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Titlebook: Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery; J. Javier Servat,Evan H. Black,Christopher J. Calv Book 2021Latest edition

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发表于 2025-3-21 16:46:38 | 显示全部楼层 |阅读模式
书目名称Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery
编辑J. Javier Servat,Evan H. Black,Christopher J. Calv
视频video
概述Fully updated and revised edition of Smith’s Ophthalmic Plastic and Reconstructive Surgery.Comprehensive text with detailed chapters that cover all aspects of the subject matter.Hundreds of images ove
图书封面Titlebook: Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery;  J. Javier Servat,Evan H. Black,Christopher J. Calv Book 2021Latest edition
描述.This landmark book is the most extensive and complete oculofacial plastic surgery guide available in the market. Updated and broadened from the three previous editions, it includes advances in the use of surgical navigation systems, and new techniques and treatments for diseases involving the eyelid, orbital and lacrimal system..Organized across 11 sections of in-depth, expertly written text, .Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery, Fourth Edition. has taken the best of the field’s classic reference text and expanded upon it.  Detailed chapters cover a multitude of topics relating to various ocular surgeries, pediatric considerations, ocular traumas, and anatomy..Supplemented with a myriad of high-quality illustrations, .Smith and Nesi’s Ophthalmic Plastic and Reconstructive Surgery, Fourth Edition .is an indispensable reference for oculoplastics surgeons and physicians in other fields.. .
出版日期Book 2021Latest edition
关键词blepharoptosis; lacrimal disease; oculoplastic; ophthalmic plastic surgery; orbital fracture
版次4
doihttps://doi.org/10.1007/978-3-030-41720-8
isbn_softcover978-3-030-41722-2
isbn_ebook978-3-030-41720-8
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 23:03:14 | 显示全部楼层
r and operating system version, installed plugins, screen resolution, hardware artifacts, Wi-Fi settings, and anything else available to the server, and then merging these attributes into uniquely identifying fingerprints. This technique is used in practice to present personalized content to repeat
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J. Javier Servat,Francesca Nesi-Eloff,Eric Baylin assessment. Mining these different information sources could bring major benefits to applications such as situation awareness in disaster zones and mapping the spread of diseases. Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial “
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Alberto G. Distefano,Emily Li,Mahsa A. Sohrab research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. How
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Shani Golan,Christopher I. Zoumalan,Frank A. Nesi,Gary J. Lelli Jr.t when an electrical component has failed? More challengingly, given a limited budget of sensors to place, how can we determine where to place them to have the highest chance of detecting such a failure? Maintaining the reliability of the electrical grid is a major challenge. An important part of ac
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Ann P. Murchison,Jurij R. Bilyk,Peter J. Savinocan measure this .. That is, we want to be able to measure whether a result is interesting from a subjective point of view..With this as our goal, we formalise how to probabilistically model real-valued data by the Maximum Entropy principle, where we allow statistics on . sets of cells as background
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mbers. In previous work, an online algorithm was proposed for this task that produces overlapping (non-disjoint) and non-exhaustive clusterings. In this paper, we parallelize this algorithm to take advantage of high-performance parallel hardware and further improve the algorithm in three ways: a ref
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