mohammad javad amani

The population growth rate and development of modern technologies are two influential factors in the development of cities. An urban heat island, which is a destructive phenomenon, was created as a result of an increase in temperature in urban areas. Increases in greenhouse gases, environmental damage, and endangering human health are among the messages of this destructive phenomenon. The expansion of vegetation, use of suitable facades and green roofs in buildings, urban change, and use of suitable pavements are among the strategies to control and reduce this destructive architecture. Considering that the roads have a large share of the configuration of the cities, the use of cool pavements, including reflective pavements, evaporative pavements, permeable pavements, porous pavements, water-retentive pavements, and energy-storing and producing (heat or energy harvesting) pavements, has been suggested in order to reduce the phenomenon of urban heating islands. In this research, by reviewing more than 400 good scientific articles, the reasons for formation, measurement methods, including remote sensing methods, laboratory methods, and the effect of cool pavements in reducing the destructive phenomenon of urban heat islands have been investigated. Studies have shown that the use of reflective pavements has reduced the temperature of the road surface by 5 to 20 degrees, depending on how the reflectivity changes. This value in evaporative pavements including permeable pavements, porous pavements, and water-retentive pavements has been reported from 5 to 35 degrees depending on the types of evaporative pavements. Also, the use of new technologies such as energy storage pavements known as energy (heat) harvesting pavements can produce energy in addition to reducing the temperature of the pavement by 3 to 5 degrees. A photovoltaic pavement is one of the most important heat-storage pavements. In addition to reducing pavement temperatures by 5 degrees Celsius, these procedures have helped reduce air pollution.

Driver behavior is a critical factor in traffic safety. Detecting abnormal driver behaviors through appropriate indicators and enforcing driving regulations will reduce high-risk driving behaviors and increase traffic safety. Detecting dangerous driver behavior is beneficial for developing warning systems and preventing accidents. Some high-risk driving behaviors, such as sudden lane changes, are dependent on determining the movement direction of the vehicle which has not received enough attention. The objective of this study is to determine the direction of movement of the vehicle and lane changes, using the sensors in the smartphone mounted on a vehicle. To achieve this goal, first, by using the Samsung Galaxy S6 smartphone and an accurate Global Positioning System (GPS), longitudinal and angular accelerometer data and GPS data are sampled as a dataset and combined by different types of neural networks. Then, combined data is fed into a suggested neural network, and lane changes are detected. Finally, the GPS data is used as the ground truth for the training of the neural network. If the GPS is not accessible, this neural network, just by receiving smartphone accelerometer data, can estimate the vehicle's direction of movement with an accuracy of 0.5 to 4.8 meters compared to the ground truth up to 8 seconds after the GPS is shut down. Using the vehicle travel path, an algorithm is proposed that can correctly detect the change of driving lane in the sample data set with 94% accuracy, 93.62% precision, 88.00% recall, and F1-score of 90.72% which are acceptable values.

Conventional production methods in heavy oil reservoirs are not efficient due to high viscosity of oil. Therefore, several new methods such as VAPEX and ES-SAGD have been developed to overcome production challenges from these reservoirs, which mainly rely on oil viscosity reduction. Solvent injection dilutes the oil and produces a complex asymmetric mixture. It is difficult to study the phase behavior of oil due to diversity of its components, and it gets more complicated by addition of solvent. One of the assumptions to simplify the problem is considering heavy oil as a single pseudo component and to investigate thermophysical properties of binary mixtures with diluents/mixtures. In this study, more than 1000 experimental data of various types of heavy oil/bitumen and several hydrocarbon and non-hydrocarbon solvents were collected and evaluated. The data were divided into two groups, below and above the critical temperature of the solvent, according to experimental temperatures. Also, modeling mixture phase behavior as a binary mixture was done using the advanced Peng-Robinson equation of state (APR-EoS). EoS tuning to match the experimental data was done by optimizing the binary interaction coefficient (kij). The results showed that binary mixture assumption could be used as a practical approach to construct P-X diagrams. Optimized kij was proposed according to dimensionless ratio Tco/Tcs (oil critical temperature to solvent critical temperature) for each solvent. The model constructed using suggested kij shows an average deviation of about 13% compared to experimental data. It is necessary to note that vapor-liquid-liquid three phase region may occur in a heavy oil/bitumen and solvent mixture. But according to the Gibbs phase rule, with two components and three phases in the system, the degree of freedom equals one. Therefore, investigating impacts of pressure change on three phase region is not possible using this model.