Entrapping multifunctional dendritic nanoparticles into a hydrogel for local therapeutic delivery and synergetic immunochemotherapy

Nano Research - Developing multifunctional nanoparticles to support new therapy models is a promising and challenging task to address the current dilemma on antitumor treatment. Herein, we...     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

三维实体数据文件的建立

建立几何体数据文件是计算机图形显示的首要任务,本文介绍了简单几何体和不规则体的数据建立过程,并在微机上用Ｃ语言实现了这些算法．     

Some notes on Zadeh’s extensions

This paper shows that the Zadeh’s extensions of sendograph-metric-continuous fuzzy-valued functions are sendograph-metric-continuous fuzzy functions.     

基于CORBA和TLl的STP网元集中管理系统实现

设计了STP网元集中管理系统,阐述了该系统的体系结构和各模块的功能以及各模块间的通信方式.通过使用CORBA构建技术、IDL接口描述语言以及文本描述语言TLl,使得系统具有灵活性、可维护性和可扩展性的特点.该系统的实现,使得对信令网中的STP设备进行集中管理和统一维护成为可能,为进一步对信令网的检测和历史数据的采集分析提供了基础.     

单磁悬浮系统的纵向解耦控制研究

磁悬浮系统是复杂的强耦合系统,研究中未充分考虑轨道的弹性形变,会造成磁浮列车和轨道之间产生耦合振动的现象,而考虑这一因素对系统的影响无疑增加了研究的难度;为了便于问题的求解,采用非线性系统理论解耦的方法,对单电磁铁悬浮系统进行纵向解耦控制,并对解耦后的子系统进行极点配置使其满足要求的性能指标;仿真结果表明该方法有效地使复杂问题简单化,并且能够实现系统的全局稳定和良好的动态性能.     

《交换空间》栏目引导新的家装理念浅析

本文通过表述传播学五大要素在《交换空间》栏目中的体现,主要介绍了家装从规划设计到具体实施的整个过程在《交换空间》栏目中的体现,分析了《交换空间》作为一种具体的可实施性强的节目对家庭装饰装修理念新的诠释。     

基于统计不相关矢量集的独立成分分析

针对随机数据在高斯分布条件下,独立成分分析在本质上不可能实现的问题,提出在统计不相关矢量集上进行独立成分分析。与一般白化变换后的数据相比,基于统计不相关矢量集的数据具有更好的不相关性,在估计独立分量时性能更优越。在ORL人脸数据库上进行的实验结果证明了该算法的有效性。