Catalytic Mechanism of the Hotdog‐Fold Thioesterase PA1618 Revealed by X‐ray Structure Determination of a Substrate‐Bound Oxygen Ester Analogue Complex

Thioesterase activity accounts for the majority of the activities in the hotdog‐fold superfamily. The structures and mechanisms of catalysis for many hotdog enzymes have been elucidated by X‐ray crystallography and kinetics to probe the specific substrate usage and cellular functions. However, structures of hotdog thioesterases in complexes with substrate analogues reported to date utilize ligands that either represent truncations of the substrate or include additional atoms to prevent hydrolysis. Here we present the synthesis of an isosteric and isoelectronic substrate analogue—benzoyl‐OdCoA—and the X‐ray crystal structure of a complex of the analogue with Pseudomonas aeruginosa hotdog thioesterase PA1618 (at 1.72 Å resolution). The complex is compared with that of the “imperfect” substrate analogue phenacyl‐CoA, refined to a resolution of 1.62 Å. Kinetic and structural results are consistent with Glu64 as the catalytic residue and with the involvement of Gln49 in stabilization of the transition state. Structural comparison of the two ligand‐bound structures revealed a crucial ordered water molecule coordinated in the active site of the benzoyl‐OdCoA structure but not present in the phenacyl‐CoA‐bound structure. This suggests a general base mechanism of catalysis in which Glu64 activates the coordinated water nucleophile. Together, our findings reveal the importance of a closely similar substrate analogue to determine the true substrate binding and catalytic mechanism.     

类X-51A飞行器巡航状态气动与燃烧流场特性分析

以类X-51A 高超声速飞行器为研究对象,重构了其机体外形及内部超燃冲压发动机的一体化模型,并通过数值方法研究了该目标巡航状态下的气动与燃烧耦合流场特性。数值分析结果表明:类X-51A 飞行器高压区主要集中在机体头部、进气道压缩段、进气道入口侧壁及尾翼前缘,且内部燃烧室的总体压力显著高于机体表面的;燃烧室内靠近两侧壁面的喷孔燃料喷注深度高于中间喷孔的,下游喷孔喷注深度高于上游喷孔的,凹腔附近喷孔喷注深度高于无稳焰结构的壁面喷孔的;燃烧产物羽流长度超过50 m,且持续向机体上方发生偏移。计算结果可为类X-51A 及相关飞行器红外探测与目标识别等技术提供流场数据参考。     

基于光谱法的法珀压力传感器腔长解调方法研究

针对白光干涉复合式光纤法珀压力传感器反射光谱复杂,其准确解调易受光源光谱不稳定与电路噪声等因素影响的问题,通过理论分析,确认了反射光谱包络信号来源于传感器对压力敏感的空气腔,其频率上的周期直接正比于腔长,提取了复合式光纤法珀压力传感器反射光谱的包络信号,通过正弦频率估计的方法,优化四参数正弦拟合为三参数正弦拟合,降低正弦拟合的计算量,并准确寻找包络信号两个峰的峰值位置,通过腔长与信号周期的关系可最终得到准确的腔长值。对该解调算法进行了详细地理论分析与数值仿真,通过实验验证了光谱法中峰值检测方法对复合式光纤法珀压力传感器80 μm空气腔静态腔长的精确解调,相对误差为0.13%。     

Water-retaining properties of NCZ composite dust suppressant and its wetting ability to hydrophobic coal dust

Coal dust is a primary threat to underground coal miners.The most common approach to control coal dust is hydraulic methods,such as water spray and coal seam wa...     

基于模糊神经网络的水轮机调速器PID控制

针对水轮机调节系统参数时变、运行工况多变以及包含复杂非线性环节的特点,传统的PID控制策略难以满足系统各项运行指标.根据水轮机调速系统的数学模型,结合模糊控制的推理能力和神经网络的自学能力,提出基于模糊神经网络的水轮机调速器PID参数控制策略.仿真结果表明,相比于传统PID控制和模糊控制,所提控制策略的系统响应拥有更小...     

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.     

海事监控视频舰船目标检测研究现状与展望

舰船目标检测是海域监控、港口流量统计、舰船身份识别以及行为分析与取证等智能海事应用的基石。随着我国海洋强国建设的推进,智慧航运和智慧海洋工程迅速发展,对通过海事监控视频开展有效的舰船目标检测识别以确保航运和海洋工程安全的需求日益紧迫。本文针对基于海事监控视频的舰船目标检测任务,回顾了舰船目标检测数据集及性能评价指标、基于传统机器学习和基于卷积神经网络的深度学习的目标检测方法等方面的国内外研究现状,分析了海洋环境中舰船目标检测任务面临的舰船目标尺度的多样性、舰船类别的多样性、海洋气象的复杂性、水面的动态性、相机的运动性和图像的低质量等技术难点,并通过实验验证,在多尺度特征融合、数据增广和能耗降低等方面提出了舰船目标检测的优化方法;同时,结合前人研究指出舰船目标检测数据集的发展应关注分类粒度的适宜性、标注的一致性和数据集的易扩充性,应加强对多尺度目标(尤其是小型目标)检测的模型结构的研究,为进一步提升舰船目标检测任务的综合性能,促进舰船目标检测技术的应用提供了新的思路。     

不确定环境下含云计算数据中心的电网韧性增强调度

为解决飓风来临前路径不确定时输电线路随机故障等带来的难题,提出了适用于含云计算数据中心的电网韧性增强日前调度策略,并将其构建为两阶段风险规避的分布鲁棒优化问题.以飓风对输电线路的时空影响为出发点,采用蒙特卡洛模拟获得飓风路径不确定时线路的离散故障集合,并构建基于L1距离度量的分布鲁棒模糊集合.然后,在日前调度中,对机组...     

Fold 2D Woven DNA Origami to Origami+ Structures

DNA origami strategy has greatly promoted the development of DNA nanotechnology as it can construct delicate 2D/3D nanostructures. The current method to prepare DNA origami is through a woven approach to fix a long strand of DNA into certain shapes. This article describes a novel strategy to fold the same 2D DNA sheet into multiple complex structures driven by introducing hydrophobic interaction. The pathway of the folding process can be adjusted by tuning the distribution and chronic order of cholesterol, resulting in different complicated structures. This method is proven efficient for constructing variable nanostructures based on the same DNA origami sheet, and termed as origami⁺. This work represents a new strategy in DNA nanotechnology and will improve the ability to manipulate objects at nanoscale.     

太阳能区域供热发展现状综述

近年来,雾霾污染已经成为中国迫切需要解决的环境问题,冬季燃煤锅炉污染物的排放是雾霾发生的主要原因.太阳能是一种可再生能源,不产生污染,采用太阳能资源代替燃煤锅炉是解决大气污染的重要措施.本文在介绍了国内外对太阳能区域供热系统应用的现状之后,结合中国的实际情况分析了太阳能区域供热系统在中国发展中应注意的问题,以及太阳能区域供热在中国的适应性,并预测了太阳能区域供热技术在中国的发展前景.本文总结了太阳能区域供热技术发展的建议,期望可以为中国太阳能区域供热的发展提供一定的理论支持.