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Titlebook: High Resolution Focused Ion Beams: FIB and its Applications; The Physics of Liqui Jon Orloff,Mark Utlaut,Lynwood Swanson Book 2003 Springer

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樓主: Debilitate
11#
發(fā)表于 2025-3-23 13:18:16 | 只看該作者
Introduction,t that the practice of focused ion beam technology is so widespread makes it easy to justify the effort spent on development of the theory of the liquid metal ion source, aside from its intrinsic interest from a scientific point of view. So, we are trying to follow the dictum of Confucius.
12#
發(fā)表于 2025-3-23 13:51:24 | 只看該作者
Ion Optics for LMIS,ystem in a way that gives a metric for optimization or for comparison of different systems. The resolution definition is based on a method devised for the wave optical treatment of electron beam systems, and so a brief outline of wave optical methods is also provided.
13#
發(fā)表于 2025-3-23 19:21:27 | 只看該作者
14#
發(fā)表于 2025-3-24 01:52:46 | 只看該作者
Because of the dedicated hardware visualization has been significantly accelerated and today’s software uses only the GPU for rasterization. Besides the graphical devices, the central processing unit (CPU) has also made remarkable progress. Multi-core architectures and new instruction sets have app
15#
發(fā)表于 2025-3-24 04:59:01 | 只看該作者
Jon Orloff,Mark Utlaut,Lynwood Swansonhey apply and deepen their knowledge. We will not cover all the related theory and assume that the reader has a basic knowledge of cryptography and information security, perhaps from other courses or books. Nevertheless, we will summarize some of the more central notions that are relevant for the ex
16#
發(fā)表于 2025-3-24 08:08:57 | 只看該作者
Jon Orloff,Mark Utlaut,Lynwood Swansonsometimes lead to inconsistent judgment outcomes. To mitigate this, it is imperative to establish a comprehensive algorithm to aid in resolving this issue. This study introduces a novel deep learning architecture that integrates convolutional neural network (CNN) and capsule neural network (CapsNet)
17#
發(fā)表于 2025-3-24 11:56:46 | 只看該作者
Jon Orloff,Mark Utlaut,Lynwood Swansonpression data were collected from the GEO dataset, subjected to rigorous differential expression analysis to curate genes for subsequent scrutiny. Based on the P-Net and PASNet models, we have developed a pathway-related deep learning model that integrates PD-associated gene expression data with est
18#
發(fā)表于 2025-3-24 18:11:49 | 只看該作者
Jon Orloff,Mark Utlaut,Lynwood Swansonpression data were collected from the GEO dataset, subjected to rigorous differential expression analysis to curate genes for subsequent scrutiny. Based on the P-Net and PASNet models, we have developed a pathway-related deep learning model that integrates PD-associated gene expression data with est
19#
發(fā)表于 2025-3-24 21:19:36 | 只看該作者
20#
發(fā)表于 2025-3-24 23:48:39 | 只看該作者
Jon Orloff,Mark Utlaut,Lynwood Swansonises, and unknown system parameters. To construct such observers, we use not the original system but its reduced-order model of the original system of minimal dimension which is insensitive to the disturbances. The observers are designed in such a way to estimate the prescribed linear function of th
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