Probabilistic Model Checking-Based Survivability Analysis in Vehicle-to-Vehicle Networks

Probabilistic model checking has been widely applied to quantitative analysis of stochastic systems, e.g., analyzing the performance, reliability and survivability of computer and communication systems. In this paper, we extend the application of probabilistic model checking to the vehicle to vehicle(V2V) networks. We first develop a continuous-time Markov chain(CTMC) model for the considered V2V network, after that, the PRISM language is adopted to describe the CTMC model, and continuous-time stochastic logic is used to describe the objective survivability properties. In the analysis, two typical failures are considered, namely the node failure and the link failure, respectively induced by external malicious attacks on a target V2V node, and interrupt in a communication link. Considering these failures, their impacts on the network survivability are demonstrated. It is shown that with increasing failure strength, the network survivability is reduced. On the other hand, the network survivability can be improved with increasing repair rate. The proposed probabilistic model checking-based approach can be effectively used in survivability analysis for the V2V networks, moreover, it is anticipated that the approach can be conveniently extended to other networks.

Dynamic probabilistic analysis of stress and deformation for bladed disk assemblies of aeroengine

In order to describe and control the stress distribution and total deformation of bladed disk assemblies used in the aeroengine, a highly efficient and precise method of probabilistic analysis which is called extremum response surface method(ERSM) is produced based on the previous deterministic analysis results with the finite element model(FEM). In this work, many key nonlinear factors, such as the dynamic feature of the temperature load, the centrifugal force and the boundary conditions, are taken into consideration for the model. The changing patterns with time of bladed disk assemblies about stress distribution and total deformation are obtained during the deterministic analysis, and at the same time, the largest deformation and stress nodes of bladed disk assemblies are found and taken as input target of probabilistic analysis in a scientific and reasonable way. Not only their reliability, historical sample, extreme response surface(ERS) and the cumulative probability distribution function but also their sensitivity and effect probability are obtained. Main factors affecting stress distribution and total deformation of bladed disk assemblies are investigated through the sensitivity analysis of the model. Finally, compared with the response surface method(RSM) and the Monte Carlo simulation(MCS), the results show that this new approach is effective.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

A select link analysis method based on logit–weibit hybrid model

Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.

Review: Recent Developments in the Non-Probabilistic Finite Element Analysis

Generally, the finite element analysis of a structure is completed under deterministic inputs.However,uncertainties corresponding to geometrical dimensions,material properties, boundary conditions cannot be neglected in engineering applications. The probabilistic methods are the most popular techniques to handle these uncertain parameters but subjective results could be obtained if insufficient information is unavailable. Non-probabilistic methods can be alternatively employed,which has led to the procedures for nonprobabilistic finite element analysis. Each non-probabilistic finite element analysis method consists of two individual parts,including the core algorithm and pre-processing procedure. In this context,three types of algorithms and two typical pre-processing procedures as well as their effectiveness are described in detail,based on which novel hybrid algorithms can be conceived for the specific problems and the future work in this research field can be fostered.

Data Analysis of Multiplex Sequencing at SOLiD Platform:A Probabilistic Approach to Characterization and Reliability Increase

New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.

Assessing citizen science opportunities in forest monitoring using probabilistic topic modelling

Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of ＂citizen scientists＂ can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. Methods： We conduct a systematic meta-analysis of 1015 publication abstracts addressing ＂forest monitoring＂ and ＂citizen science＂ in order to explore the combined topical landscape of these subjects. We employ ＇topic modelling an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. Results： We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on ＂forest monitoring＂ and ＂citizen science＂ and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. Conclusions： We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.

A Probabilistic Description of the Impact of Vaccine-Induced Immunity in the Dynamics of COVID-19 Transmission

The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.

Probabilistic seismic landslide hazard assessment: a case study in Tianshui, Northwest China

Probabilistic analysis in the field of seismic landslide hazard assessment is often based on an estimate of uncertainties of geological, geotechnical,geomorphological and seismological parameters.However, real situations are very complex and thus uncertainties of some parameters such as water content conditions and critical displacement are difficult to describe with accurate mathematical models. In this study, we present a probabilistic methodology based on the probabilistic seismic hazard analysis method and the Newmark’s displacement model. The Tianshui seismic zone(105°00′-106°00′ E, 34°20′-34°40′ N) in the northeastern Tibetan Plateau were used as an example. Arias intensity with three standard probabilities of exceedance(63%, 10%, and 2% in 50 years) in accordance with building design provisions were used to compute Newmark displacements by incorporating the effects of topographic amplification.Probable scenarios of water content condition were considered and three water content conditions(dry,wet and saturated) were adopted to simulate the effect of pore-water on slope. The influence of 5 cm and 10 cm critical displacements were investigated in order to analyze the sensitivity of critical displacement to the probabilities of earthquake-induced landslide occurrence. The results show that water content in particular, have a great influence on the distribution of high seismic landslide hazard areas. Generally, the dry coverage analysis represents a lower bound for susceptibility and hazard assessment, and the saturated coverage analysis represents an upper bound to some extent. Moreover, high seismic landslide hazard areas are also influenced by the critical displacements. The slope failure probabilities during future earthquakes with critical displacements of 5 cm can increase by a factor of 1.2 to 2.3 as compared to that of 10 cm. It suggests that more efforts are required in order to obtain reasonable threshold values for slope failure. Considering the probable scenarios of water content condition which is varied with seasons, seismic landslide hazard assessments are carried out for frequent, occasional and rare earthquake occurrences in the Tianshui region, which can provide a valuable reference for landslide hazard management and infrastructure design in mountainous seismic zones.

A practical framework for performance-based reliability analysis of subway stations based on a faultestructure combined system

It is necessary to pay particular attention to the uncertainties that exist in an engineering problem to reduce the risk of seismic damage of infrastructures against natural hazards.Moreover,certain structural performance levels should be satisfied during strong earthquakes.However,these performance levels have been only well described for aboveground structures.This study investigates the main uncertainties involved in the performance-based seismic analysis of a multi-story subway station.More than 100 pulse-like and no pulse-like ground motions have been selected.In this regard,an effective framework is presented,based on a set of nonlinear static and dynamic analyses performed by OpenSees code.The probabilistic seismic demand models for computing the free-field shear strain of soil and racking ratio of structure are proposed.These models result in less variability compared with existing relations,and make it possible to evaluate a wider range of uncertainties through reliability analysis in Rtx software using the Monte Carlo sampling method.This work is performed for three different structural performance levels(denoted as PL1ePL3).It is demonstrated that the error terms related to the magnitude and location of earthquake excitations and also the corresponding attenuation relationships have been the most important parameters.Therefore,using a faultestructure model would be inevitable for the reliability analysis of subway stations.It is found that the higher performance level(i.e.PL3)has more sensitivity to random variables than the others.In this condition,the pulse-like ground motions have a major contribution to the vulnerability of subway stations.

Tour Level Mode Choice Analysis of Madison Area in Wisconsin

This study is to investigate what factors and how they affect tours (trip chains) behavior. The key issue is the understanding and definition of tour and tour level mode. Also, these definitions should fit for the data. A semi-home based tour definition is stated, and a competing mode based tour mode is defined. Based on the definition, this study used Madison Area Data from National Household Survey to estimate a MNL structured model. It is found that travel distance could be a positive factor for car mode. Meanwhile, the number of trips is also a positive factor for choosing car.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

Empirical analysis of the farmer's behavior under the new rural financial cooperatives in China

It's been well recognized for the big role played by innovative rural cooperative financial organizations in terms of spreading farmers' operation risk,increasing farmers' income,and developing rural economy.However,no sufficient research has been conducted regarding those factors which may have effects on the farmer's willingness to participate in the new rural financial organization.This paper tries to fill out the gap of identifying various factors which may have potential influence on the farmer's willingness to participate in the new type of rural financial cooperatives.In the process,442 farmer households and small-micro-enterprises are sampled from the cooperative finance experiment villages in Panjin municipality of Liaoning province.The potential influencing factors are classified into four categories,including the famer household's characteristics,financial cooperative reputation,transaction costs,and service quality.A discrete Logit model is used for the parameter estimations.The results show that most assumed factors display statistical significance effect on the farmer's willingness to take part in the rural cooperative financing organizations but with different level of sensitivity.The cause and effect are fully discussed following by addressing policy issues related to the rural financing cooperative reforms.

基于随机参数Logit的市域旅客出行方式选择影响研究

为了探究市域火车新线开通对旅客出行路径选择的影响,以西安鄠邑区至主城区市域出行旅客为研究对象,分析市域火车开通对旅客出行方式选择的影响。传统多项Logit模型无关方案独立性,无法定量研究旅客出行方式选择的差异性,故本文应用随机参数Logit模型分析市域旅客出行方式选择。选取旅客的个体属性、市域出行方式属性、到离站属性设计问卷,采用线上线下相结合的方式开展调查,获取了1205份有效数据,基于此建立并求解随机参数Logit模型。结果表明:在旅客选择大巴出行时,市域出行距离、职业、购票方式、到站方式和到站时间对旅客出行方式选择影响显著,市域出行时间服从(-0.15,0.146^(2))正态分布的随机参数特征;对市域出行时间随机参数进行边际效应分析,市域出行时间超过30 min后,旅客选择大巴和小汽车出行的概率降低,选择市域火车出行的概率增加;对旅客出行方式选择影响显著变量进行弹性分析,到站费用每增加1%,旅客选择大巴的概率增加0.394%,选择高铁的概率下降1.888%,选择市域火车的概率增加0.569%,选择小汽车的概率降低0.030%。结果证实,旅客对市域出行时间的偏好具有异质性,市域出行时间过长将降低旅客选择大巴和小汽车的概率,减少到站时间可以提高旅客选择市域火车的概率。

Probabilistic equivalent model of DFIG-based wind farms and its application in stability analysis

A probabilistic equivalent method for doubly fed induction generator (DFIG) based wind farms is proposed in this paper.First,the wind farm equivalent model is assumed to be composed of three types of equivalent DFIGs with different dynamic characteristics.The structure of equivalent model remains constant,whereas the parameters change with the migration of different scenarios in the wind farm.Then,historical meteorological data are utilized to investigate the probability distribution of key equivalent parameters,such as capacity,wind speed and electrical impedance to the point of common coupling.Each type of equivalent DFIG is further clustered into several groups according to their active power output.Combinations are created to generate representative scenarios.The probabilistic equivalent model of wind farm is finally achieved after removing invalid combinations.Most matched representative scenarios can be predicted according to the real-time measurement.The equivalentmodel is applied to the probabilistic power flow calculation and the stability analysis of test systems.

Theoretical analysis of non-probabilistic reliability based on interval model

The aim of this paper is to propose a theoretical approach for performing the nonprobabilistic reliability analysis of structure.Due to a great deal of uncertainties and limited measured data in engineering practice,the structural uncertain parameters were described as interval variables.The theoretical analysis model was developed by starting from the 2-D plane and 3-D space.In order to avoid the loss of probable failure points,the 2-D plane and 3-D space were respectively divided into two parts and three parts for further analysis.The study pointed out that the probable failure points only existed among extreme points and root points of the limit state function.Furthermore,the low-dimensional analytical scheme was extended to the high-dimensional case.Using the proposed approach,it is easy to find the most probable failure point and to acquire the reliability index through simple comparison directly.A number of equations used for calculating the extreme points and root points were also evaluated.This result was useful to avoid the loss of probable failure points and meaningful for optimizing searches in the research field.Finally,two kinds of examples were presented and compared with the existing computation.The good agreements show that the proposed theoretical analysis approach in the paper is correct.The efforts were conducted to improve the optimization method,to indicate the search direction and path,and to avoid only searching the local optimal solution which would result in missed probable failure points.

基于面板数据和动态Logit方法的金融危机预警模型

笔者通过梳理1990—2012年间6次主要金融危机的相关文献,选取19个样本国家的18个主要金融经济指标建立了初始金融预警指标体系,并通过格兰杰因果检验,初步筛选出11个主要影响指标。但考虑到保留下来的解释变量数目较多,处理起来较为繁琐,且同一经济体的各金融经济指标之间往往具有较强的相关性,笔者通过全局主成分分析对这11个主要指标的原始数据进行了降维处理,得到价格指数因子、货币供给因子、财政负担因子和对外关系因子四个相互独立的主要因子以代表11个金融预警指标的整体信息。进而,以得到的4个主要因子为解释变量,以金融危机发生的概率为被解释变量,分别建立了基于静态Logit方法和动态Logit方法的金融危机预警模型。最后,通过样本内检验和样本外检验,得出动态Logit预警模型优于静态Logit预警模型的结论。

大学基础研究人力资源瓶颈问题的影响因素——基于Ordered Logit模型分析

基础研究对我国科研水平及经济发展有重要影响,大学人力资源是我国基础研究的核心力量,本文基于对13所大学的40多位专家的访谈以及60所大学基础研究工作者765份问卷调查的数据分析,实证研究大学基础研究人力资源瓶颈问题的影响因素。本文应用OrderedLogit模型估计6个影响因素对大学基础研究人力资源瓶颈问题认同度的影响。实证结果发现:基础研究人员人均收入水平、基础研究者的竞争压力等两个因素对基础研究团队建设的影响程度较高;大学聘用标准和职称评选机制因素对基础研究领军人才缺乏、基础研究拔尖人才流失严重的影响程度较高;国家基础研究经费投入、资源分配的行政参与等两个因素对大学基础研究领军人才缺乏、基础研究拔尖人才流失、基础研究团队建设匮乏等人力资源瓶颈问题有显著的影响。本文挖掘大学基础研究人力资源各类影响因素,明晰各个利益相关者对于大学基础研究人力资源的关注点,以期为我国优化大学基础研究的顶层设计、完善相关制度提供客观、科学的依据。

我国中小企业信用评级质量检验的实证研究——基于因子分析模型和有序Logit模型的分析

通过对我国中小企业的发展能力、盈利能力、运营能力、偿债能力等指标进行统计分析,运用因子分析法提取公因子成分并建立有序回归模型,对我国中小企业的信用评级质量进行检验。结果表明,有序回归模型对于构建中小企业信用评级质量验证机制有着良好的效果。最后,提出提高我国信用评级质量的政策建议。

基于Logit模型的私家车出行影响因素分析

文章旨在分析影响人们出行需求及行为的因素,并寻找其内在的规律性。在进行大量数据调查及分析的基础上,通过SPSS软件运用Logistic回归模型分析得出相关影响因素,并对相关影响因素进行分析,得出动态交通信息服务在一定程度上能够决定私家车出行路线选择,交通信息发布的实时性和准确性有待进一步提高,而且出行者更不愿意遇到交通事故或交通拥堵。通过分析动态交通信息背景下出行者交通出行行为的变化趋势,可以更好的缓解北京的交通拥堵情况。