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Forest burned area (FBA) detection using remote sensing (RS) data is critical for timely forest management and recovery attempts after wildfires. This study introduces a dual-path attention residual-based U-Net (DPAttResU-Net), a novel end-to-end deep learning (DL) model tailored for FBA detection using dual-source post-fire Sentinel-1 (S1) and Sentinel-2 (S2) satellite RS imagery. To better distinguish FBAs from other land cover types, DPAttResU-Net incorporates a dual-pathway structure to exploit complementary geometrical/physical and spectral features from S1 and S2, respectively. An integral component in the proposed architecture is the channel-spatial attention residual (CSAttRes) block, which emphasizes salient features through the channel and spatial attention modules, thus improving the burned area feature representation. To compare DPAttResU-Net to state-of-the-art DL models, experiments were conducted on benchmark FBA datasets collected over 12 areas, where ten datasets were used as training data and two datasets were used to test the trained DL models. The experimental results demonstrate the high proficiency of the proposed deep model in meticulously delineating FBAs. In further detail, DPAttResU-Net, with a PFN of 17.96 (%) in the first case and an IoU of 89.31 (%) in the second case, outperformed the existing U-Net-based models. Accordingly, through dual-path integration and attention mechanisms, DPAttResU-Net contributes to accurately identifying FBAs by preserving their geometrical details, making it a promising tool for post-wildfire forest management.

Key Clinical Message In this study, we introduced one of the rarest concomitants of the absence of left pulmonary artery (LPA), which was seen in our patient along with patent ductus arteriosus (PDA) and ventricular septal defect (VSD). Abstract Unilateral absence of pulmonary artery (UAPA) is a congenital heart disease in association with other abnormalities such as tetralogy of Fallot and septal defects or isolated in 30% of cases and occurs in the right lung in two thirds of cases. Our case is a 33‐year‐old man who was hospitalized with symptoms of cough, shortness of breath, and hemoptysis. The echocardiography revealed a large ventricular septal defect, absent left pulmonary artery, and severe pulmonary hypertension (PH) along with patent ductus arteriosus. These findings were confirmed by CT angiography. This association has rarely been found in past studies. Due to PH and pulmonary infection, the patient was treated with intravenous prostaglandin and antibiotics. However, in cases of timely diagnosis and treatment of UAPA, fatal complications such as pulmonary hypertension, morbidity, and mortality are reduced. This case emphasizes the importance of awareness of this abnormality and its associated anomalies to enable early diagnosis and treatment.

ObjectivesThe percentage of shunt fraction significantly impacts the management of patients with congenital shunts, influencing strategic choices such as surgical or interventional procedures. This study compared the estimated shunt fraction (the ratio of pulmonary-to-systemic flow, Qp/Qs) for quantifying the left-to-right shunt in children with ventricular septal defect (VSD) using heart catheterization, four-dimensional (4D) flow, and two-dimensional (2D) flow magnetic resonance imaging (MRI). The goal was to establish a non-invasive and reliable measurement ratio between pulmonary and systemic blood flow in these patients.MethodsBetween July 2022 and June 2023, patients scheduled to undergo invasive right heart catheterization were included in this study. MRI was performed one hour before the catheterization procedure. The correlation of shunt fraction was assessed between all methods after calculating the Qp/Qs ratio from 2D and 4D flow MRI and catheterization.ResultsA total of 24 patients (aged 3–15 years, eight females) were ultimately included in the study. The Qp/Qs ratios obtained from 4D flow had a robust correlation (correlation coefficient r = 0.962) compared to those obtained during catheterization. Cardiac catheterization recorded the mean shunt fraction at 1.499 ± 0.396, while 4D flow measured it at 1.403 ± 0.344, with no significant difference between the two techniques. Moreover, there was a reasonable correlation (r = 0.894) between 2D flow measurements of Qp/Qs and the results obtained from catheterization, with a mean shunt fraction of 1.326 ± 0.283.Conclusion4D flow MRI has the potential to be a non-invasive method for accurately measuring the left-to-right shunt in children with VSD.

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

Background and Aim: Beta thalassemia often leads to iron overload in various tissues, including the heart. Current diagnostic methods rely on MRI, which may not be feasible in certain situations. This study aimed to investigate the correlation and diagnostic accuracy between serum ferritin levels, echocardiography results, and T2-weighted cardiac MRI values in patients with beta-thalassemia major to diagnose myocardial iron overload without the necessity of MRI. Methods: This cross-sectional study included 102 patients with beta-thalassemia major who underwent echocardiography and MRI. Serum ferritin levels were measured, and myocardial T2 weighting was calculated. The cut-off values for MRI interpretation were defined as T2>20 milliseconds for normal myocardial iron load, T2*≤2010 milliseconds for mild to moderate myocardial iron burden, and T2*≤10 for severe myocardial iron burden. Results: The mean ferritin level was 2418.7±2860.8 ng/ml, mean heart T2* was 22.6±14.7 ms, and mean ejection fraction (EF) based on MRI was 51.7±11.2%. The echocardiographic parameter MVDT was identified as a suitable diagnostic indicator for reduced and severely reduced T2*. The sensitivity and specificity of ≤170 MVDT for T2 reduction were 0.72 and 0.62, respectively, and the sensitivity and specificity of ≤150 MVDT for severely reduced T2 were 0.52 and 0.96, respectively. Conclusion: The findings suggest that serum ferritin levels, EF, and MVDT in echocardiography have the highest diagnostic efficiency for detecting reduced T2*. In settings where cardiac MRI is unavailable or contraindicated, the MVDT parameter and ferritin levels can be valuable for evaluation purposes.

Background: Multi-grade classes, in which a teacher is responsible for teaching students in different educational levels, is a way to make rural children more accessible to education. Despite the negative effects of this type of education, such as less attention to the individual needs of learners, few researches point to the increase of learners' social skills, richer educational environment, better social participation and growth of self-esteem. One of the fields to resolve this contradiction is the comparison of multiple intelligences in multi-grade and one-grade students, but there is a research gap in the relevant research background. There are different views on multi-grade classes. Some researches consider multi-grade classes as one of the problems of the educational system that must be eliminated, and some others consider the existence of these classes as an opportunity for optimal education. The results of previous studies on the benefits and limitations of multi-grade classes for students are contradictory. One of the areas to resolve this contradiction is the comparison of multiple intelligences in multi- grade and one-grade students, about which there is a research gap. Aims: The present study was conducted with the aim of comparing moral intelligence, emotional intelligence and social intelligence in students of one-grade and multi-grade classes. Methods: The design of the current research was a causal-comparative description. The statistical population of the research included all one-grade and multi-grade students of Khodaafrin city in the academic year of 2021-2022, who were studying in the sixth grade. The sample size was determined based on Morgan's table (239 people) and then sampling was done in three levels: cluster, class and simple random. In this way, to determine a sample of single-grade students from among the one-gradm schools of Khoda Afarin city, a few schools were randomly selected. Also, some schools were randomly selected from the multi-grade rural schools. Then, according to the size of each class (one-grade and multi-grade) in the statistical population, a total of 239 students (147 one-grade students and 92 multi-grade students) were selected randomly. In order to collect data, questionnaires of emotional intelligence (Schutte et al., 1998), moral intelligence (Lennick & Kiel, 2005) and Tromsoe social intelligence (Silvera et al., 2001) were used. Multivariate analysis of variance and SPSS26 software were used for data analysis. Results: The results showed that the students of multi-grade classes have obtained higher average grades in all three variables of emotional intelligence, moral intelligence and social intelligence, but this difference was not significant in terms of moral intelligence scores (P>0.05), but significant in emotional intelligence and social intelligence. was (P

Immunoglobulin G4-related disease (IgG4-RD) is a chronic fibro-inflammatory disorder of obscure etiology characterized by significant infiltration of IgG4-positive plasma cells toward several organs. Coronary artery involvement is rarely seen in IgG4-RD patients; thereby, we aim to outline the noninvasive imaging findings of this rare case. Cardiac magnetic resonance (CMR) and coronary computed tomography angiography (CCTA) from a 15-year-old female diagnosed with IgG4-RD via histopathological assessment of orbital biopsy, were analyzed. CMR showed a severely reduced left ventricular ejection fraction and akinesia of the basal to mid-lateral, anterior, and septal walls. Inflammation of the basal to apical lateral wall and subendocardial infarction of the basal to apical lateral and mid inferoseptal walls were also evident. CCTA findings showed stenosis in branches of the left main artery (LM), left anterior descending artery (LAD), and right coronary artery (RCA), aortitis, and aortic wall thickening. After courses of proper treatment with prednisolone, Cellcept, and adalimumab, follow-up CMR showed significant improvement in LV systolic function and resolution of inflammation. Although IgG4-RD is an uncommon cause of coronary artery disease, it can cause lethal complications such as myocardial infarction. Hence, clinicians should be aware of cardiac complications in these patients.

A new, rapid, simple, and sensitive preconcentration method and Spectrophotometry determination technique have been presented for the microextraction and determination of trace amount of Tartrazine dye in food samples. In the present system, which is called “Magnetic stirring in syring dispersive liquid-liquid microextraction''(MSIS- DLLME), a cloudy state result is formed in a homemade glass syringe by magnetic agitation. In the MSIS-DLLME system, Tartrazine colour was uprooted into an organic detergent (Toluene) after many twinkles. Subsequently, the organic detergent which was placed on top of the result was transferred into a narrow neck by moving the piston overhead. The effective parameters on the extraction recovery were studied and optimized by Central Composite design (CCD). Under the optimum conditions, the estimation cure is direct in the range of 0.1–1(μg L− 1). The limit of detection (LOD), relative standard divagation and enrichment factor were 0.03 μg L −1, ±4.6 (n = 10) and 166, independently. The advanced system was successfully applied for microextraction of Tartrazine in food samples.

Relative radiometric normalization (RRN) is a critical pre-processing step that enables accurate comparisons of multitemporal remote-sensing (RS) images through unsupervised change detection. Although existing RRN methods generally have promising results in most cases, their effectiveness depends on specific conditions, especially in scenarios with land cover/land use (LULC) in image pairs in different locations. These methods often overlook these complexities, potentially introducing biases to RRN results, mainly because of the use of spatially aligned pseudo-invariant features (PIFs) for modeling. To address this, we introduce a location-independent RRN (LIRRN) method in this study that can automatically identify non-spatially matched PIFs based on brightness characteristics. Additionally, as a fast and coregistration-free model, LIRRN complements keypoint-based RRN for more accurate results in applications where coregistration is crucial. The LIRRN process starts with segmenting reference and subject images into dark, gray, and bright zones using the multi-Otsu threshold technique. PIFs are then efficiently extracted from each zone using nearest-distance-based image content matching without any spatial constraints. These PIFs construct a linear model during subject–image calibration on a band-by-band basis. The performance evaluation involved tests on five registered/unregistered bitemporal satellite images, comparing results from three conventional methods: histogram matching (HM), blockwise KAZE, and keypoint-based RRN algorithms. Experimental results consistently demonstrated LIRRN’s superior performance, particularly in handling unregistered datasets. LIRRN also exhibited faster execution times than blockwise KAZE and keypoint-based approaches while yielding results comparable to those of HM in estimating normalization coefficients. Combining LIRRN and keypoint-based RRN models resulted in even more accurate and reliable results, albeit with a slight lengthening of the computational time. To investigate and further develop LIRRN, its code, and some sample datasets are available at link in Data Availability Statement.

Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the interactions between oceans and the atmosphere. In this regard, synthetic aperture radar (SAR) systems, with unique capabilities (e.g., day-night and almost all-weather data acquisition), provide valuable datasets for ocean studies. Many studies have exploited the applications of SAR imagery for oceans and have proposed numerous methods to study oceanographic parameters. In this study, a brief introduction to SAR and the interaction between microwave signals and the ocean surface are initially provided. Then, the important spaceborne and airborne SAR systems for oceanographic applications are summarized. Subsequently, 12 different applications of SAR systems in the ocean are comprehensively discussed, and the advantages and disadvantages of SAR systems for ocean studies are extensively explored. Finally, the research trend on SAR applications in the ocean is provided by analyzing all the relevant papers published between 1973 and the end of December 2022, and the existing challenges are discussed for future studies.

Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the potential of the proposed graph-based method for individual tree detection in a complex forest region in Mazandaran, Iran. In particular, the graph-based method obtained Precision, Recall, and F1-score values of 0.64, 0.73, and 0.68, respectively. Furthermore, the intercomparison with the well-known and most used Local Maximum (LM) suggested the applicability of the proposed method. After point cloud generation, the proposed method was implemented entirely in Python using open-source packages, which increases its applicability for other scholars and managers. The source code of the proposed algorithm can be found at https://github.com/Seyed-Ali-Ahmadi/Graph-based_ITCD.

Background: Evidence suggests that low family differentiation leads to anxiety throughout the family and affects children's social functioning and psychosomatic symptoms. Despite numerous studies examining system family therapy's efficacy on various pathological outcomes, including increased differentiation and psychological health, there are rare studies on psychosomatic disorders. Aims: This study aimed to evaluate the efficacy of Bowen Family System Therapy on chronic anxiety and differentiation in patients with psychosomatic disorders (migraine, gastrointestinal disorders, and cardiovascular disorders). Method: The present study was quasi-experimental with a pretest-posttest design and a control group. The statistical population of this study consisted of all patients with three types of psychosomatic disorders including migraine, gastrointestinal and cardiovascular disorders which were referred to Shariati Hospital and Tehran Heart Center in 2020. Thirty eligible people were selected by the available sampling method and randomly assigned to two groups. All of them completed the Beck Anxiety Inventory (BAI; 1988) and short-form of Differentiation of Self Inventory-Revised (DSI-R; Skowron & Friedlander, 1998) in the pretest and posttest stages. The experimental group received Bowen Family System Therapy. Finally, after data collection, a multivariate analysis of covariance was used to analyze the data using SPSS-version 19 software. Results: The results showed that there was a significant difference between the two groups in both variables of anxiety, differentiation, and all their components (P

When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003).

Background: The coronary artery calcium score (CACS) indicates cardiovascular health. A concern in this regard is the ionizing radiation from computed tomography (CT). Recent studies have tried to introduce low-dose CT techniques to assess CACS. We aimed to investigate the accuracy of iterative reconstruction (IR) and threshold modification while applying low tube voltage in coronary artery calcium imaging. Methods: The study population consisted of 107 patients. Each subject underwent an electrocardiogram-gated CT twice, once with a standard voltage of 120 kVp and then a reduced voltage of 80 kVp. The standard filtered back projection (FBP) reconstruction was applied in both voltages. Considering Hounsfield unit (HU) thresholds other than 130 (150, 170, and 190), CACS was calculated using the FBP-reconstructed 80 kVp images. Moreover, the 80 kVp images were reconstructed utilizing IR at different strength levels. CACS was measured in each set of images. The intraclass correlation coefficient (ICC) was used to compare the CACSs. Results: A 64% reduction in the effective dose was observed in the 80 kVp protocol compared to the 120 kVp protocol. Excellent agreement existed between CACS at high-level (strength level = 5) IR in low-kVp images and the standard CACS protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05). Increasing the threshold density to 190 HU in FBP-reconstructed low-kVp images yielded excellent agreement with the standard protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05) and good agreement in score zero (ICC = 0.84 and p = 0.02). Conclusions: The modification of the density threshold and IR provides an accurate calculation of CACS in low-voltage CT with the potential to decrease patient radiation exposure.

Background: Spinal Anesthesia (SA) as a safe, reliable, and cost-effective method is widely used for Cesarean Section (CS). However, it is sometimes complicated by Post-Dural Puncture Headache (PDPH). Objectives: We investigated the frequency of PDPH in CS under SA and related risk factors in an academic referral hospital in the north of Iran. Materials & Methods: This descriptive cross-sectional prospective study was conducted at Alzahra hospital as an academic referral center affiliated with the Guilan University of Medical Sciences on 147 eligible cases During from May 2019 to October 2019. Then questionnaires containing questions about patients’ demographic data and some PDPH-related factors were filled out via face-to-face interviews. The collected data were analyzed by SPSS v. 21 using Chi-squared and and Fisher’s exact test. Results: The incidence of PDPH was 6.1%. Early ambulation, the number of attempts, and having a history of headache were significantly associated with a higher incidence of PDPH (P=0.001), while body mass index (P=0.106), age (P=0.093), and residents’ experience (P=0.384) had no significant association. None of our cases experienced a severe or persistent headache. Conclusion: The main predisposing factors for PDPH were found to be the history of HA, early ambulation, and the number of dura punctures. Women at risk for PDPH should be objectively screened before CS under SA.

Objective: Due to the importance of citizen participation in governance, a lot of research has been done to explain this concept and each of them has looked at it from one angle, and this has caused confusion and dispersion in explaining the concept of citizen participation. The purpose of this study is to provide the conceptual model of citizen participation in order to organize previous studies Methods: This qualitative research has an applied orientation and its approach is deductive and its time horizon is one-section and the data collection method is documentary study and thematic analysis technique has been used to analyze the data. Results: In this research, in the first stage four overarching themes of “Types of citizen participation", "factors affecting citizen participation", "requirements of citizen participation" and "consequences of citizen participation" were defined through studying the literature of participation and citizen participation. Then the main themes and sub themes of these overarching themes were identified by gathering and analysing the studies about these overarching themes. In consequence the main themes of participation method, degree of concentration, participants, subject, context and time were identifed under the overarching theme of “Types of citizen participation", the main themes of human factors and organizational factors were identifed under the overarching theme of "factors affecting citizen participation", and the similar main themes of related to governance and related to citizens were identifed under the overarching themes of "requirements of citizen participation" and "consequences of citizen participation". Conclusion: It is expected that this research, while providing a comprehensive picture of the concept of citizenship participation, can theoretically organize it and create a common concept in this field and help to realize it in practice.

Background: Decreasing signal intensity of the spleen assessed by T2* MRI is a frequent finding in patients with beta-thalassemia due to iron deposition within the reticuloendothelial cells in this organ. This parameter can also be applied to determine the candidates for blood cell transfusion. However, the association between splenic siderosis and iron overload in other vital organs such as heart and liver remains unclear. The present study aimed to assess the correlation between iron deposition in splenic, hepatic and myocardial tissues by T2* relaxometry technique. Methods: This cross-sectional study included 39 consecutive patients with a definitive diagnosis of beta-thalassemia major who underwent spleen, liver and heart MRI examinations for iron deposition and cardiac function. Results: No significant correlation was found between the heart and splenic T2* relaxation time (R=0.206, P=0.357). We revealed a strong correlation between the splenic T2* relaxation time and hepatic calculated T2*s (R=0.515, P=0.014). The liver T2* values can be predicted from the splenic T2*s by a new linear equation. According to the ROC curve analysis, the splenic T2* could significantly, but moderately predict moderate to severe from mild liver iron excess (AUC=0.667). Conclusion: Our study demonstrated a significant linear correlation between the splenic and hepatic T2* relaxation time, probably indicative of the same iron deposition mechanism, and made us available to write a linear model that would predict the deposited iron density in the spleen with the use of the magnetic resonance T2* values.

Background: The stressful work environment of emergency departments needs competent nurses to be successful in their careers. Although the work environment significantly affects nurses’ quality of care and job satisfaction, studies on the relationship between the work environment and career success are still limited. Therefore, it is imperative to investigate how individual and environmental factors predict the career success of nurses in the emergency department. Purpose: This study was conducted to identify the predictors of career success of emergency nurses based on work index and contextual variables. Methods: This cross-sectional study was conducted in eight university hospitals in Tabriz, Iran. The career success scale and the Nursing Work Index (PES-NWI) were completed by 193 emergency nurses selected using a stratified random sampling technique. Data were analyzed using a multivariate regression analysis. Results: The mean and standard deviation of nurses’ scores were 168.59±15.54 for the career success scale (range: 39-195) and 2.64±0.48 for the PES-NWI (range: 1-4). According to multiple linear regression analysis, nursing work index and hospital type were significant predictors of nurses’ career success (R2=0.17, adjusted R2=0.11, p

The lack of empirical vision in the graduates and the lack of skills necessary to be effective in their jobs, puts a lot of criticism on the educational programs of universities. Internships with the aim of filling this gap have been included in the subject of technical and engineering courses, but so far it has not been able to achieve its goals at the desired level. The purpose of this study is to analyze internships and explain the factors affecting the productivity of these courses. The statistical sample of this study, which was selected as a snowball, is made up of university faculty members, industrialists, and students with internships. Semi-structured interviews were used to collect data and the Grounded theory was used to analyze them. Finally, 16 categories were counted after two coding steps and then, using axial coding, the categories in the form of axial phenomena. Causal, contextual, intervening conditions, strategies and consequences emerged in the selective coding stage, the narration of the categories was done around the central phenomenon, and at the end, and suggestions were made to increase the productivity of the internships.

This article compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) in keypoint-based relative radiometric normalization (RRN) of unregistered bitemporal multispectral images. The keypoints matched between subject and reference images represent possible unchanged regions and form a radiometric control set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multispectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality, and quantity of the samples in the RCS, and computational time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN, with an expense of higher computational cost. The source code and samples of datasets used in this study are made available at https://github.com/ArminMoghimi/keypoint-based-RRN to support reproducible research in remote sensing.

Abstract The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

Designing and optimizing the dimensions of drainage systems is very important for keeping the downstream shells dry and preventing the increase of pore water pressure in the body of earth dams. By optimizing the drainage dimensions, the minimum factor of safety, and consequently the construction costs, can be reduced. The purpose of this research was to optimize the size of horizontal drainage that is affected by some important parameters of the dam. In this study, a homogeneous earth dam was modeled using the Geostudio software. The minimum factor of safety was obtained by changing drainage dimensions, materials, and the slope of the dam body. A two-layer neural network was used to predict the least factor of safety resulted from different scenarios created in the software. By training the neural network based on the data obtained from homogeneous dams, the minimum factor of safety for drainage optimization was extracted. For optimal, an Mfile was fitted to the trained neural network function, by which the optimal values of the dam parameters were calculated.The results showed that the optimum values of drainage dimensions obtained for homogeneous dams for three heights of 10, 20, and 30 m could be generalized to other heights between 10 and 30 m with a simple interpolation.

Background: Numerous studies have examined paranoid, schizotypal, social anxiety, and social problem-solving traits, but research that examines the relationship between paranoid and schizotypal traits with social anxiety and social problem-solving among students has been neglected. Aims: The purpose of this study was to investigate the relationship between paranoid and schizotypal traits with social anxiety and social problem solving among students. Method: The research method was correlation. In this study 387 students of Payam Noor University of Tabriz were selected by stratified random sampling. Research questionnaires included the Paranoid Scales of Green et al. (2008); Schizotypy Claridge and Brooks (1984); Druzila et al. (2002) problem solving ability; Data were analyzed using Pearson correlation test and multiple regression. Results: The results showed a significant negative relationship between schizotypal traits with logical problem solving and paranoid traits with positive orientation and logical problem solving (P< 0/01). Also, paranoid and schizotypal traits had a significant positive relationship with negative orientation, arousal style, avoidant style and social anxiety components (P< 0/01). Concurrent regression results showed that predictors of social anxiety and social problem solving predict 46% and 17% variance of paranoid and schizotypal traits, respectively. Conclusions: Paranoid and schizotypal traits can predict social anxiety.

Today, organizational knowledge is the most important asset of any organization that is openly or covertly available to human capital. Establishing knowledge management leads to organizational transformation and productivity increases dramatically. This issue is very important for municipalities, whose performance is directly related to increasing the welfare of citizens and the quality of urban life. This study tries to help the policymakers and implementers in the direction of knowledge management by analyzing the current situation of knowledge management and its establishment in Tehran Municipality. Along with this, 18 employees who are currently dealing with knowledge management in Tehran Municipality were interviewed semi-structured. The data were presented after implementation using the data theory strategy of the Analysis Foundation. The findings were presented within the framework of the Strauss and Corbin model. Findings indicate that despite the formation of the Knowledge Management Steering Committee in Tehran Municipality and the unified communication of regulations for the entire organization, lack of support from senior managers for the implementation of knowledge management, lack of macro-management in managers, high turnover rate, and the like, It has created challenges for the optimal deployment of knowledge management in this important organization with 65,000 manpower. In this regard, it is suggested that while confronting any kind of trial and error, the policy-making body be merged with the knowledge management implementation body in the municipality, and for some related organizational positions in different municipal units, the mission-related to knowledge management be defined.

Background and Aim: Lip reading is extraction of speech data from the activity of the lower part of face, particularly jaws, lips, tongue, and teeth that is a natural skill in people with hearing loss. The main purpose of lip reading is to increase the independence of people with hearing loss. Looking at the speakers facial movements significantly increase the ability to understand the spoken words, particularly in the environments where noise is present. In fact this reflects the important role of visual signals. In current study we consider the effects of education on lip reading ability by planning and instructing a lip reading program in male subjects. Methods & Materials: Sara lip reading test 1 and 2 were used for the assessment of lip reading skills and for studying the effects of lip reading training on recognition of speech. This intervention as a semi-experimental study, was conducted on 27 male students, selected by available sampling. For data analysis, statistical analysis of variance with repeated measurements and Mcnemar was used. Ethical Considerations: This research with the code IR.SBMU.REC.1394.144 has been approved by the ethics committee of Shahid Beheshti University of Medical Sciences. Results: There were significant differences between Sara lip reading test 1 scores in 2 positions (P=0.000).Lip reading test scores also showed significant differences in Sara lip reading test 2 in 2 positions (P=0.000). Also between the scores of Consonant-vowel, consonant-vowel syllables, in the position before teaching lip-reading and then ,was significant difference (P

Immediately after an earthquake, rapid disaster management is the main challenge for relevant organizations. While satellite images have been used in the past two decades for building-damage mapping, they have rarely been utilized for the timely damage monitoring required for rescue operations. Unmanned aerial vehicles (UAVs) have recently become very popular due to their agile deployment to sites, super-high spatial resolution, and relatively low operating cost. This paper proposes a novel deep-learning-based method for rapid post-earthquake building damage detection. The method detects damages in four levels and consists of three steps. First, three different feature types—non-deep, deep, and their fusion—are investigated to determine the optimal feature extraction method. A “one-epoch convolutional autoencoder (OECAE)” is used to extract deep features from non-deep features. Then, a rule-based procedure is designed for the automatic selection of the proper training samples required by the classification algorithms in the next step. Finally, seven famous machine learning (ML) algorithms—including support vector machine (SVM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), decision trees (DT), k-nearest neighbors (KNN), and adaBoost (AB)—and a basic deep learning algorithm (i.e., multi-layer perceptron (MLP)) are implemented to obtain building damage maps. The results indicated that auto-training samples are feasible and superior to manual ones, with improved overall accuracy (OA) and kappa coefficient (KC) over 22% and 33%, respectively; SVM (OA = 82% and KC = 74.01%) was the most accurate AI model with a slight advantage over MLP (OA = 82% and KC = 73.98%). Additionally, it was found that the fusion of deep and non-deep features using OECAE could significantly enhance damage-mapping efficiency compared to those using either non-deep features (by an average improvement of 6.75% and 9.78% in OA and KC, respectively) or deep features (improving OA by 7.19% and KC by 10.18% on average) alone.

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Background and purpose: Auditory-verbal memory decreases with age. One method to compensate this weakness is transcranial direct current stimulation. The current study investigated the effect of Anodal Transcranial Direct Current Stimulation on auditory-verbal memory performance of healthy elderly people. Materials and methods: In a randomized clinical trial, 42 healthy subjects with no history of cognitive problems were divided into experimental and control groups (n=21 per group). All participants received 10 sessions of electrical stimulation at 2 mA. In order to evaluate the auditory-verbal memory of the subjects, the Persian version of the Rey Auditory Verbal Learning Test (RAVLT) was used before and after electrical current delivery. Results: In experimental group, the mean scores for all stages of the RAVLT, were found to be significantly different after electrical stimulation compared to those before stimulation (P

It is important to design and optimize the dimensions of the dam drainage system to keep the dam's downstream shell dry and to prevent the increase of pore water pressure in the earth dam body. It will also be possible to find the minimum factor of safety (FOS) to reduce construction costs by optimizing the drainage dimensions. In this study, Marvak earth dam was modeled by GeoStudio software with real material parameters, and by changing the dimensions of drainage, the material of the material, and slope of the dam, the minimum factor of safety of the dam was obtained. To predict the minimum factor of safety, the software results were used in different cases in the two-layer neural network. By training the neural network from the data obtained from the modeling of the Marvak dam, the minimum factor of safety for horizontal drainage was obtained. To optimize, a command appropriate to the neural network function is taught, by which the optimal values of the dam parameters are calculated. The results of the study show that the two factors of the internal friction angle of the drainage material and the slope of the dam have the greatest impact on determining the minimum factor of safety of the dam.

The variety and prevalence of substance abuse are growing and its consequences on physical and psychological health, as well as, on global social and economic concerns are heavy. General self-efficacy is a critical factor underlying substance abuse and its successful treatment. The present study was aimed to investigate the effect of hope therapy on general self-efficacy among substance abusers. This study was quasi–experimental with pretest–posttest and control group. The statistical population comprised all of the substance abusers who referred to addiction treatment centers. Convenience sampling method was used to select 40 participants among volunteers. The participants randomly were assigned into the experimental and control groups. The experimental group received eight sessions of two-hour of hope therapy. Both groups responded to general self-efficacy scale before and after the treatment. There was a significant improvement in general self-efficacy of substance abusers after hope therapy sessions. According to research findings, using of hope therapy could have important role in increasing the general self-efficacy of substance abusers and thereby can lead to successful treatment programs and other positive outcomes in the treatment of substance abuse.

Background: Attachment styles and personality types are key determinants of intimate relationships such as life and sexual satisfaction. The issue of the present study is the lack of sufficient data regarding the relationship between attachment styles and personality types with life and sex satisfaction considering the interactive effects between them. Aims: The aim of this research is to compare the rate of life satisfaction and sexual satisfaction with respect to attachment styles in two personality types A and B. Method: The study was a causal-comparative design. Statistical population contains all married students of Payame Nour University of Tabriz in academic year 2018-2019, which amounts to 7000 subjects of whom 400 subjects were selected as sample through classified random sampling method. Diner's life satisfaction questionnaire(2008), Hadson's sexual satisfaction questionnaire(1981), Hazen and Shaiver's attachment style questionnaire(1987) and Burtner's personality type questionnaire(1967) were carried out on these subjects at first, and finally after eliminating 15 subjects from these samples because of errors contained in questionnaires, two-factor variance analysis of sample containing 385 subject was done. Results: Findings show that the rate of life satisfaction is affected by the interaction between the type of attachment style of subjects and their personality types so that subjects with immune attachment style have higher rate of life satisfaction in both personality types, (p0/05). Conclusions: Accordingly rate of life satisfaction is affected by their personality type according to their attachment style.

Background: research showed having a child with autism disorder can have adverse effects on mother’s psychological condition, but can be compare them with those who have normal children. Aims: The present study aimed to compare cognitive (executive functions, mindfulness ability) and psychological (depression, anxiety, stress) functions between mothers with autistic child and mothers with normal child. Method: The present study was a causal-comparative study. Two groups involving 30 mothers with autistic children and 30 mothers with normal children were selected. Thirty mothers with autistic children were selected among the mothers of autistic children who referred to rehabilitation centers and autism center of the city of Tabriz for rehabilitation interventions for their children in autumn and winter 2018. Two groups completed Barkley (1997) deficits in executive functioning scale, Beer, Ward, & Moar (2013) Kentucky inventory of mindfulness skills, and Lovibond and Lovibond (1995) 42-item depression anxiety and stress scales. Results: Multivariate analysis of variance showed that the levels of deficits in executive functioning (p=0/007) as well as depression, anxiety, and stress (p=0/001) in mothers with autistic children were significantly higher than mothers with normal children. The mindfulness level in mothers with autistic children was significantly lower than mothers with normal children (p=0/012). Conclusions: the mothers of autistic children indicated more depression, anxiety, stress but lower mindfulness comparing with other group of mothers.

The purpose of this study was to investigate the compliance of electrical engineering ethics codes among students of electrical faculty of Tabriz University and its related factors. In this descriptive-analytic cross-sectional study, 120 students of electrical engineering with a history of activity were selected purposefully. Data were collected by a researcher-made questionnaire. The content validity index was calculated based on the Bausell& Waltz scale for the total tool of 83.21 and the Cronbach's alpha was 0.76. The questionnaire contains 40 items in three dimensions: individual, technical and professional, and social. Data were analyzed by SPSS software version 20 and comprehensive descriptive information of participants was used for inferential information using T, Friedman, analysis of variance and Tukey's post hoc test. The findings of the research show that the rate of students' compliance with the ethical codes of engineering in all three areas was higher than the average of society and the rankings of these subscales were respectively social ethics, individual morality and technical ethics. Gender variables, job experience and level of familiarity with ethical codes did not show significant differences with respect to ethics codes of electrical engineering. But the level of education varied significantly with ethical codes. By adopting approaches such as designing and codifying the ethical code of electrical engineering and incorporating it into the professional ethics of engineering, teachig and internalization of these ethical codes, the degree of observance of these codes among the students of electrical engineering can be increased.

Aim and Background: The reinforcement sensitivity theory (RST) is important and useful in various areas such as personality, emotion and psychopathology. Sensitivity to Punishment and Sensitivity to Reward Questionnaire-Revised and Clarified (SPSRQ-RC) was developed to measure this theory and address the problem of Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ). The purpose of this study was to investigate the psychometric properties of the Persian version of SPSRQ-RC. Methods and Materials: This was a descriptive-correlative study. The statistical population was University of Bojnord students that 485 of whom were selected as the research sample using stratified random sampling method. Data were collected using SPSRQ-RC, Behavioral Inhibition System/Behavioral Activation Scales (BIS/BAS Scales), Positive and Negative Affect Schedule (PANAS). The collected data were analyzed through Cronbach’s alpha, Pearson correlation coefficient, test-retest method, exploratory factor analysis and confirmatory factor analysis. Findings: The principal components method by Varimax rotation led to the extraction of two factors; the sensitivity to Punishment (SP) and sensitivity to Reward (SR) for SPSRQ-RC. The two-factor model explained 38.51 percent of the data variance. 10 items of SPSRQ-RC on the first factor (sensitivity to Punishment) and 10 items on the second factor (sensitivity to Reward) have the significant factor load. All items belonging to each of the sensitivity to punishment and sensitivity to reward factors had a high factor load and confirmed the SPSRQ-RC factor structure. The results of the SPSRQ-RC reliability using internal consistency and test re-test showed that this tool is reliable. Also, the results showed that SPSRQ-RC has a convergent and divergent validity. Conclusions: SPSRQ-RC has good psychometric properties in Iranian society and provides a more appropriate assessment for RST.

Background: The aims of this study were to predict positive and negative symptoms of psychosis via early maladaptive schemas (EMSs) in patients with schizophrenia (SZ); to compare EMSs among SZ patients, non-patients with high schizotypal traits, and non-patients with low schizotypal traits; and to compare EMSs among subgroups concerning depression severity. Methods: We applied three groups of participants including SZ patients (n = 105), non-patients with low schizotypal traits (n = 90), and non-patients with high schizotypal traits (n = 90). Participants completed the Young Schema Questionnaire-Short Form (YSQ-SF), the Positive and Negative Syndrome Scale (PANSS), the Schizotypal Personality Scale (STA), and the Beck Depression Inventory-II (BDI-II). Results: The results indicated that the mistrust/abuse and social isolation schemas were significant predictors of positive and negative symptoms in SZ patients respectively. SZ patients and non-patients with high schizotypal traits exceeded non-patients with low schizotypal traits on all EMSs. There were no differences between SZ patients and non-patients with high schizotypal traits regarding EMSs. In the subgroups with high depression, SZ patients showed higher levels of EMSs than non-patients with low and high schizotypal traits. In the subgroups with low depression, both SZ patients and non-patients with high schizotypal traits had higher scores on EMSs than non-patients with low schizotypal traits. Conclusion: The findings suggest that mistrust/abuse and social isolation may be specific to psychosis symptoms. Also, SZ and schizotypal traits may overlap in relation to EMSs. In addition, the activated EMSs may contribute to high depression in SZ.

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.

Objective. In hypertrophic cardiomyopathy (HCM), myocardial fibrosis is routinely shown by late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging. We evaluated the efficacy of 2 novel contrast-free CMR methods, namely, diffusion-weighted imaging (DWI) and feature-tracking (FT) method, in detecting myocardial fibrosis. Methods. This cross-sectional study was conducted on 26 patients with HCM. Visual and quantitative comparisons were made between DWI and LGE images. Regional longitudinal, circumferential, and radial strains were compared between LGE-positive and LGE-negative segments. Moreover, global strains were compared between LGE-positive and LGE-negative patients as well as between patients with mild and marked LGE. Results. All 3 strains showed significant differences between LGE-positive and LGE-negative segments (P

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster’s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.

Background and Objective: Depression is among most disabling mental disorders and its prevalence is increasing. Identifying the risk factors and understanding the process of developing depressive symptoms is essential for prompt prevention and early interventions. The present study aimed to determine the interactive relationship between stressors and maladaptive strategies of cognitive coping in predicting depressive symptoms. Materials and Methods: This descriptive correctional study was conducted with participation of 373 students of Shahid Chamran University in the first semester of 2014-2015. Stratified random sampling was used for selecting participants. Life Experiences Survey questionnaire, Cognitive Emotion Regulation questionnaire, and Beck Depression Inventory-second edition were used for data collection. Data were analyzed by the multiple regression analysis with the SPSS-24 software. Results: The mean (SD) stressors score was 19.9 (10.1), maladaptive strategies of cognitive coping 39.4 (7.4), and depressive symptoms 11.5 (6.9). The β value for stressors was 0.51, Maladaptive Cognitive Coping 0.24 and their interactions 0.11 (P< 0.01). The variance of maladaptive strategies of cognitive coping was 5.2% (P

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

Objective(S): The chitosan - Fe3O4 core - shell nanoparticles were synthesized. The nanoparticles should be coated properly in the shape of core-shell, so that they remain hidden from the body's immune system after coating. Effects of different molecular weight in coating were investigated. Methods: Nanoparticles coated with low and medium molecular weight chitosan were synthesized in one step. In this way, first, the nanoparticles were prepared by co-precipitation method. Then, the surface of the nanoparticles was modified using oleic acid. Finally, the nanoparticles were coated with low or medium molecular weight chitosan. The properties of particles were investigated by TEM, XRD, VSM and FT-IR devices as well as the Debye Scherrer method. In biocompatibility study, the nanoparticles were transferred to a medium containing fibroblast cells which were extracted from the mouse embryo and cultivated in an incubator. Then, dead and live fibroblast cells were counted. Results: The growth of fibroblast cells that were adjacent to the nanoparticles were different. The percentage of live cells in the container containing uncoated particles in the sixth day was 20%. Also, the percentage of live cells in a container containing particles covered with low and medium molecular weight chitosan were 90 and 98% in the sixth day respectively. Conclusions: The molecular weight of chitosan can have a significant effect on the toxicity of nanoparticles in biological environments throughout time, so it shows that the medium cells containing particles coated with medium molecular weight chitosan had better growth than low molecular weight chitosan coated particles.

In this study, we present a new approach for unsupervised change detection in multitemporal synthetic aperture radar (SAR) images based on integrating thresholding with level set method (LSM), which is free of any prior assumption about modeling the data distribution in the difference image. The proposed approach exploits a discrete wavelet transform fusion strategy aimed at achieving the optimal difference image from the mean-ratio and log-ratio difference images. The generated binary change map (CM), by applying a thresholding method on the fused difference image, is used as the initial contour to produce a final CM on fused difference image using the LSM. Several non-fuzzy and fuzzy thresholding methods are considered to assess the generation of the initial contour for the LS segmentation. To indicate the effectiveness of the proposed method, experiments are implemented on 2 sets of multitemporal SAR images from TerraSAR-X and ERS–2 satellites, respectively. Results of the proposed method were compared with results of some existing state-of-the-art unsupervised change detection methods. Experimental results prove the competence of the proposed method in terms of computational time and accuracy over the unsupervised change detection procedure.

Today, the quality of insurance services is the determining factor in the success of a company in a competitive environment. Knowledge sharing will contribute to improving the quality of services in insurance companies. This study examines the impact of knowledge sharing on the quality of insurance services. In terms of the purpose of the study, the method of study is applied and in terms of data collection is descriptive correlation. The population consisted of all employees and corporate clients of group therapy field of Dana insurance company in Mashhad, the sample size is equal to all members of society for all. The measurement tool is a questionnaire, its validity was confirmed, according to experts in the field of management, and reliability is obtained (82.0, 78/0) using Cronbach's alpha coefficient for knowledge sharing and variable quality of insurance services, respectively. The results of structural equation modeling using LISREL software indicates that knowledge sharing does not have significant effect on the quality of insurance services; but aspects of knowledge sharing, including outright transfers, expertise and strategic has positive and significant effect on the quality of insurance services in Dana insurance company in Mashhad.

Background & Objectives: Major depression and obsessive - compulsive disorders have some comorbidities. The objective of this study is to compare perfectionism and its components in patients with depression, obsessive - compulsive disorders and normal individuals. Materials & Methods: The current study was conducted in cross-sectional context. The study population included all patients who referred to the clinics, psychiatric centers and counseling centers in the city of Tabriz. The sample was divided into three groups, one group of 30 with a diagnosis of obsessive - compulsive disorders, another group of 30 with a diagnosis of major depression disorder, and the other 30 were normal. Participants answered to Hill perfectionism questionnaire. To analyze the data, multivariate analysis of variance was used. Results: The results showed that the mean scores of perfectionism and its positive- negative dimensions were higher in patients with obsessive - compulsive and depression disorders, in comparison with normal individuals. Also no significant differences in perfectionism and its positive- negative dimensions scores was observed between people with these two mentioned disorders. Conclusion: The perfectionism components are common component in obsessive - compulsive and major depression disorders.

Background and Objectives: Obsessive compulsive symptoms are prevalent in individuals with eating disorders at clinical level. The purpose of this study was to investigate the correlation between obsessive compulsive features and pathological eating attitudes. Methods: This research is a correlational study. A sample of 790 university students were selected using stratified random sampling method and investigated by Obsessive Compulsive Inventory-Revised (OCI-R), and Eating Attitudes Test (EAT-26) questionnaires. Data were analyzed using multivariate regression analysis. Results: There were a correlation between obsessive-compulsive features and pathological eating attitudes (p

Update information on urban regions has been substantial for management communities. In this research, a novel method was developed for Urban Land Cover Mapping (ULCM). Textural-spectral features obtained from Hyperspectral Thermal Infrared (HTIR) data were fused with spatial-spectral features of the visible image for ULCM. The proposed method consists of three hierarchical steps. First, trees and vegetation classes were classified based on spatial-spectral features extracted from the visible image. Also, two new vegetation indices were introduced. By studying spectral signatures of trees and vegetation classes on the HTIR data, it was shown that trees and vegetation classes could be discriminated by visible data. Second, textural-spectral features of the HTIR data were fused with visible image features to extract bare soil, (gray; concrete; red) roof buildings and roads classes. Using HTIR textural features increased the overall accuracy and Kappa coefficient values about 6% and 8% correspondingly. Third, the results of the first and second steps were overlaid and post processing has been done. The obtained results for overall accuracy and Kappa coefficient values were 94.96% and 0.928 respectively. The comparison of the achieved results with the results of the contest announced by IEEE has shown the efficiency of the proposed method.

A 21-year-old girl with congenital aneurysm of the left atrial appendage represented with recent-onset palpitation and exertional dyspnea. The diagnosis was established by transthoracic and transesophageal echocardiography and chest multislice computed tomography scan. Surgical excision of the aneurysm and repair of the neck were done under extracorporal circulation and left ventricular function (ejection fraction = 50%) improved with mild mitral regurgitation.