Ag/LaNixCo1-xO3触点材料的制备及电接触性能研究

本文运用溶胶-凝胶燃烧法合成了双钙钛矿型LaNixCo1-xO3(LNCO)纳米材料,利用粉末冶金和热挤压技术制备了相应的Ag/LNCO触点材料及元件样品。重点考察了不同Ni、Co含量对Ag/LNCO触点材料微观结构、物相组成、物理性能、力学性能及电寿命服役能力的影响,对其电弧侵蚀失效行为进行了研究,并与SnO2粉体增强Ag基触点材料进行对比。结果表明:溶胶凝胶法合成的LNCO纳米颗粒粒径为20-30 nm,经粉末冶金工艺制备的Ag/LaNi0.5Co0.5O3触点材料电学性能和电寿命都优于Ag/SnO2触点材料,其电阻率低至2.10 μΩ?cm,电寿命性能达到51287次。表明Ag/LaNi0.5Co0.5O3触点材料性能较佳,是一种可以取代Ag/CdO的新型触点材料。     

Application of Magnetron Sputtering in Fabricating MEMS Microbatteries

With the development of MEMS and the electronic devices's miniaturization and integration, a new kind of power sources that can satisfy the need for high energy density is required. Microbatteries are being researched abroad for their advantages of extreme thinness and long-term power supply. The development of MEMS microbatteries are reviewed and suggestedmagnetron sputtering in fabricating a high-performance microbattery. The technics as annealing are analyzed. The microbattery with a LiNi1-x Cox O2 cathode exhibited stable cycle ability and a high specific discharge capacity, which was attributed to the alloying effect of the LiNiO2and LiCoO2.     

有色合金电子束焊接的发展状况

随着有色合金的广泛应用,其焊接这种连接方法也愈加重要,其中的电子束焊接与传统的焊接工艺相比,具有不可替代的特点和优势。结合近年来国内外电子束焊接的研究,着重介绍了镁、铝、钛、镍基、铜合金的电子束焊接的研究现状,并展望了有色合金电子束焊接的发展趋势与研究方向。     

显像管玻壳退火炉热平衡测试及评估

通过对4台玻壳退火炉的热平衡测试、计算及分析,得到了玻壳退火炉热平衡收支数据,计算出玻璃成品的单位质量产品耗热量、单位质量产品燃气耗量等主要技术经济指标。提出了进一步提高热效率的技术措施。     

协同优化方法及铰接式自卸车系统设计协同优化模型

对协同优化方法的基本思想、计算框架、研究现状及发展进行了分析论述,并将协同优化方法引入到铰接式自卸车(ADT)系统设计中,叙述了对铰接式自卸车进行协同优化的必要性,对ADT系统进行分解,建立协同优化模型,并提出协同优化方法的求解思路。     

低透气性高瓦斯煤层立体多层次瓦斯综合治理技术

介绍了祁南煤矿按照"大流量、多抽泵、大管径、多回路"的原则,掘进采用超前探测、煤层注水、边掘边抽和回采采用顺层孔、高位钻孔、深孔爆破增透、采空区埋管、立管及上隅角吸空管等的立体抽放方法,解决了低透气性高瓦斯煤层的瓦斯综合治理问题,提供了一种可靠、有效、实用的综合治理瓦斯技术。     

基于Vague集的Apriori算法的改进

当前研究的关联规则挖掘隐含着一个假定：待挖掘数据库中每条记录的全部属性都是完整的。然而现实中得到的数据可能是不完整的,在这种情况下将无法使用传统的算法。为了解决这个问题,引入Vague集并给出基于Vague集的支持度和置信度定义,将经典的Apriori算法推广到Vague集上。最后通过一个例子来验证算法的有效性。     

多接口移动主机最优化费用和性能方法研究

目前移动主机普遍具有多个网络接口,可以由这些接口通过不同的网络服务提供商(ISP)接入到Internet中.因此,移动主机与Internet之间就可能存在多条路径,通信的费用和性能取决于ISP的收费策略和网络通信性能.文中首先对移动主机与Internet通信进行建模,然后提出一种基于主动探测可用路径的最优化费用和性能的...     

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.