LMI Approach to Exponential Stability and Almost Sure Exponential Stability for Stochastic Fuzzy Markovian-Jumping Cohen-Grossberg Neural Networks with Nonlinear p-Laplace Diffusion

The robust exponential stability of delayed fuzzy Markovian-jumping Cohen-Grossberg neural networks (CGNNs) with nonlinear p-Laplace diffusion is studied. Fuzzy mathematical model brings a great difficulty in setting up LMI criteria for the stability, and stochastic functional differential equations model with nonlinear diffusion makes it harder. To study the stability of fuzzy CGNNs with diffusion, we have to construct a Lyapunov-Krasovskii functional in non-matrix form. But stochastic mathematical formulae are always described in matrix forms. By way of some variational methods in W1,p(Ω), Itô formula, Dynkin formula, the semi-martingale convergence theorem, Schur Complement Theorem, and LMI technique, the LMI-based criteria on the robust exponential stability and almost sure exponential robust stability are finally obtained, the feasibility of which can efficiently be computed and confirmed by computer MatLab LMI toolbox. It is worth mentioning that even corollaries of the main results of this paper improve some recent related existing results. Moreover, some numerical examples are presented to illustrate the effectiveness and less conservatism of the proposed method due to the significant improvement in the allowable upper bounds of time delays

Surface plasmon induction in multiwalled carbon nanotube arrays

Disclosed are optical devices including one or more carbon nanotubes that can function as plasmon waveguides. The presently disclosed devices advantageously utilize the existence of surface plasmons on carbon nanotubes through the generation and transport of surface plasmon polaritons across the nanotubes. Also disclosed are methods for tuning the devices through particular formation parameters for the nanotubes and/or selection of particular substrate materials. Systems of the present invention can provide optical data concerning a sample, for instance via construction of an NSOM image, as well as topological date concerning a sample via construction of an AFM image. In one embodiment, the disclosed systems can provide simultaneous acquisition of optical images and topological images

LMI-Based Stability Criterion of Impulsive T-S Fuzzy Dynamic Equations via Fixed Point Theory

By formulating a contraction mapping and the matrix exponential function, the authors apply linear matrix inequality (LMI) technique to investigate and obtain the LMI-based stability criterion of a class of time-delay Takagi-Sugeno (T-S) fuzzy differential equations. To the best of our knowledge, it is the first time to obtain the LMI-based stability criterion derived by a fixed point theory. It is worth mentioning that LMI methods have high efficiency and other advantages in largescale engineering calculations. And the feasibility of LMI-based stability criterion can efficiently be computed and confirmed by computer Matlab LMI toolbox. At the end of this paper, a numerical example is presented to illustrate the effectiveness of the proposed methods

Physics-informed Deep Super-resolution for Spatiotemporal Data

High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in leveraging deep learning to augment scientific data based on the coarse-grained simulations, which is of cheap computational expense and retains satisfactory solution accuracy. However, the major existing work focuses on data-driven approaches which rely on rich training datasets and lack sufficient physical constraints. To this end, we propose a novel and efficient spatiotemporal super-resolution framework via physics-informed learning, inspired by the independence between temporal and spatial derivatives in partial differential equations (PDEs). The general principle is to leverage the temporal interpolation for flow estimation, and then introduce convolutional-recurrent neural networks for learning temporal refinement. Furthermore, we employ the stacked residual blocks with wide activation and sub-pixel layers with pixelshuffle for spatial reconstruction, where feature extraction is conducted in a low-resolution latent space. Moreover, we consider hard imposition of boundary conditions in the network to improve reconstruction accuracy. Results demonstrate the superior effectiveness and efficiency of the proposed method compared with baseline algorithms through extensive numerical experiments

miR-638 is a new biomarker for outcome prediction of non-small cell lung cancer patients receiving chemotherapy.

MicroRNAs (miRNAs), a class of small non-coding RNAs, mediate gene expression by either cleaving target mRNAs or inhibiting their translation. They have key roles in the tumorigenesis of several cancers, including non-small cell lung cancer (NSCLC). The aim of this study was to investigate the clinical significance of miR-638 in the evaluation of NSCLC patient prognosis in response to chemotherapy. First, we detected miR-638 expression levels in vitro in the culture supernatants of the NSCLC cell line SPC-A1 treated with cisplatin, as well as the apoptosis rates of SPC-A1. Second, serum miR-638 expression levels were detected in vivo by using nude mice xenograft models bearing SPC-A1 with and without cisplatin treatment. In the clinic, the serum miR-638 levels of 200 cases of NSCLC patients before and after chemotherapy were determined by quantitative real-time PCR, and the associations of clinicopathological features with miR-638 expression patterns after chemotherapy were analyzed. Our data helped in demonstrating that cisplatin induced apoptosis of the SPC-A1 cells in a dose- and time-dependent manner accompanied by increased miR-638 expression levels in the culture supernatants. In vivo data further revealed that cisplatin induced miR-638 upregulation in the serum derived from mice xenograft models, and in NSCLC patient sera, miR-638 expression patterns after chemotherapy significantly correlated with lymph node metastasis. Moreover, survival analyses revealed that patients who had increased miR-638 levels after chemotherapy showed significantly longer survival time than those who had decreased miR-638 levels. Our findings suggest that serum miR-638 levels are associated with the survival of NSCLC patients and may be considered a potential independent predictor for NSCLC prognosis

Impacts of wet market modernization levels and hygiene practices on the microbiome and microbial safety of wooden cutting boards in Hong Kong

Accessing food through wet markets is a common global daily occurrence, where fresh meat can be purchased to support an urbanizing world population. Similar to the wet markets in many other metropolitan cities in Asia, Hong Kong wet markets vary and are characterized by differing hygiene routines and access to essential modern technologies. The lack of risk assessments of food contact surfaces in these markets has led to substantial gaps in food safety knowledge and information that could help improve and maintain public health. Microbial profiling analyses were conducted on cutting boards that had been used to process pork, poultry, and seafood at 11 different wet markets. The markets differed in hygiene protocols and access to modern facilities. Irrespective of whether wet markets have access of modern infrastructure, the hygiene practices were largely found to be inefficient based on the prevalence of bacterial species typically associated with foodborne pathogens such as Campylobacter fetus, Clostridium perfringens, Staphylococcus aureus, and Vibrio parahaemolyticus; indicator organisms such as Escherichia coli; as well as nonfoodborne pathogenic bacterial species potentially associated with nosocomial infections, such as Klebsiella pneumoniae and Enterobacter cloacae. Other Vibrio species, V. parahaemolyticus and V. vulnificus, typically associated with contaminated raw or undercooked seafood with the potential to cause illness in humans, were also found on wooden cutting boards. This study indicated that the hygienic practices used in Hong Kong wet markets are not sufficient for preventing the establishment of spoilage or pathogenic organisms. This study serves as a basis to review current hygiene practices in wet markets and provides a framework to reassess existing safety protocols

Questioning the source of identified non-foodborne pathogens from food-contact wooden surfaces used in Hong Kong's urban wet markets

In this study, a phylogenic analysis was performed on pathogens previously identified in Hong Kong wet markets' cutting boards. Phylogenetic comparisons were made between phylotypes obtained in this study and environmental and clinical phylotypes for establishing the possible origin of selected bacterial species isolated from wet market cutting board ecosystems. The results reveal a strong relationship between wet market bacterial assemblages and environmental and clinically relevant phylotypes. However, our poor knowledge of potential cross-contamination sources within these wet markets is further exacerbated by failing to determine the exact or presumed origin of its identified pathogens. In this study, several clinically relevant bacterial pathogens such as Klebsiella pneumoniae, Streptococcus suis and Streptococcus porcinus were linked to cutting boards associated with pork; Campylobacter fetus, Staphylococcus aureus, Escherichia coli, and A. caviae in those associated with poultry; and Streptococcus varanii, A. caviae, Vibrio fluvialis, and Vibrio parahaemolyticus in those associated with seafood. Identifying non-foodborne clinically relevant pathogens in wet market cutting boards in this study confirms the need for safety approaches for wet market meat, including cold storage. The presented study justifies the need for future systematic epidemiological studies to determine identified microbial pathogens. Such studies should bring about significant improvements in the management of hygienic practices in Hong Kong's wet markets and work towards a One Health goal by recognizing the importance of wet markets as areas interconnecting food processing with animal and clinical environments

Fixed Points and Exponential Stability for Impulsive Time-Delays BAM Neural Networks via LMI Approach and Contraction Mapping Principle

The fixed point technique has been employed in the stability analysis of time-delays bidirectional associative memory (BAM) neural networks with impulse. By formulating a contraction mapping in a product space, a new LMI-based exponential stability criterion was derived. Lately, fixed point methods have educed various good results inspiring this work, but those criteria cannot be programmed by a computer. In this paper, LMI conditions of the obtained result can be applicable to computer Matlab LMI toolbox which meets the need of the large-scale calculation in real engineering. Moreover, a numerical example and a comparable table are presented to illustrate the effectiveness of the proposed methods