Nonlinear dynamics modeling and analysis of disc brake squeal considering acting process of brake force

Disc brake squeal of automobile is one of the hottest and most difficult issues concerned by automobile manufacturers and researchers. Considering the acting process of brake force, a simplified nonlinear dynamics model is developed in this paper. The nonlinear dynamics equations are set up and solved by theoretical method and numerical calculation. By studying the effects of key parameters on the system’s behavior, the mechanism of brake squeal are analyzed and discussed. The results indicate that the state of system is more sensitive to the fluctuation of brake force than the variation of the negative slope of friction coefficient against the relative velocity between pad and disc. The dynamic characteristics of brake system are greatly connected with the components stiffness. The brake system may become weakly stable and easily produce brake squeal when tangential contact stiffness, normal contact stiffness and connection stiffness satisfy a certain relationship

MF-Net: multi-scale feature extraction-integration network for unsupervised deformable registration

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods

Achieving ultrahigh energy storage density in super relaxor BCZT-based lead-free capacitors through multiphase coexistence

Dielectric capacitors own great potential in next-generation energy storage devices for their fast charge-discharge time, while low energy storage capacity limits their commercialization. Enormous lead-free ferroelectric ceramic capacitor systems have been reported in recent decades, and energy storage density has increased rapidly. By comparing with some ceramic systems with fashioned materials or techniques, which lacks repeatability, as reported latterly, we proposed a unique but straightforward way to boost the energy storage capacity in a modified conventional ferroelectric system. Through stoichiometric ratio regulation, the coexistence of the C-phase and T-phase was obtained in 0.85(Ba1-xCax)(ZryTi1-y)O3-0.15BiSmO3-2 wt. % MnO ceramics with x = 0.1 and y = 0.15 under the proof of the combination of Rietveld XRD refinement and transmission electron microscope measurement. The Wrec of 3.90 J/cm3, an excellent value for BCZT-based ceramic at the present stage, was obtained because of the co-contribution of the optimization of electric field distribution and the additional interfacial polarization triggered at the higher electric fields. The finite element simulation and physical deduction, which fits very well with our experimental result, were also performed. As to the practical application, stable performance in a long-time cycle and frequency stability was obtained, and excellent discharge behaviors were also achieved.</p

Genome-Wide Histone H3K27 Acetylation Profiling Identified Genes Correlated With Prognosis in Papillary Thyroid Carcinoma

Thyroid carcinoma (TC) is the most common endocrine malignancy, and papillary TC (PTC) is the most frequent subtype of TC, accounting for 85–90% of all the cases. Aberrant histone acetylation contributes to carcinogenesis by inducing the dysregulation of certain cancer-related genes. However, the histone acetylation landscape in PTC remains elusive. Here, we interrogated the epigenomes of PTC and benign thyroid nodule (BTN) tissues by applying H3K27ac chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) along with RNA-sequencing. By comparing the epigenomic features between PTC and BTN, we detected changes in H3K27ac levels at active regulatory regions, identified PTC-specific super-enhancer-associated genes involving immune-response and cancer-related pathways, and uncovered several genes that associated with disease-free survival of PTC. In summary, our data provided a genome-wide landscape of histone modification in PTC and demonstrated the role of enhancers in transcriptional regulations associated with prognosis of PTC

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Automatic Kidney Segmentation Method Based on an Enhanced Generative Adversarial Network

When deciding on a kidney tumor’s diagnosis and treatment, it is critical to take its morphometry into account. It is challenging to undertake a quantitative analysis of the association between kidney tumor morphology and clinical outcomes due to a paucity of data and the need for the time-consuming manual measurement of imaging variables. To address this issue, an autonomous kidney segmentation technique, namely SegTGAN, is proposed in this paper, which is based on a conventional generative adversarial network model. Its core framework includes a discriminator network with multi-scale feature extraction and a fully convolutional generator network made up of densely linked blocks. For qualitative and quantitative comparisons with the SegTGAN technique, the widely used and related medical image segmentation networks U-Net, FCN, and SegAN are used. The experimental results show that the Dice similarity coefficient (DSC), volumetric overlap error (VOE), accuracy (ACC), and average surface distance (ASD) of SegTGAN on the Kits19 dataset reach 92.28%, 16.17%, 97.28%, and 0.61 mm, respectively. SegTGAN outscores all the other neural networks, which indicates that our proposed model has the potential to improve the accuracy of CT-based kidney segmentation

Dynamic optimization of constrained layer damping structure for the headstock of machine tools with modal strain energy method

Dynamic stiffness and damping of the headstock, which is a critical component of precision horizontal machining center, are two main factors that influence machining accuracy and surface finish quality. Constrained Layer Damping (CLD) structure is proved to be effective in raising damping capacity for the thin plate and shell structures. In this paper, one kind of high damping material is utilized on the headstock to improve damping capacity. The dynamic characteristic of the hybrid headstock is investigated analytically and experimentally. The results demonstrate that the resonant response amplitudes of the headstock with damping material can decrease significantly compared to original cast structure. To obtain the optimal configuration of damping material, a topology optimization method based on the Evolutionary Structural Optimization (ESO) is implemented. Modal Strain Energy (MSE) method is employed to analyze the damping and to derive the sensitivity of the modal loss factor. The optimization results indicate that the added weight of damping material decreases by 50%; meanwhile the first two orders of modal loss factor decrease by less than 23.5% compared to the original structure

CRISPR/Cas9 for hepatitis B virus infection treatment

Abstract Hepatitis B virus (HBV) infection remains a global health challenge. Despite the availability of effective preventive vaccines, millions of people are at risk of cirrhosis and hepatocellular carcinoma. Current drug therapies inhibit viral replication, slow the progression of liver fibrosis and reduce infectivity, but they rarely remove the covalently sealed circular DNA (cccDNA) of the virus that causes HBV persistence. Alternative treatment strategies, including those based on CRISPR/cas9 knockout virus gene, can effectively inhibit HBV replication, so it has a good prospect. During chronic infection, some virus gene knockouts based on CRISPR/cas9 may even lead to cccDNA inactivation. This paper reviews the progress of different HBV CRISPR/cas9, vectors for delivering to the liver, and the current situation of preclinical and clinical research