Coating Cellulosic Material with Ag Nanowires to Fabricate Wearable IR-Reflective Device for Personal Thermal Management: The Role of Coating Method and Loading Level

Textiles coated with silver nanowires (AgNWs) are effective at suppressing radiative heat loss without sacrificing breathability. Many reports present the applicability of AgNWs as IR-reflective wearable textiles, where such studies partially evaluate the parameters for practical usage for large-scale production. In this study, the effect of the two industrial coating methods and the loading value of AgNWs on the performance of AgNWs-coated fabric (AgNWs-CF) is reported. The AgNWs were synthesized by the polyol process and applied onto the surface of cotton fabric using either dip- or spray-coating methods with variable loading levels of AgNWs. X-ray diffraction, scanning electron microscopy (SEM), infrared (IR) reflectance, water vapor permeability (WVP), and electrical resistance properties were characterized. The results report the successful synthesis of AgNWs with a 30 μm length. The results also show that the spray coating method has a better performance for reflecting the IR radiation to the body, which increases with a greater loading level of the AgNWs. The antibacterial results show a good inhibition zone for cotton fabric coated by both methods, where the spray-coated fabric has a better performance overall. The results also show the coated fabric with AgNWs maintains the level of fabric breathability similar to control samples. AgNWs-CFs have potential utility for cold weather protective clothing in which heat dissipation is attenuated, along with applications such as wound dressing materials that provide antibacterial protection.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network. The results show that the proposed network can obtain higher precision than the existing models for the bladder cancer dataset. The MD-Unet can achieve state-of-art performance compared with other methods.     

基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

基于神经网络的NMR谱图自动相位校正综合算法

目前,已有多种算法被应用在核磁共振谱图自动相位校正中,由于各种算法本身特性和所基于谱图的具体特性的差异,不同算法对于特征不同的谱图的适用性也各不相同. 针对这一情况,文在综合研究多种现有自动相位校正算法的基础上,提出了一种基于神经网络的,可以根据谱图的特征来选取最合适的算法进行自动相位校正的综合算法. 实验表明,本算法可以获得比以往方法更好的计算结果.     

遥感地表参数反演中的一种不确定性知识处理方法

在基于贝叶斯网络的遥感参数反演模型中引入虚拟节点,提出不确定性知识表示与知识更新方法,推导了遥感反演中的参数后验信息更新公式.用北京顺义遥感实验的多尺度数据对冬小麦的叶面积指数和叶绿素面质量进行反演计算,验证了多知识源反演过程中不确定性知识对参数后验分布概率的调整模式.     

新农村建设中农村电网建设的探讨

为了解决新农村建设中农村用电水平的不断提高,特别是随着农村经济水平的不断发展,当前实际运行中农村配电网已出现的诸如网络损耗较大而导致供电成本过高、供电可靠性较低、供电电压和频率不满足要求等问题,作者通过对一个典型的农村电网的潮流进行分析,从网络的整体规划布局到局部的输配电设备(如:线路、变压器及无功补偿装置等)方面提出了关于降低网络损耗、提高电压质量和可靠性的具体措施.通过对改建前后的线损率和电压合格率进行计算,表明笔者所提出的方法对现有农村电网改造是有效的.最后,提出了农村电网的发展趋势.     

环型配网优化规划的遗传算法

直接使用闭环结构约束,采用遗传算法对仅有2个变电站的环型配网进行优化规划.在配网优化规划的同时考虑到了结构上的环状约束和运行上的辐射状约束.首先介绍了环型配电网优化规划中采用的数学模型,然后介绍了遗传算法理论及其在环型配电网中的应用,并使用Visual C 编程语言,设计、开发环型配网优化规划软件.最后通过对一个17节点的试验系统的优化规划,验证了方法的实用性和有效性.     

基于偏最小二乘的BP网络模型及其应用

基于BP网络模型具有拟合非线性数据的特性,提出一种基于非线性迭代偏最小二乘算法(NIPALS)的BP网络的构造策略,构造了新的PLS-BP网络模型,使BP网络减少迭代步数,提高学习效率.采用非线性迭代偏最小二乘算法预处理数据,将得到主成分数、自变量和因变量的主成分数的权重以及主成分间的关系矩阵B,以此用来确定BP网络的隐节点数和输入层、输出层的初始权值以及隐节点的关联系数.最后,进行仿真实验,并将它与PLS模型、标准的BP网络模型进行了比较,仿真结果表明,拟合和预测效果较好.     

区域公路交通网络演变的理论与方法应用研究——以郑州市高速公路网络演变为例

区域公路网络的演变是区域公路交通网络规划和布局的基础.本文以郑州市的高速公路网络为研究对象,探讨了区域公路网络的合理规模和区域公路网络成长的规律,并提出了郑州市高速公路网在地域形态上的变化规律呈现出由孤立径道网络—树状网络—格状网络—格状 放射网络的从低级到高级的演化过程.