Molecular Dynamics Simulations of the Elusive Matrix-Open State of Mitochondrial ADP/ATP Carrier

We studied and compared in detail an elusive matrix open (m-state) and cytoplasmic open (c-state) state of ADP/ATP carrier (AAC) protein embedded in the DOPC bilayer by microsecond molecular dynamics (MD) simulations. We analyzed both states with and without cardiolipin (CDL) molecules, with a special emphasis on the recently obtained crystallographic structure of the AAC m-state. The obtained results show that both states of the protein are stable in the DOPC bilayer and impermeable to water. In comparison with the c-state of AAC, the m-state is more dynamic, but at the same time possesses a larger occluded area in the protein cavity. Both states of the protein are less flexible in simulations when CDL molecules are present, which is especially visible for the m-state. Finally, the analysis of the protein conformational changes during MD simulations shows that protein parts at the protein/lipid boundaries are prone to larger conformational changes in contrast to central region of the protein embedded in the bilayer core, thus further supporting the cycling mechanism suggested for ADP/ATP exchange by AAC.     

Genetic interactions link ARF1, YPT31/32 and TRS130

A genetic screen for synthetic lethal interactions with arf1(-) identified a recessive mutation in TRS130, one of 10 components in the trafficking protein particle (TRAPP) complex (Sacher et al., 2000). As TRS130 is an essential gene, the synthetic lethal allele (trs130-101) is a novel one that requires ARF1 for viability. This allele was found to exhibit no defects in secretory function, i.e. processing of carboxypeptidase Y or invertase. YPT31 and YPT32 were identified in a subsequent screen as high-copy suppressors of arf1(-)trs130-101. Increasing the gene dosage of YPT31/32 also suppressed lethality resulting from deletion of TRS130 or TRS120 but not three other essential TRAPP subunit-encoding genes. Although unable to suppress defects in several alleles of ARF1, increasing the gene dosage of YPT31/32 suppressed the cold sensitivity of gcs1(-), an Arf GTPase-activating protein (GAP). Thus, these genetic interactions provide initial evidence for linkage of Arf and TRAPP signalling and for Ypt31/32 proteins functioning downstream of both components in the TRAPP complex and of Arf signalling via the Gcs1 Arf GAP.     

碳纤维/环氧预浸料的热固化工艺研究

以先进拉挤(Advanced Pultrution)[1]ADP成型技术为研究背景,借助差示扫描量热法(DSC)对USN12500碳纤维/环氧预浸料固化所涉及的温度和时间进行了研究与分析。以弯曲强度作为考察指标设计正交试验,优选了模拟的拉挤成型固化工艺参数。试验结果表明,①后固化温度下的保温时间对制品的力学性能影响最大,预处理温度下的保温时间影响最小;②预处理温度80℃下处理25min、热压温度130℃下保温保压25min以及后固化温度150℃下保温1.5h为本组试验的优方案。     

Argonaut SL在某隧洞过水断面水文测验中的应用

声学多普勒水流剖面仪(ADP)近十年来得到了较为广泛的应用,与传统测流仪器相比,它具有方便、快捷、经济、安全、数据准确、适应性强等优点.某隧洞已运行了12年,为了提供科学依据进行安全评价,必须测量不同流量下的隧洞进出口水位、流速等参数,为此结合该工程实例阐述Argonaut SL的应用及洞内水位的测量方法.     

一种基于以太网接口的ARM仿真器的设计

在分析传统的ARM系统的调试和仿真机制的基础上,给出了以Samsung S3C44B0X处理器为主CPU的基于以太网接口的ARM JTAG仿真器的设计方案。利用这种仿真器不仅可以体现以太网接口速度快的特点,还可以实现仿真器资源的共享。     

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.     

基于马氏决策过程模型的动态系统学习控制:研究前沿与展望

基于马氏决策过程(Markov decision process, MDP)的动态系统学习控制是近年来一个涉及机器学习、控制理论和运筹学等多个学科的交叉研究方向, 其主要目标是实现系统在模型复杂或者不确定等条件下基于数据驱动的多阶段优化控制. 本文对基于MDP的动态系统学习控制理论、算法与应用的发展前沿进行综述,重点讨论增强学习(Reinforcement learning, RL)与近似动态规划(Approximate dynamic programming, ADP)理论与方法的研究进展,其中包括时域差值学习理论、求解连续状态与行为空间MDP的值函数逼近方法、 直接策略搜索与近似策略迭代、自适应评价设计算法等,最后对相关研究领域的应用及发展趋势进行分析和探讨.     

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming (ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method.