http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Pharmacological options for pain control in patients with vertebral fragility fractures
Nuttan Kantilal Tanna,Terence Ong 대한골다공증학회 2022 Osteoporosis and Sarcopenia Vol.8 No.3
This review considers the evidence base and current knowledge for pharmacological treatment options that are available for pain control in patients with vertebral fractures sustained after a low trauma incident. Due care needs to be taken when considering prescribed options for pain control. The decision should be based on first establishing whether the presentation is one of acute severe pain at the time of a new vertebral fragility fracture incident or whether the complaint is one of the debilitating, longer term chronic back pain syndrome, accompanied by a clinical suspicion of a possible new fracture. The article also presents currently debated questions in this important area of clinical and patient care and will be of interest to the readership worldwide.
Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions
Raj, Desh,Gupta, Aditya,Garg, Bhuvnesh,Tanna, Kenil,Rhee, Frank Chung-Hoon Institute of Electrical and Electronics Engineers, 2018 IEEE transactions on fuzzy systems Vol.26 No.2
<P>Due to the numerous applications that utilize different types of fuzzy membership functions (MFs), it may sometimes be difficult to choose an appropriate MF for a particular application. In this paper, we establish preliminary guidelines to direct this selection by proposing a three-stage method. In the “forward” stage, different MFs, such as crisp MFs, type-1 (T1) fuzzy MFs, and type-2 (T2) fuzzy MFs, are generated from multidimensional data sets. Next, in the “reverse” stage, data is generated back from these MFs by considering different bin sizes. In doing so, various data sets may be generated for different applications which require fuzzy data. Finally, for the “similarity analysis” stage, we propose an iterative algorithm that makes use of the results of Wilcoxon signed rank (WSR) and Wilcoxon rank sum (WRS) tests to compare the original data and the generated data. From the results of these tests, recommendations concerning the suitability of MFs for a specific application may be suggested by observing the accuracy of representation and the requirements of the application. With this analysis, the objective is to gain insight on when T2 fuzzy sets may be considered to outperform T1 fuzzy sets, and vice versa. Several examples are provided using synthetic and real data to validate the iterative algorithm for data sets in various dimensions.</P>