Robust H

The robust H∞ filtering problem for a class of network-based systems with random sensor delay is investigated. The sensor delay is supposed to be a stochastic variable satisfying Bernoulli binary distribution. Using the Lyapunov function and Wirtinger’s inequality approach, the sufficient conditions are derived to ensure that the filtering error systems are exponentially stable with a prescribed H∞ disturbance attenuation level and the filter design method is proposed in terms of linear matrix inequalities. The effectiveness of the proposed method is illustrated by a numerical example

Chemotherapy-induced nausea and vomiting among cancer patients in Shanghai: a cross-sectional study

Background and purpose: Chemotherapy-induced nausea and vomiting (CINV) can cause severe damage to body functions and even lead to death. The prevention of CINV is critically important in patients receiving emetogenic chemotherapy regimen. This study aimed to investigate the prevalence and treatment of CINV in Grade-A tertiary hospitals in Shanghai and explore risk factors of CINV to improve its management. Methods: The clinical data of 376 cancer patients in Grade-A tertiary hospitals in Shanghai from October 2022 to December 2022 were collected retrospectively. The questionnaire was used to conduct a cross-sectional study. The univariate and multivariable logistic regression models were used to evaluate the influencing factors of CINV. Results: The management and coincidence of the guideline in 2022 significantly improved compared to five years ago. For patients receiving high-emetic-risk chemotherapy regimen, the coincidence of the guideline increased from 21.6% to 67.0%. For patients receiving moderate-emetic-risk chemotherapy regimen, the neurokinin-1 (NK-1) receptor antagonist was not significantly associated with CINV. Multivariable analysis showed that the chemotherapy regimen was the only risk factor for CINV during the whole period (P<0.05). Conclusion: The chemotherapy regimen is the main risk factor for CINV. To control CINV better, clinical practitioners should focus on the intrinsic risk of chemotherapy regimens preferentially, estimate the risk and adhere better to guidelines

Self‐adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy

This study presents a novel method for solving facial expression recognition (FER) tasks which uses a self‐adaptive weighted synthesised local directional pattern (SW‐SLDP) descriptor integrating sparse autoencoder (SA) features based on improved multiple kernel learning (IMKL) strategy. The authors’ work includes three parts. Firstly, the authors propose a novel SW‐SLDP feature descriptor which divides the facial images into patches and extracts sub‐block features synthetically according to both distribution information and directional intensity contrast. Then self‐adaptive weights are assigned to each sub‐block feature according to the projection error between the expressional image and neutral image of each patch, which can highlight such areas containing more expressional texture information. Secondly, to extract a discriminative high‐level feature, they introduce SA for feature representation, which extracts the hidden layer representation including more comprehensive information. Finally, to combine the above two kinds of features, an IMKL strategy is developed by effectively integrating both soft margin learning and intrinsic local constraints, which is robust to noisy condition and thus improve the classification performance. Extensive experimental results indicate their model can achieve competitive or even better performance with existing representative FER methods

Preparation and Thermal Shock Performance of ZrO₂/NiCrAlY Thermal Barrier Coating

Electric spark deposition (ESD) combining with micro-arc oxidation (MAO) were employed to fabricate ZrO2/NiCrAlY thermal barrier coating (TBC) on GH4169 substrate. Firstly, a NiCrAlY coating with thickness of 250 μm was deposited on GH4169 substrate by ESD. Secondly, a Zr coating with thickness of 150 μm was deposited on NiCrAlY coating followed by MAO of the Zr coating. The thermal shock performances of the coatings under different temperatures were investigated. The results indicate that the thermal cycle numbers of the coatings at 750℃, 850 ℃ and 950℃ are 51, 32, and 19 respectively

A 3-D Reconstruction Method of Dense Bubbly Plume based on Laser Scanning

Bubbly flow widely exists in many industrial applica-tions of energy, metallurgy, and chemistry, etc. Due to the clusters and overlap of dense bubbles at a high void fraction, it is nearly impossible to obtain the information of flow structures and char-acteristics in the spatial field with traditional measurement meth-ods. In this paper, a novel laser scanning based three-dimensional (3-D) reconstruction method for dense bubbly plume is developed. The measurement area is scanned by a laser sheet through a rotat-ing hexagonal optical prism, and a high-speed camera captures the sequentially-sliced images in the flow field, which is parallel to the scanning direction. Meanwhile, a scanning mathematic model is established, and its linearization is analyzed in detail. An image pre-processing method is developed to extract the features of the bubbly plume. To be specific, a method involves adaptive wavelet threshold denoising is developed to remove the noise. Also, methods regarding sliced image-matching and interpolation based on Log-polar transformation are presented to improve the spatial resolution effectively, and a set of image evaluation standards are designed to investigate the interpolation efficiency and accuracy. The experimental results conclude that the reported 3-D recon-struction method for dense bubbly flow based on laser scanning is valid with high precision, which explores a new way for the visual-ization of the 3-D structures and measurement of the volumetric flow field and the complex flow characteristics

The Relationship between Secondary Structure and Biodegradation Behavior of Silk Fibroin Scaffolds

Silk fibroin has a unique and useful combination of properties, including good biocompatibility and excellent mechanical performance. These features provided early clues to the utility of regenerated silk fibroin as a scaffold/matrix for tissue engineering. The silk fibroin scaffolds used for tissue engineering should degrade at a rate that matches the tissue growth rate. The relationship between secondary structure and biodegradation behavior of silk fibroin scaffolds was investigated in this study. Scaffolds with different secondary structure were prepared by controlling the freezing temperature and by treatment with carbodiimide or ethanol. The quantitative proportions of each secondary structure were obtained by Fourier transform infrared spectroscopy (FTIR), and each sample was then degraded in vitro with collagenase IA for 18 days. The results show that a high content of β-sheet structure leads to a low degradation rate. The random coil region in the silk fibroin material is degraded, whereas the crystal region remains stable and the amount of β-sheet structure increases during incubation. The results demonstrate that it is possible to control the degradation rate of a silk fibroin scaffold by controlling the content of β-sheet structure

Acylation Modification of Antheraea pernyi Silk Fibroin Using Succinic Anhydride and Its Effects on Enzymatic Degradation Behavior

The degradation rate of tissue engineering scaffolds should match the regeneration rate of new tissues. Controlling the degradation behavior of silk fibroin is an important subject for silk-based tissue engineering scaffolds. In this study, Antheraea pernyi silk fibroin was successfully modified with succinic anhydride and then characterized by zeta potential, ninhydrin method, and FTIR. In vitro, three-dimensional scaffolds prepared with modified silk fibroin were incubated in collagenase IA solution for 18 days to evaluate the impact of acylation on the degradation behavior. The results demonstrated that the degradation rate of modified silk fibroin scaffolds was more rapid than unmodified ones. The content of the β-sheet structure in silk fibroin obviously decreased after acylation, resulting in a high degradation rate. Above all, the degradation behavior of silk fibroin scaffolds could be regulated by acylation to match the requirements of various tissues regeneration

Prevalence, Awareness, Treatment and Control of Diabetes Mellitus in a Chinese Population.

The purpose of this study is to evaluate the prevalence, awareness, treatment and glycemic control of diabetes mellitus (DM) in a Chinese population. The findings from this study are expected to offer scientific evidence to better prevent and control the growing number of reported and untreated cases.A cross-sectional survey was conducted in Jiangsu, China. We recruited permanent residents over 18 years of age from eight towns in Jintan (JT) and six towns in Yangzhong (YZ) using a three-stage stratified cluster sampling method. The rates of DM prevalence, awareness, treatment and control as well as their related factors were analyzed.A total number of 15,404 people were entered into the analysis. The DM prevalence, awareness, treatment and control rates were 7.31%, 58.35%, 51.87% and 14.12%, respectively. Multivariable logistic regression analysis showed that being female was positively related to prevalence (OR=1.21, 95% CI: 1.07-1.37), awareness (OR=1.52, 95% CI: 1.19-1.93), treatment (OR=1.48, 95% CI: 1.17-1.88) and control (OR=1.87, 95% CI: 1.30-2.67) of DM. Having a family history of diabetes was significantly correlated with DM risk (OR=1.86, 95% CI: 1.37-2.54) and increased awareness (OR=3.12, 95% CI: 2.19-4.47), treatment (OR=3.47, 95% CI: 2.45-4.90) and control (OR=1.81, 95% CI: 1.22-2.68) of DM. Former smoking status (OR=1.82, 95% CI: 1.23-2.71), overweight (OR=2.11, 95% CI: 1.72-2.60) and obesity (OR=3.46, 95% CI: 2.67-4.50) were related to the risk of DM. Additionally, we found current drinking status to be positively correlated with DM risk (OR=1.30, 95% CI: 1.01-1.66) and negatively correlated with DM awareness (OR=0.41, 95% CI: 0.29-0.59) and treatment (OR=0.41, 95% CI: 0.29-0.59). Our study highlights the high prevalence and inadequate awareness, treatment and control of DM in the Chinese population.Management and prevention of DM-related complications should be considered an essential strategy by governments and society. This study assessed the reasons why DM has been increasing and established the first step in determining where to start regarding preventative methods

Metabolite Profiling of Feces and Serum in Hemodialysis Patients and the Effect of Medicinal Charcoal Tablets

Background/Aims: Recently, the colon has been recognized as an important source of various uremic toxins in patients with end stage renal disease. Medicinal charcoal tablets are an oral adsorbent that are widely used in patients with chronic kidney disease in China to remove creatinine and urea from the colon. A parallel fecal and serum metabolomics study was performed to determine comprehensive metabolic profiles of patients receiving hemodialysis (HD). The effects of medicinal charcoal tablets on the fecal and serum metabolomes of HD patients were also investigated. Methods: Ultra-performance liquid chromatography/mass spectrometry was used to investigate the fecal and serum metabolic profiles of 20 healthy controls and 31 HD patients before and after taking medicinal charcoal tablets for 3 months. Results: There were distinct metabolic variations between the HD patients and healthy controls both in the feces and serum according to multivariate data analysis. Metabolic disturbances of alanine, aspartate and glutamate metabolism, arginine and proline metabolism figured prominently in the serum. However, in the feces, alterations of tryptophan metabolism, lysine degradation and beta-alanine metabolism were pronounced, and the levels of several amino acids (leucine, phenylalanine, lysine, histidine, methionine, tyrosine, and tryptophan) were increased dramatically. Nineteen fecal metabolites and 21 serum metabolites were also identified as biomarkers that contributed to the metabolic differences. Additionally, medicinal charcoal treatment generally enabled the serum and fecal metabolomes of the HD patients to draw close to those of the control subjects, especially the serum metabolic profile. Conclusion: Parallel fecal and serum metabolomics uncovered the systematic metabolic variations of HD patients, especially disturbances in amino acid metabolism in the colon. Medicinal charcoal tablets had an impact on the serum and fecal metabolomes of HD patients, but their exact effects still need to be studied further

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study

Abstract Objective To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. Methods Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. Results There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models’ classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively. Conclusions There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future