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This study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI). Twenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NM RC-SNpc) and volume (NM volume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage. The PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NM volume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NM RC-SNpc. The PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NM volume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients. [ABSTRACT FROM AUTHOR]

The visualization and identification of the deep cerebellar nuclei (DCN) (dentate [DN], interposed [IN] and fastigial nuclei [FN]) are particularly challenging. We aimed to visualize the DCN using quantitative susceptibility mapping (QSM), predict the contrast differences between QSM and T2* weighted imaging, and compare the DCN volume and susceptibility in movement disorder populations and healthy controls (HCs). Seventy‐one Parkinson's disease (PD) patients, 39 essential tremor patients, and 80 HCs were enrolled. The PD patients were subdivided into tremor dominant (TD) and postural instability/gait difficulty (PIGD) groups. A 3D strategically acquired gradient echo MR imaging protocol was used for each subject to obtain the QSM data. Regions of interest were drawn manually on the QSM data to calculate the volume and susceptibility. Correlation analysis between the susceptibility and either age or volume was performed and the intergroup differences of the volume and magnetic susceptibility in all the DCN structures were evaluated. For the most part, all the DCN structures were clearly visualized on the QSM data. The susceptibility increased as a function of volume for both the HC group and disease groups in the DN and IN (p <.001) but not the FN (p =.74). Only the volume of the FN in the TD‐PD group was higher than that in the HCs (p =.012), otherwise, the volume and susceptibility among these four groups did not differ significantly. In conclusion, QSM provides clear visualization of the DCN structures. The results for the volume and susceptibility of the DCN can be used as baseline references in future studies of movement disorders. [ABSTRACT FROM AUTHOR]

Graph neural network (GNN) explainers aim to elucidate the prediction behavior of GNNs, facilitating their wide adoption in high-stakes tasks. Current approaches typically define multiple objective functions to construct comprehensive and accurate explainers. Nevertheless, optimizing GNN explainers with multiple objective functions is challenging because of conflicts between the gradients among these objectives, which may result in suboptimal solutions. To eliminate potential conflicts and enhance explainer optimization, we introduce GEAR, which is a novel framework that adjusts the gradients to optimize the GNN explainer. Specifically, we attempt to define comprehensive objectives from multiple perspectives that are crucial for optimizing GNN explainers, including fidelity, sparsity, counterfactual reasoning, and connectivity. Subsequently, we purposefully determine the dominant gradient and angle threshold of the conflicting gradients from a geometric perspective. More importantly, we propose a simple yet effective gradient adjuster to refine the gradients during explainer optimization. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed GEAR. In addition, state-of-the-art explainers with the incorporated gradient adjuster outperform their counterparts without the proposed gradient adjuster. The data and code are available at https://github.com/ymzhang0103/GEAR. [ABSTRACT FROM AUTHOR]

Mapping and monitoring initial burn severity is a critical aspect of forest management and landscape restoration. Recently, supervised learning has achieved great success in evaluating the post-fire forest condition, but plenty of labeled samples are needed to carry out training for supervised learning. However, due to the limitation of accessible labeled data, it is often arduous to put supervised learning into practice in the remote sensing field. In this paper, a novel semi-unsupervised image classification framework by using generative adversarial networks under the Google Earth Engine (GEE) platform is proposed to undertake the burn severity assessment with limited samples. The generative model can produce additional adversarial samples that look like the real samples; therefore, the discriminative model gains better classification capabilities in burn severity assessment with the extra training data provided by the generator. The detailed evaluation and comparative experiments are done in the context of post-fire assessment on eleven forest fires in northern California in the United States. The experimental results demonstrate that our approach significantly outperforms the two other most well-known methods. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/Simulink simulations and experimental tests based on Quanser latest self-driving car have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints. [ABSTRACT FROM AUTHOR]

This article considers the decentralized fractional-order fault-tolerant control problem for unmanned aerial vehicles (UAVs) against wind disturbances and actuator faults in a directed communication network. A new composite adaptive disturbance observer-based decentralized fractional-order fault-tolerant control (CADOB-DFO-FTC) scheme, which incorporates fractional-order (FO) sliding-mode surfaces, nonlinear disturbance observers (NDOs), fuzzy wavelet neural networks (FWNNs), and robust controllers, is developed to achieve the attitude tracking control of networked UAVs in a decentralized way. Based on the FO sliding-mode surfaces, the NDOs are first developed to estimate the lumped uncertainties due to the aerodynamic parameter perturbations, wind disturbances, and actuator faults. Then, adaptive FWNNs with updating weighting matrices, mean vectors, and deviation vectors are constructed to effectively attenuate the adverse effects induced by the NDO estimation errors. Furthermore, to compensate the FWNN approximation errors, robust controllers are integrated into the developed control scheme to enhance the approximation abilities. It is shown that by using Lyapunov methods, all UAVs can track their attitude references. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, spectral unmixing is a hot issue in the hyperspectral remote sensing technology and applications. Within various methods, sparse regression is one of the most commonly used methods in the hyperspectral unmixing community. In order to overcome the limitation of spatial correlation of existing sparse unmixing methods and improve further the accuracy of sparsity representation, a novel hyperspectral linear unmixing method via fusion of collaborative sparsity and multi-band non-local total variation is proposed. This method analyses the sparsity and spatially geometrical structure of hyperspectral pixels by studying the linear spectral mixed model of hyperspectral images, and reveals the interior spatial correlation of hyperspectral images with the non-local variation framework. Specifically, the sparsity and spatial correlation of hyperspectral imagery are discussed, and a hyperspectral linear sparse unmixing model is afterwards constructed by combining the relaxation technique and non-local total variation framework. Second, the solution procedure of the above spectral unmixing model is divided into two steps under the variable separation method: fractional abundance estimation and abundance map restoration. The alternating direction method of multipliers (ADMM) and split Bregman operator are exploited to obtain the spectral unmixing results. Finally, the proposed hyperspectral unmixing algorithm is evaluated with synthetic and real hyperspectral datasets. In the experiments with a synthetic hyperspectral dataset, the feasibility and effectiveness of the method are analysed quantitatively and qualitatively. The quantitative metrics and visual examination of the estimated fractional abundance map are also better than the performance of the current mainstream hyperspectral sparse unmixing algorithms. Furthermore, two real hyperspectral datasets are applied to the algorithm of this work to prove its practicability. [ABSTRACT FROM AUTHOR]

Various therapies are used in patients with internet gaming disorder (IGD), but their comparative effectiveness in reducing IGD symptoms is unknown. The present systematic review and meta-analysis evaluated the effectiveness of pharmacotherapy, psychotherapy and pharmacological-psychological combined therapy on IGD related-outcomes and its comorbid mental disorders. Following PRISMA guidelines, a search of MEDLINE, EMBASE, Ovid Emcare, PsycINFO and the Cochrane Clinical Trials Library was conducted by Aug 04, 2023. We pooled effect sizes as within-group Hedges's g and performed meta-regression using random effects models. Systematic review identified 43 studies and 32 of them with 1738 participants were included for meta-analysis. Pharmaco-, psycho- and combined therapies showed large-to-moderate effect sizes for IGD symptoms and comorbid mental disorders. Combined therapies (Hedges's g = −2.11, 95% confidence interval: −2.87 to −1.35) showed larger effect size than pharmacotherapies (Hedges's g = −1.10, −1.31 to −0.89) or psychotherapies (Hedges's g = −0.99, −1.22, −0.76) on IGD reduction at post-intervention. The effects maintained at follow-up in psycho- and combined therapies. Bupropion and cognitive behavioral therapy showed advantages over their counterparts. The three types of therapies are promising to reduce IGD symptoms and comorbidities, but cautious interpretation is required given the limitations and high heterogeneity in the existing interventions. • Pharmaco-, psycho- and combined therapies were promising to reduce IGD symptoms and comorbid mental disorders. • Combined therapy was more effective than pharmacotherapies or psychotherapies alone on IGD reduction at post-intervention. • The effects on IGD reduction maintained at follow-up in psycho- and combined therapies. • Bupropion and cognitive behavioral therapy showed advantages over their counterparts. • Cautious interpretation is required given the limitations and high heterogeneity in the existing interventions. [ABSTRACT FROM AUTHOR]

This article investigates the fault‐tolerant containment control (FTCC) problem for a group of fixed‐wing unmanned aerial vehicles with simultaneous considerations of faults and collision avoidance. A fractional‐order (FO) FTCC scheme is established to steer all follower UAVs into the convex hull formed by the leader UAVs with the involvements of FO calculus, disturbance observers (DOs), and interval type‐2 fuzzy neural networks (IT2FNNs). In the proposed control protocol, FO sliding‐mode surfaces with artificial potential functions are first designed to revamp the filtered containment errors. Then, the DOs with FO calculus are constructed to estimate the FO lumped disturbances due to faults and external disturbances. Moreover, to compensate for the DO estimation errors, IT2FNN learning mechanisms are introduced to improve the FTCC capability. It is shown by Lyapunov stability analysis that all follower UAVs can successfully converge into the convex hull spanned by the leader UAVs without collisions even when a portion of UAVs is encountered by faults. Simulation results are presented to show the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

Color is a crucial feature to consider when breeding and improving strains of Auricularia cornea. To uncover the mechanism of white strain formation in A. cornea, this study selected parental strains that were homozygous for the color trait and analyzed the genetic laws of A. cornea color through genetic population construction, such as test-cross, back-cross, and self-cross populations, and the statistical analysis of color trait segregation. Moreover, the study developed SSR molecular markers to construct a genetic linkage map, perform the fine mapping the color-controlling genetic locus, and verify candidate genes using yeast two-hybrid, transcriptome analysis, and different light treatments. The results of the study indicated that the color trait of A. cornea is controlled by two pairs of alleles. When both pairs of loci are dominant, the fruiting body is purple, while when both pairs of loci are recessive or one pair of loci is recessive, the fruiting body is white. Based on the linkage map, the study finely mapped the color locus within Contig9_29,619bp-53,463bp in the A. cornea genome and successfully predicted the color-controlling locus gene A18078 (AcveA), which belongs to the Velvet factor family protein and has a conserved structure domain of the VeA protein. It can form a dimer with the VelB protein to inhibit pigment synthesis in filamentous fungi. Lastly, the study validated the interaction between AcVeA and VelB (AcVelB) in A. cornea at the gene, protein, and phenotype levels, revealing the mechanism of inhibition of pigment synthesis in A. cornea. Under dark conditions, dimerization occurs, allowing it to enter the nucleus and inhibit pigment synthesis, leading to a lighter fruiting body color. However, under light conditions, the dimer content is low and cannot enter the nucleus to inhibit pigment synthesis. In summary, this study clarified the mechanism of white strain formation in A. cornea, which could aid in improving white strains of A. cornea and studying the genetic basis of color in other fungi. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative integrated path planning and trajectory tracking control framework for a quadrotor unmanned aerial vehicle (UAV) in the presence of environmental and systematic uncertainties to achieve integrated guidance and control. Firstly, in order to perform real-time path planning, a computationally cost-effective planning algorithm is designed to find an optimal and smooth path while avoiding both static and dynamic obstacles. Then, by employing the pure-pursuit path following approach, the generated geometric path is converted to a trajectory profile related to time, which serves as the reference commands for the low-level trajectory tracking controller. Finally, a novel adaptive sliding mode trajectory tracking controller is proposed to compensate model uncertainties and maintain the desired tracking performance for the studied quadrotor UAV. With the proposed adaptive schemes, overestimation of uncertain parameters can be avoided, which further contributes to avoiding control chattering of the system. The performance of the proposed framework is validated through comparative simulation and experimental tests based on a quadrotor UAV subject to model uncertainties and environmental obstacles, which confirms the effectiveness and superiority of the proposed approach for practical applications. [ABSTRACT FROM AUTHOR]

• PENCIL imaging can visualize NM with high quality compared to conventional MTC images. • PENCIL imaging showed similar diagnostic performance for differentiating PDs from HCs compared with MTC. • PENCIL has rapid whole-brain coverage using STAGE imaging, offering a one-stop quantitative assessment. Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties. [ABSTRACT FROM AUTHOR]

This article details the development and evaluation of a practical solution for path-constrained proximity maneuvers of spacecraft. Whereas no velocity measurement is utilized within the feedback structure, the controller rigorously enforces actuator magnitude constraints. Specifically, the control algorithm is constructed from a potential function method repelling the spacecraft from possible collisions. The proposed controller can guarantee potential functions to be navigated to the origin and thus overcome the stubborn local minima problem. Moreover, the control capability under any given control limit can be estimated and adjusted by changing the feedback gains. The specific performance with guaranteed safety can be also explicitly calculated by designers. The results are obtained through a Lyapunov-based stability analysis to prove uniformly ultimate boundedness. Numerical simulation results illustrate the performance and features of the developed control method. [ABSTRACT FROM AUTHOR]

This paper investigates a problem of autonomous aerial refueling point parallel rendezvous guidance and flight control subject to exogenous wind field disturbances. The tanker and uncrewed receiver aircraft approach the common refueling rendezvous point according to their respective flight plans, ultimately maintaining a fixed altitude and the same speed. According to the relative position of refueling target point and uncrewed receiver, a new continuous terminal sliding-mode guidance law combined with the prescribed performance control theory is designed under the end angle and speed constraints. Meanwhile, a finite-time observer is proposed to estimate the parameter uncertainties and wake vortex disturbances during refueling rendezvous. Based on the estimated information, a backstepping double-integral sliding-mode controller is developed for the uncrewed receiver flight control system. In the process of the guidance and flight control system, the integral variable is introduced to improve the steady-state performance. The designed guidance and control system can enable the uncrewed receiver to achieve the refueling rendezvous task within finite time and improve the efficiency of uncrewed aerial refueling. Comparative numerical simulations are provided to demonstrate the effectiveness of the proposed guidance and control scheme. [ABSTRACT FROM AUTHOR]

In a dynamic environment, moving to the destination safely and effectively is of paramount importance for an unmanned ground vehicle (UGV). This article presents a strategy of trajectory planning and tracking that aims to ensure the UGV’s safety in an uncertain environment. Specifically, based on the initial environment information, a global optimal trajectory connecting the start and the destination is predefined by an artificial fish swarm algorithm (AFSA). In the presence of unforeseen obstacles, a trial-based forward search (TFS) algorithm based on the Markov chain is proposed in the local trajectory planning module, while collision prediction is integrated as heuristic information. The vehicle’s current state is updated accordingly for the sake of avoiding entire state spaces involved in the computation. Therefore, the storage efficiency and convergence rate in local path planning are sufficiently enhanced in comparison to dynamic programming. Moreover, command signals can be calculated with the proposed multiconstrained model predictive controller (MMPC), ensuring the vehicle to track the reference trajectory and smoothen the motion. Finally, the results in both simulations and experiments reveal the effectiveness of the proposed algorithm in the presence of both static and dynamic obstacles. Note to Practitioners—This article is motivated by the unmanned ground vehicle (UGV) collision avoidance problem in practical missions, such as farming and emergency response. In recent years, various trajectory planning and tracking algorithms have been widely developed. However, the environmental complexity and the intruders’ unexpected movement pose difficulties in trajectory planning, especially in ensuring the computation time under the allowable threshold. Moreover, the UGV practical trajectory tracking is a challenging task which demands a desired response within various physical constraints. In this article, a two-stage conflict resolution system is proposed. First, a trial-based forward search (TFS) is used to generate a new trajectory deviating the UGV from the initially generated trajectory by the artificial fish swarm algorithm (AFSA), aiming to avoid the unforeseen intruders (unknown in prior) in real-time. Using these two trajectory planning algorithms alternatively, both global trajectory optimality in a cluttered environment and appropriate maneuvers with respect to unexpected intruders can be achieved. Subsequently, the UGV is modeled according to its kinematic characteristics, and thus a multiconstrained model predictive controller (MMPC) is designed to follow the reference trajectory. The physical constraints are respected by integrating them into the controller. Simulations and experimental results demonstrate that the proposed strategy can guide and control a UGV from the start to the destination safely and smoothly, even in the case of multiple obstacles with constant or varying velocities. Furthermore, the proposed collision avoidance strategy can be extended to other unmanned systems, including unmanned aerial vehicles and unmanned surface vehicles. [ABSTRACT FROM AUTHOR]

An adaptive sliding mode fault‐tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle in this article to accommodate actuator faults and model uncertainties. First, a new reaching law is proposed, with which a sliding mode control (SMC) law is constructed. The proposed reaching law is made up of a sliding variable and the distance between it and a designated boundary layer, and it can effectively suppress the unexpected control chattering while preserving the necessary system tracking performance. Then, an adaptive SMC scheme is proposed to further solve the fault and uncertainty compensation problem. The proposed adaptation law helps to prevent overestimation of the adaptive control parameters, as well as avoiding control chattering. Finally, a number of comparative simulation tests are carried out to validate the effectiveness and superiority of the proposed control strategy. The demonstrated quantitative comparison results confirm its advantages. [ABSTRACT FROM AUTHOR]

With focusing on solar photovoltaic (PV) systems operating at microgrid (MG) level, this study investigates the impacts of different physical faults and cyberattacks as well as designing an intelligent faults/attacks detection and diagnosis system. The ability to quickly identify and diagnose faults/attacks enables local controllers and energy management system to accommodate or mitigate the negative effects of physical faults or cyberattacks in a MG. This allows the MG to carry on operating without experiencing any significant interruptions. In this regard, the current paper investigates a hybrid AC/DC MG made up of different distributed energy resources (DER), including solar PV arrays, wind turbines, and battery energy storage systems. An intelligent hybrid diagnosis (IHD) scheme is introduced for online monitoring and diagnosing the data to reflect the real-time status of the PV system operating at the MG level. This hybrid system uses three parallel units, including a "fuzzy inference system (i.e., a rule-based unit)", a "power spectrum estimator (i.e., a signal-based unit)", and an "adaptive neuro-fuzzy inference system (i.e., a model-based unit)". A realistic MG benchmark model with a wide range of operating conditions and dynamic electrical loads in the presence of potential MG disturbances is used to demonstrate the high performance and resilience of the proposed IHD scheme under various types of faults/attacks scenarios. • The impacts of physical faults and cyberattacks on photovoltaic (PV) systems at microgrid (MG) level are investigated. • A faults/attacks detection and diagnosis scheme referred to as intelligent hybrid diagnosis (IHD) scheme is presented. • A hybrid AC/DC MG composed of several renewable distributed energy resources (DER) is considered. • Three parallel diagnosis units including rule-based, signal-based, and model-based units for IHD scheme are designed. • Simulations on realistic MG model with wide range of operating conditions in the presence of disturbances are performed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL‐based pipeline for automatic PD diagnosis based on QSM and T1‐weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE‐ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient‐weighted class activation mapping (Grad‐CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting. [ABSTRACT FROM AUTHOR]

This article proposes a safety control scheme for quadrotor unmanned aerial vehicle (UAV) to counteract ground effect and blade damage. The impacts of ground effect and blade damage are quantitatively analyzed in the established model. The proposed safety control scheme consists of position and attitude loops. Specifically, a proportional and derivative controller is designed as a baseline control with acceleration feedforward in the position loop. A disturbance observer (DO) using the measured Euler angles is exploited to estimate the lumped disturbance forces. The developed DO can effectively mitigate the estimation deviation caused by the angle tracking error. Subsequently, differential flatness theory is used to obtain the Euler angle references and the derivatives. In the attitude loop, sliding mode control and fixed-time sliding mode observer (SMO) are integrated to deal with ground effect and blade damage promptly. To demonstrate the effectiveness of the proposed safety control scheme, flight experiments are carried out, where the quadrotor UAV flies close to the rugged ground in the presence of blade damage. [ABSTRACT FROM AUTHOR]

Focusing on a quadrotor unmanned aerial vehicle (UAV), the presence of unintended actuator faults and external disturbances increases the risk of a crash. Although plenty of efforts have been devoted, how to integrate capability analysis into safety control design is still an open issue. This article presents the design of a safety control system for quadrotor UAVs. Firstly, a system capability analysis method based on a differential flatness algorithm is developed, so that the derivatives (i.e., velocity, jerk, and snap) of flight trajectory can be formulated as flat variables. A tradeoff between system capability and permissible flight maneuverability is made to avoid actuator saturation. Secondly, dual-loop nonlinear disturbance observers are exploited to identify the actuator faults and disturbances, which can thereby be handled by a cascade control scheme. Moreover, the trajectory is regenerated online at the expense of degraded flight maneuverability or even emergency landing, in view of the remaining actuator control authority. When comparing to the existing methods, the gap among safety control, trajectory generation, and system capability analysis is bridged to ameliorate practical flight safety. Finally, flight tests are carried out to demonstrate the unique merits of the proposed system. [ABSTRACT FROM AUTHOR]

This paper proposes a fault-tolerant cooperative control (FTCC) scheme for multiple UAVs in a distributed communication network against input saturation, full-state constraints, actuator faults, and unknown dynamics. Firstly, by considering physical limitations, an auxiliary control signal is designed to simplify the analysis process. Secondly, to avoid the difficulty in the back-stepping design caused by full-state constraints, virtual control signals are constructed to transform constrained variables, while the dynamic surface control is adopted to avoid the phenomenon of "differential explosion." Thirdly, a disturbance observer (DO) is designed to estimate the unknown uncertainty caused by parameter uncertainty and actuator fault. Moreover, a recurrent wavelet fuzzy neural network (RWFNN) is used to compensate for the estimation errors of DO. Finally, it is proved that all states are uniformly ultimately bounded (UUB) by Lyapunov and invariant set theory. The effectiveness of the proposed scheme is further demonstrated by the simulation results. [ABSTRACT FROM AUTHOR]

This article focuses on the 3-D guidance strategy for a reusable launch vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding-mode and shrinking-horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly nonlinear differential equations in the time domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and a guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of the altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by the shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy. [ABSTRACT FROM AUTHOR]

Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNR LC). The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNR LC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNR LC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNR LC and the MoCA score in PD subjects. The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD. • Exploring the path of locus coeruleus (LC) affects cognitive function. • Multimodal MRI imaging address the function of glymphatic system and LC. • LC degeneration is related to cognition in Parkinson's disease (PD). • Impaired glymphatic function as a mediator in this relationship in PD. [ABSTRACT FROM AUTHOR]

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei. [ABSTRACT FROM AUTHOR]

This article presents a single‐hidden‐layer feedforward network (SLFN) aided fault‐tolerant control (FTC) scheme for a class of nonlinear systems subject to actuator faults. First, the upper bounds of the norms of the unknown functions, which cover information of actuator faults and amplitude saturation degrees, are deployed. The upper bounds are approximated by SLFNs with adaptive techniques. Subsequently, a sliding mode control approach is presented to allow for prompt corrective reactions, with explicit consideration of multivariable conditions and control input constraints. Finally, simulation results based on a nonlinear Boeing 747 aircraft model are presented to demonstrate the effectiveness of the proposed FTC scheme. [ABSTRACT FROM AUTHOR]

Large-scale wind farms and wind farm clusters with many installed wind turbines are increasingly built around the world, and especially in offshore regions. The reliability and availability of these assets are critically important for cost-effective wind power generation. This requires effective solutions for online fault detection, diagnosis and fault accommodation to improve the overall reliability and availability of wind turbines and entire wind farms. To meet this requirement, this paper proposes a novel active fault-tolerant cooperative control (FTCC) scheme for large-scale wind farms and wind farm clusters (WFCs). The proposed scheme is based on a signal correction method at wind turbine level that is augmented with two innovative "control reallocation" mechanisms at wind farm and network operator levels. Applied to a WFC, this scheme detects, identifies and accommodates the effects of both mild and severe power-loss faults in wind turbines. Various simulation studies on an advanced WFC benchmark indicate the high efficiency and effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

• Both multiple quadcopters grouped to different blocks and one block are considered. • Spraying time is reduced by 10.8% and 13.2% in simulation and actual flight tests. • The optimization complexity of one quadcopter's path planning is significantly reduced. • A simulation prototype by MATLAB GUIs and Stateflow is built for mission planner. As the approaching of Agriculture 4.0 era and advancements of new technologies of Unmanned Aerial Vehicles (UAVs) and particularly quadcopter UAVs, plant protection quadcopters are becoming increasingly popular and practical in pesticide spraying, fertilization, pollination, seeding, and other agricultural activities. One of the main problems for plant protection quadcopters is completing planning tasks efficiently and quickly. Therefore, this paper proposes an autonomous task assignment and decision-making method for coverage path planning by multiple cooperative quadcopters. The Sequential Quadratic Programming (SQP) method is adopted to acquire the optimal solution for the proposed problem. Then, a simulation platform by MATLAB Graphical User Interfaces (GUIs) is established using the Stateflow technique, and multiple ZY-UAV-680 quadrotor UAVs are employed to carry out the actual flight tests. Finally, simulations and actual flight tests are conducted to demonstrate the effectiveness of the autonomous optimal task assignment and decision-making method. The final results show that: the proposed method can divide multiple UAVs reasonably to several blocks; the time differences between the simulation test and real flight test are only 39.8 sec and 20.6 sec, and accounts for 6.6% and 3.9% of the spraying time spent on real flight test by three cooperative quadrotors, respectively; the optimal scheme can save 60.8 sec and 80.0 sec in the simulation tests and the real flight tests, which accounts for 10.8% and 13.2% of the time spent on average scheme, respectively. Therefore, it can be concluded that the simulation results can match the real flight test results quite well regardless of average scheme or optimal scheme, and the optimal scheme is more efficient and timesaving than the average scheme. [ABSTRACT FROM AUTHOR]

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]

• 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 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]

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]

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]

• 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]