基于真实世界中风痰热腑实的证候演变规律研究

目的:通过分析中风痰热腑实证患者在中医治疗下的证候演变,探索中风痰热腑实的发生及演变规律,以期实现临床精确干预。方法:检索北京中医药大学东直门医院1990年1月1日—2020年1月1日以中风为第一诊断、发病72 h以内入院且本次住院时间累计超过30 d的患者的住院资料,病程中出现痰热腑实证的患者为痰热腑实证组,共169例,运用秩和检验及转移率矩阵分析痰热腑实的证候演变规律。结果:(1)始发状态为痰热证的患者更易出现痰热腑实(87.6%),尤其在中脏腑患者里更为突出(95.5%),而未出现痰热腑实证的患者,其始发状态以风痰瘀血痹阻脉络为主(51%);(2)痰热腑实的演变规律大致为痰热证—痰热腑实证—痰瘀阻络证—气虚证,但是中经络与中脏腑的演变规律存在部分差异。不同的证候发展阶段对于疾病总体存在不同意义。结论:痰热腑实证遵循着固定的发生及演变规律,合理地应用演变规律,可以优化治疗方案,实现精准干预。

基于RS与GIS支持下的定边县土地利用变化分析与发展趋势研究

利用遥感和地理信息系统方法,计算出了定边县1986～2000年土地利用类型转移概率矩阵,初步分析了土地利用的数量变化和各类型间的转化特征,并利用马尔可夫链模型(Markov Chain Model)对今后数年(2015、2030年)土地利用动态变化的趋势进行了定量预测研究。结果表明:2000～2030年,定边县耕地、草地年变化率下降,未利用土地和水域的年变化率基本持平,建设用地增加1354.58hm2,年变化率为0.83%,增加趋势显著。定边县土地利用类型之间发生了较为复杂的变化,土地利用向良性化发展,少量草地又有沙化趋势。随着人口增加和经济的发展,建设用地增加趋势显著。

Waveguide Optimization for a 9.0μm GaAs-Based Quantum Cascade Laser

Improved waveguide designs for 9.0μm GaAs-based quantum cascade laser （QCL） structures are presented. Modal losses and confinement factors are calculated for TM modes with the transfer matrix method （TMM） and effective index method （EIM）. The thicknesses of the cladding layer and waveguide layer, the ridge-width, and the cavity length are all taken into account. Appropriate thicknesses of epilayers are given with lower threshold gain and more economical material growth time.

An Improved Markov Chain Model Based on Autocorrelation and Entropy Techniques and Its Application to State Prediction of Water Resources

According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

Analysis of the flexural vibration of ship's tail shaft by transfer matrix method

A ship's tail shaft has serious flexural vibration due to the cantilevered nature of the propeller's blades.Analysis of the nature frequency of flexural vibration is vital to be able to provide effective shock absorption for a ship's tail shaft.A mathematic model of tail shaft flexural vibrations was built using the transfer matrix method.The nature frequency of flexural vibration for an electrically propelled ship's tail shaft was then analyzed,and an effective method for calculating it was proposed:a genetic algorithm(GA),which calculates the nature frequency of vibration of a system.Sample calculations,with comparisons by the Prohl method under conditions bearing isotropic support,showed this method to be practical.It should have significant impact on engineering design theory.

Two-way Markov random walk transductive learning algorithm

Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.

DTMC-based Modeling and Analysis of Obstacle Ad hoc Networks

One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.

The Convergence of the Abstract Evolutionary Algorithm Based on a Special Selection Mechanism

There has been a growing interest in mathematical models to character the evolutionary algorithms. The best-known one of such models is the axiomatic model colled the abstract evolutionary algorithm. In this paper, we first introduce the definitions of the abhstract selection and evolution operators, and that of the abstract evolutionary algorithm, which describes the evolution as an abstract stochastic process composed of these two fundamental abstract operators. In particular, a kind of abstract evolutionary algorithms based on a special selection mechansim is discussed. According to the sorting for the state space, the properties of the single step transition matrix for the algorithm are anaylzed. In the end, we prove that the limit probability distribution of the Markov chains exists. The present work provides a big step toward the establishment of a unified theory of evolutionary computation.

基于SMP状态空间等值的智能变电站保护系统短期可靠性评估

电气设备短期可靠性为电力系统风险评估提供数据支持,状态空间法中传统转移频率守恒等值算法基于稳态指标定义,不适用于短期可靠性评估,故需进行改进.本文研究等值条件,首次证明满足等值条件的系统等值后仍为马尔可夫过程,暂态状态概率可由矩阵相乘得到.不满足等值条件的系统,提出用半马尔可夫过程描述.确立等值前后转移率及分布函数的关系,推导半马尔可夫核矩阵与等值后转移率、分布函数的关系,以矩阵描述形式求得暂态状态概率和灵敏度指标.运用所提算法对一智能变电站保护系统进行短期可靠性评估,结果表明传统算法低估系统可用率,暂态灵敏度的峰值和出现时间也存在误差.初始状态影响系统暂态可用率以及暂态灵敏度的收敛速度.

多优先级与离开机制并存的M/M/c/N排队系统

生产中重要设备发生故障后需要及时维修,普通部分设备会因等待时间过长而离开维修系统,因此需要建立设备多优先级,并存且包含离开机制的排队系统模型。在马尔科夫过程理论基础上结合分块理论,得出分块的系统状态转移率矩阵。针对状态转移率矩阵为块三对角矩阵的特点,利用分块矩阵理论得到稳态下的状态概率以及状态转移频度。在此基础上获得关键状态转移频度、待修时间等系统运行指标,并据此提出系统盈利计算方法。最后以某汽车维修厂为实例,验证该模型在实际生产中的有效性。所提的排队系统建模方法通过引入稳态状态转移频度,建立了完备的系统指标体系,为系统配置优化提供了理论基础。

Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

Scheduling is a major concern in construction planning and management, and current construction simulation research typically targets the shortest total duration. However, uncertainties are inevitable in actual construction, which may lead to discrepancies between the actual and planned schedules and increase the risk of total duration delay. Therefore, developing a robust construction scheduling technique is of vital importance for mitigating disturbance and improving completion probability. In the present study, the authors propose a robustness analysis method that involves underground powerhouse construction simulation based on the Markov Chain Monte Carlo(MCMC) method. Specifically, the MCMC method samples construction disturbances by considering the interrelationship between the states of parameters through a Markov state transition probability matrix, which is more robust and efficient than traditional sampling methods such as the Monte Carlo(MC) method. Additionally, a hierarchical simulation model coupling critical path method(CPM) and a cycle operation network(CYCLONE) is built, using which construction duration and robustness criteria can be calculated. Furthermore, a detailed measurement method is presented to quantize the robustness of underground powerhouse construction, and the setting model of the time buffer is proposed based on the MCMC method. The application of this methodology not only considers duration but also robustness, providing scientific guidance for engineering decision making. We analyzed a case study project to demonstrate the effectiveness and superiority of the proposed methodology.

THE TRANSITION PROBABILITY MATRIX OF A MARKOV CHAIN MODEL IN AN ATM NETWORK

In this paper we consider a Markov chain model in an ATM network, which has been studied by Dag and Stavrakakis. On the basis of the iterative formulas obtained by Dag and Stavrakakis, we obtain the explicit analytical expression of the transition probability matrix. It is very simple to calculate the transition probabilities of the Markov chain by these expressions. In addition, we obtain some results about the structure of the transition probability matrix, which are helpful in numerical calculation and theoretical analysis.

Effects Of Average Index Variation in Apodized Long-Period Fiber Gratings

We analyze the effects of average index variation on the transmission characteristics of an index-apodized long-period fiber grating （LPFG） by the transfer matrix method and study how these effects depend on the grating length, the grating profile, the modal dispersion factor, and the duty cycle of the index modulation. Apart from shifting the resonance wavelength and modifying the rejection band, average index variation can give rise to significant side lobes that may appear on the short-wavelength or long-wavelength side of the rejection band, depending on the signs of the average index change and the modal dispersion factor. Our results provide general guidance for the writing of LPFGs for the minimization of side lobes. Our analysis compares well with published experimental results and should be useful for the design and fabrication of LPFGs.