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Titlebook: Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm; Extensions and Appli Ali Kaveh,Ataollah Zaerreza Book 2023 The Ed

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發(fā)表于 2025-3-26 23:42:46 | 只看該作者
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發(fā)表于 2025-3-27 05:11:45 | 只看該作者
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發(fā)表于 2025-3-27 09:06:07 | 只看該作者
Ali Kaveh,Ataollah Zaerrezale will be given from Kirkwood and Sterne (Standardization, in: Medical Statistics, Chap. 25, Blackwell Science, Oxford UK 2003) studied the age and sex adjusted mortality rate of onchocerciasis patients already blind, and the authors tested it versus non blind patients using Poisson regression. The
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發(fā)表于 2025-3-27 10:15:06 | 只看該作者
Ali Kaveh,Ataollah Zaerreza3) data with inconstant variability, (4) big data. In clinical research many examples can be given like circadian phenomena, and diseases where spreading may be dependent on subsets with frailty, low weight, low hygiene, and many forms of lack of healthiness. Stratified analyses is the laborious and
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發(fā)表于 2025-3-27 17:21:37 | 只看該作者
Ali Kaveh,Ataollah Zaerrezag.Companion website with code, problem sets and additional r.This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and
36#
發(fā)表于 2025-3-27 21:27:28 | 只看該作者
Ali Kaveh,Ataollah Zaerrezaents together in an updated manner. This work is suitable for both academic coursework and corporate technical training..The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correctio
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發(fā)表于 2025-3-28 00:04:41 | 只看該作者
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發(fā)表于 2025-3-28 08:39:36 | 只看該作者
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發(fā)表于 2025-3-28 14:17:25 | 只看該作者
An Enhanced Shuffled Shepherd Optimization Algorithm and Application to Space Structures, et al. [1]. Shuffled Shepherd Optimization Algorithm (SSAO) is an optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some disadvantages, including being caught in a local optimum and starting from a random population without previous information. This chapter ai
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