Identification of Source Nature and Seasonal Variations of Arctic Aerosol byPositive Matrix Factorization

functional study of p0 proteins of two cereal yellow dwarf viruses (cydv-rpv and cydv-rps) and identification of their cellular partner

نقش سرکوبگری پروتئین p0 در دو پولروویروس کوتولگی زردی غلات cydv-rpv) و (cydv-rps، متفاوت در شدت بیماریزایی، مورد مطالعه قرار گرفت. نتایج نشان داد که هر دو پروتئین p0 p0cy-rpv) و (p0cy-rps قادر به سرکوب خاموشی آر ان ای ایجاد شده توسط ترادف های تراژن سنس و تکرار معکوس در n. benthamiana هستند. نشان داده شد که هر دو پروتئین p0 می توانند تخریب پروتئین argonaute-1 را تسهیل کنند. علاوه بر این، تمایل م...

patterns and variations in native and non-native interlanguage pragmatic rating: effects of rater training, intercultural proficiency, and self-assessment

although there are studies on pragmatic assessment, to date, literature has been almost silent about native and non-native english raters’ criteria for the assessment of efl learners’ pragmatic performance. focusing on this topic, this study pursued four purposes. the first one was to find criteria for rating the speech acts of apology and refusal in l2 by native and non-native english teachers...

Seasonal Variations of Satellite-Derived Aerosol Properties over Ten Major

Spatiotemporal variations of ambient PM10 source contributions in Beijing in 2004 using positive matrix factorization

Source contributions to ambient PM10 (particles with an aerodynamic diameter of 10μm or less) in Beijing, China were determined with positive matrix factorization (PMF) based on ambient PM10 composition data including concentrations of organic carbon (OC), elemental carbon (EC), ions and metal elements, which were simultaneously obtained at six sites through January, April, July and October in ...

Survey on the Variations and Applications of Nonnegative Matrix Factorization

Nonnegative Matrix Factorization has been proved to be valuable in many fields of data mining, especially in unsupervised learning. In this paper, we will briefly review its variations and applications in image processing, data clustering, semi-supervised clustering, bi-clustering (co-clustering) and financial data mining. Note that we cannot cover all the interesting works on NMF, but generall...