The study of Usul Fiqh only feels essential when it deals with different problems whose orders it executes not involve in the offer of the old Fiqh. Besides that, with the rising number of participants of comparative school legislation alike to discover out which idea is more powerful, since closely as the effort to transform Islamic rule, it will increase appeared in how urgent the role of Ushul Fiqh is. The paper tries to present this discussion to suggest the relevance of this idea of Fiqh in dealing with legal issues dealt with by Muslims who sometimes put on nonessential issues. The author organized research with a comparative descriptive analysis. This study organized a view of the works of Usul Fiqh along with the rules contained there in along with the development of Usul Fiqh and its benefits from time to time. The authors identify that Ushul Fiqh plays a highly considerable position in the improvement of Islamic law. Not merely that, Usul Fiqh, the essential function of Usul Fiqh, is to improve someone to find out the rules they take based on syar'i arguments, so that they do not rely very often on considering other people whose bases they do not have. There are not a few obstacles in the Islamic world community with the presence of discoveries by scientists who require answers and confidence of Islamic law.
Palmprint recognition system has been one promising biometric system used in Presence System. There are some methods to recognize the individual palmprint as well as to extract its feature. In this research, two recognition methods are compared, i.e., backpropagation neural network and similarity measure using Euclidean distance. While, for feature extraction, we implemented Principal Components Analysis (PCA) method. From the research, it can be concluded that from test results, the best recognition using backpropogation neural networks is 93.33% which is reached when parameters used are: 100 principal components, 1 hidden layer, and 75 neurons. While, implementation of similarity measure using Euclidean distance, the best recognition rate is 96.67% which is reached when 75 principal components are used. When considering the time consumed in recognition, the Euclidean distance gives the better result, i.e. 17.09 seconds, while using backpropagation neural network with 75 neurons, time consumed is 425 seconds. Therefore, from this research, recognition implementation combining both PCA and Euclidean distance are more suggested rather than using combination of PCA and backpropagation neural network.
The seal face is the main component of a mechanical seal to prevent leakage in a system with fluid flow. Seal face manufacture is generally produced by the material removal process, which causes some raw material waste. Powder metallurgy is the process of manufacturing products from metal powders with raw material efficiency of up to 97%. This study discusses the relationship between the manufacturing process parameters of seal face with SiC material through a powder metallurgy process as a substitute for manufacturing by material removal. The approach used in this research was the design of experiments with the Taguchi method and the technique of Gray Relational Analysis. Process parameters controlled were compaction pressure (CF), compaction time (CH), sintering temperature (ST), and sintering time (SH). Responses were measured in the form of surface hardness (HV) and density. The combination of process parameters that produces the optimum response is CF = 408 N/mm 2 (level 3), CH = 2 min (level 1), ST = 1050 • C (level 3), SH = 120 min (level 2) with contribution of process parameters CF = 38.06%, CH = 2.53%, ST = 49.50%, and SH = 9.91%. The optimum surface hardness and density values were 513.03 HV and 3.04 gr/mm 3 .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.