酶促合成生物柴油反应动力学

以Candidasp.99-125脂肪酶为催化剂,甘油三油酸酯和甲醇为底物,采用有序机制模型对酶促合成生物柴油的酯交换反应动力学进行了研究,并与经典乒乓机制模型进行了比较。研究结果表明,反应初速率的实验值与有序机制模型方程的计算值吻合很好。对于固定化Candidasp.99-125脂肪酶催化合成生物柴油的酯交换反应机理进行研究,采用有序机制模型比经典乒乓机制模型更为精确。反应过程中,醇抑制为竞争性抑制,在甘油三油酸酯浓度较小的范围内,醇抑制作用较为显著,醇浓度越低反应初速率越快。该有序机制模型可用来预测生物柴油的生产批次或连续反应器中酯交换反应的速率,确定最佳底物油脂和醇的浓度。     

Kinetics of methylamine conversion on a Pd catalyst in the presence of H2

The kinetics of disproportionation of methylamine to dimethylamine and ammonia in the presence of hydrogen have been investigated over a silica-supported palladium catalyst. At a fixed partial pressure of methylamine, the reaction rate generally increases with decreasing hydrogen partial pressure, approaching a limiting or maximum value at sufficiently low pressures of hydrogen. The existence of a maximum is supported by the observation that the presence of some hydrogen appears to be necessary for the reaction to proceed at a conveniently measurable rate. At a fixed hydrogen partial pressure, the reaction rate increases with increasing methylamine partial pressure. When the methylamine partial pressure is sufficiently low or the hydrogen partial pressure is sufficiently high, the reaction order with respect to methylamine can be somewhat higher than one. At such conditions, a mechanism involving a bimolecular reaction between two partially dehydrogenated methylamine molecules on the surface appears to make a significant contribution to the overall catalytic reaction.     

基于非竞争机制的改进GAF拓扑控制算法

能量问题是制约无线传感器网络发展和应用的瓶颈之一。为均衡网络节点的能量消耗,延长网络的生命周期,需要建立高效的拓扑控制机制。在深入研究GAF算法的基础上,提出了一种基于非竞争机制的改进算法。利用建立的适合度函数和顺序表优化了簇头的选举策略;通过改进虚拟单元格的划分方法,并推导出动态剩余能量阈值估算公式,有效地改善了GAF算法的相关性能。仿真结果表明,改进算法对于减少拓扑控制耗费、延长网络生命周期具有显著效果。     

关于I/O模拟的讨论

详细分析Windows的I/O机制,提出了分别在用户级、系统级、驱动级的I/O模拟操作,通过实际案例和源代码分享在I/O模拟上的得失,着重介绍鼠标与键盘的模拟。     

基于Boost消息机制研究与实现

消息通信机制可以有效的简化系统设计,增强系统的可扩展性。Boost Signal/Slot机制,为在进行消息机制开发提供了有力的工具。以Boost为基础,分析了Boost消息机制的实现方式,通过引入一个简单实例,演示了利用Boost构造消息机制的基本方法。     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

IPv6在QoS上的三项重要改进

本文主要分析了IPv6相对于IPv4在QoS上所做的基本报头、ICMP控制机制及基本报头中的QoS元素三项改进,从而为有效支持QoS提供理论支持。     

岛上原油贮罐罐底边缘板腐蚀机理分析及防护措施

对马鞭洲岛广石化码头原油罐罐底边缘板腐蚀机理进行分析，并采取相应的防护措施。实践表明，对提高码头原油贮罐的安全使用寿命是有效的。     

Inhibition behavior for copper corrosion by photoelectrochemical methods

wavelengthofmono chromatelightwas 4 2 0nm .3　RESULTSANDDISCUSSION3.1　ApplicationofphotoelectrochemicalmethodtoevaluatetheeffectsofinhibitorsforcoppercorrosionThestructuresoftheinhibitorsforcoppercorro sionshownintextareasfollows:Theinhibitioneffectsofdifferentinhibitorsforcoppercorrosioncanbeevaluatedby photoelec trochemicalmethod .AsshowninRef .[12 ],onanegativepotentialscan ,thepotentialcorrespondingtothecathodicphotocurrentJph=0isdefinedasΦV,atwhichCu2 Oontheelectrodesurfaceisc…