The transaction cost approach to modeling the demand for automobiles

The purchase of an automobile involves significant transaction costs in addition to the purchase price. Therefore, the assumptions implied by static car holding models are invalid. This paper describes a dynamic approach to the modeling of level-of-ownership and auto-type choice, based on a transaction choice model which utilizes information on past car ownership. The cost or disutility of a transaction depends on the attributes of the household and the purchased car, as well as on past car-ownership characteristics. A set of assumptions underlying the incorporation of transaction costs in the model is presented. The paper discusses the econometric implications of omitting the dynamic attributes (i.e., past ownership characteristics). A disaggregate model was estimated, using a choice-based sample consisting of a random sample of households enriched with a sample of households which transacted in the car market during the study period. This sampling method combined with a random choice of a subset of the car alternatives provides for a cost-effective method to estimate a transaction model.     

Assimilation of buoy and satellite data in wave forecasts with integral control variables

The quality of numerical wave forecasts can be improved significantly by assimilating wave observations prior to the forecast. In the present study a technique for such assimilation is developed that exploits (a) the efficiency of a limited number of integral control variables, and (b) the effectiveness of variational (model-consistent) assimilation. The formal procedure is independent of the type of control variables and of the wave model (moreover, no adjoint wave model is required). In the present study, integral control variables are chosen to represent large-scale errors in the driving wind fields and uncertainties in the wave model. The assimilation technique is validated with observations of the ERS-1 satellite altimeter and two waverider buoys in two consecutive storms in the Norwegian Sea. The assimilation of the observations reduced the errors in the forecasted significant wave height at the buoy locations typically from 25% to 12%. For low-frequency waves the effect of the assimilation is similarly significant at one buoy location but marginal at the other buoy location.     

An operational tanker scheduling optimization system: background, current practice and model formulation

We describe the operational tanker scheduling problem in detail, as it relates to Chevron Shipping Company. We review the literature and current Chevron practice. We develop a model for the scheduling situation, and describe an integer programming formulation for schedule optimization, including the feasible schedule generation process, for a realistic representation of the company scheduling practices.     

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory

The set of equations for global ocean biogeochemistry deterministic models have been formulated in a comprehensive and unified form in order to use them in numerical simulations of the marine ecosystem for climate change studies (PELAGOS, PELAgic biogeochemistry for Global Ocean Simulations). The fundamental approach stems from the representation of marine trophic interactions and major biogeochemical cycles introduced in the European Regional Seas Ecosystem Model (ERSEM). Our theoretical formulation revisits and generalizes the stoichiometric approach of ERSEM by defining the state variables as Chemical Functional Families (CFF). CFFs are further subdivided into living, non-living and inorganic components. Living CFFs are the basis for the definition of Living Functional Groups, the biomass-based functional prototype of the real organisms. Both CFFs and LFGs are theoretical constructs which allow us to relate measurable properties of marine biogeochemistry to the state variables used in deterministic models. This approach is sufficiently generic that may be used to describe other existing biomass-based ecosystem model.     

The influence of workplace environment on lung function of shopkeepers working near National Highway in Jalgaon: A note

The inferior ambient air quality was observed near highway passing through Jalgaon urban center. Among the pollutants critical level of particulates are observed at the roadside during May 2003 to April 2004. The shopkeepers working at the highway sides are at high risk of exposure to the air pollution caused by heavy highway traffic. The lung function test of the shopkeepers shows significant decrease in forced vital capacity, forced expiratory volume in one second and peak expiratory flow rate. The regular periodic health checkup and use of nose mask will protect the health of shopkeepers working near National Highway passing through Jalgaon urban center.     

Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour

This paper presents a comprehensive econometric modelling framework for daily activity program generation. It is for day-specific activity program generations of a week-long time span. Activity types considered are 15 generic categories of non-skeletal and flexible activities. Under the daily time budget and non-negativity of participation rate constraints, the models predict optimal sets of frequencies of the activities under consideration (given the average duration of each activity type). The daily time budget considers at-home basic needs and night sleep activities together as a composite activity. The concept of composite activity ensures the dynamics and continuity of time allocation and activity/travel behaviour by encapsulating altogether the activity types that are not of our direct interest in travel demand modelling. Workers’ total working hours (skeletal activity and not a part of the non-skeletal activity time budget) are considered as a variable in the models to accommodate the scheduling effects inside the generation model of non-skeletal activities. Incorporation of previous day’s total executed activities as variables introduces day-to-day dynamics into the activity program generation models. The possibility of zero frequency of any specific activity under consideration is ensured by the Kuhn-Tucker optimality conditions used for formulating the model structure. Models use the concept of random utility maximization approach to derive activity program set. Estimations of the empirical models are done using the 2002–2003 CHASE survey data set collected in Toronto.

EXPRESSION CLONING OF A PROTECTIVE LEISHMANIA ANTIGEN

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基于双群进化策略的多项式近似因式分解

针对传统算法在解决多项式的因式分解过程中,存在着只适用于精确分解而不适合系数为浮点数的多项式的因式分解问题,文中提出一种应用双种群双突变进化策略来对浮点数多项式近似分解的算法,克服了传统算法不能解决浮点数多项式的因式分解的弊端.数值计算结果表明该算法收敛速度快、精度高、稳定性强.     

An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.