Random walk modeling of wake dispersion for the exhaust tower of an underground tunnel in urban area

In this paper, some experimental studies on the impact of effluent from an exhaust tower of an underground tunnel with special construction are reported. By measuring the flow field downstream of the tower in NJU meteorological wind tunnel, some flow characteristics in the make area were established. Based on these, an advanced random\|walk dispersion model was set up and applied successfully to the simulation of dispersion in the wake area. The modelling results were in accordance with wind tunnel measurements. The computed maximum of ground surface concentration in the building case was a factor of 3-4 higher than that in the flat case and appeared much closer to the source. The simulation indicated that random walk modelling is an effective and practical tool for the wake stream impact assessment.

Application of random walk model to fit temperature in 46 gamma world cities from 1901 to 1998

Very recently, we have applied the random walk model to fit the global temperature anomaly, CRUTEM3. With encouraging results, we apply the random walk model to fit the temperature walk that is the conversion of recorded tem-perature and real recorded temperature in 46 gamma world cities from 1901 to 1998 in this study. The results show that the random walk model can fit both temperature walk and real recorded temperature although the fitted results from other climate models are unavailable for comparison in these 46 cities. Therefore, the random walk model can fit not only the global temperature anomaly, but also the real recorded temperatures in various cities around the world.

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.

Development and application of a new random walk model to simulate the transport of degradable pollutants

To simulate the pollutant transport with seif-purification in inland waters,the widely used random walk model(RWM)is modified to include a source term for the degradation and to consider the impact of land boundaries.The source term for the degradation is derived from the assumption of the first-order reaction kinetics.Parameters for the new model are determined by a comparison to the analytical results.The proposed model is then applied to simulate and analyze the transport of a test pollutant and its spatial distribution in a large reservoir in northeast China.Reasonable results are obtained,and the effects of the runoff,the flow structure,and the wind on the pollutant transport are analyzed.The results may help the risk assessment and the management of the water pollution in inland waters.

Modelling of the behavior of marine oil spills: applications based on random walk techniques

A numerical model has been developed to simulate the transport and fate of oil spilled at sea. The model combines the transport and fate processes of spilled oil with the random walk technique. Oil movement under the influence of tidal currents, wind driven currents, and turbulent eddies is simulated by the PLUME RW dispersion model developed by HR Wallingford. The weathering processes in the model represent physical and chemical changes of soil slicks with time, and comprise mechanical spreading, dispersion, evaporation and emulsification. Shoreline stranding is determined approximately using a capacity method for different shoreline types. This paper presents details of the model, and describe the results of various sensitivity tests. The model is suitable for oil spill contingency planning.

Scheduling trains on railway network using random walk method

According to random walk, in this paper, we propose a new traffic model for scheduling trains on a railway network. In the proposed method, using some iteration rules for walkers, the departure and the arrival times of trains at each station are determined. We test the proposed method on an assumed railway network. The numerical simulations and the analytical results demonstrate that the proposed method provides an effective tool for scheduling trains. Some characteristic behaviours of train movement can be reproduced, such as train delay.

An Improved Walk Model for Train Movement on Railway Network

在这篇论文，我们为在铁路网络上模仿火车运动建议一个改进散步模型。在建议方法，行人们代表火车。改进散步模型是一种基于网络的模拟分析模型。用行人运动的一些管理规则，行人能动态地在车站决定它的离开和到达时间。以便测试建议方法，我们在铁路网络的部分上模仿火车运动。数字模拟和分析结果证明改进模型是为在铁路网络上模仿火车运动的一个有效工具。而且，它能很好捕获在铁路交通安排的火车的典型行为。

Random walk immunization strategy on scale-free networks

A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows： a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.

Imperfect Trapping in a Random Walk with Both Species Mobile

It is presented here a continuous time random walk model for diffusion mediated reactions with both species mobile. The random walk is carried out over an infinite homogeneouos lattice. They are calculated the probability density for the time of reaction of a pair, the reaction rate and the time evolution of the concentration of the majority species. Analytical results are obtained in the Fourier-Laplace transform representation. Known results for a fixed trap are reobtained with appropriate marginal probabilities. It is thus justified Smoluchowski’s original approximation considering the trap at a fixed position and the majority species diffusing with a coefficient sum of the individual coefficients. The results obtained are illustrated by a one dimensional model with bias.

基于环境中移动运输代理的传感器网络建模

针对大型稀疏传感器网络中的数据获取,本文提出了一种利用环境中普遍存在的移动代理来连接稀疏传感器的网络体系结构和一种2-维网格随机游走分析模型;提出的传感器网络模型由3个抽象层构成,即由无线传感器构成的底层、由各种运输代理构成的中间层和由接入点/中央存储库构成的顶层。具体实现原理是位于中间层的移动运输代理从底层分布的无线传感器收集数据并缓冲数据,然后经过游走运输,最后将从底层的无线传感器收集的数据交付到顶层必要的接入点进行必要的存储和处理,从而实现整个传感器网络的数据获取;理论分析和仿真实验结果表明,提出的基于移动运输代理的传感器网络模型不仅具有较好的鲁棒性和可扩展性,而且相比于基站网络模型和Ad-hoc网络模型,在传感器功率消耗、数据成功率和基础设施投入成本方面有明显的优势。

基于随机游走麻雀搜索算法的多特征结构尺寸熔融沉积成型工艺参数优化

在熔融沉积成型过程中,打印参数对成型样件精度有着重要影响.为了提高整体尺寸精度,采用随机游走的麻雀算法获得最优实验方案.首先,以熔融沉积成型的分层厚度、喷头温度、打印速度和填充率为实验变量设计4因素4水平的正交试验;然后,以样件不同特征结构尺寸的相对误差为优化对象,使用田口-灰色关联法对实验数据进行处理;最后,通过随机游走的麻雀算法计算最优参数方案.结果表明,相比常用的田口-灰色关联法,采用优化后工艺参数成型样件的综合尺寸精度提高了20%,灰色关联度提高了27%.

基于随机行走模型的人致振动舒适度研究

为研究人群密度对人行桥结构的振动影响,首先,建立了双向随机行走模型,推导了随机荷载作用下结构振动响应解析解;然后,对比分析了人群密度对结构振动响应的影响规律;最后,通过研究行人实感时程与总时程之间的相关关系,定义了舒适度放大系数,以荆州市某人行天桥为例,进行振动舒适度评价,验证了双向随机行走模型及舒适度放大系数的适用性。结果表明:人行桥结构加速度响应随人群密度增大呈现出先增大后减小的规律;行人实感时程与总时程具有显著的相关性,根据舒适度放大系数可建立对应关系;舒适度放大系数随着振动响应峰值的增大而减小,随着人群密度的增大而增大;该类结果对人行桥振动舒适度定量计算具有参考借鉴意义。

隐蔽线索对基于时间前瞻记忆的影响

本研究通过三个实验,从不同角度考察隐蔽线索对基于时间前瞻记忆的影响,以期进一步了解基于时间前瞻记忆的加工特点。实验1A旨在考察不同进行中任务线索条件下基于时间前瞻记忆的表现差异;实验1B旨在考察不同干扰任务条件下的线索对基于时间前瞻记忆的影响;实验2旨在考察嵌入不同的前瞻记忆任务线索和进行中任务线索对基于时间前瞻记忆的影响。结果表明:(1)适当的隐蔽线索对个体的基于时间前瞻表现具有促进作用。对于提示回溯成分的隐蔽线索,时间线索无论隐藏在进行中任务中还是干扰任务中都显著提高被试基于时间前瞻记忆的表现。(2)与出现在干扰任务阶段中的线索相比,出现在进行中任务阶段中的线索更能提高个体基于时间前瞻记忆的表现,说明线索越临近目标时间,促进效应越大。(3)基于时间范式的线索能够促进基于时间前瞻记忆表现,基于事件的线索则不能。本研究结果支持随机游动模型。

A time fractional model to represent rainfall process

This paper deals with a stochastic representation of the rainfall process. The analysis of a rainfall time series shows that cumulative representation of a rainfall time series can be modeled as a non-Gaussian random walk with a log-normal jump distribution and a time-waiting distribution following a tempered a-stable probability law. Based on the random walk model, a fractional Fokker-Planck equation （FFPE） with tempered a-stable waiting times was obtained. Through the comparison of observed data and simulated results from the random walk model and FFPE model with tempered a-stable waiting times, it can be concluded that the behavior of the rainfall process is globally reproduced, and the FFPE model with tempered a-stable waiting times is more efficient in reproducing the observed behavior.

富血小板纤维蛋白对拔牙术后角化龈宽度以及牙龈软组织增量的影响

目的:探究拔牙术后应用富血小板纤维蛋白(Platelet-rich fibrin,PRF)对患者角化龈宽度及牙龈软组织增量的影响。方法:选择2020年12月-2021年12月于笔者医院行拔牙术的90例患者为研究对象,采用随机数字表法分为对照组(45例)和观察组(45例)。拔牙后,对照组采用新鲜血凝块填充拔牙窝,观察组采用PRF凝胶填充。比较两组患者拔牙创术区黏膜愈合时间、术后3 d和7 d的愈合率、黏膜颜色分级、肿胀程度分级、视觉模拟评分(Visual analogue score,VAS);比较两组患者术前及术后6个月的角化龈宽度、牙龈软组织增量。随机行走模型评价PRF的应用对患者上述指标的影响。比较两组患者术后并发症发生情况。结果:术后,观察组黏膜愈合时间明显短于对照组(P<0.05);术后3 d和7 d,观察组的黏膜愈合率明显高于对照组,黏膜颜色分级、肿胀程度分级、VAS评分明显低于对照组(P<0.05);术后6个月,两组患者的角化龈宽度明显高于术前,牙龈软组织增量明显低于术前,且观察组角化龈宽度、牙龈软组织增量明显低于观察组(P<0.05)。随机行走模型结果显示,观察组术后VAS评分、角化龈宽度改善显著,观察组能够维持患者牙龈软组织增量的相对稳定。观察组术后并发症发生率明显低于对照组(P<0.05)。结论:拔牙术后应用PRF能够促进拔牙创术区黏膜的愈合,减轻患者术后疼痛,保持角化龈宽度及牙龈软组织增量。

On Mixed Model for Improvement in Stock Price Forecasting

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low accuracy,slow convergence,and complex parameters.This study aims to employ a mixed model to improve the accuracy of stock price prediction.We present how to use a random walk based on jump-diffusion,to obtain stock predictions with a good-fitting degree by adjusting different parameters.Aimed at getting better parameters and then using the time series model to predict the data,we employed the time series model to smooth the sequence utilizing logarithm and difference,which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure.According to the comparative analysis,which focuses on checking the mean absolute error,including root mean square error and R square evaluation index,we have drawn a clear conclusion that our mixed model prediction effect is relatively good.In the context of Chinese stocks,the hybrid random walk model is very suitable for predicting stocks.It can“interpret”the randomness of stocks very well,and it also has an unparalleled prediction effect compared with other models.Based on the time series model’s application in this paper,the abovementioned series is more suitable for predicting trends.

Construction of Clonal Growth Models in Trifolium repens

The suitability of models for describing the clonal growth of Trifolium repens population was discussed. The results showed that deterministic models were inadequate for describing its clonal growth, but the diffusion models and the randomwalk models suited for the clonal growth characteristics of the population. And it was found that random-walk models were better than diffusion models for describing a population in an environment with rich natural resources, and the latter was better in a poor environment.

Sensitivity of Lagrangian Particle Tracking Based on a 3D Numerical Model

The aim of the paper is to investigate in detail the sensitivity of particles displacement based on method of Lagrangian particle tracking in combination with a 3D Eulerian numerical model that was developed by the first author, namely FSUM. The characteristic parameters used for this research include the possibilities of random movement, settling velocity of solid particle, horizontal and vertical diffusion coefficients and condition of particle fixed with a constant distance under water surface. The first part is on the fluid flow model. It includes 3D Navier-Stokes equations together with the initial and boundary conditions that were numerically solved with the finite difference method and coded with FORTRAN 90/95 using parallel technique with OpenMP. A semi-Lagrangian treatment of the advective terms was used. The second part is related to Lagrangian particle tracking model and was solved with the fourth Runge-Kutta method. Model was applied for Strait of Johor and has been calibrated by using measured data on water level and velocity at one station. Eight cases of simulations with many different options were carried out. Through computed cases it shows that random term and settling velocity are very important factors for the behavior of particle trajectory. Although the random diffusion is minor in comparison with flow velocity, but it can rearrange the initial distribution of particles then the cluster of particles become more dispersive during the process of movement. In addition, introducing settling velocity of particle makes a big change on the trajectory of particle that becomes more suitable to sediment transport. The study gave a comprehensive picture on particle movement. The model also showed its possibilities of multiform applications in simulation and prediction for the different problems in practice.