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There are significant advantages using the autonomous underwater vehicle (AUV) for underwater search. Compared with a single AUV, multi-AUV offers greater efficiency and better stability in underwater search. At the same time, the theoretical and technical level of autonomous navigation and cooperative control of multi-AUV formation is the key to the implementation of the underwater search task. The following key factors are worth discussing in the application of multi-AUV in underwater search: task allocation, path planning, and formation control. The purpose of this paper is to grasp the application and development trend of multi-AUV formation in underwater search, so as to summarize the past, present, and future research and development trends of this investigation field in detail. • To the best of authors' knowledge, this is the first systematic and comprehensive survey of multi-AUV underwater search, and is intended to assist research and development of multi-AUV underwater search applications. • Several aspects of task allocation, path planning, and formation control are considered and reviewed. The strengths, weaknesses, limitations, and challenges of the respective algorithms of the above techniques are explicitly discussed, to gain a deeper understanding of how multi-AUV multi-target search is conducted. • The opportunities and challenges of this research are listed based on the purpose of using multi-AUV underwater multi-target search. [ABSTRACT FROM AUTHOR]

This paper explores the fault-tolerant control (FTC) problem for full-state error constrained attitude tracking of an uncertain spacecraft. The existing FTC solutions to this problem generally rely on an uniform strong controllability (USC) assumption that may not hold in the presence of multiplicative actuator faults. In this work, a sufficient condition for the USC assumption is presented to help evaluate the applicability of existing FTC solutions. Then, based on the unified error transformation method and symmetric barrier Lyapunov functions, a saturated adaptive robust FTC algorithm is proposed, which can eliminate the USC assumption without requiring the multiplicative actuator faults to be differentiable, while ensuring that (i) full-state error constraints are never violated; (ii) all closed-loop signals are bounded, despite the simultaneous presence of inertia uncertainties, disturbances, and actuator faults. Finally, simulation results are provided to verify our theoretical findings. [ABSTRACT FROM AUTHOR]

• A novel AHNM, which not only compensates l 1 -norm with l 2 -norm, but also adaptively evaluates the importance of each singular value by introducing a significance factor, is proposed. • The proposed AHNM is efficiently solved by using the alternating minimization method. In particular, all the subproblems associated with the proposed AHNM model have closed-form solutions. • Based on the proposed AHNM, we construct a general image restoration framework and develop an efficient image restoration algorithm. • Extensive experimental results demonstrate that our proposed AHNM method outperforms many state-of-the-art methods in terms of quantitative and qualitative quality. Rank minimization methods have achieved promising performance in various image processing tasks. However, there are still two challenging problems in the existing works. One is that most of the current methods only regularize singular values by using a single l 1 -norm, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM). Consequently, many small singular values are shrunk to zero, which is unbeneficial for restoring image details. The other is that how to adaptively evaluate the importance of each singular value is still a suspending problem. In this paper, we propose a novel rank minimization method, namely adaptive hybrid norm minimization (AHNM) model, to solve the above problems. Specifically, for each singular value, we employ l 2 -norm to compensate for l 1 -norm, and introduce a significance factor to assess its importance adaptively. More importantly, we show that closed-form solutions for all subproblems can be derived simply by using alternating optimization. With the aid of the proposed AHNM model, we further develop a general yet effective image restoration algorithm based on the nonlocal self-similarity (NSS) of images. Numerous experimental results demonstrate that the proposed AHNM model consistently outperforms many state-of-the-art restoration methods, including model-based methods and deep learning-based methods. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of air-breathing hypersonic vehicles subject to input nonlinearities, aerodynamic uncertainties and flexible modes. An adaptive backstepping controller and a dynamic inverse controller are developed for the altitude subsystem and the velocity subsystem, respectively, where the former eliminates the problem of “explosion of terms” inherent in backstepping control. Moreover, a modified smooth inverse of the dead-zone is proposed to compensate for the dead-zone effects and reduce the computational burden. Based on this smooth inverse, an input nonlinear pre-compensator is designed to handle input saturation and dead-zone nonlinearities, which leads to a simpler control design for the altitude subsystem subject to these two input nonlinearities. It is proved that the proposed controllers can guarantee that all closed-loop signals are bounded and the tracking errors converge to an arbitrarily small residual set. Simulation results are carried out to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SN NM) boundaries and iron-based SN (SN QSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SN NM volume, SN QSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SN NM , and 0.87 and 1.05 for the SN QSM , respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SN NM , and 0.87 and 0.96 for the SN QSM , respectively. The SN QSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SN NM volume, SN QSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SN QSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SN NM volume, SN QSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically. [ABSTRACT FROM AUTHOR]

A centralized multi-AGV motion planning method is proposed. In contrast to the prevalent planners with decentralized (decoupled) formulations, a centralized planner contains no priority assignment, decoupling, or other specification strategies, thus is free from being case-dependent and deadlock-involved. Although centralized motion planning is computationally expensive, it deserves investigations in schemes that are sensitive to solution quality but insensitive to computation time. Specifically, centralized multi-AGV motion planning is formulated as an optimal control problem in this work, wherein differential algebraic equations are used to describe the AGV dynamics, mechanical restrictions, and exterior constraints. Orthogonal collocation direct transcription method is adopted to discretize the original infinite-dimensional optimal control problem into a large-scale nonlinear programming (NLP) problem, which is solved using interior point method thereafter. Exhaustive simulations are conducted on 10-AGV formation reconfiguration tasks. Simulation results show the validation, unification, and real-time implementation potential of the introduced centralized planner. Particularly, the computation time on a PC reduces to several seconds with near-optimal initial guess in the NLP solving process, making receding horizon replanning possible via this centralized planner. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) are widely utilized for graph data mining, attributable to their powerful feature representation ability. Yet, they are prone to adversarial attacks with only slight perturbations of input data, limiting their applicability to critical applications. Vulnerability analysis of GNNs is thus essential if more robust models are to be developed. To this end, a Layer-wise Relevance Propagation based Adversarial attacking (LRP2A) model is proposed 1 1 Data and code are available at https://github.com/duiyady/LRP2A.. Specifically, to facilitate applying LRP to the "black-box" victim model, we train a surrogate model based on a sophisticated re-weighting network. The LRP algorithm is then leveraged for unraveling "contributions" among the nodes in the downstream classification task. Furthermore, the graph adversarial attacking algorithm is intentionally designed to be both interpretable and efficient. Experimental results prove the effectiveness of the proposed attacking model on GNNs for node classification. Additionally, the adoption of LRP2A allows the choice of the adversarial attacking strategies on the GNN interpretable, which in turn can gain deeper insights on the GNN's vulnerability. [ABSTRACT FROM AUTHOR]

A fault-tolerant control approach without rate sensors is presented for the attitude stabilization of a satellite being developed. External disturbances, reaction wheel faults, actuator saturation, and unavailable angular velocity are addressed. A sliding-mode observer is proposed by using attitude feedback only, and the unavailable angular velocity is estimated by this observer in finite time. Using the attitude and the estimated velocity, another sliding-mode observer is proposed to reconstruct actuator faults and disturbances. It is proven that reconstruction with zero observer error is achieved in finite time. With the reconstructed value, a velocity-free controller is then developed to asymptotically stabilize the attitude. Simulation results are also provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

• STN-DBS and GPi-DBS exhibit a similar effect of topological restoration on the motor brain network. • The ability of STN-DBS and GPi-DBS to improve motor function is reflected in enhanced local efficiency of motor brain regions. • STN-DBS and GPi-DBS decrease and increase, respectively, regional information processing capacity and transmission efficiency of distinct non-motor regions. • The effect of STN-DBS on non-motor topological features persists after DBS is switched off, while that of GPi-DBS dose not. The subthalamic nucleus (STN) and globus pallidus internus (GPi) are the two most common and effective target brain areas for deep brain stimulation (DBS) treatment of advanced Parkinson's disease. Although DBS has been shown to restore functional neural circuits of this disorder, the changes in topological organization associated with active DBS of each target remain unknown. To investigate this, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 34 medication-free patients with Parkinson's disease that had DBS electrodes implanted in either the subthalamic nucleus or internal globus pallidus (n = 17 each), in both ON and OFF DBS states. Sixteen age-matched healthy individuals were used as a control group. We evaluated the regional information processing capacity and transmission efficiency of brain networks with and without stimulation, and recorded how stimulation restructured the brain network topology of patients with Parkinson's disease. For both targets, the variation of local efficiency in motor brain regions was significantly correlated (p < 0.05) with improvement rate of the Uniform Parkinson's Disease Rating Scale-III scores, with comparable improvements in motor function for the two targets. However, non-motor brain regions showed changes in topological organization during active stimulation that were target-specific. Namely, targeting the STN decreased the information transmission of association, limbic and paralimbic regions, including the inferior frontal gyrus angle, insula, temporal pole, superior occipital gyri, and posterior cingulate, as evidenced by the simultaneous decrease of clustering coefficient and local efficiency. GPi-DBS had a similar effect on the caudate and lenticular nuclei, but enhanced information transmission in the cingulate gyrus. These effects were not present in the DBS-OFF state for GPi-DBS, but persisted for STN-DBS. Our results demonstrate that DBS to the STN and GPi induce distinct brain network topology reconstruction patterns, providing innovative theoretical evidence for deciphering the mechanism through which DBS affects disparate targets in the human brain. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimal reliability in over-actuated systems. Overloading an actuator decreases its overall lifetime and reduces its average performance over a long time. Therefore, performance and reliability are two conflicting requirements. While appropriate reliability is related to average loads, good performance is related to fast response and sufficient loads generated by actuators. Actuator redundancy allows us to address both performance and reliability at the same time by properly allocating desired loads among redundant actuators. The main contribution of this paper is the on-line optimization of the overall plant reliability according to performance objective using an MIT (Massachusetts Institute of Technology) rule-based method. The effectiveness of the proposed method is illustrated through an experimental application to an octocopter helicopter testbed. [ABSTRACT FROM AUTHOR]

Background: We previously demonstrated that macrophage low-density lipoprotein receptor (LDLR)-related protein 1 (LRP1) deficiency increases atherosclerosis despite antiatherogenic changes including decreased uptake of remnants and increased secretion of apolipoprotein E (apoE). Thus, our objective was to determine whether the atheroprotective effects of LRP1 require interaction with apoE, one of its ligands with multiple beneficial effects.Methods and Results: We examined atherosclerosis development in mice with specific deletion of macrophage LRP1 (apoE(-/-) MΦLRP1(-/-)) and in LDLR(-/-) mice reconstituted with apoE(-/-) MΦLRP1(-/-) bone marrow. The combined absence of apoE and LRP1 promoted atherogenesis more than did macrophage apoE deletion alone in both apoE-producing LDLR(-/-) mice (+88%) and apoE(-/-) mice (+163%). The lesions of both mouse models with apoE(-/-) LRP1(-/-) macrophages had increased macrophage content. In vitro, apoE and LRP1 additively inhibit macrophage apoptosis. Furthermore, there was excessive accumulation of apoptotic cells in lesions of both LDLR(-/-) mice (+110%) and apoE(-/-) MΦLRP1(-/-) mice (+252%). The apoptotic cell accumulation was partially due to decreased efferocytosis as the ratio of free to cell-associated apoptotic nuclei was 3.5-fold higher in lesions of apoE(-/-) MΦLRP1(-/-) versus apoE(-/-) mice. Lesion necrosis was also increased (6 fold) in apoE(-/-) MΦLRP1(-/-) versus apoE(-/-) mice. Compared with apoE(-/-) mice, the spleens of apoE(-/-) MΦLRP1(-/-) mice contained 1.6- and 2.4-fold more total and Ly6-C(high) monocytes. Finally, there were 3.6- and 2.4-fold increases in Ly6-C(high) and CC-chemokine receptor 2-positive cells in lesions of apoE(-/-) MΦLRP1(-/-) versus apoE(-/-) mice, suggesting that accumulation of apoptotic cells enhances lesion development and macrophage content by promoting the recruitment of inflammatory monocytes.Conclusion: Low-density lipoprotein receptor protein 1 exerts antiatherogenic effects via pathways independent of apoE involving macrophage apoptosis and monocyte recruitment. [ABSTRACT FROM AUTHOR]

This paper presents a dual adaptive fault-tolerant control strategy for a quadrotor helicopter based on adaptive sliding mode control and adaptive boundary layer. Within the proposed adaptive control strategy, both model uncertainties and actuator faults can be compensated without the knowledge of the uncertainty bounds and fault information. By virtue of the proposed adaptive control scheme, the minimum discontinuous control gain is adopted, which significantly reduces the control chattering effect. As compared to the existing adaptive sliding mode control schemes in the literature, larger actuator faults can be tolerated by employing the proposed control scheme while suppressing control chattering. Moreover, boundary layer is used to smoothen control discontinuity and further eliminate control chattering. Nevertheless, the choice of boundary layer thickness is a trade-off between system stability and tracking accuracy. By explicitly considering this fact, an adaptive boundary layer is developed and synthesized with the proposed adaptive control framework to ensure stability and tracking accuracy of the considered system. When the control parameter tends to be overestimated, the thickness of boundary layer can be appropriately adjusted to avoid control parameter overestimation. Simulation and experimental tests of a quadrotor helicopter are both conducted to validate the effectiveness of the proposed control scheme. Its advantages are demonstrated in comparison with a conventional adaptive sliding mode control scheme. [ABSTRACT FROM AUTHOR]