We studied the normality conditions in families of meromorphic functions, improved the results of Fang and Zalcman [Fang ML, Zalcman L, Normal families and shared values of meromorphic functions, Computational Methods...We studied the normality conditions in families of meromorphic functions, improved the results of Fang and Zalcman [Fang ML, Zalcman L, Normal families and shared values of meromorphic functions, Computational Methods and Function Theory, 2001, 1 （1）： 289-299], and generalized two new normality criterions. Let F be a family of meromorphic functions in a domain D, a a non-zero finite complex number, B a positive real number, and k and m two positive integers satisfying m〉2k＋4. If every function denoted by f belonging to F has only zeros with multiplicity at least k and satisfies f^m（z）f^（k）（Z）=α→ ｜^f（k）（z）｜ ≤B or f^m（z）f^（k）（z）=α→｜f（z）｜ ≥, then F is normal in D.展开更多
From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order ...From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order to review the overall situation and provide information on major accidents of coal mines in China, we investigated 26 major coal mining accidents in China between the years of 1949 and 2009 through statistical methods, each of which led to more than 100 fatalities. Statistical characteristics about accident-related factors such as time, death toll, accident reasons, characters and nature of enterprise were analyzed. And some special conclusions have been achieved. For example, although we have made great progress, the safety situation in China coal mining industry is still serious, and the reasons for the mining accidents are all human errors which are not inevitable. Such results may be helpful to prevent major accidents in coal mines. Moreso, based on both the knowledge of other countries which have good safety situation nowadays and the safety management situation of China, we made suggestion on safety management of China coal mining. In conclusion, countermeasures were proposed in accordance with the results of statistical studies and the analyses of problems existed in coal mines, including the perfec- tion of safety supervision organization, the establishment of cooperating agency among government, coal mines and workers, the perfection of safety rules and regulations, the improvement of safety investment, the enhancement of safety training, the development of safety technique, and the development of emer- gency rescue technique and equipment.展开更多
Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast a...Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.展开更多
文摘We studied the normality conditions in families of meromorphic functions, improved the results of Fang and Zalcman [Fang ML, Zalcman L, Normal families and shared values of meromorphic functions, Computational Methods and Function Theory, 2001, 1 （1）： 289-299], and generalized two new normality criterions. Let F be a family of meromorphic functions in a domain D, a a non-zero finite complex number, B a positive real number, and k and m two positive integers satisfying m〉2k＋4. If every function denoted by f belonging to F has only zeros with multiplicity at least k and satisfies f^m（z）f^（k）（Z）=α→ ｜^f（k）（z）｜ ≤B or f^m（z）f^（k）（z）=α→｜f（z）｜ ≥, then F is normal in D.
基金support from the Science and Technology Programming Project of Shandong Provincein China (No. 2010GSF10808)the National Natural Science Foundation of China (No. 51074100)
文摘From the year of 1949 to the present, the China national coal output has been increasing quickly and became first in the world in 2009. But at the same time, major coal mining accidents still exist nowadays. In order to review the overall situation and provide information on major accidents of coal mines in China, we investigated 26 major coal mining accidents in China between the years of 1949 and 2009 through statistical methods, each of which led to more than 100 fatalities. Statistical characteristics about accident-related factors such as time, death toll, accident reasons, characters and nature of enterprise were analyzed. And some special conclusions have been achieved. For example, although we have made great progress, the safety situation in China coal mining industry is still serious, and the reasons for the mining accidents are all human errors which are not inevitable. Such results may be helpful to prevent major accidents in coal mines. Moreso, based on both the knowledge of other countries which have good safety situation nowadays and the safety management situation of China, we made suggestion on safety management of China coal mining. In conclusion, countermeasures were proposed in accordance with the results of statistical studies and the analyses of problems existed in coal mines, including the perfec- tion of safety supervision organization, the establishment of cooperating agency among government, coal mines and workers, the perfection of safety rules and regulations, the improvement of safety investment, the enhancement of safety training, the development of safety technique, and the development of emer- gency rescue technique and equipment.
基金Indian Institute of Technology,Kharagpur in India for supporting this work
文摘Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.
