محمد شریف

One of the current weather phenomena that has affected Iran nationwide is the dust storm. Hormozgan providence, located in the country's south (in the global arid and semiarid region), is prone to wind erosion and dust storms due to its proximity to the Central Persian and Arabian deserts and its lack of vegetation cover. According to a review of the literature, earlier studies on simulating dust storms in the Hormozgan region have primarily used MODIS products on a daily time scale for the study area. The aim of this study was to model dust storms using spectral indices, such as the Normalized Difference Dust Index (NDDI) and Brightness Temperature Difference (BTD), based on the Artificial Neural Network (ANN) and Random Forest (RF) methods, two of the most well-known and effective machine learning techniques in the modeling and prediction fields, on an hourly time scale using Spinning Enhanced Visible and InfraRed Imager (SEVIRI) METEOSAT images. The indicators were computed using METEOSAT images during the selected dates (21–24 November 2016 and 3–9 December 2016) for dusty days with visibility of less than 1000 m (8 images per day). In order to model and predict dust storms, the Artificial Neural Network (ANN) and Random Forest (RF) methods were used. NDVI and BT were used as dependent variables, and Air Temperature (AT), Wind Speed (WS), Air Pressure (P), and Absolut Humidity (AH) extracted from NASA GES DISK were used as dependent variables. Time scales for SIVIRI images and NASA GES DISK climate reanalysis data were 4 and 3 hours, respectively. Climate reanalysis data were extrapolated to 4 hours each day. The findings demonstrate that the NDDI's Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) were, respectively, 0.08, 0.31, and 0.24 based on ANN. While the BT index values were 0.42, 6 and 4.6, respectively. Based on the RF technique, the NDVI model had R2, RMSE, and MAE values of 0.55, 0.3, and 0.23, whereas the corresponding values for BT were 0.69, 4.4, and 2.8. The results show that RF models combined with climate reanalysis data have a good performance in modeling and predicting dust storms in the Iranian province of Hormozgan. BT index generated from SIVIEI images with a 4-hour time resolution and RF models are also of outstanding performance. Further research is needed to evaluate the performance of the method used in this research in other regions of Iran.

With the increasing growth of positioning technologies and the use of navigation systems, a large volume of moving point object data, such as people, cars, ships, and animals, is available. However, the lack of integrity and incompleteness of these data for systemic, human, and environmental reasons challenges the analysis of trajectories and their effective application in various fields. Therefore, the reconstruction of missing data plays an important role in maximizing the capacity of movement data, particularly in navigation and track tracking. In this study, using the similarity measurement of trajectories approach, trajectories containing gaps are reconstructed. In this regard, the context-based dynamic time warping (CDTW) method, along with speed and direction movement parameters, are used to measure the similarity and reconstruct the trajectories of vessels in two regions of the Atlantic and Pacific Oceans. Two mechanisms, a constant number of trajectories and a specified threshold, are considered for reconstruction. The results show that using a constant number of trajectories in comparison with the specified threshold reduces the root mean square error (RMSE) and mean absolute error (MAE) from 1.5 and 1.4 to 0.5 and 0.4, respectively. In addition, increasing the length of the trajectories improves the RMSE and MAE values from 0.5 to 0.1 in the case of a constant number of trajectories and 1.5 to 0.3 in the case of the specified threshold.

Dust storms are natural disasters that have severely affected human life and the environment. The majority of research in dust storm has been dedicated to the forecasting of storm-prone areas. However, developing models to predict the movement of these storms plays a significant role in the prevention and management of dust storms, because they reveal the transport pathway and identify the next vulnerable areas against the storm. In this research, a hybrid convolutional neural network (CNN) model has been developed to predict the path of dust storms based on airborne optical depth (AOD) data of MERRA-2 product for the next 12 hours. 40 storm events including 2489 storm hours in a dry region in Central and South Asia have been used for training the model. The results show that the proposed model provides an accurate prediction of the storm's path, so that for the time steps of 3, 6, 9, and 12 hours, the overall accuracy values are 0.9806, 0.9810, 0.9813, and 0.9790, respectively, the F1 score values are 0.8490, 0.8524, 0.8530, and 0.8384, respectively, and the Kappa coefficient values are 0.8387, 0.8424, 0.8431, and 0.8273, respectively.

With the developments in navigation, positioning, and tracking technologies, a large amount of moving point data (e.g., human, vehicle, animal) have been produced. Through moving an object in the course of time, a sequence of its position is recorded which is known as trajectory. Studying the behaviors of point objects and analyzing their trajectories have recently received great attentions among researchers in different fields of science, especially in geographic information science. Such studies contribute to better understanding of movement-behavior patterns of moving objects. Data mining, as one of the main approaches in geographic knowledge discovery, is normally used in moving databases to extract information from moving point objects’ trajectories. Analyzing the similarity of trajectories as one of the frequently used approaches in geographic data mining, is of great importance, which is normally performed by distance functions.
The way an object moves is highly influenced from the condition and situation (known as context) where movement takes place. Therefore, merely analyzing trajectories from their spatiotemporal dimension may not clearly reflect the conditions and situations (contexts) during the move, and may lead to unrealistic results. Accordingly, most of the present distance functions are only able to measure the similarity of trajectories at spatial and spatiotemporal dimensions and neglect the underlying context during the move. Therefore, developing a new trajectory similarity measure approach, capable of handling a wide range of context information in conjunction with spatiotemporal dimension becomes crucial.
According to the complexity and variety in the moving objects’ datasets, especially in their context parameters, in this research, a fuzzy model is developed which is able to exploit a wide range of internal and external context information in similarity measure process of multi-length trajectories. Specifically, a fuzzy inference system (FIS) is used for measuring the similarity between trajectories based on qualitative and quantitative context information. To enhance the effectiveness of the system, a subjective hierarchical fuzzy inference system (HFIS) is designed and the membership functions and rules are generated and adjusted based on learning. The HFIS controls the rules by dividing a FIS into several simple sub-systems that are hierarchically connected to each other. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is used for defining and adjusting the FIS.
The performance of the system is assessed by applying it on real trajectories of pedestrians’ and cyclists’ datasets in the Central Park of Manhattan, New York, USA, while accounting contextual information. The movement parameters and context information were recorded in qualitative and quantitative formats. Two types of context information are used: 1) internal context (i.e., all of the properties that directly relate to the moving object, such as eagerness), and 2) external context (i.e., any factor that extrinsically influences the process of movement, such as slope). According to the nature of data, four models of spatial, spatiotemporal, spatiotemporal-internal context, and spatiotemporal-internal-external context are applied on the dataset to see the added value of each parameter.
The achievements of the implementations can be discussed from two main perspectives: 1) system performance, and 2) similarity results. From the system performance aspect, the control surface plots indicate the consistency of the rules in the developed fuzzy inference systems. In addition, the Pareto diagrams for both pedestrians’ and cyclists’ models cover the Pareto fronts in all four models in an appropriate manner.
From the similarity results aspect that are shown in boxplots, the commonalities between both pedestrians’ and cyclists’ trajectories are very high at the spatial dimension in the first model, while by adding the temporal dimension as the second model, the similarity range at the spatiotemporal dimension increases. This issue is directly related to the traversed paths and the elapsed times of the moving objects. By importing the internal contexts (i.e., speed, direction, length, eagerness) as the third model, the similarity range again increases but the median decreases. However, the distribution in the pedestrians’ similarity values is much more than the cyclists’ values. The fourth model is composed of all the previous parameters plus the external contexts (i.e., path difficulty and slope). The same as previous trends, the similarity range increases but the median decreases. Although the number of trajectories that share common characteristics are low when taking into account all the spatial, temporal, internal contexts, and external contexts in the implementation, the results become much more realistic and close to reality for both datasets.
In addition, to assess the influence of the internal and external contexts on the similarity output, a one-way analysis of variance (ANOVA) test, with a 95% confidence level, is applied on the results. Furthermore, a number of sensitivity analyses are applied on the datasets. The outcomes demonstrate meaningful influence of contexts on the similarity results of trajectories.
In this research a novel approach based on fuzzy systems is developed for multi-dimensional similarity measure of trajectories while accounting both internal and external context information. This approach enhances the previous similarity functions that only use spatial and temporal data. The developed models were applied on two real trajectory datasets, which were capable of handling both quantitative and qualitative information. The results indicate the effectiveness of the proposed context-aware similarity measure approach in revealing the commonalities between trajectories in multi dimensions.

The study of the effect of fertility and female labor force participation on economic growth is of particular significance. Thus in this paper, we examined the relationship between fertility, female labor force participation and economic growth using a comparative study between Iran and the G7 countries. For this purpose, we used the data from the overall fertility rate, the female labor force participation rate and the economic growth rate of the World Bank for the period 1990-2015. We employed the ARDL Bounds method and the panel constant effect method to estimate the Iran model and the model of G7 countries respectively.The results showed that with a one-percent increase in fertility, economic growth in Iran in the short and long term reduces by 0.177% and 0.166% respectively and in G7 countries it decreases by 0.012 %. The findings also indicated that a one-percent increase in the female labor force participation rate in Iran would increase 0.036% in the short run and 0.635% in the long run in economic growth. And such a change in economic growth in the G7 countries will affect 0.059% in a positive direction.

Movement of objects is taking place in geographical contexts. Context directly/indirectly influences movement process and causes different reactions to moving objects. Therefore, considering context in movement studies and the development of movement models are of vital importance. In this regard, incorporating context can play a crucial role in similarity measurement of objects movements and their corresponding trajectories. Trajectories of moving point objects, beside their spatial and temporal dimensions, have another aspect which is called contextual dimension. This dimension, however, has been less considered so far and a few researches in trajectory analysis domain have investigated it. To this end, this research develops a method based on Euclidean distance in which individual spatial, temporal, and contextual dimensions as well as their integration can be explored in the process of similarity measurement of trajectory. Beside the simplicity of the method, it is developed in a way for taking into account every small change in each type of dimension(s). To validate the proposed method and survey the role of contextual data in similarity measurement of trajectories, three experiments are performed on commercial airplane dataset. Accordingly, geographical coordinates and altitude of airplane as spatial dimension, travel time as temporal dimension, and airplane speed, wind speed, and wind direction as contextual dimension are utilized in these experiments.
The first experiment measures the correspondence of trajectories in different dimensions. Also, it explores the role of dimensions weights individually and collaboratively along the similarity measure process. The results demonstrate that weights severely affect similarity values, while they are totally application dependent. Meanwhile, it can be confirmed that contexts may increase or decrease the values of trajectories similarities. This effect can be seen in the average of relative similarity values of commercial airplanes trajectories in spatial (0.60), spatial-temporal (0.51), and spatial-temporal-contextual (0.46) dimensions. Contexts can enhance and restrict movements as well. To justify this statement, the second experiment is conducted to explore how movement and geographical contexts interact in similarity measure process. To this end, four sample trajectories are compared with respect to different dimensions. For a pair of trajectory, the relative similarity value at spatial dimension is 0.04. By incorporating time dimension, this value increases to 0.30 at spatio-temporal dimension. Given the high similarity of these two trajectories in wind direction, wind speed, and airplane speed (0.85), the ultimate similarity of them becomes 0.48. In contrast, for another pair of trajectory, the spatial and spatio-temporal similarity values are 0.85 and 0.91, respectively. Considering the similarity value of these two trajectories in wind direction, wind speed, and airplane speed (0.37), the final relative similarity becomes 0.73. The third experiment sought for the role of motivation context in similarity measure process. Although such context is very difficult to capture and in many applications will remain inaccessible, we consider the pilots decisions in handling the airplanes during the approaching and landing phases (i.e., continuous descent final approach or dive and drive) as the motivation context in this application. Choosing either of these techniques highly affects the figure of trajectories where quantifying them can be accomplished by measuring the similarity of trajectories at spatial and spatial-temporal dimensions. All in all, the results of the above experiments yield the robustness of the proposed method in similarity measurement of trajectories as well as its sensitivity to slight alterations in dimensions.

Movement is part of all the ecosystems and most of the spatio-temporal processes. Studying movement and movement patterns aid in knowledge extraction and discovering meaningful relationships of dynamic events in our surroundings. Movement of mobile point objects, in the course of time, generates trajectories which analyzing them is highly imperative in different sciences. Among the similarity analysis approaches, detection and similarity measurement has been at focal attentions. In this regard, this research firstly investigates the concept of movement and the way a trajectory is forming. Afterward, moving spaces are presented and the pros and cons of each are outlined. In the following, similarity measurement approaches are introduced and their positive and negative features are concisely addressed. In the end, related studies which have used diverse similarity measurement approaches for various applications are surveyed comprehensively. It is hope that this research provides researchers a better understanding and perception of the process of point objects’ movement and helping them in choosing and implementing the proper similarity measurement approach.

About insert of establishment of the supreme administrative council in paragraph 31 of the second program code for development (1994) and confirmation its sanctioned regulations by President of Iran, it was been propounded some importants questions: What is legal place of the supreme administrative council? Do approved regulations of the council can derogate approvals of the Parliament of Iran? and...