Ultralow feeding gas flow guiding growth of large-scale horizontally aligned single-walled carbon nanotube arrays

On the basis of the rational analysis about the fluidic property of the system, an ultralow gas flow chemical vapor deposition (CVD) strategy was designed to prepare large-scale horizontally aligned ultralong single-walled carbon nanotube (SWNT) arrays. SWNT arrays could be well obtained under extremely low feeding flow of 1.5 sccm in a 1 in. quartz tube reactor. It was confirmed that the tubes grew floatingly and could cross microtrenches or climb over micro-obstacles in ultraslow gas flow. SWNTs arrays also could be formed no matter the substrate was placed vertically or upside down. The growth mechanism was discussed. Both the buoyancy effect induced by gas temperature/density difference and gas flow stability played dominant roles. More attractively, simultaneous batch-scale preparation of SWNT arrays was realized by the ultralow gas flow strategy. This new strategy turns to be more abstemious, efficient, promising, and flexible compared with the high gas flow rate fast-heating CVD processes.     

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

再谈电容器真空浸渍"单台注油"装置

介绍了电容器单台注油装置的构成及工作原理,对关键零部件作了改进,对其作用进行了分析,并说明如何安装使用。     

一种高效Cable Modem SOC软硬件协同验证平台

通过对基于龙芯一号CPU的双向有线调制解调器SOC的研究,利用软件仿真和硬件模拟相结合的方法,设计了一种软硬件协同验证平台TWCNP,可分别对EuroDOCSIS协议栈、EuroDOCSIS协议处理器、整个双向有线网络SOC等软硬件进行验证,并实现了验证和设计的统一.为了提高TWCNP的验证时效性,提出了一种基于TWCNP-OS的软硬件协同验证方法,缩短了开发周期,保证了SOC设计的可靠性.     

The integration of image segmentation maps using region and edgeinformation

We present an algorithm that integrates multiple region segmentation maps and edge maps. It operates independently of image sources and specific region-segmentation or edge-detection techniques. User-specified weights and the arbitrary mixing of region/edge maps are allowed. The integration algorithm enables multiple edge detection/region segmentation modules to work in parallel as front ends. The solution procedure consists of three steps. A maximum likelihood estimator provides initial solutions to the positions of edge pixels from various inputs. An iterative procedure using only local information (without edge tracing) then minimizes the contour curvature. Finally, regions are merged to guarantee that each region is large and compact. The channel-resolution width controls the spatial scope of the initial estimation and contour smoothing to facilitate multiscale processing. Experimental results are demonstrated using data from different types of sensors and processing techniques. The results show an improvement over individual inputs and a strong resemblance to human-generated segmentation     

电力通信网络安全可靠性定量评估研究方法

当前电力通信网络的安全可靠性评估缺少量化的评估手段,针对该问题,文章提出了完整的定量评估研究方法,该方法建立了分层结构的定量评估模型(包括分层结构指标体系、指标权重模型),并定义了安全可靠性指标值与对应的评估结论等级。该方法为电力通信网络的规划、建设、优化提供定量依据和基础数据,具有理论研究价值和工程指导意义,并已在国网上海电力项目中得到应用。     

不同压紧件对IEGT工作表面压应力分布的影响

介绍了大功率器件IEGT在压装中,不同结构形式的压紧件对IEGT工作面压应力分布的影响,结合实例并通过有限元模拟及实验验证来确定压紧件的结构形式,以保证IEGT工作面压应力分布均匀,满足其在电力系统中使用要求。     

A Delay-Dependent Approach to Robust H ∞ Control for Uncertain Stochastic Systems with State and Input Delays

In this paper, the problem of delay-dependent robust H _∞ control for uncertain stochastic systems with state and input delays is investigated. The time delays are assumed to be bounded and time varying and the uncertainties are assumed to be norm bounded. By using the Lyapunov functional method, a new delay-dependent robust H _∞ control scheme is presented in terms of linear matrix inequalities (LMIs). Some numerical examples are given to illustrate the effectiveness of the proposed approach. This work is partially supported by the Natural Science Foundation of China (60674055, 60774047), and the Taishan Scholar Programme of Shandong Province. An erratum to this article can be found at