Reaction Mechanism Reduction for Ozone-Enhanced CH4/Air Combustion by a Combination of Directed Relation Graph with Error Propagation, Sensitivity Analysis and Quasi-Steady State Assumption

In this study, an 18-steps, 22-species reduced global mechanism for ozone-enhanced CH4/air combustion processes was derived by coupling GRI-Mech 3.0 and a sub-mechanism for ozone decomposition. Three methods, namely, direct relation graphics with error propagation, (DRGRP), sensitivity analysis (SA), and quasi-steady-state assumption (QSSA), were used to downsize the detailed mechanism to the global mechanism. The verification of the accuracy of the skeletal mechanism in predicting the laminar flame speeds and distribution of the critical components showed that that the major species and the laminar flame speeds are well predicted by the skeletal mechanism. However, the pollutant NO was predicated inaccurately due to the precursors for generating NO were removed as redundant components. The laminar flame speeds calculated by the global mechanism fit the experimental data well. The comparisons of simulated results between the detailed mechanism and global mechanism were investigated and showed that the global mechanism could accurately predict the major and intermediate species and significantly reduced the time cost by 72%Peer reviewe

Zinc ferrite based gas sensors: A review

Flammable, explosive and toxic gases, such as hydrogen, hydrogen sulfide and volatile organic compounds vapor, are major threats to the ecological environment safety and human health. Among the available technologies, gas sensing is a vital component, and has been widely studied in literature for early detection and warning. As a metal oxide semiconductor, zinc ferrite (ZnFe2O4) represents a kind of promising gas sensing material with a spinel structure, which also shows a fine gas sensing performance to reducing gases. Due to its great potentials and widespread applications, this article is intended to provide a review on the latest development in zinc ferrite based gas sensors. We first discuss the general gas sensing mechanism of ZnFe2O4 sensor. This is followed by a review of the recent progress about zinc ferrite based gas sensors from several aspects: different micro-morphology, element doping and heterostructure materials. In the end, we propose that combining ZnFe2O4 which provides unique microstructure (such as the multi-layer porous shells hollow structure), with the semiconductors such as graphene, which provide excellent physical properties. It is expected that the mentioned composites contribute to improving selectivity, long-term stability, and other sensing performance of sensors at room or low temperature

Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing

Decentralized federated learning (DFL) has gained popularity due to its practicality across various applications. Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process. Especially when distributed nodes suffer from limitations in communication or computational resources, DFL will experience extremely inefficient and unstable training. Motivated by these challenges, in this paper, we develop a novel algorithm based on the framework of the inexact alternating direction method (iADM). On one hand, our goal is to train a shared model with a sparsity constraint. This constraint enables us to leverage one-bit compressive sensing (1BCS), allowing transmission of one-bit information among neighbour nodes. On the other hand, communication between neighbour nodes occurs only at certain steps, reducing the number of communication rounds. Therefore, the algorithm exhibits notable communication efficiency. Additionally, as each node selects only a subset of neighbours to participate in the training, the algorithm is robust against stragglers. Additionally, complex items are computed only once for several consecutive steps and subproblems are solved inexactly using closed-form solutions, resulting in high computational efficiency. Finally, numerical experiments showcase the algorithm's effectiveness in both communication and computation

Isosorbide mononitrate inhibits myocardial fibrosis in diabetic rats by up-regulating exosomal MiR-378

Purpose: To investigate the effect of isosorbide mononitrate on diabetic cardiomyopathy (DCM), and the potential mechanism of action.Methods: The effects of isosorbide mononitrate and isosorbide mononitrate + GW4869 on cardiac function and myocardial fibrosis in DCM rats were determined via hemodynamics, hematoxylin-eosin (H&E) staining and Masson staining. Exosomes were extracted from the serum, and the differential expressions of microribonucleic acids (miRNAs) related to myocardial fibrosis were determined by reverse transcription-polymerase chain reaction (qRT-PCR). Western blotting was performed to determine the effects of isosorbide mononitrate and isosorbide mononitrate + GW4869 on IGF1R/STAT3 signaling pathway.Results: Isosorbide mononitrate exerted a protective effect against DCM--induced cardiac dysfunction and myocardial fibrosis, while such a protective effect was suppressed by the exosome inhibitor GW4869 (p < 0.05). The expression of miR-378 in exosomes significantly rose in isosorbide mononitrate group. The increased expression of miR-378 in vitro inhibited the proliferation of primary myocardial fibroblasts, and reduced the expression of myocardial fibrosis markers (p < 0.05). Luciferase reporter assay data showed that miR-378 negatively regulated the expression of IGF1R by direct binding to IGF1R mRNA 3'-untranslated region (3'UTR). In primary myocardial fibroblasts, miR-378 mimic significantly reduced the protein expressions of IGF1R, p-STAT3/STAT3 and c-Myc (p < 0.05). Isosorbide mononitrate lowered the protein expressions of IGF1R, p-STAT3/STAT3 and c-Myc, but the inhibitory effect was weakened by the exosome inhibitor, GW4869 (p < 0.05).Conclusion: Isosorbide mononitrate inhibits myocardial fibrosis in diabetic rats by up-regulating exosomal miR-378, and targeting the axis of STAT3/IGF1R. The results of this study may provide a new insight into the treatment of DCM

Verification and Validation of a Low-Mach-Number Large-Eddy Simulation Code against Manufactured Solutions and Experimental Results

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).To investigate turbulent reacting flows, a low-Mach number large-eddy simulation (LES) code called ‘LESsCoal’ has been developed in our group. This code employs the Germano dynamic sub-grid scale (SGS) model and the steady flamelet/progress variable approach (SFPVA) on a stagger-structured grid, in both time and space. The method of manufactured solutions (MMS) is used to investigate the convergence and the order of accuracy of the code when no model is used. Finally, a Sandia non-reacting propane jet and Sandia Flame D are simulated to inspect the performance of the code under experimental setups. The results show that MMS is a promising tool for code verification and that the low-Mach-number LES code can accurately predict the non-reacting and reacting turbulent flows. The validated LES code can be used in numerical investigations on the turbulent combustion characteristics of new fuel gases in the future.Peer reviewedFinal Published versio

Classification for glucose and lactose Terahertz spectra based on SVM and DNN methods

In recent decades, terahertz (THz) radiation has been widely applied in many chemical and biomedical areas. Due to its ability to resolve the absorption features of many compounds noninvasively, it is a promising technique for chemical recognition of substances such as drugs or explosives. A key challenge for THz technology is to be able to accurately classify spectral measurements acquired in unknown complicated environments, rather than those from ideal laboratory conditions. Support vector machine (SVM) and deep neural networks (DNNs) are powerful and widely adopted approaches for complex classification with a high accuracy. In this article, we explore and apply the SVM and DNN methods for classifying the frequency spectra of glucose and lactose. We measured 372 groups of independent signals under different conditions to provide a sufficient training set. The classification accuracies achieved were 99% for the SVM method and 89.6% for the DNN method. These high classification accuracies demonstrate great potential in chemical recognition