循环经济模式下的资源利用问题分析

基于循环经济的3R原则,本文将厂商的输入资源按其来源分为原生资源、回用资源和循环资源3种类型,在一系列假设条件下,利用转移矩阵描述了生态产业链上资源回用和梯次传递的关系,进而从总量上对厂商的资源利用问题进行了简化分析,为循环经济的效果分析提供了一种数量分析的基本框架.

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.

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.