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Home Uterine Activity Monitoring (HUAM) has demonstrated to be of great value for preventing preterm labor in recent years. In this research, a low cost monitoring device for non-invasive monitoring of the uterine activity in pregnant women is presented. The new device has been designed based on an inductive Colpitz oscillator and vertical displacement of a ferrite core in a coil. The resulting frequency of the oscillator is proportional to the pressure in the external surface of the abdomen. This frequency is measured by the portable monitor. A low-power static random access memory (SRAM) provides long-term data storage. The proposed sensor for detecting uterine contractions has light weight, high stability and low cost. This sensor is very simple to manufacture and use for long-term ambulatory monitoring of the uterine activity. Furthermore, this sensor can be calibrated using software. Other features of the sensor are, resolution of 0.13 mmHg, repeatability close to 96% and input range from 0 to 95.32 mmHg.

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This paper gives a detailed analysis of direct torque control (DTC) strategy in a five-level drive and proposes a 24-sector switching table. The overvoltage problem due to high dv/dt is reduced compared to the 12-sector DTC. Using all vectors leads to better flexibility and reduces speed oscillations. Simulation and experimental results for a 3kVA prototype confirm the proposed solutions. A TMS320F2812 is used to implement the above strategy.

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The echoes obtained from ultrasonic testing of materials contain valuable information about the geometry and grain structure of the test specimen. These echoes can be modeled by Gaussian pulses in a model-based estimation process. For precise modeling of an echo, the parameters of the Gaussian pulse should be estimated as accurately as possible. There are a number of algorithms that can be used for this purpose. In this study, three different algorithms are used: Gauss-Newton (GN), particle swarm optimization (PSO), and genetic algorithm (GA). The pros and cons of each of these three algorithms are reviewed and by combining them, the benefits of each algorithm are used while its shortcomings are avoided. For signals containing multiple echoes, the minimum description length (MDL) principle is used to estimate the numbers of required Gaussian echoes followed by space alternating generalized expectation maximization (SAGE) technique to translate it to separate echoes and to estimate the parameters of each echo. The performance of the proposed algorithms for simulated and experimental signals with overlapping and non-overlapping echoes is evaluated and shows to be quite effective.

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Nowadays, cities as a place of living and human activity are facing serious challenges in providing human needs. Increasing in population growth, vehicle ownership and communication development has led to complexity of the transportation system and its problems, including congestion, environmental pollution and the consumption of non-renewable resources. Therefore, changes in urban transport policies and efforts to develop and more use of the public transport, especially the bus, are one of the most important concerns in urban transport planning. A review of various studies suggests that planning for efficient use of bus infrastructures and enhancing the efficiency of public transportation operation in the world, require information on the infrastructure and passenger demand for lines and bus stations. Accordingly, it is necessary to carry out studies to predict passenger demand for bus stations in Tehran. Thus, this study predicts bus stations passenger demand for future short-term periods, using data gathered by AFC (Automated Fare Collection) and AVL (Automatic Vehicle Location). For this purpose, firstly AFC and AVL data was sorted according to the time for each bus line. Since passengers use their smart card while they are getting off the bus it means at the exit station thus identifying their origin station is vital, so that in second step, data of two data bases is compared and matched by writing computer code in Matlab software to determine the origin stations of passengers and then forming origin-destination demand matrix for each bus line in terms of its stations. This matrix is considered as the main data base of the study, a time series analysis, a seasonal autoregressive integrated moving average (SARIMA) and neural network as an artificial model are calibrated based on the available data. Both models’ goodness of fit indices are compared in terms of learning and generalization capabilities. For this purpose, initial data is divided into two subsets called learning and test data sets and comparison indices are computed for both aforementioned sets. The models’ results show that the multi-layer perceptron neural network model in terms of goodness of fit indices in both learning and generalization capabilities in prediction of bus station passenger demand is better than SARIMA model; however, the manner of influencing different factors such as day of week or month of year in passenger demand in each station is more clear in time series analysis. The passenger demand for each stations in first month in spring is different from the rest months in this season. Months in summer is also show different trends for passenger demand, while all months in fall and the first two months in winter have similar passenger demand in various stations. Official holidays has also significant influence on passenger demand so that reduce passenger demand by approximately 256 persons on average. All days in week have meaningful effects on passenger demand in comparison with Friday so that Monday and Thursday have the highest and the lowest effect on weekday passenger demand in bus stations in comparison with Friday, respectively. This analysis comparison show that if the precision of future prediction is important then neural network outweigh time series regression, while the identification of influential variables on passenger demand is better done by time series analysis.

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Concerns for climate change, reduction of greenhouse gas emission and environmental pollution, besides economically dependency on fossil fuels and political aspect motivate governments and policy makers to take into account replacing usual vehicle with alternative fuel vehicles (AFVs) such as Compressed Natural Gas Vehicles (CNGV) and Gasoline-Electric Hybrid Vehicles (GEHV). The air pollution in Tehran is a serious concern that based on this problem, CNGV has been introduced to Iranian market from 10 years ago. On the other hand, with the approval and notification of the removal of the electric vehicle importation’s tariff law, GEHV has been entered into the market of Iran as a new entrant. The purposes of this paper is to identify the effective factors to choose AFVs for drivers in Tehran and the assessment of effects of the incentive policies that increase AFV shares and computing their willingness to pay (WTP) for AFV under different incentives. This study designed a questionnaire which includes 3 parts: current vehicle features, dominant travel characteristics, socio-economic properties and the prioritization of effective factors on new vehicle purchase, and the tendency of AFVs choice with different scenarios representing different features. A random sample of 365 respondents was interviewed in a face-to-face survey in February 2016 in the technical inspection centers and in compressed natural gas stations. Finally, for the determination of effective factors on current and new vehicle purchase with revealed preference information and the assessment of AFVs usage tendency with stated preference information, the Multinomial Logit models have been used and WTPs are calculated. The incentive policy in Tehran, like previous studied countries, was the most influential factor in motivating consumers to buy AFVs in comparison to improvement of AFV specifications. The results show that drivers’ WTP is 5 MT for free access to even-odd area for CNGV and 12 MT for GEHV, also WTP for free access to pricing area in Tehran central business district is equal to 10 MT for CNGV; i.e. people tend to pay this extra cost for AFVs to access to pricing areas. These values are comparable with similar studies in cities located in developed countries. However, the results of this study show that WTP for fuel cost in Iran is considerably less than WTP of people driving in developed countries. The fuel cost and access time to gas stations are influential variables on CNGV choice. The vehicle acceleration and driving range are influential variables on GEHV choice.

 

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In this paper, a model based on GMDH Type Neural Network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. The results suggest that GMDH Neural Network model, according to the Root Mean Squared Error (RMSE) and Direction statistics (Dstat) statistics are more effective than OLS method. Also, first lag of gas price in the future market is the most efficient variable in predicting gas price in spot market.

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Developments in the oil contracts of countries indicate that most countries have abandoned the use of service contracts only and resorted to the contract model of production sharing contracts and new concessions, or by modifying, have driven their contract models to a variety of partnership contracts. However, this study shows that although many legal efforts have been made in Iran and Iraq to use this contract model, but the lack of clear law in this regard has led to the reform process of oil contracts leading to the implementation of models of long-term service contracts. The results of comparing the economic evaluation of Iranian petroleum contracts with service contracts and production sharing contract in Iraq show that the production sharing contract has higher economic efficiency, while creating the necessary incentives for the contractor to implement risky projects, in various economic conditions creates greater alignment between the interests of the parties to the contract, establishes a more efficient and equitable distribution of technical and economic risks between them, which is an important factor for the commitment of the parties to the implementation and termination of the contract. Therefore, it seems that due to the prevailing conditions in the oil market, including competition and declining trends in oil prices the use of the contract model of production sharing contract, at least for fields with difficult conditions and common fields, is a solution, but to use it like other countries, more effective legal measures such as the enactment of a law is essential.

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One of the information needed for all planning problems and specifically transportation planning is to have accurate prediction about the future. Traffic variables prediction is one of the efficient tools in travel demand management. Using this tool and advanced traveler information systems (ATIS), the predicted traffic variables are informed to the users and transportation system operators to make plans and set policies. In this study, the average speed and traffic volume of the Karaj to Chalus suburban road with the high variation of traffic variables in the north of Iran is predicted. The Karaj to Chalous road is part of the route from Tehran as the capital of Iran to the country's northern coast. Along the Karaj to Chalous road, three parallel roads, with different lengths, connect Tehran with the cities of the north. In general, finding the pattern of non-mandatory trips is more complicated than mandatory trips. Generally, the predictive methods are divided into three groups, naïve, parametric and non-parametric methods. Among the various predictive models, the SARIMA as a parametric model and the artificial neural network and the support vector machine as nonparametric models are employed. In the data pre-processing step, the variables affecting the average speed and traffic volume are extracted and added to the dataset as predictor variables. These variables are related to time, calendar, holidays, weather, and roads blockage. Also, because of the importance of the maximum and minimum values of traffic speed and volume, as critical values and rare events, models are evaluated with emphasis on the prediction of rare events compared to normal values. The results show that, for the test data, the lowest root mean square error of predicting the average traffic speed and traffic volume are obtained using artificial neural network and support vector machine models equals 139 vehicles per hour and 5 kilometers per hour, respectively. In terms of R² of prediction-observation plot, the performance of SARIMA for predicting the average speed and traffic volume is the same for the test dataset. In contrast the R² of hourly traffic volume prediction is higher for the training data. The R² of artificial neural network model and the support vector machine for traffic volume prediction is higher than traffic speed prediction. The lowest root mean square error of predicting the first and fourth quartile of the observed average traffic speed values is obtained by support vector machine models and artificial neural network, respectively. Also, predicting the first quartile and fourth quartile of the observed traffic volume values by the support vector machine model is more accurate than two other models. Using predicted traffic parameters and providing them to travelers and transportation agencies by intelligent transportation systems leads to make a balance between travel demand and travel supply in the near future which is the main aim of this study. Travelers can have a better personal plan for their future trips based on these predictions. Also, the transportation agencies are more prepared to deal with critical traffic situations and can prevent traffic congestion.

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 The most important point in performing the Pile Integrity Test (PIT) is the correct interpretation of the results. This insitu tests is very useful in the estimation of the pile length embedded in the soil or the control of the cross section of bored piled where the quility of the pile construction is in doubt. Two common defects of the bored piles are buldging and necking of the pile cross section which correspond to the over-size and narrowing of the pile diameter alonng the pile length. Thses two anolamies in the pile geometry inflence the pile functionality and an approperiate reaction is required. Correct identifications of the length and dpeth of an anomaly are among the factors that are influenced by the anomaly location and the interaction of the waves passing through the pile. In this research, an attempt is made to interpret the results of PIT by examining the dimensions and location of anomalies in different parts of the pile as well as the effect of the presence of soil on the obtained results. PIT is simulated by the numerical finite difference method and the results have been investigated. The pile head is loaded by a semi-sinusoidal impact which is defined as a compressive pressure over a circular region at the cross section centroid during a short period of time. The verification of the simulations is established by the compariosn of the results with those one-dimentional wave theory which is based on the arrival time of the impact wave to the reciever situated on the pile head. In addition, by changing the position of the wave vreciving in the numenrical mode, it was shown that the best place to install the accelerometer as the recivier would be at the distance of 0.6R from the pile center where R is the pile radius. This finding is consistent with the results of previous studies which confirms the validiy of the simulations. According to the results, the existance of the soil around the pile causes to deform the figure of the waves and it required to modify the records before a correct interpretation. The soil atound the pile plays a role of damper of the waves passing through the pile and it causes that the magnitude of the peaks observed in the records diminish and the interpretaion may not be so easy as that in a free pile. For the pile embeded in the soil, the closer the anomaly location is to the pile head, the less the damping effect of the soil is and thus, the wave forms are more similar to the free pile. Based on the findings of this study, to interprete correctly the PIT results, it is recommended to use the first peak of the recorded velocity if there is a necking defect, while the use of the second peak is recommneded for a buldging defect to estimate the anomaly depth based on the free pile diagrams.It is also seen that as the defect length increases to about twice the diameter of the pile, the peak value of the velocity changes (in most cases, it increases) and remains almost constant at larger lengths.