Study on Craft Culture and Features of Shandong Lu Brocade of China

China is one of the nations producing manual textiles at the earliest. With a history of a thousand years in southwest area of Shandong in China, Lu brocade is mainly distributed in Jining, Heze and other areas. As a kind of folk manual cotton textile in Shandong, Lu brocade takes cotton as main raw material, and adopts manual spinning, manually dyeing, and manual weaving, achieving colorful cloth, just like brocade and embroidery. This is why it is called Lu Brocade. The reason for Lu brocade to be passed on from generation to generation concerns a lot of the folk marriage of Shandong area. There are eight parts in its main process: spinning, dyeing thread, starching thread, wrapping, healding, crossing column, spindling, and weaving. Each process is divided into several sub-processes. Technically it can be mainly categorized into jersey technique, jacquard technique, tuck stitch technique, incision-making technique and package flower technique. For Lu brocade, jersey is the basic technique and jacquard is the core technique. Lu Brocade has distinct local specialty and practical value. Currently intangible cultural heritage protection is widely concerned, we should develop market, plan package, and conduct publicity and marketing for Lu Brocade, so as to promote the industrialized operation of the splendid traditional culture. Practice of several decades proves that the R&D and promotion of Lu Brocade products have affected the quality of Chinese manual textiles and people’s life in a positive manner

A Wideband Coaxial-to-Ridge waveguide Adaptor

A Coaxial-to-ridge waveguide adaptor covering the entire K and Ka band has been demonstrated in this article. The adaptor is in the form of asymmetric double ridge waveguides and characteristic impedances of each step is determined by Chebyshev polynomial. A new method of calculating the characteristic impedance of the asymmetric double ridge waveguide is presented and the wideband adaptor is designed on this basis. The simulated results for the proposed adaptor in HFSS show that the return loss is better than 17.8dB in the entire K and Ka band and the insertion loss is better than 0.1dB. The simulated results for the back-to-back confi guration show that return loss is better than 15 dB and insertion loss is better than 0.2dB. To demonstrate its performance, the adaptor is fabricated and then measured on the vector network analyser. The measured results show that the average insertion loss of the adaptor is about 1dB in the whole band

Eicosapentaenoic acid increases cytochrome P-450 2J2 gene expression and epoxyeicosatrienoic acid production via peroxisome proliferator-activated receptor γ in endothelial cells

Summaryω-3 fatty acids, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have beneficial effects on cardiovascular diseases. Cytochrome P-450 (CYP) 2J2 that is expressed in endothelial cells metabolizes arachidonic acids to biologically active epoxyeicosatrienoic acids (EETs) that possess anti-inflammatory and anti-thrombotic effects.We studied the effects of EPA and DHA on the expression of CYP 2J2 mRNA by reverse transcription-polymerase chain reaction in cultured human umbilical vein endothelial cells and found that EPA, but not DHA, increased the expression of CYP 2J2 mRNA in a dose-dependent and a time-dependent manner. EPA-induced CYP 2J2 expression was significantly inhibited by pretreatment with a peroxisome proliferator-activated receptor (PPAR) γ antagonist, GW9662. EPA, but not DHA, caused a significant increase in cellular levels of 11,12-dihydroxyeicosatrienoic acid that is a stable metabolite of 11,12-EET, which was blocked by pretreatment with GW9662.These data demonstrate that EPA increases CYP 2J2 mRNA expression and 11,12-EET production via PPARγ in endothelial cells and indicate a novel protective role of EPA and PPARγ against vascular inflammation

Dissecting the γ\gamma-ray emissions of the nearby galaxies NGC 1068 and NGC 253

Intrigued by recent high-energy study results for nearby galaxies with gamma-ray emission and in particular NGC~1068 that has been detected as a neutrino-emitting source by the IceCube Neutrino Observatory, we conduct detailed analysis of the $\gamma$-ray data for the galaxies NGC~1068 and NGC~253, obtained with the Large Area Telescope onboard {\it the Fermi Gamma-ray Space Telescope}. By checking for their possible spectral features and then constructing light curves in corresponding energy ranges, we identify flare-like activity from NGC ~1068 in $\geq$2\,GeV energy range and significant long-term variations of NGC~253 in $\geq$5\,GeV energy range. In the former, the emission appears harder in the two half-year flare-like events than that in the otherwise `quiescent' state. In the latter, there is a 2-times decrease in the flux before and after MJD~57023, which is clearly revealed by the test-statistic maps we obtain. Considering studies carried out and models proposed for the $\gamma$-ray emissions of the two sources, we discuss the implications of our findings. The jet in NGC~1068 may contribute to the \gr\ emission. The nature of the long-term variations in NGC~253 is not clear, but the variation part of the emission may be connected to the very-high-energy (VHE) emission of the galaxy and could be verified by VHE observations.Comment: 9 pages, 6 figures, 2 tables, submitted to Ap

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations