다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Mathematical Problems in Engineering

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

/pdf/federated-learning-in-mobile-edge-networks-a-comprehensive-5dgelc5m6h.pdf

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

Multifunctional mesoporous silica nanoparticles are developed in order to deliver anticancer drugs to specifi c cancer cells in a targeted and controlled manner. The nanoparticle surface is functionalized with amino- β -cyclodextrin rings bridged by cleavable disulfi de bonds, blocking drugs inside the mesopores of the nanoparticles. Poly(ethylene glycol) polymers, functionalized with an adamantane unit at one end and a folate unit at the other end, are immobilized onto the nanoparticle surface through strong β -cyclodextrin/adamantane complexation. The non-cytotoxic nanoparticles containing the folate targeting units are effi ciently trapped by folate-receptor-rich HeLa cancer cells through receptormmediated endocytosis, while folate-receptor-poor human embryonic kidney 293 normal cells show much lower endocytosis towards nanoparticles under the same conditions. The nanoparticles endocytosed by the cancer cells can release loaded doxorubicin into the cells triggered by acidic endosomal pH. After the nanoparticles escape from the endosome and enter into the cytoplasm of cancer cells, the high concentration of glutathione in the cytoplasm can lead to the removal of the β -cyclodextrin capping rings by cleaving the pre-installed disulfi de bonds, further promoting the release of doxorubicin from the drug carriers. The high drug-delivery effi cacy of the multi functional nanoparticles is attributed to the co-operative effects of folatemediated targeting and stimuli-triggered drug release. The present delivery system capable of delivering drugs in a targeted and controlled manner provides a novel platform for the next generation of therapeutics.

Multifunctional Mesoporous Silica Nanoparticles for Cancer-Targeted and Controlled Drug Delivery

Multifunctional mesoporous silica nanoparticles are developed in order to deliver anticancer drugs to specifi c cancer cells in a targeted and controlled manner. The nanoparticle surface is functionalized with aminoβ -cyclodextrin rings bridged by cleavable disulfi de bonds, blocking drugs inside the mesopores of the nanoparticles. Poly(ethylene glycol) polymers, functionalized with an adamantane unit at one end and a folate unit at the other end, are immobilized onto the nanoparticle surface through strong β -cyclodextrin/adamantane complexation. The non-cytotoxic nanoparticles containing the folate targeting units are effi ciently trapped by folate-receptor-rich HeLa cancer cells through receptormmediated endocytosis, while folate-receptor-poor human embryonic kidney 293 normal cells show much lower endocytosis towards nanoparticles under the same conditions. The nanoparticles endocytosed by the cancer cells can release loaded doxorubicin into the cells triggered by acidic endosomal pH. After the nanoparticles escape from the endosome and enter into the cytoplasm of cancer cells, the high concentration of glutathione in the cytoplasm can lead to the removal of the β -cyclodextrin capping rings by cleaving the pre-installed disulfi de bonds, further promoting the release of doxorubicin from the drug carriers. The high drug-delivery effi cacy of the multi functional nanoparticles is attributed to the co-operative effects of folatemediated targeting and stimuli-triggered drug release. The present delivery system capable of delivering drugs in a targeted and controlled manner provides a novel platform for the next generation of therapeutics.

Multifunctional Mesoporous Silica Nanoparticles for CancerTargeted and Controlled Drug Delivery

Engineering multifunctional nanocarriers for targeted drug delivery shows promising potentials to revolutionize the cancer chemotherapy. Simple methods to optimize physicochemical characteristics and surface composition of the drug nanocarriers need to be developed in order to tackle major challenges for smooth translation of suitable nanocarriers to clinical applications. Here, rational development and utilization of multifunctional mesoporous silica nanoparticles (MSNPs) for targeting MDA-MB-231 xenograft model breast cancer in vivo are reported. Uniform and redispersible poly(ethylene glycol)-incorporated MSNPs with three different sizes (48, 72, 100 nm) are synthesized. They are then functionalized with amino- β cyclodextrin bridged by cleavable disulfi de bonds, where amino- β -cyclodextrin blocks drugs inside the mesopores. The incorporation of active folate targeting ligand onto 48 nm of multifunctional MSNPs (PEG-MSNPs48-CDPEG-FA) leads to improved and selective uptake of the nanoparticles into tumor. Targeted drug delivery capability of PEG-MSNPs48-CD-PEG-FA is demonstrated by signifi cant inhibition of the tumor growth in mice treated with doxorubicin-loaded nanoparticles, where doxorubicin is released triggered by intracellular acidic pH and glutathione. Doxorubicin-loaded PEG-MSNPs48CD-PEG-FA exhibits better in vivo therapeutic effi cacy as compared with free doxorubicin and non-targeted nanoparticles. Current study presents successful utilization of multifunctional MSNP-based drug nanocarriers for targeted cancer therapy in vivo.

Biocompatible, Uniform, and Redispersible Mesoporous Silica Nanoparticles for Cancer‐Targeted Drug Delivery In Vivo

Complex fabrication process and expensive materials have restricted the development of smart three-dimensional (3D) lightweight structures, which are expected to possess self-shaping, self-folding and self-unfolding performances. Here we present a simple approach to fabricate smart lightweight structures by triggering shape transformation from thin printed composite sheets. The release of the internal strain in printed polymer materials enables the printed composite sheet to keep flat under heating and transform into a designed 3D configuration when cooled down to room temperature. The 3D lightweight structure can be switched between flat and 3D configuration under appropriate thermal stimuli. Our work exploits uniform internal strain in printed materials as a controllable tool to fabricate smart 3D lightweight structures, opening an avenue for possible applications in engineering fields.

/pdf/smart-three-dimensional-lightweight-structure-triggered-from-217aiavavi.pdf

Smart three-dimensional lightweight structure triggered from a thin composite sheet via 3D printing technique.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Quan Zhang

Author Tools

Chat about Author