A novel tread model for tire modelling using experimental modal parameters

Modal parameter model acts as an analytical model of tire, because it can represent the entire tire characteristics, when applied to the tire modelling. However, without considering the effect of transfer characteristics of adjacent tread spring elements, the accuracy of modal parameter model may be limited in previous studies. Based on contact mechanics and the modal parameter model, a novel tread model is proposed considering tread transfer characteristics, and then a modified model using the modal parameters was obtained using the novel tread model. The effects of tire external characteristics were analyzed by modifying the modal parameter static vertical model. Finally, the experimental and calculated data were compared, the proposed tread model improved the accuracy of calculation. The results show that the tread transfer characteristics significantly affect contact length, i.e., by improving the predictive accuracy by 50 %, and the effect on improving the squat of tire can be ignored. The developed model may help to optimize tire modelling

Tie-dye technique and pattern features

Relationship between tie-dye technique and image pattern has been studied. Based on digital image processing, average value of HSV (hue, saturation, value) tri-component of valid tie-dye area, proportion of incompletely dyed area in HSV color space and the Tamura first three texture features of digital tie-dye image have been extracted. Then, the repeated tests and analysis of variance (ANOVA) of rotation speed and concentration have been done. The results show the obvious impact of concentration and rotation speed on the color and texture feature of tie-dye pattern. Furthermore, back propagation neutral network is developed and partial pattern features with low correlation is used to forecast the tie-dye concentration and rotation speed. The experiment results show the 100% rate of forecast, which proves that pattern features can effectively achieve the technique forecast

Inhibition of Arterial Allograft Intimal Hyperplasia Using Recipient Dendritic Cells Pretreated with B7 Antisense Peptide

Background. Low expression or absence of dendritic cell (DC) surface B7 molecules can induce immune tolerance or hyporesponse. Whether DCs could induce indirect allogeneic-specific cross-tolerance or hyporesponse to recipient T cells remains unclear. Methods. Generated from C3H/He mice bone marrow cells pulsed with donor antigen from C57BL/6 mice, recipient DCs were incubated with B7 antisense peptide (B7AP). Immune regulatory activities were examined in vitro by a series of mixed lymphocyte reactions. Murine allogeneic carotid artery orthotopic transplantation was performed from C57BL/6 to C3H/He. Recipients were given B7AP-treated DCs 7 days before transplantation. Allograft pathological analysis was done 2 months after transplantation. Results. B7AP-pretreated DCs markedly inhibited T-cell proliferation compared with untreated group. Pretreated T cells exhibited markedly reduced response to alloantigen versus third-party antigen. Pathological analysis of arterial allografts demonstrated significant reduction of intimal hyperplasia in B7-AP pretreated group versus control. Conclusion. Blockade of B7 molecules by B7AP could induce indirect allogeneic-specific hyporesponse and inhibit arterial allograft intimal hyperplasia, which may be involved in future strategies for human allograft chronic rejection

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

This paper presents a novel approach for improving the estimation accuracy of the road profile for a vehicle suspension system. To meet the requirements of road profile estimation for road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on road profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher road estimation accuracy compared to traditional KF methods

Large-scale phosphoproteome analysis in seedling leaves of Brachypodium distachyon L.

BACKGROUND: Protein phosphorylation is one of the most important post-translational modifications involved in the regulation of plant growth and development as well as diverse stress response. As a member of the Poaceae, Brachypodium distachyon L. is a new model plant for wheat and barley as well as several potential biofuel grasses such as switchgrass. Vegetative growth is vital for biomass accumulation of plants, but knowledge regarding the role of protein phosphorylation modification during vegetative growth, especially in biofuel plants, is far from comprehensive. RESULTS: In this study, we carried out the first large-scale phosphoproteome analysis of seedling leaves in Brachypodium accession Bd21 using TiO(2) microcolumns combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and MaxQuant software. A total of 1470 phosphorylation sites in 950 phosphoproteins were identified, and these phosphoproteins were implicated in various molecular functions and basic cellular processes by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Among the 950 phosphoproteins identified, 127 contained 3 to 8 phosphorylation sites. Conservation analysis showed that 93.4% of the 950 phosphoproteins had phosphorylation orthologs in other plant species. Motif-X analysis of the phosphorylation sites identified 13 significantly enriched phosphorylation motifs, of which 3 were novel phosphorylation motifs. Meanwhile, there were 91 phosphoproteins with both multiple phosphorylation sites and multiple phosphorylation motifs. In addition, we identified 58 phosphorylated transcription factors across 21 families and found out 6 significantly over-represented transcription factor families (C3H, Trihelix, CAMTA, TALE, MYB_related and CPP). Eighty-four protein kinases (PKs), 8 protein phosphatases (PPs) and 6 CESAs were recognized as phosphoproteins. CONCLUSIONS: Through a large-scale bioinformatics analysis of the phosphorylation data in seedling leaves, a complicated PKs- and PPs- centered network related to rapid vegetative growth was deciphered in B. distachyon. We revealed a MAPK cascade network that might play the crucial roles during the phosphorylation signal transduction in leaf growth and development. The phosphoproteins and phosphosites identified from our study expanded our knowledge of protein phosphorylation modification in plants, especially in monocots. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-375) contains supplementary material, which is available to authorized users

A Novel Deep Model with Meta-learning for Rolling Bearing Few-shot Fault Diagnosis

Machine learning, especially deep learning, has been highly successful in data- intensive applications, however, the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement. This leads to the so-called Few-Shot Learning (FSL) problem, which requires the model rapidly generalize to new tasks that containing only a few labeled samples. In this paper, we proposed a new deep model, called deep convolutional meta-learning networks (DCMLN), to address the low performance of generalization under limited data for bearing fault diagnosis. The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data. The proposed method was compared to several few-shot learning methods, including methods with and without pre-training the embedding mapping, and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain. The comparisons are carried out on one-shot and ten-shot tasks using the CWRU bearing dataset and a cylindrical roller bearing dataset. The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions. In addition, we found that the pre-training process does not always improve the prediction accuracy

The QE numerical simulation of PEA semiconductor photocathode

Several kinds of models have already been proposed for explaining the photoemission process. The exact photoemission theory of semiconductor photocathode was not well established after decades of research. In this paper an integral equation of quantum efficiency (QE) is constructed to describe the photoemission of positive electron affinity (PEA) semiconductor photocathode based on three-step photoemission model. The influences of forbidden gap, electron affinity, photon energy, incident angle, degree of polarization, refractive index, extinction coefficient, initial/final electron energy, relaxation time and external electric field on the QE of PEA semiconductor photocathode are taken into account. In addition, a computer code is also programmed to calculate the QE of K2CsSb photocathode theoretically at 532nm wavelength, the result is in line with the experimental value by and large. What are the reasons caused to the distinction between the experimental measuring and theoretical QE are discussed.Comment: 12 pages,3 figures,2 tables,submitted to Chinese Physics

Maintenance of Sorafenib following combined therapy of three-dimensional conformal radiation therapy/intensity-modulated radiation therapy and transcatheter arterial chemoembolization in patients with locally advanced hepatocellular carcinoma: a phase I/II study

BACKGROUND: Three-dimensional conformal radiation therapy (3DCRT)/intensity-modulated radiation therapy (IMRT) combined with or without transcatheter arterial chemoembolization (TACE) for locally advanced hepatocellular carcinoma (HCC) has shown favorable outcomes in local control and survival of locally advanced HCC. However, intra-hepatic spreading and metastasis are still the predominant treatment failure patterns. Sorafenib is a multikinase inhibitor with effects against tumor proliferation and angiogenesis. Maintenance Sorafenib would probably prevent or delay the intrahepatic and extrahepatic spread of HCC after radiotherapy, which provides the rationale for the combination of these treatment modalities. METHODS AND DESIGN: Patients with solitary lesion (bigger than 5 cm in diameter) histologically or cytologically confirmed HCC receive TACE (1-3 cycles) plus 3DCRT/IMRT 4-6 weeks later. Maintenance Sorafenib will be administered only for the patients with non-progression disease 4 to 6 weeks after the completion of radiotherapy. The dose will be 400 mg, p.o., twice a day. Sorafenib will be continuously given for 12 months unless intolerable toxicities and/or tumor progression. If no more than 3 patients discontinue Sorafenib treatment who experience dose-limiting toxicity after necessary dose modification and delay and/or radiation-induced liver disease in the first 15 enrolled patients, the study will recruit second fifteen patients for further evaluating safety and efficacy of treatment. Hypothesis of the current study is that Sorafenib as a maintenance therapy after combined therapy of 3DCRT/IMRT and TACE is safe and superior to radiotherapy combined with TACE alone in terms of time to progression (TTP), progression-free survival (PFS) and overall survival (OS) in comparison to historical data. DISCUSSION: A recent meta-analysis showed TACE in combination with radiotherapy, improved the survival and the tumor response of patients, and was thus more therapeutically beneficial. In this study, local therapy for HCC is the combination of TACE and radiotherapy. Radiation exposure as a kind of stress might induce the compensatory activations of multiple intracellular signaling pathway mediators, such as PI3K, MAPK, JNK and NF-kB. Vascular endothelial growth factor (VEGF) was identified as one factor that was increased in a time- and dose-dependent manner after sublethal irradiation of HCC cells in vitro, translating to enhanced intratumor angiogenesis in vivo. Therefore, Sorafenib-mediated blockade of the Raf/MAPK and VEGFR pathways might enhance the efficacy of radiation, when Sorafenib is followed sequentially as a maintenance modality. (ClinicalTrials.gov number, NCT00999843.

An assembly oriented design framework for product structure engineering and assembly sequence planning

The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology