Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations

Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

Parametrizing the Kepler exoplanet period-radius distribution with the bivariate normal inverse Gaussian distribution

This paper presents a simple and robust method for obtaining a comprehensive understanding of the joint period and radius distribution in Kepler exoplanets. The proposed method is based on particle swarm optimization and bivariate Normal Inverse Gaussian distribution. Furthermore, in the construction of the probability density function, this study selects planet-host stars with the GK-type. The injecting approach is also employed to solve the survey completeness of sample. The resulting occurrence rate of Earth analogs is 0.025 with a 95% bootstrap confidence interval between 0.023 and 0.032.     

基于加权案例推理模型族的软件成本SVR组合估算

精确地估算软件成本是软件项目成功开发的一个重要保证,直接影响着软件的风险控制和质量保证.为了更好地解决单一估算模型的不足,提出了集成多案例推理(CBR)模型的软件成本组合估算模型.首先,采用六种距离计算公式刻画新旧项目相似度,构建了六种CBR模型,并运用粒子群算法(PSO)来优化CBR模型族中的属性权重.其次,在CBR模型族的基础上,运用支持向量回归机(SVR)模型将不同CBR模型的估算结果进行集成,提高软件成本估算结果的精度.利用Deshamais数据库对模型有效性进行检验,实证结果表明,在六种CBR模型中Euc-CBR、Min-CBR、Gau-CBR和Mah-CBR模型估算结果没有明显差异,Gre-CBR和Man-CBR模型略优;提出的SVR组合估算模型估算精度明显优于单CBR模型和其他线性组合估算模型,能有效提高软件成本的估算精度.     

Crying for Food: The Mexican Myths of ‘La Llorona’ and ‘The Hungry Woman’ in Cherríe L. Moraga

The Mexican legend of ‘La Llorona’ (‘The Weeping Woman’), who drowned her children out of revenge for being abandoned by her lover, and the Aztec creation myth of ‘The Hungry Woman’ — crying constantly for food, with mouths all around her body — have inspired Chicana writers in the symbolic representation of their own yearning, be it sexual, identity-building, or anti-patriarchal. This essay seeks to lay the mythical groundwork within this topic, as well as to illustrate with some particular examples the different reappropriations of these myths in Cherríe L. Moraga, mainly in her play The Hungry Woman: A Mexican Medea (2001). With a view to opening up a past ‘that can provide a kind of road map to our future’ (: ix), these examples of transgressive women will be deprived of the feminine colonial passivity imbued by the dominant male discourse, and analysed as a complex, active, polyvalent mythological female corpus that integrates both life and death, womb and grave. This hybrid approach is inherent to the Aztec mythology on which Moraga relies in order to transcend Manichaeistic resolutions and probe the social, political, and gender reasons leading a hungry mother to commit infanticide.     

Pollutant Ground Concentrations of Nonneutrally Buoyant Particles

A methodology is suggested for the estimation of the mass density and the cumulative ground deposition of a nonvolatile, nonneutrally buoyant, air pollutant (liquid or solid) released from a polluted column (following an explosion caused during routine operation in, e.g., the chemical industry or due to any kind of hostile act) and deposited on the ground via gravitational settling. In many cases, the deposited mass due to gravitational settling constitutes a significant fraction of the original inventory released from the source. Implementation of the methodology in preliminary risk assessments can serve as an efficient tool for emergency planning for both immediate and long‐term measures such as evacuation and decontamination. The methodology considers, inter alia, an estimation of the critical particle diameter, particle size, and mass distributions along the polluted column. This methodology was developed to apply in rural regions since proper application of relevant meteorological input data can be accomplished mainly for such areas.     

Objective Bayesian analysis of counting experiments with correlated sources of background

Searches for faint signals in counting experiments are often encountered in particle physics and astrophysics, as well as in other fields. Many problems can be reduced to the case of a model with independent and Poisson-distributed signal and background. Often several background contributions are present at the same time, possibly correlated. We provide the analytic solution of the statistical inference problem of estimating the signal in the presence of multiple backgrounds, in the framework of objective Bayes statistics. The model can be written in the form of a product of a single Poisson distribution with a multinomial distribution. The first is related to the total number of events, whereas the latter describes the fraction of events coming from each individual source. Correlations among different backgrounds can be included in the inference problem by a suitable choice of the priors.     

混合学习法前向网络多属性储层参数预测

应用前向网络描述地震属性和储层参数间的非线性映射关系时，经典的误差反向传播算法存在收敛速度慢，易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术，具有较快的收敛速度和较强的全局搜寻能力；误差反向传播算法本质上是梯度下降算法，注重局部搜索。混合学习法为两种学习算法交替执行，首先以粒子群优化算法训练网络，当误差能量在规定的迭代次数内不再发生变化时，采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点，具有良好的应用前景。     

中国高技术产业技术创新能力分行业评价研究——基于微粒群算法的实证分析

首先通过表征化、形象化和抽象化三个步骤构建技术创新能力评价的杠杆模型，然后基于杠杆模型建立了中国高技术产业技术创新能力的评价指标体系。在此基础上，通过构建基于微粒群算法的评价模型，对中国高技术产业5大行业的17个细分行业技术创新能力进行评价分析。结果表明：17个细分行业的技术创新能力存在着明显差异，排名前3的行业依次为：通信设备制造业、家用视听设备制造业和飞机制造业及修理业；并且根据各细分行业技术创新能力的3个组成能力的分值和排名，将17个细分行业划分为“基本匹配型”、“弱匹配型”和“不匹配型”3种类型，其中，只有化学药品原药制造业、仪器仪表制造业2个行业处于“基本匹配型”。     

EM浸种处理对玉米叶色值的影响

以玉米品种农大108为试验材料,用SPAD-502仪分别测定了不同浓度EM稀释液浸种处理后的叶色值。试验结果表明:利用不同浓度EM(有效微生物群)稀释液浸种及在玉米拔节期叶面喷施并灌根,可显著提高玉米叶片的叶绿素含量。并遴选出玉米施用EM的最佳浓度为500倍稀释液。