Non-Fragile Guaranteed Cost Control of Nonlinear Systems with Different State and Input Delays Based on T-S Fuzzy Local Bilinear Models

This paper focuses on the non-fragile guaranteed cost control problem for a class of Takagi-Sugeno (T-S) fuzzy time-varying delay systems with local bilinear models and different state and input delays. A non-fragile guaranteed cost state-feedback controller is designed such that the closed-loop T-S fuzzy local bilinear control system is delay-dependent asymptotically stable, and the closed-loop fuzzy system performance is constrained to a certain upper bound when the additive controller gain perturbations exist. By employing the linear matrix inequality (LMI) technique, sufficient conditions are established for the existence of desired non-fragile guaranteed cost controllers. The simulation examples show that the proposed approach is effective and feasible

Psychological resilience and cognitive reappraisal mediate the effects of coping style on the mental health of children

IntroductionThis study explored the effects of coping style and two potential intermediately factors (cognitive reappraisal and psychological resilience) on the mental health of middle school students during the normalization of epidemic prevention and control in China.MethodsAnswers on questionnaires designed to assess coping style, cognitive reappraisal, psychological resilience, and mental health among 743 middle school students (386 boys, 357 girls, 241 first graders, 235 second graders, and 267 third graders) were analyzed using structural equation modeling.ResultsThe results showed that coping style, cognitive reappraisal, and psychological resilience directly predicted mental health. The negative effect of a negative coping style on mental health was significantly stronger than the positive effect of a positive coping style. Coping style affected mental health through the independent mediating effects of cognitive reappraisal and psychological resilience and through their chain mediation.DiscussionThe use of positive coping styles by most students led to greater cognitive reappraisal, strengthened psychological resilience, and thus few mental health problems. These findings provide empirical evidence and may guide educators in the prevention and intervention of mental health problems among middle school students

Performance Evaluation of Mesophilic Anaerobic Digestion of Chicken Manure with Algal Digestate

Dilution is considered to be a fast and easily applicable pretreatment for anaerobic digestion (AD) of chicken manure (CM), however, dilution with fresh water is uneconomical because of the water consumption. The present investigation was targeted at evaluating the feasibility and process performance of AD of CM diluted with algal digestate water (AW) for methane production to replace tap water (TW). Moreover, the kinetics parameters and mass flow of the AD process were also comparatively analyzed. The highest methane production of diluted CM (104.39 mL/g volatile solid (VS)) was achieved with AW under a substrate concentration of 8% total solid (TS). The result was markedly higher in comparison with the group with TW (79.54-93.82 mL/gVS). Apart from the methane production, considering its energy and resource saving, nearly 20% of TW replaced by AW, it was promising substitution to use AW for TW to dilute CM. However, the process was susceptible to substrate concentration, inoculum, as well as total ammonia and free ammonia concentration

Outdoor particulate matter exposure affects metabolome in chronic obstructive pulmonary disease: Preliminary study

IntroductionThe metabolomic changes caused by airborne fine particulate matter (PM2.5) exposure in patients with chronic obstructive pulmonary disease (COPD) remain unclear. The aim of this study was to determine whether it is possible to predict PM2.5-induced acute exacerbation of COPD (AECOPD) using metabolic markers.MethodsThirty-eight patients with COPD diagnosed by the 2018 Global Initiative for Obstructive Lung Disease were selected and divided into high exposure and low exposure groups. Questionnaire data, clinical data, and peripheral blood data were collected from the patients. Targeted metabolomics using liquid chromatography-tandem mass spectrometry was performed on the plasma samples to investigate the metabolic differences between the two groups and its correlation with the risk of acute exacerbation.ResultsMetabolomic analysis identified 311 metabolites in the plasma of patients with COPD, among which 21 metabolites showed significant changes between the two groups, involving seven pathways, including glycerophospholipid, alanine, aspartate, and glutamate metabolism. Among the 21 metabolites, arginine and glycochenodeoxycholic acid were positively associated with AECOPD during the three months of follow-up, with an area under the curve of 72.50% and 67.14%, respectively.DiscussionPM2.5 exposure can lead to changes in multiple metabolic pathways that contribute to the development of AECOPD, and arginine is a bridge between PM2.5 exposure and AECOPD

Short-term exposure to fine particulate matter and genome-wide DNA methylation in chronic obstructive pulmonary disease: A panel study conducted in Beijing, China

BackgroundFine particulate matter (PM2.5) is a crucial risk factor for chronic obstructive pulmonary disease (COPD). However, the mechanisms whereby PM2.5 contribute to COPD risk have not been fully elucidated. Accumulating evidence suggests that epigenetics, including DNA methylation, play an important role in this process; however, the association between PM2.5 exposure and genome-wide DNA methylation in patients with COPD has not been studied.ObjectiveTo evaluate the association of personal exposure to PM2.5 and genome-wide DNA methylation changes in the peripheral blood of patients with COPD.MethodsA panel study was conducted in Beijing, China. We repeatedly measured and collected personal PM2.5 data for 72 h. Genome-wide DNA-methylation of peripheral blood was analyzed using the Illumina Infinium Human Methylation BeadChip (850 k). A linear-mixed effect model was used to identify the differentially methylated probe (DMP) associated with PM2.5. Finally, we performed a functional enrichment analysis of the DMPs that were significantly associated with PM2.5.ResultsA total of 24 COPD patients were enrolled and 48 repeated DNA methylation measurements were associated in this study. When the false discovery rate was < 0.05, 19 DMPs were significantly associated with PM2.5 and were annotated to corresponding genes. Functional enrichment analysis of these genes showed that they were related to the response to toxic substances, regulation of tumor necrosis factor superfamily cytokine production, regulation of photosensitivity 3-kinase signaling, and other pathways.ConclusionThis study provided evidence for a significant relationship between personal PM2.5 exposure and DNA methylation in patients with COPD. Our research also revealed a new biological pathway explaining the adverse effects of PM2.5 exposure on COPD risk

A protocol for dual calcium-voltage optical mapping in murine sinoatrial preparation with optogenetic pacing

Among the animal models for studying the molecular basis of atrial and sinoatrial node (SAN) biology and disease, the mouse is a widely used species due to its feasibility for genetic modifications in genes encoding ion channels or calcium handling and signaling proteins in the heart. It is therefore highly valuable to develop robust methodologies for studying SAN and atrial electrophysiological function in this species. Here, we describe a protocol for performing dual calcium-voltage optical mapping on mouse sinoatrial preparation (SAP), in combination with an optogenetic approach, for studying SAP membrane potential, intracellular Ca2+ transients, and pacemaker activity. The protocol includes the details for preparing the intact SAP, robust tissue dual-dye loading, light-programmed pacing, and high-resolution optical mapping. Our protocol provides an example of use of the combination of optogenetic and optical mapping techniques for investigating SAP membrane potential and intracellular Ca2+ transients and pacemaker activity with high temporal and spatial resolution in specific cardiac tissues. Thus, our protocol provides a useful tool for studying SAP physiology and pathophysiology in mice

Pharmacological therapy for stable chronic obstructive pulmonary disease

Abstract In recent years, emphasis has shifted from preventing and treating chronic obstructive pulmonary disease (COPD) to early prevention, early treatment, and disease stabilization, with the main goal of improving patients’ quality of life and reducing the frequency of acute exacerbations. This review summarizes pharmacological therapies for stable COPD

Construction of the "Three-integration" Rolling Development Mode in the Urban-rural Fringe from the Perspective of Thematic Business Engine: A Case Study of "Zhucheng Dinosaur Eco-city" in Shandong Province

Starting from the transformation and upgrading of traditional culture, the optimization and development of traditional industries, and the innovation of traditional models, this paper used cultural entrepreneurship to trigger the thematic business engine, and explored Zhucheng City with its unique dinosaur culture. It proposed "Three-integration" rolling development mode to build Zhucheng into a new highland of cultural entrepreneurship and create new competitiveness under the background of agricultural and rural modernization development. This paper also explored from the perspective of multi-subject collaboration of cultural entrepreneurship. Government needs to establish rolling development pilot projects and promote their application; enterprises need to play the engine role of thematic business through the mode of "getting larger to help smaller ones"; the masses need to pay attention to the reaction of culture to the economy, so as to promote the wave of high-level promotion of cultural inheritance and urban-rural integration

Classification of Transmission Line Corridor Tree Species Based on Drone Data and Machine Learning

Tree growth in power line corridors poses a threat to power lines and requires regular inspections. In order to achieve sustainable and intelligent management of transmission line corridor forests, a transmission line corridor tree barrier management system is needed, and tree species classification is an important part of this. In order to accurately identify tree species in transmission line corridors, this study combines airborne LiDAR (light detection and ranging) point-cloud data and synchronously acquired high-resolution aerial image data to classify tree species. First, individual-tree segmentation and feature extraction are performed. Then, the random forest (RF) algorithm is used to sort and filter the feature importance. Finally, two non-parametric classification algorithms, RF and support vector machine (SVM), are selected, and 12 classification schemes are designed to perform tree species classification and accuracy evaluation research. The results show that after using RF for feature filtering, the classification results are better than those without feature filtering, and the overall accuracy can be improved by 3.655% on average. The highest classification accuracy is achieved when using SVM after combining a digital orthorectification map (DOM) and LiDAR for feature filtering, with an overall accuracy of 85.16% and a kappa coefficient of 0.79

Classification of Transmission Line Corridor Tree Species Based on Drone Data and Machine Learning

Tree growth in power line corridors poses a threat to power lines and requires regular inspections. In order to achieve sustainable and intelligent management of transmission line corridor forests, a transmission line corridor tree barrier management system is needed, and tree species classification is an important part of this. In order to accurately identify tree species in transmission line corridors, this study combines airborne LiDAR (light detection and ranging) point-cloud data and synchronously acquired high-resolution aerial image data to classify tree species. First, individual-tree segmentation and feature extraction are performed. Then, the random forest (RF) algorithm is used to sort and filter the feature importance. Finally, two non-parametric classification algorithms, RF and support vector machine (SVM), are selected, and 12 classification schemes are designed to perform tree species classification and accuracy evaluation research. The results show that after using RF for feature filtering, the classification results are better than those without feature filtering, and the overall accuracy can be improved by 3.655% on average. The highest classification accuracy is achieved when using SVM after combining a digital orthorectification map (DOM) and LiDAR for feature filtering, with an overall accuracy of 85.16% and a kappa coefficient of 0.79