مریم سجادیان

UAV-based images are now widely used to generate large-scale orthophoto mosaics. The generation of an orthophoto mosaic is divided into two stages: image registration and image stitching. Using the DSM associated with each image, orthophotos are generated throughout the image registration. The single orthophotos generated are then joined to each other step by step in the second stage, utilizing various methods of image stitching. Image stitching methods depend on the complex and challenging processes of image matching and seamline determination. In addition, the registration and stitching of images in UAV-based mapping projects with a significant number of images is a time-consuming process. In this study, a straightforward method is provided for creating large-scale true orthophoto mosaics from UAV-based images without the requirement to generate single orthophotos, image registration and seamline network determination. Instead of registrating the images and then stitching them step-by-step, this method processes the DSM of the entire area and all of the images simultaneously. First, for each DSM point, the optimal image is determined from among all the visible images based on an optimization procedure. Optimization is based on two criteria: distance from nadir point and distance from the projection center. Using the determined optimal images, the differential rectification procedure is then run, and the orthophoto mosaic cells are filled. The results of this investigation demonstrated that the proposed method yielded a mosaic with minimal changes along the seamline. In addition, the proposed method is compared with the conventional orthophoto mosaic production method, which is based on image matching and determination of seamlines. Evaluations indicate that the proposed method is able to increase the production rate of orthophoto mosaic by 39% and 45% in dataset 1 and 2 respectively. Additionally, the geometric accuracy calculated using the checkpoints in the orthophoto mosaic generated by the suggested method has decreased by an average of 2 cm, indicating more precise results.

Auditory-verbal working memory is the ability to learn, retain, and recall syllables and words. Rey auditory – verbal learning test is one of the common and popular methods to evaluate auditory – verbal memory. Age and education are demographic factors affecting this parameter. The purpose of this study was to investigate these factors on the results of the Persian version of the test.

This descriptive, analytic study was performed on 50 women aged 40-60 years with normal hearing at two age groups (25 women 40 to 49 years old and 25 women 50 to 59 years old), who were selected by convenience sampling method using the Persian version of the Rey Auditory-Verbal Learning test.

A significant correlation was observed between the mean of total recall score, recall after interference score, and delayed score at women 40 to 49 years old (p< 0.01). Also there was a significant positive correlation between education and these scores (p< 0.01).

According to the results of this study, it seems that the beginning of decrement changes in memory occurs before the aging, which necessitates the need to consider and take preventive measures to reduce the incidence of inability in the elderly more and more. Furthermore, education can be considered as a major demographic characteristic on this test.

Genetic identification is now a standard and efficient method for identifying individuals and investigating claimed kinship cases. The aim of this study was to investigate the relationship between Y-STR and autosomal STR markers.

This case report is according to a claimed kinship that was done with available sampling of 7 relatives referred to forensic medicine in 2020. These members were 2 men and 2 women without identity cards and 3 men with identity cards claiming the uncle-nephew and aunt-nephew relationships. After authentication and obtaining the complete biographies of the nominees, the genealogy was drawn. Blood samples were analyzed using autosomal STR and Y-STR markers by Yfiler ™ Plus and GlobalFiler ™ Express kits and were electrophoresed with ABI 3500 genetic analyzer. Since to prove the claimed relative relationship, the common father of the grandfatherchr('39')s child must be common, so the allelic inheritance between them was examined.

The results of the Y-STR indicated that the alleged uncle-nephew relationship was not ruled out. However, the results of autosomal STR markers indicated the status of Amelogenin Y-null in the profiles of all males in the pedigree. Comparison of the obtained genetic profiles showed that there was no inheritance of the obligatory paternal allele in the 4 loci D8S1179, D2S441, THO1 and D10S1248 between A and H, which definitely negated the existence of a father-son relationship between the two individuals; Hence, the alleged uncle-nephew and aunt-nephew relationship was rejected.

Autosomal STR markers should be used to examine complex relative relationships, and the use of Y-STR markers should be limited to genetic testing of male specimens in rape evidence, crime scenes, and non-paternity cases.

Auditory verbal working memory is defined as the ability to learn, retain, and recall verbal syllables or words. Various factors like age, gender, education, and change in endocrine secretions, such as gonads, can affect the acquisition of memory and learning test scores. In the present study, the effect of menopause on changes in hormonal secretion was studied on women's working memory.  A descriptive-analytic study was performed on 25 menopause women, aged 50–59, with normal hearing and who had had their menopause during the past two years or more.  Sampling method was available sampling which was perfromed using the Persion version of the Rey auditory – verbal test. In order to investigate the status of data distribution we used Shapirovailk test, to examine the relationship between immediate recall and delayed recall and delayed recall, Wilcoxon test was run, to study the correlation between stages 1 to 5, Friedman test was used, and to investigate the relationship between age, education, and years of menopause, Spearman's correlation coefficient was run. The results showed that there is a significant relationship between the scores of trails 1 to 5. There was also a significant relationship between delayed memory with recognition score and also between education and some scores of memory test. Menopause in the age range of 50 to 59 years does not affect verbal auditory memory and it seems that the level of education will have a greater impact on the acquisition of scores in a decade of life.

As there is not any study on the relationship between temperament and personality traits and personality disorders in parents of children with attention deficithyperactivity disorder; moreover, many challenges posed in parents of these children. Therefore, the current study aimed to determine the relationship between temperament and personality traits and personality disorders in parents of children with attention deficit hyperactivity disorder compared to the healthy ones.
Methods and Materials: This cross-sectional descriptive-analytic study has been conducted at Roozbeh psychiatric hospital in Tehran in 2017. Samples included 45 adults having children with attention deficit hyperactivity disorder and 45 adults having healthy children. Data were collected through Conner’s Parent Rating Scales, Strengths and Difficultries Questionnaire (SDQ), Temperament and Character Inventory (TCI), Symptom Checklist-90-Revised (SCL- 90-R), and the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II). Data were analyzed through Pearson correlation using SPSS version 20.
The findings demonstrated that there was a significant correlation between personality traits and personality disorders (PDiscussion and Persistence does not predict personality disorder. Selfdirectiveness and low-level co-operativeness are predictors of almost all types of personality disorder. Ultimately, self-transcendence trait is not related to personality disorder.

Nowadays the three-dimensional presentation of real world features is very important and useful, and attracted researchers in various branches such as photogrammetry and geographic information systems and those interested in three-dimensional reconstruction of the building. Buildings are the most important part of a three dimensional model of a city, therefore extraction and modeling buildings of remote sensing data are important steps to build a urban digital model.
Although many efforts to reconstruct the three-dimensional building of LiDAR data have been made by researchers in recent years, but challenges still exist in this area, especially in urban areas. In previous studies, dense vegetation and tall trees in the vicinity of the buildings cause to difficulty in the building extraction process and reduction in the accuracy of the modeling results. The aim of this article is extraction and reconstruction of buildings by using LiDAR point clouds in urban areas with high vegetation. In this study, factors such as the LiDAR return pulse, the height of points and area of the region is used to separate the non-structural parts. Ground points in segment-based method by changing the size segment in each iteration by mean and standard deviation of the height of points in any segment extracted. The vegetation points are extracted and identified using LIDAR return pulse and a new method called "three-dimensional imaging of points on two-dimensional surfaces ". The projection process is done in the planes of XY, XZ and YZ. Using Illustration of points and changing the angle of view makes the point clouds be evaluated in different directions. Region expanding algorithm and length constraints imposed in different planes has an important role in the separation of dense vegetation. The modeling of building is done by using break lines and important vertices of the building roof in layers of roof height. Extraction of building edge points and height layers of roof is done separately in each building. This points are isolated by height analysis of the roof points. In the line approximations grouping the points in each height layer, line fitting and adjustment of line directions are factors that caused the break lines and points of the building roof to be correctly created. In roof modeling, the basic structure of the roof is modeled and then the parts on the roof are added to the model. The overall structure of the roof is made by roof vertexes and normal vector of generated planes. At the end, by calculating the point’s distances from roof plane, the roof parts are identified and the model of this components are added to the roof. The proposed method is evaluated on LiDAR point clouds in an area of the Stuttgart, Germany, with a density of 4 points per square meter.
The accuracy of the proposed method is evaluated by visual interpretation and quantitative comparisons with information extracted by a human operator. The accuracy of proposed method is about 96 percent in extracting building points and modeling error at the corner of the building is approximately 44 cm. Overall, the results represent the success of the proposed method in extracting and modeling of buildings in areas with dense vegetation.

In two-thirds of cases, Attention Deficient Hyperactivity Disorder (ADHD) Continuing ADHD to adulthood causes many problems and disturbs the persons social, educational, and family performance. This study aimed to compare the personality traits of patients with ADHD with healthy individuals.
Methods and Materials: This cross-sectional descriptive-analytic study was conducted in Roozbeh Psychiatric Hospital in Tehran during the years of 2016-2017. 100 adults with ADHD and 100 healthy adults were participated in the present study. Data were collected through The TCI, SCL-90 and DIVA questionnaires. Data were analyzed by one way analysis of variance.
There were statistically significant differences in the 7 dimensions of TCI (Novelty seeking, Harm Avoidance, Reward Dependence, Persistence, Co-operativeness, Selfdirectiveness, Self-Transcedenc) between ADHD patients and healthy subjects (p Discussion and The personality traits of patients with ADHD were significantly different from healthy ones.

Attention deficit hyperactivity disorder (ADHD) is common in adults, and associated with comorbidities and negative consequences in many parents of children with ADHD. The aim of this study was to determine ADHD and personality disorder in parents of children with ADHD compared to the healthy group.
This cross-sectional descriptive and the analytic study were conducted in 2015 in Roozbeh Psychiatric Hospital Tehran and the sample group consisted of 45 adult subjects with ADHD children and 45 healthy adults who were chosen control method. questionnaires Conner’s adult attention deficit hyperactivity disorder questionnaires, Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II), Conner’s parent’s scales between the two groups were completed, and Strengths and Difficulties Questionnaire (SDQ) and Symptom Checklist -90- Revised (SCL-90-R) were completed in the group controlling. And for parents who had a score above the cutting point for the diagnostic assessment of personality disorder Structured Clinical Interview for DSM-IV Axis II Disorders, to assess personality disorders, as well as for the diagnostic assessment of ADHD in adults Diagnostic Interview for ADHD in Adults (DIVA) was conducted.
The incidence of ADHD in parents of children with ADHD than parents with healthy children was 6/5 equivalent and the prevalence of B cluster disorders, C cluster disorders, passive-aggressive personality disorder and depressive personality disorder in parents of children with ADHD was higher than the cut-off point parents with healthy children (P˂0.01). The most common subtypes in parents of children with ADHD were respectively mixed (attention- hyperactivity) (38.5%) and attention deficit (30.8%).
The prevalence of ADHD and personality disorders in parents of children with ADHD are more than parents with healthy.

In recent years, the techniques of surface representation have been changed and three-dimensional models have been replaced with two-dimensional maps. Airborne laser scanner as a powerful system has been known in remote sensing as a valuable source for 3D data acquisition from the Earth’s surface which can mainly be employed for 3D reconstruction and modeling. 3D reconstruction of buildings as an important element of 3D city models, based on LiDAR point clouds has been considered in this study. A new non-parametric method is proposed for generation of 3D model of buildings with flat and tilted roof. The approach comprises of four steps for 3D building reconstruction as: (A) Simultaneous building extraction and segmentation, (B) Edge detection, (C) Line approximation, and (D) 3D modeling. In step (A) a multi-agent method is proposed for extraction of buildings from LiDAR point clouds and segmentation of roof points at the same time. In this method five criteria such as height values, number of returned pulses, length, normal vector direction based on Constrained Delaunay Triangulation, and area are utilized. Next, in step (B) the edge points of roof segments are detected. Points of triangles having no neighboring triangles are extracted as primary edge points. In the extraction process, noises, external objects, and tree points on the roofs are eliminated. It is an advantage of the proposed method, however it leads to create the undesired edge points. There is the same problem regarding to segments which contain overlap with each other (like flat building). These undesired edge points as internal points are known and must be removed. In this paper, a method named “Grid Erosion” is employed for removing these internal points and therefore finding real edge points. After detecting the final edge points, a RANSAC-based algorithm is employed to approximate building lines in step (C). RANSAC is a powerful technique in line fitting and in comparison with general least square method, especially with noisy data, it provides robust results. In order to reduce the sensitivity of RANSAC to select parameters and no need for heavy post-processing, edge points are grouped by considering the angle between two consecutive connecting convex points. After classification of edge points, a RANSAC algorithm is separately applied on each classified edge-points group to produce primary lines. The regularization constraints should be applied on primary lines to generate the final lines. Finally, by modeling of the roofs and walls, 3D buildings model is reconstructed in step (D). The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. Building roof model is manually digitized from LiDAR point clouds and compared with building roof models that reconstructed using proposed method. In model reconstruction, the dominant errors are close to 30 cm which is calculated in horizontal distance. The main advantage of this method is its capability for segmentation and reconstruction of flat buildings containing parallel roof structures even with very small height differences (e.g. 10 cm). The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models.

Nowadays, automatic processing and object extraction from data acquired by airborne sensors has been an important research topic in photogrammetric institutes. Airborne laser scanning system, which is commonly referred to as LiDAR, is a superior technology for three-dimensional spatial data acquisition from Earth’s surface in high speed and density. 3D city modeling is one of the main applications of LiDAR system. An important first step to reconstruct the building as one of the most important components of urban models is to identify and separate the building points from other points such as; terrain, trees, and vegetation. In addition, building roof segmentation is another important step in point cloud processing. In this paper, a multi-agent strategy is proposed for simultaneous extraction of building and segmentation of roof points from LiDAR point cloud. First, the ground and vegetation candidate points are removed from building points using local minimums of heights and returned pulse. Then different segments are extracted by analysis of the triangles formed on the remaining points by means of region growing method based on normal vectors. Finally, building segments are separated from other segments using area criterion and the united building segments are detected using a new method named ‘Grid Dilation’. The proposed method has been tested on the LiDAR data of the Vaihingen city, Germany. In addition to a visual interpretation, a quantitative assessment has been done. Due to lack of a ground truth, control points was selected manually from LiDAR point cloud and compared with points that classified using proposed method. The proposed method extracts the roof of buildings with an accuracy of 93%. Overall, the results indicate that proposed method could successfully identify the building segments without using additional resources, such as map or aerial photo. The main advantage of this method is its capability for extraction and segmentation of buildings containing parallel multi-level roof structures even with a very small height differences (e.g. 10 cm).