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11.
Faraz Malik Mahmood A. Kayani M. Ansar Obaid Ullah Muhammad Shafeeq Shahid Chohan Yassir Abbas Saqib Shazad Ali Raza Rahat Rehman Faizan Raiz Qurat-ul-ain Muhammad Hassan Siddiqi Allah Rakha Zia ur Rehman Zahoor Ahmed 《西安交通大学学报(英文版)》2008,20(4)
For the development of 19-plex Y STR system and polymorphism studies in locl ethnic populations sixteen markers of non-recombining regions (NRY) of Y chromosome, which show high power of discrimination among individuals, were selected in this study. Blood samples (600) were e.ollected from the males of three most common castes of Pakistani population (Arnin, Awan and Rajput) with different parent lineages. Three markers (DYS385a/b, DYS389Ⅰ/Ⅱ and YCAⅡa/b) among 16 Y STRs are double-targeted regions of the Y chromosome and thus provide two polymorphie peaks for each respective primer set. These 16 Y-STRs were developed into Megaplex system for simultaneous amplification of all markers within the population. The overall power of discrimination observed in focused populations was 60.5%, 66.5% and 55% in Rajput, Awan and Arain casts respectively. This discrimination power will be helpful in haman identification for forensic casework studies including sexual assaults and paternity testing. 相似文献
12.
Abbas Khosravi Ehsan Mazloumi Saeid Nahavandi Doug Creighton J.W.C. Van Lint 《Transportation Research Part C: Emerging Technologies》2011,19(6):1364-1376
The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability. 相似文献