Management of Abnormal Visual Developments

When human beings recognize the external world, more than 80% of the information come from visual function and visual system. Normal visual development and normal binocularity are the fundamental of good visual acuity and visual functions. Any abnormal visual experience would cause abnormality, such as refractive error, strabismus, amblyopia and other diseases. The patients with abnormal visual developments were reported to have abnormal, lonely, and other psycho problems. In this chapter, we will describe the normal developmental of visual function, summarize the abnormal developments and the correction or treatment

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency

Surface Plasmon Effect Dominated High-Performance Triboelectric Nanogenerator for Traditional Chinese Medicine Acupuncture

Available, effectively converting low-frequency vibration into available electricity, triboelectric nanogenerator (TENG) is always research hot nowadays. However, the enhancing effect of the existing methods for the output have all sorts of drawbacks, i.e., low efficiency and unstable, and its practical applications still need to be further explored. Here, leveraging core-shell nanoparticles Ag@SiO2 doping into tribo-materials generates the surface plasmon effect to boost the output performance of the TENG. On one hand, the shell alleviated the seepage effect from conventional nanoparticles; on the other hand, the surface plasmon effect enabled the core-shell nanoparticles to further boost the output performance of TENG. We circumvent the limitations and present a TENG whose output power density can be up to 4.375 mW/cm2. Points is that this article novelty investigate the high-performance TENG applicating for traditional Chinese medicine and develop a pratical self-powered acupuncture system. This technology enables rapid, routine regulation of human health at any age, which has potential applications in nearly any setting across healthcare platforms alike.FIMA

Surface Plasmon Effect Dominated High-Performance Triboelectric Nanogenerator for Traditional Chinese Medicine Acupuncture

Available, effectively converting low-frequency vibration into available electricity, triboelectric nanogenerator (TENG) is always research hot nowadays. However, the enhancing effect of the existing methods for the output have all sorts of drawbacks, i.e., low efficiency and unstable, and its practical applications still need to be further explored. Here, leveraging core-shell nanoparticles Ag@SiO2 doping into tribo-materials generates the surface plasmon effect to boost the output performance of the TENG. On one hand, the shell alleviated the seepage effect from conventional nanoparticles; on the other hand, the surface plasmon effect enabled the core-shell nanoparticles to further boost the output performance of TENG. We circumvent the limitations and present a TENG whose output power density can be up to 4.375 mW/cm2. Points is that this article novelty investigate the high-performance TENG applicating for traditional Chinese medicine and develop a pratical self-powered acupuncture system. This technology enables rapid, routine regulation of human health at any age, which has potential applications in nearly any setting across healthcare platforms alike

EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research

It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, open-source, and community-supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account “Meta-genome.” Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon)