景观动态的Markov模型研究——以长白山自然保护区为例

在 Markov模型假设的基础上 ,利用长白山自然保护区 1975年 MSS、1985年和 1997年 TM卫星遥感数据 ,在遥感图象处理软件和 GIS软件协助下 ,对遥感影像的计算机监督分类结果 (共分为 13类 )进行处理 ,对 Markov模型的可利用性进行分析与检验 ,得出长白山自然保护区景观变化无后效性 ,符合 Markov模型条件。根据 1985～ 1997年转移概率计算步长 10 a(1985～ 1995年 )的转移概率矩阵 ,从 1975年计算 1985年各景观类型的面积与 1985年各景观类型的实际面积值对比 ,计算得 χ2 >χ20 .0 5(12 ) ;再分别用 1975～ 1985年和 1985～ 1997年的转移矩阵计算 1995年和 2 0 4 7年各景观类型的面积 ,分析得χ2 >χ20 .0 5(12 ) ;对两阶段的转移概率矩阵分析得到 χ2 >χ20 .0 5(14 4 ) ;说明两阶段的 Markov转移过程不具同一性 ,属于两个不同的 Markov过程。不同景观类型转移方式对χ2 值的贡献率可以说明其对景观动态的重要性 ,分析结果表明有重要贡献的类型分别为 :阔叶红松林 5 2 .0 0 % >山杨白桦林 2 4 .6 6 % >云冷杉林 11.4 2 % >落叶松林 2 .4 3% ,说明这 4种景观类型的转移方式对长白山自然保护区的景观动态起重要作用 ,尤其以阔叶松林的作用最大 ;同时对 Markov模型在长白山自然保护区长期景观变化预测的可

Realization of negative differential resistance and switching devices based on copper phthalocyanine by the control of evaporation rate

We have observed, respectively, a negative differential resistance (NDR) and switching conduction in current-voltage (I-V) characteristics of organic diodes based on copper phthalocyanine (CuPc) film sandwiched between indium-tin-oxide (ITO) and aluminum (Al) by controlling the evaporation rate. The NDR effect is repeatable which can be well, controlled by sweep rate and start voltage, and the switching exhibits write-once-read-many-times (WORM) memory characteristics. The traps in the organic layer and interfacial dipole have been used to explain the NDR effect and switching conduction. This opens up potential applications for CuPc organic semiconductor in low power memory and logic circuits.

Electrically-controlled near-infrared chiroptical switching of enantiomeric dinuclear ruthenium complexes

Enantiomerically pure dinuclear ruthenium complexes with 1,2-dicarbonylhydrazide as a bridging ligand are optically active in the visible and near infrared spectral regions depending on the oxidation states of the metal centers and are useful as an electrochemically driven near infrared chiroptical switch.

Comparison of photobioreactors and optimal light regime for rapid vegetative propagation of transgenic Undaria pinnatifida gametophytes

BACKGROUND: Previously, tachyplesin gene (tac) has been successfully transferred into Undaria pinnatifida gametophytes using the method of microprojectile bombardment transformation. The objectives of this study were to compare and evaluate the performance of bubble-column and airlift bioreactors to determine a preferred configuration of bioreactor for vegetative propagation of transgenic U. pinnatifida gametophytes, and to then investigate the influence of light on vegetative propagation of these gametophytes, including incident light intensity, photoperiod and light quality to resolve the problems of rapid vegetative propagation within the selected bioreactor. RESULTS: Experimental results showed that final dry cell density in the airlift bioreactor was 12.7% higher than that in the bubble-column bioreactor under the optimal aeration rate of 1.2 L air min(-1) L-1 culture. And a maximum final dry cell density of 2830 mg L-1 was obtained within the airlift bioreactor using blue light at 40 mu mol m(-2) s(-1) with a light/dark cycle of 14/10 (h). Polymerase chain reaction (PCR) analysis indicated that genes (bar and tac) were not lost during rapid vegetative propagation within the airlift bioreactor. CONCLUSION: The airlift bioreactor was shown to be much more suitable for rapid vegetative propagation of transgenic U. pinnatifida gametophytes than the bubble-column bioreactor in the laboratory. The use of blue light allows improvement of vegetative propagation of transgenic U. pinnatifida gametophytes. (C) 2009 Society of Chemical Industry

Optimal light regime for the cultivation of transgenic Laminaria japonica gametophytes in a bubble-column bioreactor

Fluctuating light intensity had a more significant impact on growth of gametophytes of transgenic Laminaria japonica in a 2500 ml bubble-column bioreactor than constant light intensity. A fluctuating light intensity between 10 and 110 mu E m(-2) s(-1), with a photoperiod of 14 h:10 h light:dark, was the best regime for growth giving 1430 mg biomass l(-1).

Intra-habitat heterogeneity of microbial food web structure under the regime of eutrophication and sediment resuspension in the large subtropical shallow Lake Taihu, China

Planktonic microbial community structure and classical food web were investigated in the large shallow eutrophic Lake Taihu (2338 km(2), mean depth 1.9 m) located in subtropical Southeast China. The water column of the lake was sampled biweekly at two sites located 22 km apart over a period of twelve month. Site 1 is under the regime of heavy eutrophication while Site 2 is governed by wind-driven sediment resuspension. Within-lake comparison indicates that phosphorus enrichment resulted in increased abundance of microbial components. However, the coupling between total phosphorus and abundance of microbial components was different between the two sites. Much stronger coupling was observed at Site 1 than at Site 2. The weak coupling at Site 2 was mainly caused by strong sediment resuspension, which limited growth of phytoplankton and, consequently, growth of bacterioplankton and other microbial components. High percentages of attached bacteria, which were strongly correlated with the biomass of phytoplankton, especially Microcystis spp., were found at Site 1 during summer and early autumn, but no such correlation was observed at Site 2. This potentially leads to differences in carbon flow through microbial food web at different locations. Overall, significant heterogeneity of microbial food web structure between the two sites was observed. Site-specific differences in nutrient enrichment (i.e. nitrogen and phosphorus) and sediment resuspension were identified as driving forces of the observed intra-habitat differences in food web structure.

Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.

Column switching-back flushing technique for the analysis of aromatic compounds in gasoline

A simple method, based on the technique of capillary column switching-back flushing, has been developed for the detailed analysis of aromatic compounds in gasoline. The sample was first separated on a 30-m long OV-2330 polar precolumn and then backflushed onto a nonpolar analytical column. The early eluting components from the precolumn and the components of interest (aromatic compounds plus heavier compounds) eluting from the analytical column are all directed to the same flame ionization detection system through a T piece, which permits the quantitative analysis of aromatic hydrocarbons in gasoline by a normalization method using correcting factors. The switching time window of the method is +/-5 s, resulting in easier operation and higher reliability. The reproducibility of the quantitative analysis was less than or equal to3% RSD for real gasoline samples. (C) 2002 Elsevier Science B.V. All rights reserved.

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

Switching Between Discrete and Continuous Process Models to Predict Molecular Genetic Activity

Two kinds of process models have been used in programs that reason about change: Discrete and continuous models. We describe the design and implementation of a qualitative simulator, PEPTIDE, which uses both kinds of process models to predict the behavior of molecular energetic systems. The program uses a discrete process model to simulate both situations involving abrupt changes in quantities and the actions of small numbers of molecules. It uses a continuous process model to predict gradual changes in quantities. A novel technique, called aggregation, allows the simulator to switch between theses models through the recognition and summary of cycles. The flexibility of PEPTIDE's aggregator allows the program to detect cycles within cycles and predict the behavior of complex situations.