Arisan Baca Tulis: Pemberantasan Buta Aksara Melalui Metode Arisan Yang Unik Dan Menyenangkan

Surabaya as the capital of East Java was haven\u27t free from illiteracy yet, the condition was mostly of them were women aged 30 years and over who haven\u27t completed the compulsory education for nine years. In addition, women are also considered capable of transmitting knowledge and their knowledge to the family, especially the children as closest network. With the fun activity, we expect the new atmosphere of togetherness and gathering with the same degree of age are able to create its own preoccupations and improving the confidence to learn to read and write. This program, is modified such that mothers are often going ahead and daring to write or read the sentences given by instructor, will get a roll of paper that bearing their names. Thus, the more quantity Mothers forward and dare to write and read, the more the roll of their names, and a chance to win the raffle of “Arisan” at the end of training will be even greater. With different and unique method, able to attract mothers to be more diligent in reading and writing, so that illiteracy slowly be decrease, the mothers began to realize how important education started early, and spirit and motivation for learning has begun to form, evidenced by an increase in the ability of mothers to read and write, 77% of mothers who previously could not read, or only able to spell, and be able to read haltingly, and 68% of mothers who can not write, or can only write a few letters, as well as stuttering to write, has increased its ability to 86% have been able to read fluently, and 77% have been able to write correctly

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data

Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly obtain with traditional optical microscopy. In addition, there are trade-offs between the parameters of traditional optical microscopes. Generally, a higher resolution results in a smaller field of view (FOV) and narrower depth of field (DOF). To overcome these drawbacks, we report a novel semi-supervised deep learning-based hybrid network framework, termed ContransGAN, which can be used in traditional optical microscopes with different magnifications to obtain high-quality quantitative phase images. This network framework uses a combination of convolutional operation and multiheaded self-attention mechanism to improve feature extraction, and only needs a few unpaired microscopic images to train. The ContransGAN retains the ability of the convolutional neural network (CNN) to extract local features and borrows the ability of the Swin-Transformer network to extract global features. The trained network can output the quantitative phase images, which are similar to those restored by the transport of intensity equation (TIE) under high-power microscopes, according to the amplitude images obtained by low-power microscopes. Biological and abiotic specimens were tested. The experiments show that the proposed deep learning algorithm is suitable for microscopic images with different resolutions and FOVs. Accurate and quick reconstruction of the corresponding high-resolution (HR) phase images from low-resolution (LR) bright-field microscopic intensity images was realized, which were obtained under traditional optical microscopes with different magnifications

Visualization 1: Simple calculation of a computer-generated hologram for lensless holographic 3D projection using a nonuniform sampled wavefront recording plane

Visualization 1 shows the continuous accommodation depth cue in numerical experiments. Originally published in Applied Optics on 01 October 2016 (ao-55-28-7988