A Straightforward Solvent-Pair-Enabled Multicomponent Coassembly Approach toward Noble-Metal-Nanoparticle-Decorated Mesoporous Tungsten Oxide for Trace Ammonia Sensing.

The straightforward synthesis of noble-metal-nanoparticle-decorated ordered mesoporous transition metal oxides remains a great challenge due to the difficulty of balancing the interactions between precursors and templates. Herein, a solvent-pair-enabled multicomponent coassembly (SPEMC) strategy is developed for straightforward synthesis of noble-metal-nanoparticle-decorated nitrogen-doped ordered mesoporous tungsten oxide (abbreviated as NM/N-mWO, NM = Pt, Rh, Pd). The amphiphilic poly(ethylene oxide)-block-polystyrene (PEO-b-PS) copolymers coassemble with ammonium metatungstate (AMT) clusters and different kinds of hydrophilic noble metal precursors without phase separation.

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.

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.

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.

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.

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.

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.

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.

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.

Micellar Nanoreactors Enabled Site-Selective Decoration of Pt Nanoparticles Functionalized Mesoporous SiO /WO Composites for Improved CO Sensing.

Site-selective and partial decoration of supported metal nanoparticles (NPs) with transition metal oxides (e.g., FeO ) can remarkably improve its catalytic performance and maintain the functions of the carrier.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method.

As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper.

Ex vivo study of prostate cancer localization using rolling mechanical imaging towards minimally invasive surgery.

Rolling mechanical imaging (RMI) is a novel technique towards the detection and quantification of malignant tissue in locations that are inaccessible to palpation during robotic minimally invasive surgery (MIS); the approach is shown to achieve results of higher precision than is possible using the human hand. Using a passive robotic manipulator, a lightweight and force sensitive wheeled probe is driven across the surface of tissue samples to collect continuous measurements of wheel-tissue dynamics. A color-coded map is then generated to visualize the stiffness distribution within the internal tissue structure.

Serum Chemerin Levels in relation to Osteoporosis and Bone Mineral Density: A Case-Control Study.

Background: To evaluate serum chemerin levels in patients with osteoporosis and healthy controls and to investigate the relationship between serum chemerin levels and bone mineral density (BMD).

Methods: An age- and gender-matched case-control study was conducted. Pearson's correlation test was performed to investigate the relationship between serum chemerin levels and BMD.

An atom-economic route to thiophenes and 2,2'-bithiophenes by intramolecular transannulation of gem-dialkylthio enynes.

An atom-economic route to thiophenes and bithiophenes has been developed starting from the readily available gem-dialkylthio enynes. A range of functionalized thiophenes and bithiophenes, bearing a pendent vinylthio group, were obtained in good to high yields under mild conditions.

Correlation between steroid hormonal levels and cardiac function in women during controlled ovarian hyperstimulation.

During in vitro fertilization-embryo transfer (IVF-ET) treatment, most women require controlled ovarian hyperstimulation (COH). COH with gonadotropins results in an increase in steroid hormonal levels; however, it is unclear what impact these high concentrations of steroid hormones have on cardiac heart function. The purpose of this study was to examine the effect of high levels of estradiol (E2) and progesterone (P) during COH treatment on cardiac function in women undergoing IVF-ET.