Mussel byssus-inspired dual-functionalization of zirconia dental implants for improved bone integration.

Zirconia faces challenges in dental implant applications due to its inherent biological inertness, which compromises osseointegration, a critical factor for the long-term success of implants that rely heavily on specific cell adhesion and enhanced osteogenic activity. Here, we fabricated a dual-functional coating that incorporates strontium ions, aimed at enhancing osteogenic activity, along with an integrin-targeting sequence to improve cell adhesion by mussel byssus-inspired surface chemistry. The results indicated that although the integrin-targeting sequence at the interface solely enhances osteoblast adhesion without directly increasing osteogenic activity, its synergistic interaction with the continuously released strontium ions from the coating, as compared to the release of strontium ions alone, significantly enhances the overall osteogenic effect. More importantly, compared to traditional polydopamine surface chemistry, the coating surface is enriched with amino groups capable of undergoing various chemical reactions and exhibits enhanced stability and aesthetic appeal. Therefore, the synergistic interplay between strontium and the functionally customizable surface offers considerable potential to improve the success of zirconia implantation.

Distributed Dynamic Task Allocation for Moving Target Tracking of Networked Mobile Robots Using k -WTA Network.

Tasks allocation plays a pivotal role in cooperative robotics. This study proposes a novel fully distributed task allocation method for target tracking, by which mobile robots only need to share state information with communication neighbors. The proposed method adopts a distributed k winners-take-all ( k -WTA) network to select the k mobile robots closest to the moving target to perform the target tracking task. In addition, an innovative robot control law is designed, incorporating speed feedback and nonlinear activation functions to achieve finite-time error convergence. Unlike previous approaches, our distributed task allocation method yields finite-time error convergence, does not rely on consensus filters, and eliminates the need for a central computing unit to get the k -WTA result during the control process. We demonstrate the effectiveness of the proposed method through theoretical analysis and simulations. Compared to traditional methods, our method leads to smaller total moving distances and speed norms, which underscores the significance of our method in enhancing the efficiency and performance of mobile robots in dynamic task allocation.

N-alkylamides from Litsea cubeba (Lour.) Pers. with potential anti-inflammatory activity.

Six amides, including a new N-alkylamide (1), four known N-alkylamides (2-5) and one nicotinamide (6) were isolated from Litsea cubeba (Lour.) Pers., which is a pioneer herb traditionally utilized in medicine. Their structures were elucidated on the basis of 1D and 2D NMR experiments and by comparison of their spectroscopic and physical data with the literature values. Cubebamide (1) is a new cinnamoyltyraminealkylamide and possessed obvious anti-inflammatory activity against NO production with IC50 values of 18.45 µM. Further in-depth pharmacophore-based virtual screening and molecular docking were carried out to reveal the binding mode of the active compound inside the 5-LOX enzyme. The results indicate that L. cubeba, and the isolated amides might be useful in the development of lead compounds for the prevention of inflammatory diseases.

Initialization-Based k-Winners-Take-All Neural Network Model Using Modified Gradient Descent.

The k -winners-take-all ( k -WTA) problem refers to the selection of k winners with the first k largest inputs over a group of n neurons, where each neuron has an input. In existing k -WTA neural network models, the positive integer k is explicitly given in the corresponding mathematical models. In this article, we consider another case where the number k in the k -WTA problem is implicitly specified by the initial states of the neurons. Based on the constraint conversion for a classical optimization problem formulation of the k -WTA, via modifying the traditional gradient descent, we propose an initialization-based k -WTA neural network model with only n neurons for n -dimensional inputs, and the dynamics of the neural network model is described by parameterized gradient descent. Theoretical results show that the state vector of the proposed k -WTA neural network model globally asymptotically converges to the theoretical k -WTA solution under mild conditions. Simulative examples demonstrate the effectiveness of the proposed model and indicate that its convergence can be accelerated by readily setting two design parameters.

Dynamic Moore-Penrose Inversion With Unknown Derivatives: Gradient Neural Network Approach.

Finding dynamic Moore-Penrose inverses (DMPIs) in real-time is a challenging problem due to the time-varying nature of the inverse. Traditional numerical methods for static Moore-Penrose inverse are not efficient for calculating DMPIs and are restricted by serial processing. The current state-of-the-art method for finding DMPIs is called the zeroing neural network (ZNN) method, which requires that the time derivative of the associated matrix is available all the time during the solution process. However, in practice, the time derivative of the associated dynamic matrix may not be available in a real-time manner or be subject to noises caused by differentiators. In this article, we propose a novel gradient-based neural network (GNN) method for computing DMPIs, which does not need the time derivative of the associated dynamic matrix. In particular, the neural state matrix of the proposed GNN converges to the theoretical DMPI in finite time. The finite-time convergence is kept by simply setting a large parameter when there are additive noises in the implementation of the GNN model. Simulation results demonstrate the efficacy and superiority of the proposed GNN method.

Distributed k-Winners-Take-All Network: An Optimization Perspective.

In this article, we proposed an equivalent formulation of the k-winners-take-all (k-WTA) problem as a constrained optimization problem by including the Laplacian matrix of the undirected connected communication graph to adapt to the distributed computing scenario, where an additional auxiliary variable is introduced. To solve the optimization problem in a distributed fashion, we design projection neural networks by using the convex optimization theory, leading to the emergence of a distributed k-WTA network. Our theoretical analysis shows that the proposed distributed k-WTA network has a globally asymptotically stable equilibrium that is identical to the optimal solution to the optimization problem, that is, the correct k-WTA solution. The effectiveness and advantages, including the extendability to constrained k-WTA problems, of the proposed k-WTA network are demonstrated via simulations.

5-Demethoxy-10'-ethoxyexotimarin F, a New Coumarin with MAO-B Inhibitory Potential from Murraya exotica L.

Rutaceae plants are known for being a rich source of coumarins. Preliminary molecular docking showed that there was no significant difference for coumarins in Clausena and Murraya, both of which had high scoring values and showed good potential inhibitory activity to the MAO-B enzyme. Overall, 32 coumarins were isolated from Murraya exotica L., including a new coumarin 5-demethoxy-10'-ethoxyexotimarin F (1). Their structures were elucidated on the basis of a comprehensive analysis of 1D and 2D NMR and HRMS spectroscopic data, and the absolute configurations were assigned via a comparison of the specific rotations and the ECD exciton coupling method. The potential of new coumarin (1) as a selective inhibitor of MAO-B was initially evaluated through molecular docking and pharmacophore studies. Compound (1) showed selectivity for the MAO-B isoenzyme and inhibitory activity in the sub-micromolar range with an IC50 value of 153.25 ± 1.58 nM (MAO-B selectivity index > 172).

GNN Model for Time-Varying Matrix Inversion With Robust Finite-Time Convergence.

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient neural network (GNN) is recognized as an effective method for static matrix inversion with exponential convergence. However, when it comes to time-varying matrix inversion, most of the traditional GNNs can only track the corresponding time-varying solution with a residual error, and the performance becomes worse when there are noises. Currently, zeroing neural networks (ZNNs) take a dominant role in time-varying matrix inversion, but ZNN models are more complex than GNN models, require knowing the explicit formula of the time-derivative of the matrix, and intrinsically cannot avoid the inversion operation in its realization in digital computers. In this article, we propose a unified GNN model for handling both static matrix inversion and time-varying matrix inversion with finite-time convergence and a simpler structure. Our theoretical analysis shows that, under mild conditions, the proposed model bears finite-time convergence for time-varying matrix inversion, regardless of the existence of bounded noises. Simulation comparisons with existing GNN models and ZNN models dedicated to time-varying matrix inversion demonstrate the advantages of the proposed GNN model in terms of convergence speed and robustness to noises.

Distributed Estimation of Algebraic Connectivity.

The measurement algebraic connectivity plays an important role in many graph theory-based investigations, such as cooperative control of multiagent systems. In general, the measurement is considered to be centralized. In this article, a distributed model is proposed to estimate the algebraic connectivity (i.e., the second smallest eigenvalue of the corresponding Laplacian matrix) by the approach of distributed estimation via high-pass consensus filters. The global asymptotic convergence of the proposed model is theoretically guaranteed. Numerical examples are shown to verify the theoretical results and the superiority of the proposed distributed model.

Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances.

In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal control law is derived through the approximation of the integral-type quadratic performance index with respect to the tracking error, where the equivalent unknown parameters are generated online by an auxiliary system that can learn the dynamics of the controlled system. It is proved that the state differences between the auxiliary system and the corresponding controlled USV vessel are globally asymptotically convergent to zero. Besides, the approach theoretically guarantees asymptotic optimality of the performance index. The efficacy of the method is demonstrated via simulations based on the real parameters of an USV vessel.

Updating Emotional Stimuli in Heroin Abstainers' Working Memory: An Event-Related Potential Investigation.

BACKGROUND: It is well-documented that heroin users demonstrate aberrant emotion-processing abilities. However, the mechanism by which heroin users process emotional information after it has captured their attention and entered their working memory is unclear. OBJECTIVES: A modified emotional 2-back task was used to examine whether heroin abstainers demonstrate specific bias patterns in updating emotional stimuli in their working memory. METHODS: In total, 26 male heroin abstainers and 29 healthy controls were asked to identify whether the current picture was the same as a picture that had appeared two trials earlier, while behavioral data and electroencephalogram data were collected. RESULTS: Contrary to predictions, the heroin abstainers and healthy controls demonstrated a similar pattern of P300 activity in response to emotional stimuli with no between-group differences in accuracy or reaction time. More specifically, the P300 amplitudes were larger for negative pictures than for positive and neutral pictures. Surprisingly, we found larger P300 amplitudes at Fz electrodes than at Cz and Pz electrodes in the control group, whereas there was no significant difference at midline electrodes in the heroin abstainers. CONCLUSIONS/IMPORTANCE: Although subtle differences may exist in attentional engagement toward incoming emotional stimulus between two groups, the similar P300 pattern may indicate partial preservation of emotional working memory capacity associated with adaptive emotion regulation in heroin abstainers. These results deepen our understanding of the emotion regulation impairments associated with chronic drug use.

Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints.

Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the proposed controller.

Neural Network-Based Model-Free Adaptive Near-Optimal Tracking Control for a Class of Nonlinear Systems.

In this paper, the receding horizon near-optimal tracking control problem about a class of continuous-time nonlinear systems with fully unknown dynamics is considered. The main challenges of this problem lie in two aspects: 1) most existing systems only restrict their considerations to the state feedback part while the input channel parameters are assumed to be known. This paper considers fully unknown system dynamics in both the state feedback channel and the input channel and 2) the optimal control of nonlinear systems requires the solution of nonlinear Hamilton-Jacobi-Bellman equations. Up to date, there are no systematic approaches in the existing literature to solve it accurately. A novel model-free adaptive near-optimal control method is proposed to solve this problem via utilizing the Taylor expansion-based problem relaxation, the universal approximation property of sigmoid neural networks, and the concept of sliding mode control. By making approximation for the performance index, it is first relaxed to a quadratic program, and then, a linear algebraic equation with unknown terms. An auxiliary system is designed to reconstruct the input-to-output property of the control systems with unknown dynamics, so as to tackle the difficulty caused by the unknown terms. Then, by considering the property of the sliding-mode surface, an explicit adaptive near-optimal control law is derived from the linear algebraic equation. Theoretical analysis shows that the auxiliary system is convergent, the resultant closed-loop system is asymptotically stable, and the performance index asymptomatically converges to optimal. An illustrative example and experimental results are presented, which substantiate the efficacy of the proposed method and verify the theoretical results.

A Neural Controller for Image-Based Visual Servoing of Manipulators With Physical Constraints.

Main issues in visual servoing of manipulators mainly include rapid convergence of feature errors to zero and the safety of joints regarding joint physical limits. To address the two issues, in this paper, an image-based visual servoing scheme is proposed for manipulators with an eye-in-hand configuration. Compared with existing schemes, the proposed one does not require performing pseudoinversion for the image Jacobian matrix or inversion for the Jacobian matrix associated with the forward kinematics of the manipulators. Theoretical analysis shows that the proposed scheme not only guarantees the asymptotic convergence of feature errors to zero but also the compliance with joint angle and velocity limits of the manipulators. Besides, simulation results based on a PUMA560 manipulator with a camera mounted on the end effector verify the theoretical conclusions and the efficacy of the proposed scheme.

Z-type control of populations for Lotka-Volterra model with exponential convergence.

The population control of the Lotka-Volterra model is one of the most important and widely investigated issues in mathematical ecology. In this study, assuming that birth rate is controllable and using the Z-type dynamic method, we develop Z-type control laws to drive the prey population and/or predator population to a desired state to keep species away from extinction and to improve ecosystem stability. A direct controller group is initially designed to control the prey and predator populations simultaneously. Two indirect controllers are then proposed for prey population control and predator population control by exerting exogenous measure on another species. All three control laws possess exponential convergence performances. Finally, the corresponding numerical simulations are performed. Results substantiate the theoretical analysis and effectiveness of such Z-type control laws for the population control of the Lotka-Volterra model.