Design of MERS-CoV entry inhibitory short peptides based on helix-stabilizing strategies.

Interaction between Middle East respiratory syndrome coronavirus (MERS-CoV) spike (S) protein heptad repeat-1 domain (HR1) and heptad repeat-2 domain (HR2) is critical for the MERS-CoV fusion process. This interaction is mediated by the α-helical region from HR2 and the hydrophobic groove in a central HR1 trimeric coiled coil. We sought to develop a short peptidomimetic to act as a MERS-CoV fusion inhibitor by reproducing the key recognition features of HR2 helix. This was achieved by the use of helix-stabilizing strategies, including substitution with unnatural helix-favoring amino acids, introduction of ion pair interactions, and conjugation of palmitic acid. The resulting 23-mer lipopeptide, termed AEEA-C16, inhibits MERS-CoV S protein-mediated cell-cell fusion at a low micromolar level comparable to that of the 36-mer HR2 peptide HR2P-M2. Collectively, our studies provide new insights into developing short peptide-based antiviral agents to treat MERS-CoV infection.

Micellar Nanoreactors Enabled Site-Selective Decoration of Pt Nanoparticles Functionalized Mesoporous SiO2 /WO3-x Composites for Improved CO Sensing.

Site-selective and partial decoration of supported metal nanoparticles (NPs) with transition metal oxides (e.g., FeOx ) can remarkably improve its catalytic performance and maintain the functions of the carrier. However, it is challenging to selectively deposit transition metal oxides on the metal NPs embedded in the mesopores of supporting matrix through conventional deposition method. Herein, a restricted in situ site-selective modification strategy utilizing poly(ethylene oxide)-block-polystyrene (PEO-b-PS) micellar nanoreactors is proposed to overcome such an obstacle. The PEO shell of PEO-b-PS micelles interacts with the hydrolyzed tungsten salts and silica precursors, while the hydrophobic organoplatinum complex and ferrocene are confined in the hydrophobic PS core. The thermal treatment leads to mesoporous SiO2 /WO3-x framework, and meanwhile FeOx nanolayers are in situ partially deposited on the supported Pt NPs due to the strong metal-support interaction between FeOx and Pt. The selective modification of Pt NPs with FeOx makes the Pt NPs present an electron-deficient state, which promotes the mobility of CO and activates the oxidation of CO. Therefore, mesoporous SiO2 /WO3-x -FeOx /Pt based gas sensors show a high sensitivity (31 ± 2 in 50 ppm of CO), excellent selectivity, and fast response time (3.6 s to 25 ppm) to CO gas at low operating temperature (66 °C, 74% relative humidity).

Nonlocal delay gives rise to vegetation patterns in a vegetation-sand model.

The vegetation pattern generated by aeolian sand movements is a typical type of vegetation patterns in arid and semi-arid areas. This paper presents a vegetation-sand model with nonlocal interaction characterized by an integral term with a kernel function. The instability of the Turing pattern was analyzed and the conditions of stable pattern occurrence were obtained. At the same time, the multiple scales method was applied to obtain the amplitude equations at the critical value of Turing bifurcation. The spatial distributions of vegetation under different delays were obtained by numerical simulation. The results revealed that the vegetation biomass increased as the interaction intensity decreased or as the nonlocal interaction distance increased. We demonstrated that the nonlocal interaction between vegetation and sand is a crucial mechanism for forming vegetation patterns, which provides a theoretical basis for preserving and restoring vegetation.

A Predefined-Time Adaptive Zeroing Neural Network for Solving Time-Varying Linear Equations and Its Application to UR 5 Robot.

Time-varying linear equations (TVLEs) play a fundamental role in the engineering field and are of great practical value. Existing methods for the TVLE still have issues with long computation time and insufficient noise resistance. Zeroing neural network (ZNN) with parallel distribution and interference tolerance traits can mitigate these deficiencies and thus are good candidates for the TVLE. Therefore, a new predefined-time adaptive ZNN (PTAZNN) model is proposed for addressing the TVLE in this article. Unlike previous ZNN models with time-varying parameters, the PTAZNN model adopts a novel error-based adaptive parameter, which makes the convergence process more rapid and avoids unnecessary waste of computational resources caused by large parameters. Moreover, the stability, convergence, and robustness of the PTAZNN model are rigorously analyzed. Two numerical examples reflect that the PTAZNN model possesses shorter convergence time and better robustness compared with several variable-parameter ZNN models. In addition, the PTAZNN model is applied to solve the inverse kinematic solution of UR 5 robot on the simulation platform CoppeliaSim, and the results further indicate the feasibility of this model intuitively.

Maillard Reaction Inspired Microexplosion toward Fast Synthesis of Two-Dimensional Mesoporous Tin Oxides for Efficient Chemiresistive Gas Sensing.

Two-dimensional (2D) mesoporous transition metal oxides are highly desired in various applications, but their fast and low-cost synthesis remains a great challenge. Herein, a Maillard reaction inspired microexplosion approach is applied to rapidly synthesize ultrathin 2D mesoporous tin oxide (mSnO2). During the microexplosion between granular ammonia nitrate with melanoidin at high temperature, the organic species can be carbonized and expanded rapidly due to the instantaneous release of gases, thus producing ultrathin carbonaceous templates with rich functional groups to effectively anchor SnO2 nanoparticles on the surface. The subsequent removal of carbonaceous templates via calcination in air results in the formation of 2D mSnO2 due to the confinement effect of the templates. Pd nanoparticles are controllably deposited on the surface of 2D mSnO2 via in situ reduction, forming ultrathin 2D Pd/mSnO2 nanocomposites with thicknesses of 6-8 nm. Owing to the unique 2D mesoporous structure with rich oxygen defects and highly exposed metal-metal oxide interfaces, 2D Pd/mSnO2 exhibits excellent sensing performance toward acetone with high sensitivity, a short response time, and good selectivity under low working temperature (100 °C). This fast and convenient microexplosion synthesis strategy opens up the possibility of constructing 2D porous functional materials for various applications including high-performance gas sensors.

Expression of G protein estrogen receptor (GPER) on membrane of mouse oocytes during maturation.

PURPOSE: To determine expression of G-protein estrogen receptor (GPER) in mouse oocyte membrane during maturation. METHODS: The expression of GPER from different maturation stages of oocytes, in vivo and in vitro matured oocytes as well as aging oocytes was examined by immune-fluorescence GPR30 antibody and the images were analyzed by laser scanning confocal microscope. Further confirmation was performed by Western blots for cell fractionation. RESULTS: Significant fluorescent signal was observed on the surface of mouse oocytes. The image expression was lower in germinal vesicle (GV) stage than mature metaphase-II (M-II) stage oocytes. There was high expression in in-vivo matured oocytes compared to in vitro matured oocytes. The highest expression was observed in aging oocytes compared with other oocytes. CONCLUSIONS: The changes of expression of GPER on mouse oocytes plasma membrane confirm oocyte membrane maturation, suggesting that those changes of GPER may be related to the functional role of oocyte maturation.

A Dynamic Gain Fixed-Time Robust ZNN Model for Time-Variant Equality Constrained Quaternion Least Squares Problem With Applications to Multiagent Systems.

A dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address time-variant problems. The DFTRZNN model is constructed with a novel dynamic gain parameter and a novel activation function (NAF), which differs from previous zeroing neural network (ZNN) models. Moreover, the comprehensive theoretical derivation of the FXT stability and robustness of the DFTRZNN model is presented in detail. Simulation results further confirm the availability and superiority of the DFTRZNN model for solving TV-EQLS. Finally, the consensus protocols of multiagent systems are presented by utilizing the design scheme of the DFTRZNN model, which further demonstrates its practical application value.

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

ZNNs With a Varying-Parameter Design Formula for Dynamic Sylvester Quaternion Matrix Equation.

This article aims to studying how to solve dynamic Sylvester quaternion matrix equation (DSQME) using the neural dynamic method. In order to solve the DSQME, the complex representation method is first adopted to derive the equivalent dynamic Sylvester complex matrix equation (DSCME) from the DSQME. It is proven that the solution to the DSCME is the same as that of the DSQME in essence. Then, a state-of-the-art neural dynamic method is presented to generate a general dynamic-varying parameter zeroing neural network (DVPZNN) model with its global stability being guaranteed by the Lyapunov theory. Specifically, when the linear activation function is utilized in the DVPZNN model, the corresponding model [termed linear DVPZNN (LDVPZNN)] achieves finite-time convergence, and a time range is theoretically calculated. When the nonlinear power-sigmoid activation function is utilized in the DVPZNN model, the corresponding model [termed power-sigmoid DVPZNN (PSDVPZNN)] achieves the better convergence compared with the LDVPZNN model, which is proven in detail. Finally, three examples are presented to compare the solution performance of different neural models for the DSQME and the equivalent DSCME, and the results verify the correctness of the theories and the superiority of the proposed two DVPZNN models.

Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem.

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

Stimulation enhancement effect of the combination of exoskeleton-assisted hand rehabilitation and fingertip haptic stimulation.

Introduction: Providing stimulation enhancements to existing hand rehabilitation training methods may help stroke survivors achieve better treatment outcomes. This paper presents a comparison study to explore the stimulation enhancement effects of the combination of exoskeleton-assisted hand rehabilitation and fingertip haptic stimulation by analyzing behavioral data and event-related potentials. Methods: The stimulation effects of the touch sensations created by a water bottle and that created by cutaneous fingertip stimulation with pneumatic actuators are also investigated. Fingertip haptic stimulation was combined with exoskeleton-assisted hand rehabilitation while the haptic stimulation was synchronized with the motion of our hand exoskeleton. In the experiments, three experimental modes, including exoskeleton-assisted grasping motion without haptic stimulation (Mode 1), exoskeleton-assisted grasping motion with haptic stimulation (Mode 2), and exoskeleton-assisted grasping motion with a water bottle (Mode 3), were compared. Results: The behavioral analysis results showed that the change of experimental modes had no significant effect on the recognition accuracy of stimulation levels (p = 0.658), while regarding the response time, exoskeleton-assisted grasping motion with haptic stimulation was the same as grasping a water bottle (p = 0.441) but significantly different from that without haptic stimulation (p = 0.006). The analysis of event-related potentials showed that the primary motor cortex, premotor cortex, and primary somatosensory areas of the brain were more activated when both the hand motion assistance and fingertip haptic feedback were provided using our proposed method (P300 amplitude 9.46 µV). Compared to only applying exoskeleton-assisted hand motion, the P300 amplitude was significantly improved by providing both exoskeleton-assisted hand motion and fingertip haptic stimulation (p = 0.006), but no significant differences were found between any other two modes (Mode 2 vs. Mode 3: p = 0.227, Mode 1 vs. Mode 3: p = 0.918). Different modes did not significantly affect the P300 latency (p = 0.102). Stimulation intensity had no effect on the P300 amplitude (p = 0.295, 0.414, 0.867) and latency (p = 0.417, 0.197, 0.607). Discussion: Thus, we conclude that combining exoskeleton-assisted hand motion and fingertip haptic stimulation provided stronger stimulation on the motor cortex and somatosensory cortex of the brain simultaneously; the stimulation effects of the touch sensations created by a water bottle and that created by cutaneous fingertip stimulation with pneumatic actuators are similar.

Solvent-pair surfactants enabled assembly of clusters and copolymers towards programmed mesoporous metal oxides.

Organic-inorganic molecular assembly has led to numerous nano/mesostructured materials with fantastic properties, but it is dependent on and limited to the direct interaction between host organic structure-directing molecules and guest inorganic species. Here, we report a "solvent-pair surfactants" enabled assembly (SPEA) method to achieve a general synthesis of mesostructured materials requiring no direct host-guest interaction. Taking the synthesis of mesoporous metal oxides as an example, the dimethylformamide/water solvent pairs behave as surfactants and induce the formation of mesostructured polyoxometalates/copolymers nanocomposites, which can be converted into metal oxides. This SPEA method enables the synthesis of functional ordered mesoporous metal oxides with different pore sizes, structures, compositions and tailored pore-wall microenvironments that are difficult to access via conventional direct organic-inorganic assembly. Typically, nitrogen-doped mesoporous ε-WO3 with high specific surface area, uniform mesopores and stable framework is obtained and exhibits great application potentials such as gas sensing.

Adaptive Cross Entropy for ultrasmall object detection in Computed Tomography with noisy labels.

Conventional size object detection has been extensively studied, whereas researches concerning ultrasmall object detection are rare due to lack of dataset. Here, considering that the stapes in the ear is the smallest bone in our body, we have collected the largest stapedial otosclerosis detection dataset from 633 stapedial otosclerosis patients and 269 normal cases to promote this direction. Nevertheless, noisy classification labels in our dataset are inevitable due to various subjective and objective factors, and this situation prevails in various fields. In this paper, we propose a novel and general noise tolerant loss function named Adaptive Cross Entropy (ACE) which needs no fine-tuning of hyperparameters for training with noisy labels. We provide both theoretical and empirical analyses for the proposed ACE loss and demonstrate its effectiveness in multiple public datasets. Besides, we find high-resolution representations crucial for ultrasmall object detection and present an auxiliary backbone called W-Net to address it accordingly. Extensive experiments demonstrate that the proposed ACE loss is able to boost the diagnosis performance under noisy label setting by a large margin. Furthermore, our W-Net can help extract sufficient high-resolution representations specialized for ultrasmall objects and achieve even better results. Hopefully, our work could provide more clues for future research on ultrasmall object detection and learning with noisy labels.

Design and Analysis of a Novel Distributed Gradient Neural Network for Solving Consensus Problems in a Predefined Time.

In this article, a novel distributed gradient neural network (DGNN) with predefined-time convergence (PTC) is proposed to solve consensus problems widely existing in multiagent systems (MASs). Compared with previous gradient neural networks (GNNs) for optimization and computation, the proposed DGNN model works in a nonfully connected way, in which each neuron only needs the information of neighbor neurons to converge to the equilibrium point. The convergence and asymptotic stability of the DGNN model are proved according to the Lyapunov theory. In addition, based on a relatively loose condition, three novel nonlinear activation functions are designed to speedup the DGNN model to PTC, which is proved by rigorous theory. Computer numerical results further verify the effectiveness, especially the PTC, of the proposed nonlinearly activated DGNN model to solve various consensus problems of MASs. Finally, a practical case of the directional consensus is presented to show the feasibility of the DGNN model and a corresponding connectivity-testing example is given to verify the influence on the convergence speed.

The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography.

BACKGROUND: The purpose of this study was to explore the common characteristics of fenestral otosclerosis (OS) which are misdiagnosed, and develop a deep learning model for the diagnosis of fenestral OS based on temporal bone high-resolution computed tomography scans. METHODS: We conducted a study to explicitly analyze the clinical performance of otolaryngologists in diagnosing fenestral OS and developed an explainable deep learning model using 134,574 temporal bone high-resolution computed tomography (HRCT) slices collected from 1,294 patients for the automatic diagnosis of fenestral OS. We prospectively created an external test set with 31,774 CT slices from 144 patients, which contained 86 fenestral OS ears and 202 normal ears and used it to evaluate the performance of our otosclerosis-Logical Neural Network (LNN) model to assess its potential clinical utility. In addition, we compared the diagnostic acumen of seven otolaryngologists with the otosclerosis-LNN approach in the clinical test set, which was mixed with 78 fenestral OS and 62 normal ears. Finally, to evaluate the assisting value of the model, the seven participants were again invited to classify all cases in the clinical test set after referring to the diagnostic results of the model, to which they were blinded. RESULTS: The diagnostic performance of otologists was not satisfactory, and those CT samples which were misdiagnosed had similar characteristics. Based on this finding, we defined three subtypes of fenestral OS lesions that are suitable for clinical diagnosis guidance: "focal", "transitional", and "typical" fenestral OS. The most encouraging result is that the model achieved an area under the curve (AUC) of 99.5% (per-ear-sensitivity of 96.4%, per-ear-specificity of 98.9%) on the prospective unknown external test. Furthermore, we used this model to assist otologists and observed a consistent and significant improvement in diagnostic performance, especially for the newly defined focal and transitional fenestral OS, which led to the initial high misdiagnosis rate. CONCLUSIONS: Our findings of the fine-grained classification of fenestral OS could have implications for future diagnosis and prevention programs. In addition, our deep OS localization network is an effective approach providing assistance to otologists to deal with the significant challenge of the misdiagnosis of fenestral OS.

Noble Metal Nanoparticles Decorated Metal Oxide Semiconducting Nanowire Arrays Interwoven into 3D Mesoporous Superstructures for Low-Temperature Gas Sensing.

Mesoporous materials have been extensively studied for various applications due to their high specific surface areas and well-interconnected uniform nanopores. Great attention has been paid to synthesizing stable functional mesoporous metal oxides for catalysis, energy storage and conversion, chemical sensing, and so forth. Heteroatom doping and surface modification of metal oxides are typical routes to improve their performance. However, it still remains challenging to directly and conveniently synthesize mesoporous metal oxides with both a specific functionalized surface and heteroatom-doped framework. Here, we report a one-step multicomponent coassembly to synthesize Pt nanoparticle-decorated Si-doped WO3 nanowires interwoven into 3D mesoporous superstructures (Pt/Si-WO3 NWIMSs) by using amphiphilic poly(ethylene oxide)-block-polystyrene (PEO-b-PS), Keggin polyoxometalates (H4SiW12O40) and hydrophobic (1,5-cyclooctadiene)dimethylplatinum(II) as the as structure-directing agent, tungsten precursor and platinum source, respectively. The Pt/Si-WO3 NWIMSs exhibit a unique mesoporous structure consisting of 3D interwoven Si-doped WO3 nanowires with surfaces homogeneously decorated by Pt nanoparticles. Because of the highly porous structure, excellent transport of carriers in nanowires, and rich WO3/Pt active interfaces, the semiconductor gas sensors based on Pt/Si-WO3 NWIMSs show excellent sensing properties toward ethanol at low temperature (100 °C) with high sensitivity (S = 93 vs 50 ppm), low detection limit (0.5 ppm), fast response-recovery speed (17-7 s), excellent selectivity, and long-term stability.

Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging.

BACKGROUND: Non-magnifying endoscopy with narrow-band imaging (NM-NBI) has been frequently used in routine screening of esophagus squamous cell carcinoma (ESCC). The performance of NBI for screening of early ESCC is, however, significantly affected by operator experience. Artificial intelligence may be a unique approach to compensate for the lack of operator experience. AIM: To construct a computer-aided detection (CAD) system for application in NM-NBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging (WLI). METHODS: A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected from three institutions (Zhongshan Hospital of Fudan University, Xuhui Hospital, and Kiang Wu Hospital) as the training dataset, and 316 pairs of images, each pair including images obtained by WLI and NBI (same part), were collected for validation. Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems. The diagnostic results of the two CAD systems and improvement in diagnostic efficacy of endoscopists were compared in terms of sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. RESULTS: The area under receiver operating characteristic curve for CAD-NBI was 0.9761. For the validation dataset, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD-NBI were 91.0%, 96.7%, 94.3%, 95.3%, and 93.6%, respectively, while those of CAD-WLI were 98.5%, 83.1%, 89.5%, 80.8%, and 98.7%, respectively. CAD-NBI showed superior accuracy and specificity than CAD-WLI (P = 0.028 and P ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI (P = 0.006). By using both CAD-WLI and CAD-NBI, the endoscopists could improve their diagnostic efficacy to the highest level, with accuracy, sensitivity, and specificity of 94.9%, 92.4%, and 96.7%, respectively. CONCLUSION: The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI. Endoscopists can achieve the best diagnostic efficacy using both CAD-WLI and CAD-NBI.

Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization.

Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem.

A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion.

In this work, a new zeroing neural network (ZNN) using a versatile activation function (VAF) is presented and introduced for solving time-dependent matrix inversion. Unlike existing ZNN models, the proposed ZNN model not only converges to zero within a predefined finite time but also tolerates several noises in solving the time-dependent matrix inversion, and thus called new noise-tolerant ZNN (NNTZNN) model. In addition, the convergence and robustness of this model are mathematically analyzed in detail. Two comparative numerical simulations with different dimensions are used to test the efficiency and superiority of the NNTZNN model to the previous ZNN models using other activation functions. In addition, two practical application examples (i.e., a mobile manipulator and a real Kinova JACO2 robot manipulator) are presented to validate the applicability and physical feasibility of the NNTZNN model in a noisy environment. Both simulative and experimental results demonstrate the effectiveness and tolerant-noise ability of the NNTZNN model.