Adaptive general type-2 fuzzy model-based control for nonlinear networked systems with packet dropouts.

In this paper, a novel adaptive general type-2 fuzzy model-based control (AGT2-FMBC) is proposed for networked control systems (NCSs) with packet dropouts. A general type-2 fuzzy model (GT2FM) is designed to represent the nonlinear system with uncertainties online, wherein the antecedents of fuzzy rules are defined by general type-2 fuzzy sets and the consequents by Takagi-Sugeno type models. Utilizing the Bernoulli distribution, packet dropouts in the two communication channels are effectively modeled.

A new bacterial phospholipase D with specificity for phosphatidylethanolamine over phosphatidylcholine.

Phospholipases D (PLDs) are key enzymes involved in numerous processes in all living organisms. PLD catalyzes, notably, the hydrolysis of different phospholipids (PLs) generating phosphatidic acid (PA). PA is an important moiety at the crossroads of multiple metabolic pathways and it is involved in signaling reactions, cancer genesis in mammals, bacterial infections and the defense response in plants.

Contrastive independent subspace analysis network for multi-view spatial information extraction.

Multi-view classification integrates features from different views to optimize classification performance. Most of the existing works typically utilize semantic information to achieve view fusion but neglect the spatial information of data itself, which accommodates data representation with correlation information and is proven to be an essential aspect. Thus robust independent subspace analysis network, optimized by sparse and soft orthogonal optimization, is first proposed to extract the latent spatial information of multi-view data with subspace bases.

CK19 protein expression: the best cutoff value on the prognosis and the prognosis model of hepatocellular carcinoma.

Background And Objective: In clinical practice, CK19 can be an important predictor for the prognosis of HCC. Due to the high incidence and mortality rates of HCC, more effective and practical prognostic prediction models need to be developed urgently.

Methods: A total of 1,168 HCC patients, who underwent radical surgery at the Guangxi Medical University Cancer Hospital, between January 2014 and July 2019, were recruited, and their clinicopathological data were collected.

Ammonium Sensing Patch with Ultrawide Linear Range and Eliminated Interference for Universal Body Fluids Analysis.

Ammonium level in body fluids serves as one of the critical biomarkers for healthcare, especially those relative to liver diseases. The continuous and real-time monitoring in both invasive and non-invasive manners is highly desired, while the ammonium concentrations vary largely in different body fluids. Besides, the sensing reliability based on ion-selective biosensors can be significantly interfered by potassium ions.

Layer-by-layer self-assembly coatings on strontium titanate nanotubes with antimicrobial and anti-inflammatory properties to prevent implant-related infections.

One way to effectively address endophyte infection and loosening is the creation of multifunctional coatings that combine anti-inflammatory, antibacterial, and vascularized osteogenesis. This study started with the preparation of strontium-doped titanium dioxide nanotubes (STN) on the titanium surface. Next, tannic acid (TA), gentamicin sulfate (GS), and pluronic F127 (PF127) were successfully loaded into the STN via layer-by-layer self-assembly, resulting in the STN@TA-GS/PF composite coatings.

Adaptive fuzzy fixed-time bipartite consensus control for stochastic nonlinear multi-agent systems with performance constraints.

This paper investigates the fixed-time bipartite consensus control problem of stochastic nonlinear multi-agent systems (MASs) with performance constraints. A constraint scaling function is proposed to model the performance constraints with user-predefined steady-state accuracy and settling time without relying on the initial condition. Technically, the local synchronization error of each follower is mapped to a new error variable using the constraint scaling function and an error transformation function before being used to design the controller.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data.

Variational Distillation for Multi-View Learning.

Information Bottleneck (IB) provides an information-theoretic principle for multi-view learning by revealing the various components contained in each viewpoint. This highlights the necessity to capture their distinct roles to achieve view-invariance and predictive representations but remains under-explored due to the technical intractability of modeling and organizing innumerable mutual information (MI) terms. Recent studies show that sufficiency and consistency play such key roles in multi-view representation learning, and could be preserved via a variational distillation framework.

Gelatin-mediated gallium-doped SrHA composite coatings with sequential antimicrobial and osteogenic functions for infected bone defect repair.

In this study, gallium- and gelatin-modified strontium-doped hydroxyapatite (SrHA-Gel-Ga) bilayer coatings were prepared on titanium substrates by electrodeposition and spin-coating techniques. The results showed that gallium and gelatin were uniformly doped into the SrHA coatings, which exhibited good hydrophilicity and bioactivity. Furthermore, SrHA-Gel-Ga demonstrated good antimicrobial properties against E.

Perturbed Progressive Learning for Semisupervised Defect Segmentation.

Recently, with the development of intelligent manufacturing, the demand for surface defect inspection has been increasing. Deep learning has achieved promising results in defect inspection. However, due to the rareness of defect data and the difficulties of pixelwise annotation, the existing supervised defect inspection methods are too inferior to be implemented in practice.

An Overview of Image Generation of Industrial Surface Defects.

Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem.

Reinforcement Learning-Based Adaptive Optimal Control for Nonlinear Systems With Asymmetric Hysteresis.

This article investigates the adaptive optimal tracking problem for a class of nonlinear affine systems with asymmetric Prandtl-Ishlinskii (PI) hysteresis nonlinearities based on actor-critic (A-C) learning mechanisms. Considering the huge obstacles arising from the uncertainty of hysteresis nonlinearity in actuators, we develop a scheme for the conflict between the construction of Hamilton functions and hysteresis nonlinearity. The actuator hysteresis forces the input into a hysteresis delay, thus preventing the Hamilton function from getting the current moment's input instantly and thus making optimization impossible.

Comprehensive Analysis of Cuproptosis-Related Genes in Prognosis and Immune Infiltration of Hepatocellular Carcinoma Based on Bulk and Single-Cell RNA Sequencing Data.

Background: Studies on prognostic potential and tumor immune microenvironment (TIME) characteristics of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are limited. Methods: A multigene signature model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The cuproptosis-related multivariate cox regression analysis and bulk RNA-seq-based immune infiltration analysis were performed.

Learning All-In Collaborative Multiview Binary Representation for Clustering.

Multiview clustering via binary representation has attracted intensive attention due to its effectiveness in handling large-scale multiple view data. However, these kind of clustering approaches usually ignore a very important potential high-order correlation in discrete representation learning. In this article, we propose a novel all-in collaborative multiview binary representation for clustering (AC-MVBC) framework, where multiview collaborative binary representation and clustering structure are learned in a joint manner.

Reinforcement learning based adaptive optimal control for constrained nonlinear system via a novel state-dependent transformation.

Existing schemes for state-constrained systems either impose feasibility conditions or ignore the optimality. In this article, an adaptive optimal control scheme for the strict-feedback nonlinear system is proposed, which benefits from two design steps. Firstly, a novel nonlinear state-dependent function (NSDF) is formulated to equivalently transform the system into a non-constrained one to deal with state constraints without the requirements on feasibility conditions.

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations.

A Regioselectively Oxidized 2D Bi/BiOx Lateral Nano-Heterostructure for Hypoxic Photodynamic Therapy.

Optoelectronic science and 2D nanomaterial technologies are currently at the forefront of multidisciplinary research and have numerous applications in electronics and photonics. The unique energy and optically induced interfacial electron transfer in these nanomaterials, enabled by their relative band alignment characteristics, can provide important therapeutic modalities for healthcare. Given that nano-heterostructures can facilitate photoinduced electron-hole separation and enhance generation of reactive oxygen species (ROS), 2D nano-heterostructure-based photosensitizers can provide a major advancement in photodynamic therapy (PDT), to overcome the current limitations in hypoxic tumor microenvironments.

Mg/Cu-doped TiO nanotube array: A novel dual-function system with self-antibacterial activity and excellent cell compatibility.

Many studies were conducted to change the surface morphology and chemical composition of Ti implants for the improvement of antibacterial ability and osseointegration between medical Ti and surrounding bone tissue. In this study, we successfully prepared a novel dual-function coating on pure Ti surface, i.e.