碳纳米纤维增强聚甲醛复合材料制备及性能

通过纳米碳纤维(CNFs)在聚甲醛(POM)基体中的均匀分散以及取向,制备了具有优异力学性能和热性能的POM/CNFs复合材料。利用扫描电子显微镜、透射电子显微镜、拉伸性能测试、热重分析、动态热机械分析测试表征了POM/CNFs复合材料的结构和力学、热学性能。结果表明,CNFs与POM分子链形成氢键相互作用,促进了CNFs在POM基体内分散,同时使POM/CNFs复合材料的结晶度显著提高。随着CNFs含量增加,POM/CNFs复合材料的拉伸强度、储能模量和损耗模量均得到提高。当添加0.5%的CNFs时,拉伸强度、储能模量及损耗模量分别提高了20.5%,127%和58%。进一步研究了高温拉伸对POM/CNFs复合材料性能的影响。结果表明,CNFs沿拉伸方向定向排列,同时复合材料拉伸后结晶度提高,拉伸强度显著增加。     

基于2M光接口的线路保护通信通道设计与应用

传统的光纤复用保护通道需将保护装置发出的光信号通过光电转换设备变成电信号后再复用进SDH传输,对端也需将收到的电信号转换成光信号后送到保护装置.这种方式增加了中间转换环节,致使信号传输效率低、时延较大、故障隐患较多.针对上述问题,设计了一种基于2M光接口的光纤复用保护通道,省去中间转换环节.应用结果表明,通道方式结构简单,传输稳定,安装调试环节减少,能够满足保护通道要求.     

基于曲线拟合和SVM的眼动信号分类算法研究

为提高基于EOG的眼动信号分类算法精度,改进基于EOG的人—机交互系统性能,提出了一种基于曲线拟合(curve fitting)与支持向量机(SVM)的眼动信号分类算法(CF-SVM),并设计了新的实验范式,增加了“扫视保持”环节.该算法采用曲线拟合方法进行特征提取,在此基础上,使用SVM分类器对眼动信号进行分类.实验室环境下,对9名眼部活动正常的受试者进行了眼动数据采集与识别,CF-SVM算法的平均分类准确率达到98.3％,与其他几种眼动识别方法相比较,其平均正确率分别提升了9.4％、5.9％、1.0％.实验结果表明,CF-SVM算法在眼动信号识别中表现了良好的性能,具有高的分类精度和鲁棒性.     

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper)

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.     

GM2NAS: multitask multiview graph neural architecture search

Knowledge and Information Systems - Graph neural network-based multitask learning models on multiview graphs have achieved acceptable results in different real-world applications. However,...     

A weighted ADI scheme with variable time steps for diffusion-wave equations

Calcolo - We present a local projection stabilization for the virtual element discretization of the nonlinear convection–diffusion–reaction equation. We consider a...     

A Self-Assembled Vertical-Gradient and Well-Dispersed MXene Structure for Flexible Large-Area Perovskite Modules

Advancing hole transport layers (HTL) to realize large-area, flexible, and high-performance perovskite solar cells (PSCs) is one of the most challenging issues for its commercialization. Here, a self-assembled gradient Ti₃C₂T_x MXene incorporated PEDOT:PSS HTL is demonstrated to achieve high-performance large-area PSCs by establishing half-caramelization-based glucose-induced MXene redistribution. Through this process, the Ti₃C₂T_x MXene nanosheets are spontaneously dispersed and redistributed at the top region of HTL to form the unique gradient distribution structure composed of MXene:Glucose:PEDOT:PSS (MG-PEDOT). These results show that the MG-PEDOT HTL not only offers favorable energy level alignment and efficient charge extraction, but also improves the film quality of perovskite layer featuring enlarged grain size, lower trap density, and longer carrier lifetime. Consequently, the power conversion efficiency (PCE) of the flexible device based on MG-PEDOT HTL is increased by 36% compared to that of pristine PEDOT:PSS HTL. Meanwhile, the flexible perovskite solar minimodule (15 cm² area) using MG-PEDOT HTL achieve a PCE of 17.06%. The encapsulated modules show remarkable long-term storage stability at 85 °C in ambient air (≈90% efficiency retention after 1200 h) and enhanced operational lifetime (≈90% efficiency retention after 200 h). This new approach shows a promising future of the self-assembled HTLs for developing optoelectronic devices.     

Pre-Activation of CO2 at Cobalt Phthalocyanine-Mg(OH)2 Interface for Enhanced Turnover Rate

Cobalt phthalocyanine (CoPc) anchored on heterogeneous scaffold has drawn great attention as promising electrocatalyst for carbon dioxide reduction reaction (CO₂RR), but the molecule/substrate interaction is still pending for clarification and optimization to maximize the reaction kinetics. Herein, a CO₂RR catalyst is fabricated by affixing CoPc onto the Mg(OH)₂ substrate primed with conductive carbon, demonstrating an ultra-low overpotential of 0.31 ± 0.03 V at 100 mA cm⁻² and high faradaic efficiency of >95% at a wide current density range for CO production, as well as a heavy-duty operation at 100 mA cm⁻² for more than 50 h in a membrane electrode assembly. Mechanistic investigations employing in situ Raman and attenuated total reflection surface-enhanced infrared absorption spectroscopy unravel that Mg(OH)₂ plays a pivotal role to enhance the CO₂RR kinetics by facilitating the first-step electron transfer to form anionic *CO₂⁻ intermediates. DFT calculations further elucidate that introducing Lewis acid sites help to polarize CO₂ molecules absorbed at the metal centers of CoPc and consequently lower the activation barrier. This work signifies the tailoring of catalyst-support interface at molecular level for enhancing the turnover rate of CO₂RR.