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匹配条件: “P. Ravi Shankar” ,找到相关结果约203367条。
Educating Health Professionals about Drug and Device Promotion: A Nepalese Perspective
P. Ravi Shankar
PLOS Medicine , 2007, DOI: 10.1371/journal.pmed.0040089
Sir Robert Hutchison's Petition and the Medical Humanities
P. Ravi Shankar
International Journal of Medical Education , 2010, DOI: 10.5116/ijme.4b8a.fba9
A Workshop at a Nepalese Medical School
P Ravi Shankar
Australasian Medical Journal , 2012,
Abstract: A three-day theatre of the oppressed workshop was conducted at KIST Medical College, Lalitpur, Nepal. In this manuscript the author describes salient features of the workshop.
Medical humanities
P. Ravi Shankar
Journal of Medical and Allied Sciences , 2012,
A lay person’s guide to medicine: Book review
P Ravi Shankar
Journal of College of Medical Sciences-Nepal , 2010, DOI: 10.3126/jcmsn.v6i2.3620
Abstract: DOI: 10.3126/jcmsn.v6i2.3620 Journal of college of Medical Sciences-Nepal, 2010, Vol.6, No-2, 62-63
Community-Based Learning in a Time of Conflict
Bishnu Giri ,P. Ravi Shankar
PLOS Medicine , 2006, DOI: 10.1371/journal.pmed.0030115
Ranking Fuzzy Numbers with a Distance Method using Circumcenter of Centroids and an Index of Modality
P. Phani Bushan Rao,N. Ravi Shankar
Advances in Fuzzy Systems , 2011, DOI: 10.1155/2011/178308
Abstract: Ranking fuzzy numbers are an important aspect of decision making in a fuzzy environment. Since their inception in 1965, many authors have proposed different methods for ranking fuzzy numbers. However, there is no method which gives a satisfactory result to all situations. Most of the methods proposed so far are nondiscriminating and counterintuitive. This paper proposes a new method for ranking fuzzy numbers based on the Circumcenter of Centroids and uses an index of optimism to reflect the decision maker's optimistic attitude and also an index of modality that represents the neutrality of the decision maker. This method ranks various types of fuzzy numbers which include normal, generalized trapezoidal, and triangular fuzzy numbers along with crisp numbers with the particularity that crisp numbers are to be considered particular cases of fuzzy numbers. 1. Introduction Ranking fuzzy numbers are an important tool in decision making. In fuzzy decision analysis, fuzzy quantities are used to describe the performance of alternatives in modeling a real-world problem. Most of the ranking procedures proposed so far in the literature cannot discriminate fuzzy quantities and some are counterintuitive. As fuzzy numbers are represented by possibility distributions, they may overlap with each other, and hence it is not possible to order them. It is true that fuzzy numbers are frequently partial order and cannot be compared like real numbers which can be linearly ordered. In order to rank fuzzy quantities, each fuzzy quantity is converted into a real number and compared by defining a ranking function from the set of fuzzy numbers to a set of real numbers which assign a real number to each fuzzy number where a natural order exists. Usually by reducing the whole of any analysis to a single number, much of the information is lost and hence an attempt is to be made to minimize this loss. Various ranking procedures have been developed since 1976 when the theory of fuzzy sets were first introduced by Zadeh [1]. Ranking fuzzy numbers were first proposed by Jain [2] for decision making in fuzzy situations by representing the ill-defined quantity as a fuzzy set. Since then, various procedures to rank fuzzy quantities are proposed by various researchers. Bortolan and Degani [3] reviewed some of these ranking methods [2, 4–14] for ranking fuzzy subsets. Chen [15] presented ranking fuzzy numbers with maximizing set and minimizing set. Dubois and Prade [16] presented the mean value of a fuzzy number. Lee and Li [17] presented a comparison of fuzzy numbers based on the probability
Learning How Drug Companies Promote Medicines in Nepal
Bishnu Rath Giri,P. Ravi Shankar
PLOS Medicine , 2005, DOI: 10.1371/journal.pmed.0020256
Fuzzy Critical Path Method Based on Lexicographic Ordering
Phani Bushan Rao P,Ravi Shankar Nowpada
Pakistan Journal of Statistics and Operation Research , 2012, DOI: 10.1234/pjsor.v8i1.178
Abstract: The Critical Path Method (CPM) is useful for planning and control of complex projects. The CPM identifies the critical activities in the critical path of an activity network. The successful implementation of CPM requires the availability of clear determined time duration for each activity. However, in practical situations this requirement is usually hard to fulfil since many of activities will be executed for the first time. Hence, there is always uncertainty about the time durations of activities in the network planning. This has led to the development of fuzzy CPM. In this paper, we use a Lexicographic ordering method for ranking fuzzy numbers to a critical path method in a fuzzy project network, where the duration time of each activity is represented by a trapezoidal fuzzy number. The proposed method is compared with fuzzy CPM based on different ranking methods of fuzzy numbers. The comparison reveals that the method proposed in this paper is more effective in determining the activity criticalities and finding the critical path. This new method is simple in calculating fuzzy critical path than many methods proposed so far in literature.
Learning how drug companies promote medicines in Nepal.
Giri Bishnu Rath,Shankar P Ravi
PLOS Medicine , 2005,

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