Experimental Investigation on the Early Stage Spray Characteristics with Biodiesel and Diesel

[EN] The early stage spray characteristics have a great impact on the secondary atomization progress, and thus affect the engine combustion and emission performances. The experimental investigation of the early stage spray behaviors with biodiesel and diesel was carried out by employing a laser-based Mie-scattering method. The results show that the spray tip penetration for biodiesel is higher than that for diesel at the early stage spray under the same injection pressure. Moreover, the early stage spray tip penetration can be longer under high injection pressures for two fuels. Besides, the early stage spray cone angle for biodiesel is narrower than that for diesel, and the spray cone angle is especially higher than biodiesel by 25.8% after start of injection time of 0.01ms. Furthermore, under the same injection condition, the difference of early stage spray area between diesel and biodiesel is not obvious, while the spray volume for biodiesel is larger than that for diesel, and also the spray volume can be enlarged by increasing injection pressure for both fuels.This work was supported by the contribution of China postdoctoral fund projects [grant number2013M530236]; The projects of ‘Six talent peak’ [grant number 2014-ZBZZ-014]; Research start-up found projects of Jiangsu university [grant number 13JDG104]; Natural Science Foundation of Jiangsu Province of China [grant number BK20150520]；The Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD]Yu, S.; Yin, B.; Wen, S.; Li, X.; Jia, H.; Yu, J. (2017). Experimental Investigation on the Early Stage Spray Characteristics with Biodiesel and Diesel. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 473-479. https://doi.org/10.4995/ILASS2017.2017.4651OCS47347

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect

The physical origin of the periodic activity for FRB 20180916B

Fast radio bursts (FRBs) are transient radio signals with millisecond-duration, large dispersion measure (DM) and extremely high brightness temperature. Among them, FRB 20180916B has been found to have a 16-day periodic activity. However, the physical origin of the periodicity is still a mystery. Here, we utilize the comprehensive observational data to diagnose the periodic models. We find that the ultra-long rotation model is the most probable one for the periodic activity. However, this model cannot reproduce the observed rotation measure (RM) variations. We propose a self-consistent model, i.e., a massive binary containing a slowly rotational neutron star and a massive star with large mass loss, which can naturally accommodate the wealth of observational features for FRB 20180916B. In this model, the RM variation is periodic, which can be tested by future observations.Comment: 12 pages, 8 figure