign function, the saturation function is used for the second case, and the proposed control algorithm ensures the same convergence of tracking errors via Lyapunov analysis. Finally, the effectiveness of the proposed distributed tracking control schemes is verified by the numerical examples.

The development of marine petroleum exploitation becomes an important part of marine economy development gradually. Ocean bottom flying node (OBFN) is a kind of autonomous underwater vehicle which can explore marine petroleum and natural gas in large-scale deployment. This paper investigates the prescribed performance fault-tolerant trajectory tracking control method for an OBFN in presence of modeling uncertainties, ocean current disturbances, and thruster faults. By using a new performance function and an error transformation scheme, the proposed controller is able to achieve the desired tracking performance and the maximum convergence time is known as a priori. A disturbance observer is designed to deal with the influence of the modeling uncertainties, ocean disturbances, and thruster faults. The stability of the closed-loop OBFN system is proved by using Lyapunov theory. Finally, simulation results under different types of thruster faults illustrate the effectiveness of the proposed disturbance-observer-based prescribed performance fault-tolerant trajectory tracking controller.

This paper designs a prescribed performance fault-tolerant control strategy for the underwater acoustic sensor network nodes (UASNN) trajectory tracking control in the presence of ocean current disturbances, modeling uncertainties, and thruster faults. By using a general uncertainties observer, the influence of disturbances and uncertainties are estimated. Additionally, a novel performance function which determines explicitly the maximum convergence time is utilized. Based on the new performance function and corresponding error transformation, the 6-DOF tracking errors are restricted to prescribed bounds to ensure the desired transient and steady response. Furthermore, when considering thruster saturation, we introduce an auxiliary system to compensate for the saturation. The closed-loop system stability is proved by Lyapunov theory. The numerical simulations for three thruster faults are carried out to demonstrate that the proposed strategy is effective.

Abstract Background Heterosis, a multigenic complex trait extrapolated as sum total of many phenotypic features, is widely utilized phenomenon in agricultural crops for about a century. It is mainly focused on establishing vigorous cultivars with the fact that its deployment in crops necessitates the perspective of genomic impressions on prior selection for metric traits. In spite of extensive investigations, the actual mysterious genetic basis of heterosis is yet to unravel. Contemporary crop breeding is aimed at enhanced crop production overcoming former achievements. Leading cotton improvement programs remained handicapped to attain significant accomplishments. Results In mentioned context, a comprehensive project was designed involving a large collection of cotton accessions including 284 lines, 5 testers along with their respective F1 hybrids derived from Line × Tester mating design were evaluated under 10 diverse environments. Heterosis, GCA and SCA were estimated from morphological and fiber quality traits by L × T analysis. For the exploration of elite marker alleles related to heterosis and to provide the material carrying such multiple alleles the mentioned three dependent variables along with trait phenotype values were executed for association study aided by microsatellites in mixed linear model based on population structure and linkage disequilibrium analysis. Highly significant 46 microsatellites were discovered in association with the fiber and yield related traits under study. It was observed that two-thirds of the highly significant associated microsatellites related to fiber quality were distributed on D sub-genome, including some with pleiotropic effect. Newly discovered 32 hQTLs related to fiber quality traits are one of prominent findings from current study. A set of 96 exclusively favorable alleles were discovered and C tester (A971Bt) posited a major contributor of these alleles primarily associated with fiber quality. Conclusions Hence, to uncover hidden facts lying within heterosis phenomenon, discovery of additional hQTLs is required to improve fibre quality. To grab prominent improvement in influenced fiber quality and yield traits, we suggest the A971 Bt cotton cultivar as fundamental element in advance breeding programs as a parent of choice.

Abstract In recent years, with the use of the float-based wireless sensor network in the Argo project, a large amount of ocean data has been obtained. These data can be applied to analyze the oceanic thermocline, and the forecasting trend of the thermocline through the SVR method in machine learning is presented in this paper. Firstly, this paper refines the spatial resolution with the SVR method and determines the lateral boundary of the three-dimensional thermocline through the information entropy method. Combined with BOA Argo data from 2004 to 2015, this paper then predicts the thermocline trend (10°–25° S and 55°–80° E) over the next 4 years. The results show that the trend of the three-dimensional thermocline’s lateral boundary can be effectively predicted with the application of SVR method.

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.

Fluid–structure interactions of flexible net panels are complex and lack sufficient exploration. To examine the flow characteristics of flexible net panels with large deformation, we propose a partitioned coupling scheme in this paper. The coupled fluid–structure equations are solved separately under finite volume and finite element frameworks. The interface traction from the fluid solver is considered as a Neumann boundary condition for the solid domain, and the interface velocity is applied as a Dirichlet boundary condition for the fluid problem. Then, the forces can be transferred along the interface via Dirichlet-to-Neumann mapping. The results show that both the drag coefficient and the velocity reduction increase alongside the net solidity ratio (Sn), but they decrease as the Reynolds number/attack angle increases. A comparative study of drag coefficients is made between the present numerical simulations and the analytical predictions. This paper also examines the velocity distribution and vortex formation of flexible net panels. A single vortex forms in the shear layers and the wake when Sn = 0.16, and a pair of vortices mostly forms in the wake when Sn = 0.33. The vertical net twines predominantly affect the formation of the vortex behind the net, leading to delayed vortex shedding. The flow exhibits wake interactions due to the interference between the net twines in the high-solidity net panel. No such interference occurs in the low-solidity net panel, but the altered shear layers could cause severe velocity fluctuations in the near field.

In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme.

Underwater sensor networks are facing a great challenge in designing a routing protocol with longer network lifetime and higher packet delivery rate under the complex underwater environment. In this paper, we propose an energy-aware and void-avoidable routing protocol (EAVARP). EAVARP includes layering phase and data collection phase. During the layering phase, concentric shells are built around sink node, and sensor nodes are distributed on different shells. Sink node performs hierarchical tasks periodically to ensure the validity and real-time of the topology. It makes EAVARP apply to dynamic network environment. During the data collection phase, data packets are forwarded based on different concentric shells through opportunistic directional forwarding strategy (ODFS), even if there are voids. The ODFS takes into account the remaining energy and data transmission of nodes in the same shell, and avoids cyclic transmission, flooding, and voids. The verification and analysis of simulation results show the effectiveness of our proposed EAVARP in terms of selecting performance matrics in comparison to existing routing protocols.

This paper is concerned with data-driven adaptive tracking control for unknown autonomous marine vehicles (AMVs) with uncertainties and disturbances. By deploying the data-driven technique and observer design, an equivalent data model of the AMV is firstly established. Based on the proposed data model, a novel data-driven adaptive tracking controller is designed, and the corresponding stability analysis for the closed-loop AMV system is presented theoretically. Finally, simulation studies are given to demonstrate the validity of the main results.

The ocean bottom flying node is a novel autonomous underwater vehicle that explores the oil and gas resources in deep water. Thousands of the ocean bottom flying nodes track different predefined trajectories arriving at target points in a small ocean area, respectively. A class of prescribed performance adaptive trajectory tracking control method is investigated for the ocean bottom flying node trajectory tracking problem with ocean current disturbances, model uncertainties as well as thruster faults. Based on a predefined performance function and an error transformation, the ocean bottom flying node trajectory tracking error is restricted to prespecified bounds to ensure a desired transient and steady response. Radial basis function neural network is used to approximate the general uncertainty caused by ocean current disturbances, model uncertainties, and thruster faults. Further, the upper bound of approximation error is estimated by an adaptive law. Using the adaptive laws, we propose a prescribed performance adaptive trajectory tracking controller. The simulation examples on an ocean bottom flying node system show that the proposed control scheme can compensate for the effect of the general uncertainty while obtaining the fast transient process and expected trajectory tracking accuracy.

This article investigates the trajectory tracking control problem for unmanned surface vessels with input saturation and full-state constraints. The barrier Lyapunov function is used to solve the problem of state constraints, and the adaptive method is employed to handle the unknown random disturbances and saturation problems. The proposed control approach can guarantee that the control law and signals of closed-loop system are uniformly bounded and achieve the asymptotic tracking. Finally, simulation studies are provided to show the effectiveness of the proposed method.

Aiming to optimize the truss leg structure of self-elevating unit, the sensitivity of leg strength to different chord space was analyzed, and the optimal value of chord space for legs was presented. The wind load under storm condition was obtained by wind tunnel test, and the wave and current loads were calculated based on theoretical and numerical methods. The natural vibration period and first order offset value were obtained by eigenvalue analysis, thus the inertial load considering the dynamic amplification factor and the inertial moment considering the geometric nonlinearity were obtained. It is found out through analysis that: the inertial load considering the dynamic amplification factor is more sensitive to chord space under a certain wave and current angle, and decreases with the increase of chord space in general; the inertial moment considering the geometric nonlinearity also decreases with the increase of chord space. According to the environment load, the strength of leg structures at different chord spaces were checked and the sensitivity of the structure strength to chord space was compared. Based on this, the optimal values of chord space for legs at three water depths were presented. Key words: self-elevating unit, environment load, leg strength, chord space

Underwater vehicles coordination and formation have attracted increasingly attentions since their great potential on the real-world applications. However, usually such vehicles are underactuated and with very different environmental difficulties, which are different from those vehicles (robots) on the land. This study proposes a novel approach to integrate potential field and interval type-2 fuzzy learning algorithm for autonomous underwater vehicles formation control based on formation system framework. For the system nonlinearity and complicated environment, support vector machine has been applied to generate optimal rules for the type-2 fuzzy systems. This approach can generate optimal and reasonable formation rules on the face of different situations through classification. Furthermore, reinforcement learning has been combined with fuzzy systems to deal with limited communication state during formation. Therefore, autonomous underwater vehicles can not only execute actions through the evaluation, but also can avoid coupling character between communication state and potential field. Finally, simulations and experiments results have been extensively performed to validate the proposed methods.

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

A novel time-domain body-nonlinear Green’s function method is developed for evaluating large-amplitude roll damping of two-dimensional floating bodies with consideration of viscous dissipation effects. In the method, the instantaneous wetted surface of floating bodies is accurately considered, and the viscous dissipation effects are taken into account based on the “fairly perfect fluid” model. As compared to the method based on the existing inviscid body-nonlinear Green’s function, the newly proposed method can give a more accurate damping coefficient of floating bodies rolling on the free surface with large amplitudes according to the numerical tests and comparison with experimental data for a few cases related to ship hull sections with bilge keels.

The mean water surface (interface) under the air cushion of a surface effect ship (SES) or an air cushion supported platform (ACSP) is generally lower than the outside water surface due to the overpressure of the air cushion. To precisely analyze the hydrodynamics under the air cushion, multiple water levels should be considered in numerical models. However, when using free surface Green’s functions as numerical methods, the water level difference cannot be taken into account, because free surface Green’s functions normally require users to set in the whole water domain a unique datum water surface that completely separates the air domain and the water domain. To overcome this difficulty, a multi-domain approach is incorporated into a 2.5D method that is based on a time domain free surface Green’s function with viscous dissipation effects in this paper. In the novel multi-domain 2.5D method, the water domain is partitioned into inner and outer domains, and the interface is located in the inner domain while the outside water surface is placed in the outer domain. In each domain there exists only one unique water level, while water levels in different domains are allowed to be different. Benefited from this characteristic, the multi-domain 2.5D method is able to precisely consider the water level difference and its influence on hydrodynamics. The newly proposed multi-domain 2.5D method is employed to predict the hydrodynamics of an SES, and it is confirmed that the multi-domain 2.5D method can give better numerical results than the single-domain one for the given case.

A novel time-domain Green’s function is developed for dealing with two-dimensional interaction between water waves and floating bodies with considering viscous dissipation effects based on the “fairly perfect fluid” model. In the Green’s function, the temporal (lower order viscosity coefficient term) and spatial (higher order viscosity coefficient term) viscous dissipation effects are fully considered. As compared to the methods based on the existing time-domain Green’s functions that could not account for the spatial viscous dissipation, the method based on the new time-domain Green’s function can give much better numerical results and overcome instability problems related to the existing Green’s function, according to the numerical tests and comparison with CFD modeling data for a few cases related to floating bodies with a flare angle.

Detecting QTLs (quantitative trait loci) that enhance cotton yield and fiber quality traits and accelerate breeding has been the focus of many cotton breeders. In the present study, 359 SSR (simple sequence repeat) markers were used for the association mapping of 241 Upland cotton collections. A total of 333 markers, representing 733 polymorphic loci, were detected. The average linkage disequilibrium (LD) decay distances were 8.58 cM (r2 > 0.1) and 5.76 cM (r2 > 0.2). 241 collections were arranged into two subgroups using STRUCTURE software. Mixed linear modeling (MLM) methods (with population structure (Q) and relative kinship matrix (K)) were applied to analyze four phenotypic datasets obtained from four environments (two different locations and two years). Forty-six markers associated with the number of bolls per plant (NB), boll weight (BW), lint percentage (LP), fiber length (FL), fiber strength (FS) and fiber micornaire value (FM) were repeatedly detected in at least two environments. Of 46 associated markers, 32 were identified as new association markers, and 14 had been previously reported in the literature. Nine association markers were near QTLs (at a distance of less than 1-2 LD decay on the reference map) that had been previously described. These results provide new useful markers for marker-assisted selection in breeding programs and new insights for understanding the genetic basis of Upland cotton yields and fiber quality traits at the whole-genome level.

We introduce two subequations with different independent variables for constructing exact solutions to nonlinear partial differential equations. In order to illustrate the efficiency and usefulness, we apply this method to 2+1-dimensional KdV equation, which was first derived by Boiti et al. (1986) using the idea of the weak Lax pair. As a result, we obtained many new exact solutions.

One mapping population derived from Tifrunner × GT-C20 has shown great potential in developing a high density genetic map and identifying quantitative trait loci (QTL) for important disease resistance, tomato spotted wilt virus (TSWV) and leaf spot (LS). Both F and F generation-based genetic maps were previously constructed with 318 and 239 marker loci, respectively. Higher map density could be achieved with the F map (5.3 cM per locus) as compared to the F (5.7 cM per locus). Quantitative trait loci analysis using multi-environment phenotyping data from F and higher generations for disease resistance identified 54 QTL in the F map including two QTL for thrips (12.14–19.43% phenotypic variation explained [PVE]), 15 for TSWV (4.40–34.92% PVE), and 37 for LS (6.61–27.35% PVE). Twenty-three QTL could be identified in the F map including one QTL for thrips (5.86% PVE), nine for TSWV (5.20–14.14% PVE), and 13 for LS (5.95–21.45% PVE). Consistent QTL identified in each map have shown higher phenotypic variance than nonconsistent QTL. As expected, the number of QTL and their estimates of phenotypic variance were lower in the F map. This is the first QTL study reporting novel QTL for thrips, TSWV, and LS in peanut ( L.), and therefore, future studies will be conducted to refine these QTL.

Only a few genetic maps based on recombinant inbred line (RIL) and backcross (BC) populations have been developed for tetraploid groundnut. The marker density, however, is not very satisfactory especially in the context of large genome size (2800 Mb/1C) and 20 linkage groups (LGs). Therefore, using marker segregation data for 10 RILs and one BC population from the international groundnut community, with the help of common markers across different populations, a reference consensus genetic map has been developed. This map is comprised of 897 marker loci including 895 simple sequence repeat (SSR) and 2 cleaved amplified polymorphic sequence (CAPS) loci distributed on 20 LGs (a01-a10 and b01-b10) spanning a map distance of 3, 863.6 cM with an average map density of 4.4 cM. The highest numbers of markers (70) were integrated on a01 and the least number of markers (21) on b09. The marker density, however, was lowest (6.4 cM) on a08 and highest (2.5 cM) on a01. The reference consensus map has been divided into 20 cM long 203 BINs. These BINs carry 1 (a10_02, a10_08 and a10_09) to 20 (a10_04) loci with an average of 4 marker loci per BIN. Although the polymorphism information content (PIC) value was available for 526 markers in 190 BINs, 36 and 111 BINs have at least one marker with >0.70 and >0.50 PIC values, respectively. This information will be useful for selecting highly informative and uniformly distributed markers for developing new genetic maps, background selection and diversity analysis. Most importantly, this reference consensus map will serve as a reliable reference for aligning new genetic and physical maps, performing QTL analysis in a multi-populations design, evaluating the genetic background effect on QTL expression, and serving other genetic and molecular breeding activities in groundnut.