含席夫碱结构碳点缓蚀剂的简易可扩展制备及性能研究

目的 克服目前制备碳点(Carbon dots, CDs)缓蚀剂存在的耗时、耗能等缺点,在室温下一步制备含席夫碱结构的CDs缓蚀剂,并研究其对Q235碳钢的缓蚀性能。方法 设计了一种简易、可扩展的制备方法,以邻苯二胺和对苯醌为前驱体,无需高温加热便可在室温下反应2 h,从而获得含席夫碱结构的CDs。利用TEM等方法对其结构进行表征,并采用UV和PL光谱评估其在HCl溶液中的长期分散稳定性。通过失重法、电化学测试方法研究了不同浓度CDs对Q235碳钢在1 mol/L HCl溶中的缓蚀性能。通过SEM和三维轮廓测量仪分析腐蚀后碳钢表面形貌及化学组成,提出CDs的缓蚀机理。结果 CDs含C=N键,具有多种含氧、含氮基团,有利于其在钢表面的吸附。CDs在HCl溶液中具有长期分散稳定性。当添加浓度为200 mg/L时,其对碳钢在1 mol/L HCl溶液中的缓蚀效率可达到95.05%。CDs为混合型缓蚀剂,能够同时抑制阴极和阳极反应。CDs在碳钢表面的吸附方式遵循Langmuir等温吸附模型,其缓蚀机理为通过物理和化学吸附方式在碳钢表面形成一层保护膜,从而抑制碳钢的腐蚀。结论 成功为CDs缓蚀剂的合成提供了一种简易、可扩展、高效、省时的方法,而且证明了具有席夫碱结构的CDs对碳钢在1 mol/L HCl溶液中的腐蚀具有显著的抑制能力。     

Snowball: Scalable Storage on Networks of Workstations with Balanced Load

Networks of workstations are an emerging architectural paradigm for high-performance parallel and distributed systems. Exploiting networks of workstations for massive data management poses exciting challenges. We consider here the problem of managing record-structured data in such an environment. For example, managing collections of HTML documents on a cluster of WWW servers is an important application for which our approach provides support. The records are accessed by a dynamically growing set of clients based on a search key (e.g., a URL). To scale up the throughput of client accesses with approximately constant response time, the records and thus also their access load are dynamically redistributed across a growing set of workstations. The paper addresses two problems of realistic workloads: skewed access frequencies to the records and evolving access patterns where previously cold records may become hot and vice versa. Our solution incorporates load tracking at different levels of granularity and automatically chooses the appropriate granularity for dynamic data migrations. Experimental results based on a detailed simulation model show that our method is indeed successful in providing scalable cost/performance and explicitly controlling its level.     

Mixed-Mode Programmable and Scalable Architecture for On-Chip Learning

Typical analog VLSI architectures for on-chip learning are limited in functionality, and scale poorly under variable problem size. We present a scalable hybrid analog-digital architecture for backpropagation learning in multilayer feedforward neural networks, which integrates the flexible functionality and programmability of digital control functions with the efficiency of analog parallel neural computation. The architecture is fully scalable, both in the parallel analog functions of forward and backward signal propagation through synaptic and neural functional units (SynMod and NeuMod), and in the global and local digital functions controlling recall, learning, initialization, monitoring and built-in test. The architecture includes local provisions for long-term weight storage using refresh, which is transparent to the functional operation both during recall and learning. Refresh While Learning (RWL) provides a means to compensate for the finite precision of the quantized analog weights during learning. We include simulation results for a network of 32×32 neurons, mapped in parallel onto a MassPar computational engine, which validate the functionality of the architecture on simple character recognition tasks, and demonstrate robust operation of the trained network under 4-bit quantization of the weights owing to the RWL technique.     

A Fast Parallel Clustering Algorithm for Large Spatial Databases

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dR*-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.     

可编程路由器技术研究

可编程路由器是一种能从功能上分解成若干组件与接口,并能通过配置和与编程,实现灵活而动态地部署新服务、新协议和减轻网络管理负担的路由设备.本文调研了可编程路由器相关研究工作;讨论了可编程路由器与传统路由器、可扩展路由器、主动网络节点、可重构路由器的区别;重点说明了可编程路由器的架构及其数据路径的可编程性;同时也对可编程路由器的平台重构、网络管理及创新应用等方面进行了考察,最后对工作进行了总结并讨论了未来可能的研究方向.     

Optimal Clustering of Hierarchical Hyper-Ring Multicomputers

The Hyper-Ring (HR) is presented as a hierarchical and scalable ring-based topology for small-scale to massively parallel systems which eliminates the major disadvantages of large-scale rings. With a fixed node degree, a low cost, symmetric properties, and a simple routing scheme, the HR topology is very suitable for small-scale to large-scale multicomputer systems. Assuming pipelined communication, the performance of 4- and 5-dimensional HR multicomputers is modeled, the performance model is evaluated, and the results of the performance model evaluation are analyzed. Moreover, the impact of the traffic load and message length on the system performance is analyzed. The major objective of this work is to shed light on how to cluster HRs in order to optimize the system efficiency. Assuming a uniform message arrival rate into the nodes of the HR, the results show that the efficiency of HR topologies with an equal number of nodes is best when the topologies are perfectly balanced. The next best-performing HRs are those with larger rings at the lower (outer) levels and smaller rings at the higher levels (near the root ring). The results confirm that the HR topology is suitable for massively parallel and scalable multicomputer systems as well as for networks of workstations.     

Relay-assisted hierarchical adaptation scheme for multi-user scalable video delivery to heterogeneous mobile devices

In multi-user video (MUV) delivery scenarios,the available resources of receiver devices,such as processing capability,link packet error rate (PER),and bandwidth,are usually different.We propose a relay-assisted hierarchical adaptation (RHA) scheme to maximize the total perceptual quality of all users when transmitting video streams coded via scalable video coding (SVC).First,MUV bitstreams are adaptively extracted under the constraints of network bandwidth and individual decoding capacity.Next,the relay links are introduced as substitutes of possible bad direct links for packets retransmissions.Approximately equal opportunity of transmission is allocated to each stream while the packets inside a stream are scheduled according to their priorities.The priorities are determined by the links states and packets loss distortions.Simulation results show that our RHA scheme has significant performance improvements compared with other schemes.     

An end-to-end robust approach for scalable video over the internet

With the growth of the Internet and abundant network resources, video streaming is becoming one of the increasingly important Internet applications. However, the current IP-based network provides only a single class best effort service. Video packet can be regarded as a packet loss by the video decoder either due to network congestion or due to exceeding the maximum delay threshold. It remains an open challenging task as to how to cope with the packet loss in the video streaming over the Int…