Lu Zhonghua

Microwave synthesis of LiCoO2 cathode materials

LiCoO2 is synthesized by microwave heating. It has high capacity and good cycleability. The advantages of microwave synthesis are: (i) the calcination process is very fast; (ii) the synthesized powders have small and uniform grains; (iv) the synthesis temperature can be lower, and (iv) the lithium oxide loss is smaller

Electrochemical and X-ray photospectroscopy studies of polytetrafluoroethylene and polyvinylidene fluoride in Li/C batteries

The stability of polytetrafluorethylene (PTFE) and polyvinylidene fluoride (PVDF) used as the anode binders in lithium-ion batteries was studied by electrochemical and X-ray photospectroscopy (XPS) measurements. The characteristic discharge/charge curves for PTFE and PVDF were obtained. It is shown that the behavior of PTFE and PVDF exhibits much discrepancy when they are attacked by high active lithium ion in electrochemical cells. PTFE reacts with lithium easily and decomposes during the first discharge process, which is indicated by a long plateau appearing around 1.2 V in the first discharge curve of Li/Ni(PTFE) cell. XPS results show that PTFE decomposes after one discharge/charge cycle. PVDF is rather stable due to a possible surface passivation process.

Magnetic properties of Sm2Fe17Ny with Al substituted for Fe

By means of X-ray diffraction analysis, it is shown for x < 0.4 that all the interstitial Sm-2(Fe1-xAlx)(17)N-y nitrides and the parent compounds crystallize with Th2Zn(17)-type structure. The lattice constants of the parent compounds increase linearly with Al concentration. The introduction of nitrogen atoms leads to a further increase in the lattice constants, but the increase becomes smaller with increasing Al concentration; this can be related to the fact that the nitrogen content introduced into the parent compounds decreases nearly linearly with increasing Al concentration. The dependence on composition of T-c of the parent compounds exhibits a maximum, whereas T-c of the nitrides decreases monotonically with Al content from 750 K for x=0 to 313 K for x=0.4. The introduction of nitrogen leads to enhancement of the average magnetic moment of Fe in Sm2Fe17, whereas the substitution of Al for Fe leads to a decrease in the average magnetic moment of Fe in both the nitrides and the parent compounds. The anisotropy field B-a of nitrides is almost independent of the Al concentration for x less than or equal to 0.3, then decreases very fast and becomes zero at about x = 0.4.

Global asymptotic stability of the periodic Lotka-Volterra System with two-predators and one-prey

The three species Lotka-Volterra periodic model with two predators and one prey is considered. A set of easily verifiable sufficient conditions is obtained. Finally, and example is given to illustrate the feasibility of these conditions.

Large-Scale Parallel Simulation of High-Dimensional American Option Pricing

High-dimensional American option pricing is computationally challenging in both theory and practice. We use stochastic mesh method combined with performance enhancement policy of bias reduction to solve this practical problem in classic Black-Scholes framework. We effectively parallelize this algorithm through splitting the generated mesh by row among processors, use MPI for efficient implementation, and perform large-scale numerical experiments on heterogeneous supercomputer DeepComp7000. Numerical results of parallel simulation demonstrate that parallel simulation has good scalability in different parallel environments of DeepComp7000; large-scale parallel simulation can obtain much better speedup. The convergent performance is also empirically demonstrated. The estimated option value converges with the increase of mesh size; when using smaller mesh size, the stochastic mesh method with bias reduction can underestimate the true American option value.

The Applications and Trends of High Performance Computing in Finance

Large-scale parallel simulation and modeling have changed our world. Today, supercomputers are not just for research and scientific exploration; they have become an integral part of many industries, among which finance is one of the strongest growth factors for supercomputers, driven by ever increasing data volumes, greater data complexity and significantly more challenging data analysis. In this paper, a modest application of the developments of high-performance computing in finance is studied deeply. Attentions are not only focused on the what benefits the parallel algorithm bring to the financial research, but also on the practical applications of the High-Performance Computing in real financial markets, especially some recent advances is highlighted. On that basis, some suggestions about the challenges and development directions of HPCs in finance are proposed.

Parallel Computing for Dynamic Asset Allocation Based on the Stochastic Programming

In this paper, a multi-stage stochastic programming model is constructed, for the dynamic asset allocation with the transaction cost constraints. In the mean time in order to improve the performance, the Conditional Value-at-Risk as the risk measure, which is a very important concept in the modern risk management field, is also contained. However, with the increase of the number of scenarios, the number of constrains and decisions variable is increasing dramatically. It turns out that the memory management is a major bottleneck when solving planning problems. For this reason, this paper shows that the dedicated model generations, and the specialized solution techniques based on high performance computing, are the essential elements to tackle this large-scale financial planning. The parallel code is programmed by the C language, and the Message Passing Interface (MPI) for communication is utilized. The parallel and financial performance is performed on the DeepComp7000.

Research on Pseudoreceptor Models for the Inhibitors at GABA Receptors via Flexible Atom Receptor Model

A selective pseudoreceptor models for the inhibitors at GABA receptors of fly and rat were built via Flarm program. The pseudoreceptor models simulated the receptors very well and had good predicting ability. The q(2) values of the training sets were 0.874 and 0.897, and the r(2) values of the predict sets were 0.962 and 0.733. The Flarm models predicted that there are five binding sites when the compounds bind with GABA receptors, but there might be some different favoritism between the GABA receptors of fly and rat. The results were in accordance with pharmacorphore models built in previous research, and all the results gave insight to find the rela-tions and differences between the inhibitors acting on the GABA receptor of fly and rat.

Research of Acceleration MS-Alignment Identifying Post-Translational Modifications on GPU

MS-Alignment is an unrestrictive post-translational modification (PTM) search algorithm with an advantage of searching for all types of PTMs at once in a blind mode. However, it is time-consuming, and thus it could not well meet the challenge of large-scale protein database and spectra. We use Graphic Processor Unit (GPU) to accelerate MS-Alignment for reducing identification time to meet time requirement. The work mainly includes two parts. (1) The step of Database search and Candidate generation (DC) consumes most of the time in MS-Alignment. We propose an algorithm of DC on GPU based on CUDA (DCGPU). The data parallelism way is partitioning protein sequences. We adopt several methods to optimize DCGPU implementation. (2) For further acceleration, we propose an algorithm of MS-Alignment on GPU cluster based on MPI and CUDA (MC_MS-A). The comparison experiments show that the average speedup ratio could be above 26 in the model of at most one modification and above 41 in the model of at most two modifications. The experimental results show that MC_MS-A on GPU Cluster could reduce the time of identifying 31173 spectra from about 2.853 months predicted to 0.606 h. Accelerating MS-Alignment on GPU is applicable for large-scale data requiring for high-speed processing.