فهرست مطالب a. mohammadzadeh

With the increase in world population and limited energy resources, countries have faced the high demand of energy and energy consumption problem. The crisis that threaten countries and human societies are the limited resources of non-renewable (fossil) energy and the increase in environmental pollution caused by excessive consumption of fossil fuels and global warming. These factors have motivated researchers and investors in the energy sector to control and supply energy from renewable sources. The uncertainty caused by these generations can have many effects on the costs imposed on the network and the operation of the electricity networks, such as an increase in power outages and unsupplied energy. Network development planning is one of the important issues in the power system to meet the growth of electricity demand in the coming years due to urban development, increasing social welfare, energy security, and job creation. The final objective of this model is to minimize energy losses, investment and operating costs, unsupplied energy, and environmental pollutants. The proposed methods have been implemented by MATLAB software on the Garver electricity network and the IEEE 33-bus distribution network and solved by PSO algorithms. The final model can be effectively used for planning the supply chain of the conventional electricity network with the penetration of renewable energy-based generations in various economic, environmental, and social dimensions.

In the recent three decades, building damage map production after an earthquake has been one of the most important challenges in disaster management. For decades, researchers have utilized different kinds of sensors, including airborne LiDAR, RADAR, and optical satellite imagery, in order to create earthquake-induced damage maps. LiDAR and RADAR sensors are disadvantageous due to their high cost and difficulty of acquisition. Additionally, optical satellite images are limited by the lack of timely availability of high-resolution imagery, a low signal-to-noise ratio, and the inability to reconstruct 3D data. On the other hand, the recent development of low-cost and flexible-resolution UAVs along with high accessibility has opened up a great opportunity in this area. Furthermore, UAV data make detecting damage levels possible according to the well-known EMS-98 damage standard. However, there are still a few researchers that have considered international damage standards. When developing a strategy for detecting damage, three important factors should be considered: accuracy, efficiency, and cost. Recently, a lot of attention has been paid to deep-learning models in the field of damage detection. These algorithms require enormous amounts of data, which takes a lot of time and energy. Moreover, such models are unlikely to be general enough due to the sophisticated and numerous types of damage that can occur to a building. So, these issues pose a serious limitation for their practical application in a disaster situation. Since this research put forward a novel and rapid method for detecting the damage to buildings on four levels using a designed removal decision tree. For testing the proposed method, orthophoto and DSM were derived from raw and post-event UAV images of two regions of Sarpol-e-Zahab. An overview of the presented framework for generating the damage map of buildings is depicted in figure1. In summary, our original contributions are described as follows:Taking into account three crucial parameters of crisis management including accuracy, cost, and time efficiency.Presenting a height anomaly index that distinguishes minor damage from major damage. In short, this index represents the percent of negative values in clustered DMS based on DBSCAN.Damage level detection according to EMS-98 standard in four levels, including, “no visible damage to slight damaged”, “Minor damaged”, ”major damaged”, and “collapsed”.Fig.1. The proposed method for detecting damage to buildings in 4 levelsThe results for two study areas of "Sarpol-e Zahab" reveals achieving an accuracy of 86% and a kappa coefficient of 81% for processing 142 buildings within just 15 seconds which indicates the efficiency and time efficiency of the proposed method. In addition, Because of being fully automatic (being unsupervised), the cost of this method is very low. As a consequence, our findings in this research revealed the high potential of UAV data for timely damage detection and rescue after an earthquake. However, the negative effects of shadows and trees are some of the challenges that should be considered in future research.

The educational status of students during their studies in universities is very important. Being aware of the shortcomings and factors related to the educational status and recognizing in a timely manner, it is possible to provide solutions to prevent students᾽ academic failure. Therefore, this research was conducted to study the relationship between educational status and related factors in paramedical students of Gonabad University of Medical Sciences.

This descriptive -analytical study has been done between 2018 and 2020. The study population included students of the paramedical faculty of Gonabad University of Medical Sciences who had spent at least one semester at Gonabad University of Medical Sciences and were selected by census method. Data collection tool was a questionnaire consisting of two parts: demographic information and Pham and Taylor questionnaires that were used to assess the educational status. Data were analyzed through descriptive statistics (Frequency , mean and standard deviation), Pearson and Spearman- Brown correlation coefficient test, independent t- test and one- way analysis of variance by SPSS.

Of the 330 students who were participated in this research, (43.6%) were male and (56.4%) were female. The results showed that (45.8%) had favorable educational status, (53.6%) had average and (0.6%) had unfavorable educational status. There was a statistically significant relationship between academic achievement with marriage (p = 0.006) and field of study (p <0.001). there was a significant relationship between the father’s job and academic achievement (p = 0.008). The results showed that the academic achievement scores of students with average economic status were lower and the grade point average of the whole class who had unfavorable economic status was also lower. In other words, there was a significant relationship between grade point average and economic status (p = 0.026).

According to the findings of the present study, the majority of the students had moderate academic status and some factors related to it were marital status, field of study and parent occupation. In order to promoting academic achievement and preventing students’ academic failure, programs such as student counseling, identifying and helping students at risk, empowering professors to help students and improving their academic motivation can be done.also Providing training workshops and using lesson planning is suggested.

The lack of efficient inventory tools is an old and well-known problem related to in situ forest measurements. Time of flight technology (TOF), using photogrammetric techniques and the ability to detect depth, allows the creation of 3D point clouds and the measurement of various objects, including tree stems. Therefore, this paper presents the accuracy of diameter measurement with TOF technology compared to calipers for six tree species: (Fagus orientalis Lipsky), (Quercus castanaefulia C.A.M. subsp), (Acer velutinum Boiss), (Carpinus betulus L.), (Alnus subcordata C.A.M.), (Parrotia persica C.A.Mey.), differing in stem shape and bark (20 of each species) were studied in the Darabkola Research Forest. Then, the diameters at a height of 1 and 1.30 metres of the tree trunks were measured with a vernier calliper and a point cloud was created using a Phab 2 Pro smartphone. The target diameters were measured using the CloudCompare software. The results showed that there was not much difference between the field recordings and the TOF measurement. However, Acer velutinum, Fagus orientalis and Quercus castanaefulia had fewer measurement errors than Parrotia persica, Alnus glutinosa and Carpinus betulus. Overall, the diameter RMSE at a height of 1 m and 1.30 m was 1.01 cm and 0.87 cm, respectively. According to the Bias index, the values measured with the TOF technology were higher than those measured with the caliper for most species were. Due to the results obtained and the many possibilities of TOF technology, such as the possibility to measure the diameter at different heights, the creation of RGB-3D, the high scanning speed and accuracy, low complexity, the brightness and the lower cost than other technologies, three-dimensional surveying is a good option for forest inventory verification.

Image registration is the technique of aligning two or more images with a reference image all acquired at the same geographical area with different acquisition dates, viewpoints, and imaging sensors. This technique is a fundamental process in photogrammetry and remote sensing tasks, and change detection is known as one of its important applications. Urban change detection with an arbitrary time difference between the two images faces limited access to the required datasets. The Google Earth software is known as an appropriate resource to achieve such datasets. Nevertheless, a significant limitation of this software is the co-registration error of the images acquired at different times. In this work, a combined image registration approach has been proposed to cope with this problem. The proposed method is made up of three major steps. In the first step, the KAZE algorithm is applied to extract and describe image features with high stability against illumination distortions. In the second step, the image matching and mismatch elimination procedures are conducted using the VFC (Vector Field Consensus) technique. Finally, in the third step, the geometric relation between the two images is established using the TPS (Thin-Plate Spline) adaptive transformation model. The results derived from the KAZE feature extraction and matching algorithm are compared with those of the SIFT and SURF algorithms in four satellite image pairs related to various urban areas. For datasets 1 to 4, the highest correct correspondence rates of 0.63, 0.81, 0.6, and 0.76 were obtained using the KAZE algorithm compared to the other algorithms. Moreover, the TPS transformation model established the most accurate and reliable geometric relation between the two images with RMSE values of 2.1, 1.8, 2.1, and 1.7 pixels for datasets 1 to 4, respectively. Accordingly, the results indicate the efficacy of the proposed integrated method for robust feature extraction and image registration of the Google Earth imagery with significant brightness and landscape differences.

One of the economic challenges facing developing countries is the cost of tackling air pollution and improving its quality. On the other hand, in addition to cost, the health of people in the community and existing diseases are also directly related to air quality. Therefore, the study and analysis of changes in air pollutants, including nitrogen dioxide, carbon monoxide, and ozone can provide valuable information for experts to analyze air quality. The presence of high-resolution spatial sensors to study a variety of applications has enabled experts in this field to study most environmental phenomena. In the present study, temporal and spatial changes of air pollutants were determined using Sentinel-5 satellite data in April 2016, simultaneously with the release of Covid-19 virus for Iran, and compared with the values of the same period in 1998. The spread of Covid-19 virus during this period had a variety of consequences that led to a reduction in factory activity as well as a decrease in vehicle traffic. Therefore, the present study seeks to investigate the effect of these factors by analyzing the temporal changes and spatial distribution of pollutants in the interval between these two times. The results confirm the improvement of air quality in this period compared to the previous year, April 1998. According to the results, the concentration of nitrogen dioxide in the entire barley column and tropospheric nitrogen dioxide , which is directly related to transportation and human activities, has decreased.

Change detection is one of the most important processes in photogrammetry and remote sensing, in which occurred changes in a same geographical area are identified over time. Forests are one of the national assets of any country that has a vital role in climate change, groundwater formation and prevention of floods and soil erosion. Thus, an accurate change detection method should be exploited to monitor and maintain forest regions. In this paper, an efficient unsupervised change detection method is proposed for this purpose. Here, two bitemporal sattelite images, acquired at the forest areas of Unitted stasted and Australia are employed  to evaluate the proposed change detection method. In the first step of the proposed approach, discrete wavelet transform was used to generate an efficient change index by fusing of two difference images derived by NDVI and GNDVI vegetation indices. Anisotropic diffusion filtering was then applied to obtain robust change index in which noises was reduced while change regions was highlited. In the next step, the generated index was segmented into changed and unchanged classes using an improved k-means algorithm. Finally, improved MRF model initialed with the initial change map is employed to generate final change map. The proposed MRF model include two novel improvements in the main energy function, resulting in preserveing changed region details. The proposed improved MRF contained superiority of 0.49% and 0.61% compared to traditional MRF in datasets 1 and 2, respectively. The proposed MRF also outperformed Otsu, PCA-kmeans, GAFCM and GMMMRF methods, so that reduced the total error rate by an average of 0.93% and 5.31% in data sets 1 and 2, respectively. In general, the proposed method has a high capability for accurate identifying changes for vegetated areas.

In this paper, a new radial basis function network-based model predictive control (RBFN-MPC) is presented to control the steam temperature of a power plant boiler. For the first time in this paper the Laguerre polynomials are used to obtain local boiler models based on different load modes. Recursive least square (RLS) method is used as observer of the Laguerre polynomials coefficient. Then a new locally recurrent radial basis function neural network with self-organizing mechanism is used to model these local transfer function and it used to estimate the boiler future behavior. The recurrent RBFN tracks system is dynamic online and updates the model. In this recurrent RBFN, the output of hidden layer nodes at the past moment is used in modelling, So the boiler model behaves exactly like a real boiler. Various uncertainties have been added to the boiler and these uncertainties are immediately recognized by the recurrent RBFN. In the simulation, the proposed method has been compared with traditional MPC (based on boiler mathematical model). Simulation results showed that the recurrent RBFN-based MPC perform better than mathematical model-based MPC. This is due to the neural network's online tracking of boiler dynamics, while in the traditional way the model is always constant. As the amount of uncertainty increases, the difference between our proposed method and existing methods can clearly be observed.

Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the Mean Shift Segmentation Method and the HSI Color Model for Road Detection. Initially, the multispectral images were segmented and then NDVI and NDWI spectral indices were created. In addition, the segmented images were transformed to HSI color space. Then, primary road surfaces were detected by Hue, NDVI, and NDWI spectral indices. In addition, the centerlines of roads were extracted using Voronoi diagram-based technique. After extracting of centerlines of primary roads, dangle errors were removed with emphasis on the topological rules and the lengths of dangles. In order to evaluate the proposed method, the Moonah multi-spectral Image provided by the ISPRS was used. According to the evaluation results, the parameters of completeness, accuracy and quality of the proposed method are, on average, estimated to be 98%, 84% and 84%. In addition, the results of the proposed method were compared with the results of five state of-the-art methods. The results demonstrate the high capability of the proposed method in detecting and extracting roads from satellite multispectral images in urban areas.

acquisition field reference data using conventional methods due to limited and time-consuming data from a single tree in recent years, to generate reference data for forest studies using terrestrial laser scanner data, aerial laser scanner data, radar and Optics has become commonplace, and complete, accurate 3D data from a single tree or reference trees can be recorded. The detection and identification of tree species and their precise spatial information are essential for the management of natural or man-made forests, and urban vegetation covers. Terrestrial laser scanners are active remote sensing sensors that offer the ability for generating high-level spatial information for forestry and nature conservation applications. A terrestrial laser scanner acquire detailed tree structure even in the sub-branch level. Hence, geometric information of the trees can be obtained with high accuracy from the terrestrial laser scanner point cloud data.The proposed process in this paper is to first use the laser data points of the terrestrial laser scanner of three different tree species: Quercus_petraea oak tree, Pinus_massoniana pine tree and Erythrophleum bean tree. geometric parameters of these trees These include extracted tree height, base canopy height, canopy height, canopy volume and tree diameter profiles. For each species, there were 12 single tree point cloud data of terrestrial laser scanner that were processed by the reference paper provider and the leaves of the trees were considered as noise and deleted. After the geometrical parameters of these trees have been extracted, considering these geometrical parameters (9 geometrical parameters) as a feature and using support vector machine algorithms and nearest neighbor classification of these three tree species done. It is worth noting that the accuracy of the methods for extracting the geometric parameters of trees has been evaluated by reference data that were produced non- automatically. In classification algorithm support vector machine is implemented in MATLAB programming language and RBF kernel is used for separation of three species and from each 12 point clouds of each species 8 point clouds as training data and 4 point clouds as test data are considered. In classifying the nearest neighbor, the value of K is empirically set when the algorithm is most accurate, and same as the SVM method of the 12 clouds available, 8 clouds are considered as training data and the rest of the clouds as test. One of the prominent goals of this study is to investigate the potential of the SVM and KNN for classificaction of tree species using few geometric features and few training samples.The evaluation results indicate the acceptable achieved accuracy 81% for the SVM algorithm and 74% for the KNN algorithm. In both SVM and KNN methods the accuracy of Q. petraea is 100% because the geometrical and structural features of this species are quite different from the other two species, which is clearly visualized in the images and the difference between the two The other class is completely done. The challenge of this classification relates to the other two species because they have almost identical geometrical parameters.The classification results show that the support vector machine algorithm with less training data performs better than the nearest neighbor algorithm in separating these two tree species.

This study aimed at comparing hope, academic achievement motivation, and academic self-concept among students with and without learning disabilities. To this end, a causal-comparative research method was used. All the fifth and sixth grade elementary students with learning disabilities referred to Miyaneh Learning Disabilities Center and all the fifth and sixth grade elementary students without learning disabilities in Miyaneh city in the academic year 2016-2017 constituted the population of this study. From among this population, 40 students with learning disabilities and 40 students without learning disabilities were selected as the sample through simple random sampling. Hope Scale (Snyder), Academic Achievement Motivation Scale (Harter), and Academic Self-concept Questionnaire (Mendaglio & Pyryt) were employed for data collection. The data were analyzed using multivariate analysis of variance (MANOVA). The results showed that the school students with learning disabilities had significantly lower levels of hope, academic achievement motivation, and academic self-concept than the ones without learning disabilities. The low levels of hope, academic achievement motivation, and academic self-concept among students with learning disabilities may have undesirable psychological, behavioral, and academic consequences; therefore, it appears necessary to conduct psychological intervention and instructional strategies to enhance the degree of hope, academic achievement motivation, and academic self-concept in these students.

Hypothyroidism may cause symptoms and disorders in different parts of the body which can lead to speech disorders. This research was done to recognize and identify speech disorders in patients with Hypothyroidism.

This descriptive study was done on 120 subjects with hypothyroidism, with T4 <4.5 μg/dL and TSH >4mU/L. Members of the study team interviewed all subjects, performed a complete physical examination and documented the data on a pre-designed questionnaire. Voice parameters analysis was completed by MDVP soft ware. All data was statistically analyzed. Spearman correlation coefficient was used for quantitative variables and Mann Whitney and chi square tests for qualitative variables.

One-hundred and six patients were females. The prevalence of different speech disorders was as follows: Voice disorders 81 %, Stuttering 11%, Articulation disorders 6%, Hyper nasality 6%. There was a significant decrease in basal frequency, (P<0/001), and a low voices, (bass), was present in 60% of these patients. There was also abnormal increase in most parameters of voice in studied subjects. Dryness of the laryngopharyngeal region was a common complaint, (53%). Prevalence of sensory and auditory signs was more common in women, (P<0.001). Globus sensation was reported only in women and stuttering was more common in men. Voice disorder was the only speech disorder that was directly related to the elevation of TSH level, (p < 0.003).

Voice disorder is the most common speech disorder in hypothyroid patients and may be helpful in the diagnosis of hypothyroidism. Voice disorders are more common in women. Stuttering is prevalent in hypothyroidism and hypothyroidism is an important cause of speech disorders.

Although injection of opium or anti-inflammatory drugs (IV or IM) is common for reducing postoperative pain there is limitation in use of them because of their side effects.

In this study the efficacy of intrathecal Midazolam in the management of acute postoperative pain in C/S has been examined.

151 patient scheduled for cesarean section (C/S) under spinal anesthesia was performed by 80 mg of Lidocain Hyperbaric Solution plus 2.5mg Midazolam as case group. In control group 76 patient spinal Anesthesia was performed with 80mg Lidocain Hyperbaric. In postoperative period, Visual Analoge Score determined pain evaluation at one and three hours after C/S.

The mean of VAS in 1 and 3 hours after C/S in case and Control group was (2.5 ± 1.2, 4.9± 1.9), (5.9±1.7, 8.8± 1.5) Respectively with (P< 0.0001). Ketamine injection, Hypotension and shivering in case group is lower than control group (P<0.01) (1.3% versus 50%), (29.3% Versus 42.1%), (%4 Versus 57.9%) Respectively.

Interatecal midazolam without side effect can reduce postoperative pain.

One of the anesthetic techniques with a lot of clinical benefits is spinal block that is produced by injection of local anesthetic solution into the subarachnoid space. This procedure caused 15-18% reduction in peripheral vascular resistance. Decreased blood pressure as a complication of spinal block may be seen in one third of cases. Intravenous fluids before induction is recommended for prevention of this event. There are contradictory studies in this Issue. Propose of this study was determination the effects of Crystalloid administration as a prehydration before operation on decrease of blood pressure. In this randomized clinical trial, double- blind study we selected 112 patients 60-90 years old who did not have any history of hypertension and CHF and antihypertensive medication and were candidate for elective lower abdomen operation. After taking informed consent, patients divided into 2 groups randomly. 500cc Ringer was infused 20 minutes before spinal anesthesia for the first group, second group received no intervention. Blood pressure after 5th, 10th, 15th, 20th minutes was reported. Blood pressure lower than 90-mmhg or more than %30 lower than baseline considered as decreased blood pressure and treated with 5mg Ephedrine. Data was analyzed with SPSS/9 software. We used Chi square and student T-Test, P<0.05 was considered meaningful. There was no significant difference between sex, age, weight and height with level of anesthesia in patients. The rate of decreased blood pressure in 5th minute in first group was 34(60.7%) and in second group was 27(48.2%) which was no significant difference. Ephedrine administration was 34mg in first and 27mg in second group. In both groups 5 persons (8.9%) had nausea and vomiting after spinal anesthesia. In this study Crystalloid administration as prehydration did not reduce occurrence of hypotention. This procedure is not useful for aging people and more studies for prevention of this complication is recommend.

Developing social security and therapeutic services to all population in society on one hand and non-development of facilities and the centers providing therapeutic services according to them on the other necessitates making basic improvements in medical organizations in order to increase services provision via increasing benefits and making them more effective. A branch of management discipline entitled "development and revolution in the organization" is responsible for it. Familiarization with this science especially in developing countries plays a pivotal role in the development of these countries especially when we consider that they are encountered with restrictions in resources and capital and should get the most benefit from the least facilities. In this study, first we will present a comprehensive description of this novel management discipline, and then its application in the development and enhancement of organizations providing healthcare services will be focused.