Studies on seasonal changes of heavy metal concentration in SOILS provide vital information for best management options at all times. The study investigated temporal variation in concentration of heavy metals in three towns having AUTOMOBILE service centres in Imo State. The study site is characterized by two major seasons in a year. Heavy metals were found in both arable and AUTOMOBILE SOILS, but more concentrations were recorded on the latter. Mean values of Cd, Cr, Ni, Hg and Pb were 6.2 mg/kg, 4.7 mg/kg 6.5 mg/kg, 0.02 mg/kg and 71.9 mg/kg respectively in the dry season while 2.9 mg/kg Cd, 2.2 mg/kg Cr, 1.9 mg/kg Ni, 0.01 mg/kg Hg and 51.9 mg/kg Pb were recorded during the rainy season of the experimental period. Higher values of heavy metal concentration were found in AUTOMOBILE SOILS as follows: 18.1 mg/kg Cd, 12.0 mg/kg Cr, 16.3 mg/kg Ni, 4.8 mg/kg Hg and 312.8 mg/kg Pb in rainy season, and 15.1 mg/kg Cd, 8.1 mg/kg Cr, 11.9 mg/kg Ni 2.7 mg/kg Hg and 267.9 mg/kg Pb. However, Cd showed highest variability in arable SOILS during the dry season (CV=79%) while Hg varied widely in AUTOMOBILE SOILS in the rainy season (CV=54%).

In this article we argue that for the estimation of a valid econometrics relation, an economic theory is indispensable. In many cases, it has been observed that statistical relations have been introduced as an econometric model. The main idea is that an econometric model relays heavily on economic theory, and ignoring the relevant economic theory underlying the observed relationship frequently gives rise to a nonessential model specification. The data of National Account are based on economic theory. An econometric model which uses the National Account data has to follow the basic principles of economic models which are "econometric" theories. Therefore, it is argued that many so called theories published in economic journals do not follow economic theories and therefore are fundamentally flawed. We have used the demand for AUTOMOBILEs to clarify this point further.

Civil liability is at work when there is a harmful act or harmful omission of an act. Sometimes one without perfuming or omitting a harmful act turn to have vicarious liability of other’s acts or omission of acts. In AUTOMOBILE accidents such liability occurs when one’s AUTOMOBILE cause accident while in the control of another person. The question is whether the owner of the vehicle to be responsible for such damage. The answers to the question could be of three types: 1- when there is an emplyer–emplouec relationship between the owner and the driver of the AUTOMOBILE, vicarious liability applies. 2- In all cases where the owner lends his/her AUTOMOBILE to another person, such liability applies for the owner. 3- vicarious liability does not apply in AUTOMOBILE accidents. The third category is at work in our country, Iran.

. One of the industries that has intensified the competitive environment in the industry in recent years due to continuous changes is the automotive industry, therefore, automotive companies must find ways to be able to differentiate their strategy from other automotive companies. Provide better customer service. An example of these distinctive strategies is investment decisions in attractive areas of automotive companies. In this regard, the purpose of this research is to design and explain the strategic investment model in attractive areas of the automotive industry in Zamyad Automotive Company. The statistical population of this study was the senior managers of all production and organizational lines of Zamyad Automotive Company (464 managers), of which 210 managers were selected and tested. It was suggested that the intellectual capital, financial costs and managerial risk profile play a significant role in the investment of managers in attractive areas of automotive companies.

In this paper, a disaggregate model is developed for household car ownership based on a data set from the city of Mashad. The approach adopted in this study is a two-level nested logit in which conditional probability of owning one car vs two or more is modeled at level 1. then marginal probability of owning a car is modeled at level 2. Seven groups of descriptive variables, each including one or more variables divided into two separate classes for each level. were employed in the model building process. Household demographics and sodo-economic indicators. Including head of the household characteristics like gender, age and occupation, as well as life style and life cycle stage, were among the variables which showed significant effects on car ownership. Results demonstrate the appropriateness of the nested logit structure and reject the application of the simple multinomial logit models when the II A property is not valid. Necessity of data sets-with rich information, more appropriate for disaggregate modeling applications. is further recommended.

Since the car is working in different climatic conditions, any Changes in temperature of the climatic conditions can affect various parts of the car. So it is necessary to optimize the energy consumption & keep the passengers in a suit & comfort state considering this kind of changes. The automotive air conditioning system is one of the largest ancillary loads in the passenger cars, with considerable effects on the vehicle fuel consumption. While most of the studies have represented linearized equations & simplified steady state matrixes, In the simulations of this paper, Nonlinear dynamic equations with using a model predictive controller and advanced control theory is designed for automotive air conditioning system. At the same time, a genetic algorithm technic which is a method of generation evaluation, is finding out the best answer in the whole problem space using a fitness function to select optimized inputs for sending to the plant. Also in this system a compressor capacity & fan flow rate has considered as two inputs to achieve the main objectives like minimizing the steady state error, system fast response, reduction of noise effects, minimizing of energy consumption and comforter of passengers.