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In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs.

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.

Multi-cloud is the use of multiple cloud computing in a single heterogeneous architecture. Workflow scheduling in multi-cloud computing is an NP-Hard problem for which many heuristics and meta-heuristics are introduced. This paper first presents a hybrid multi-objective optimization algorithm denoted as HGSOA-GOA, which combines the Seagull Optimization Algorithm (SOA) and Grasshopper Optimization Algorithm (GOA). The HGSOA-GOA applies chaotic maps for producing random numbers and achieves a good trade-off between exploitation and exploration, leading to an improvement in the convergence rate. Then, HGSOA-GOA is applied for scientific workflow scheduling problems in multi-cloud computing environments by considering factors such as makespan, cost, energy, and throughput. In this algorithm, a solution from the Pareto front is selected using a knee-point method and then is applied for assigning the scientific workflows’ tasks in a multi-cloud environment. Extensive comparisons are conducted using the CloudSim and WorkflowSim tools and the results are compared to the SPEA2 algorithm. The achieved results exhibited that the HGSOA-GOA can outperform other algorithms in terms of metrics such as IGD, coverage ratio, and so on.

This study was conducted to ascertain the efficacy of buccal injection of articaine compared to lidocaine in inducing palatal anesthesia in different maxillary regions.This double-blinded, randomized clinical trial included 300 patients who referred for extraction of 1 maxillary tooth. The patients were categorized into 3 strata according to the extraction area (anterior, premolar, molar), and then randomly assigned to 2 groups based on the administered medication. The first group received buccal infiltration by 0.6 mL of 2% lidocaine, whereas the second group was buccally administered using 0.6 mL of 4% articaine. After a waiting period of 2 minutes, the failure or success in achieving palatal anesthesia was assessed by the instrumentation technique. In cases of failed anesthesia, an additional 0.6 mL of the same anesthetic was given, and the procedure was repeated if palatal anesthesia was not attained after a 2-minute delay. If pain remained 2 minutes after the third injection, a supplemental palatal infiltration was administered and the extraction was attempted.The success rate of buccal infiltration in achieving palatal anesthesia was 82.7% in the articaine group and 1.3% in the lidocaine group. There was a significant difference in the success rate and drug volume required to induce palatal anesthesia between the 2 groups (P .001), but no significant difference was found between different maxillary regions, using either of the medications (P .05).Articaine can be considered as a suitable alternative to lidocaine for eliminating painful palatal infiltration in the extraction of maxillary teeth.

This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.

Despite the unique capabilities of hyperspectral images for classification tasks, handling the high dimension of these data is challenging. Therefore, dimension reduction algorithms have been proposed to solve this challenge. In this paper, an unsupervised Feature Selection (FS) algorithm was proposed for hyperspectral image classification. First, the entropy values of hyperspectral bands were employed to identify and remove noisy bands. Afterward, the Structural Similarity (SSIM) index and the k-means clustering algorithm were combined to select a few representative bands. Subsequently, the selected bands were injected into a supervised classifier, and the obtained Overall Accuracy (OA) and Kappa Coefficient (KC) were used to evaluate the performance of the proposed method. Finally, the results were compared with the ones achieved from other well-known and state-of-the-art FS approaches. The results revealed that the proposed method outperformed other FS algorithms. Furthermore, the proposed FS algorithm obtained equal or higher OA and KC in comparison with the case of employing all hyperspectral bands. Additionally, a stability analysis step was performed to investigate the consistency of the proposed method. The results suggest the potential of the FS approach for hyperspectral image classification.

Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud platform. We will encounter many sorting modes with an increase in virtual machines and the variety in task size. Reaching an order with the least makespan is an NP-hard problem. The hardness of this problem increases even more with several contradictory goals. Hence, a meta-heuristic algorithm is what required in reaching the optimal response. Thus, the algorithm is a hybridization of the ant lion optimizer (ALO) algorithm with a Sine Cosine Algorithm (SCA) algorithm and used it multi-objectively to solve the problem of scheduling scientific workflows. The novelty of the proposed algorithm was to enhance search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud computing environment. The purpose was to minimize the makespan and cost of performing tasks, to reduce energy consumption to have a green cloud environment, and to increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 workflow scheduling algorithm. The results show a decrease in the energy consumed and makespan.

The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan.

Accurate information about the location, extent, and type of Land Cover (LC) is essential for various applications. The only recent available country-wide LC map of Iran was generated in 2016 by the Iranian Space Agency (ISA) using Moderate Resolution Imaging Spectroradiometer (MODIS) images with a considerably low accuracy. Therefore, the production of an up-to-date and accurate Iran-wide LC map using the most recent remote sensing, machine learning, and big data processing algorithms is required. Moreover, it is important to develop an efficient method for automatic LC generation for various time periods without the need to collect additional ground truth data from this immense country. Therefore, this study was conducted to fulfill two objectives. First, an improved Iranian LC map with 13 LC classes and a spatial resolution of 10 m was produced using multi-temporal synergy of Sentinel-1 and Sentinel-2 satellite datasets applied to an object-based Random forest (RF) algorithm. For this purpose, 2,869 Sentinel-1 and 11,994 Sentinel-2 scenes acquired in 2017 were processed and classified within the Google Earth Engine (GEE) cloud computing platform allowing big geospatial data analysis. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final Iran-wide LC map for 2017 was 95.6% and 0.95, respectively, indicating the considerable potential of the proposed big data processing method. Second, an efficient automatic method was developed based on Sentinel-2 images to migrate ground truth samples from a reference year to automatically generate an LC map for any target year. The OA and KC for the LC map produced for the target year 2019 were 91.35% and 0.91, respectively, demonstrating the efficiency of the proposed method for automatic LC mapping. Based on the obtained accuracies, this method can potentially be applied to other regions of interest for LC mapping without the need for ground truth data from the target year.

Change Detection (CD) problem from remotely sensed images is a popular topic among researchers. Because of the diversity in the problem of change detection and the complexity of the study areas it cannot be claimed that there is an appropriate and prevalent algorithm which is more effective for different types of the case study. As a fundamental investigation, it is critical to recognize the weaknesses of the state of artworks in change detection. Also, those examined weaknesses have to be improved aptly to develop a new strong method. This paper presents a thresholding algorithm improved by the Genetic Algorithm (GA) in CD problems, which focuses on minimizing a novel cost function. The suggested cost function can be adopted for local and global change variations in difference images without any prior assumptions. The presented algorithm was tested on two data sets (i.e., Alaska region and Uremia Lake) to validate its effectiveness. Experimental results demonstrated that the proposed algorithm in this work has improved the accuracy of change detection (changed pixel accuracy term) in the Alaska region about 8%–12% and also in Uremia Lake approximately between 8%–13% in comparison with other conventional methods including Fuzzy C- Means (FCM), Otsu thresholding, K-Means, and K-Medoid.

Change Detection from remote sensing images is one of the most challenging and controversial topics among researchers in this field. Given the variety of types of surveyed areas and the data used, it cannot be claimed that there is a comprehensive way to identify different types of remote sensing data.This research aims to represent three robust and novel cost functions based on improving conventional thresholding techniques (i.e., Otsu, Fisher, and Kittler & Illingworth) combined with Particle Swarm Optimization method. The aim of the research is to detect the local-global variation of bi-temporal remotely sensed images based on images statistics but without any a-prior assumptions. The proposed method consists of four fundamental steps. The first step is a preprocessing step, as an indispensable step, in order to equalize the spectral domains of inputs images and also the geometric correction of them. The second step is a process of difference image generation to highlight the changed regions in the study areas. Then, the generated difference image is split to smaller size non-overlapped blocks with the aim of local change analysis. The third step emphasizes the importance of the proposed method and also discusses in improving the cost functions of conventional thresholding techniques as local-global techniques in detail. Next, the final change map is created based on the maximum vote method and image bands combination results. At the final step, accuracy assessment and sensitivity analysis were applied to evaluate the performance of the proposed method. Experimental result on Landsat data (i.e., Alaska region and Uremia Lake) demonstrated the accuracy improvement of three proposed change detection methods about (2% -11%) over different thresholding cost functions. Therefore, the results prove the efficiency and robustness of the proposed cost function compared to the initial cost function.

In this paper, DTFL an image classifier based on Decision Tree and Fuzzy Logics is proposed. At the beginning of classification using DTFL, each pixel is located at the highest level of a decision tree where it belongs to the combination of all classes. DTFL transfers a pixel to a lower level of the decision tree where the pixel belongs to a combination of fewer classes. Decision-making about transfers is based on fuzzy logic with seven different membership functions including triangular-shaped, trapezoidal-shaped, π-shaped, bell-shaped, Gaussian, differential S-shaped and multiplicative S-shaped. Eventually, pixels will reach the lowest level of the decision tree where it belongs to only one class. For accuracy assessment, DTFL was used to classify a GeoEye-1 image. The overall accuracy of 96.14% and a kappa coefficient of 96.06% were reached by DTFL. In comparison, the overall accuracy of 89.91% and a kappa coefficient of 89.77% were reached by a Maximum Likelihood Classifier, MLC. In the case of applying a threshold in MLC to reach the same accuracy as DTFL, 8.73% of pixels take the non-classified label while using DTFL all the pixels get a proper label. The results indicate that the proposed classifier extracts more information from images.

Convolutional neural networks (CNNs) are extensively used in cardiac image analysis. However, heart localization has become a prerequisite to these networks since it decreases the size of input images. Accordingly, recent CNNs benefit from deeper architectures in gaining abstract semantic information. In the present study, a deep learning-based method was developed for heart localization in cardiac MR images. Further, Network in Network (NIN) was used as the region proposal network (RPN) of the faster R-CNN, and then NIN Faster-RCNN (NF-RCNN) was proposed. NIN architecture is formed based on “MLPCONV” layer, a combination of convolutional network and multilayer perceptron (MLP). Therefore, it could deal with the complicated structures of MR images. Furthermore, two sets of cardiac MRI dataset were used to evaluate the network, and all the evaluation metrics indicated an absolute superiority of the proposed network over all related networks. In addition, FROC curve, precision-recall (PR) analysis, and mean localization error were employed to evaluate the proposed network. In brief, the results included an AUC value of 0.98 for FROC curve, a mean average precision of 0.96 for precision-recall curve, and a mean localization error of 6.17 mm. Moreover, a deep learning-based approach for the right ventricle wall motion analysis (WMA) was performed on the first dataset and the effect of the heart localization on this algorithm was studied. The results revealed that NF-RCNN increased the speed and decreased the required memory significantly.

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change., We appreciate the I.R. of Iran Meteorological Organiza-tion (IRIMO) that provided us with meteorological data.

Background: Inevitable prolonged fasting time before surgery leads to inflammatory reactions, surgery-related stress response, and consequently unfavorable outcomes; thus, developing strategies to mitigate these consequences is crucial. Objectives: In this study, we evaluated the effect of ascorbic acid on stress response reduction in abdominal hysterectomy following prolonged fasting time. Methods: Eligible women candidates for abdominal hysterectomy following prolonged fasting time were enrolled in the study and divided into 2 groups of vitamin C [group C; 1 g intravenously (IV) before surgery] and placebo (group P). Before induction of anesthesia, C-reactive protein (CRP), mean arterial pressure (MAP), heart rate (HR), and blood sugar (BS) were measured and compared between the 2 groups at 4-point times, 24 and 48 hours immediately after extubation. Results: Finally, the data of 80 patients were included for the final analysis. A statistically significant difference was observed between the 2 groups in terms of CRP and BS values in T1 CRP serum levels; at the end of the study, CRP values were 0.5 ± 0.55 and 0.92 ± 0.69 in groups C and P, respectively (P = 0.012), and BS levels were 124.12 ± 18.11 and 152.0 ± 17.36 in groups C and P, respectively (P = 0.0001). However, this significant difference was not observed at T2 regarding CRP (P = 0.145) and BS (P = 0.135), as well as at T3 regarding CRP (P = 0.282) and BS (P = 0.213). However, according to both CRP and BS values, the trend of changes from T0 to T3 was significant in the 2 groups (P < 0.0001). Hemodynamic parameters were not significantly different between the 2 groups. No adverse event was reported in the 2 groups. Conclusions: We found that ascorbic acid could induce short-term positive effects in abdominal hysterectomy following prolonged fasting time. Obviously, the optimal dosage, timing, and specific cases that benefit the most from this intervention should be investigated.

Introduction: The COVID-19 has been associated with many problems for the general public and especially health care workers (HCWs). This study conducted to provide predictors of COVID-19 outcomes on HCWs in Rasht, Iran.Methods: In a retrospective cross-sectional study, 381 HCWs with positive RT-PCR or high-resolution lungs computed tomography for COVID-19 from February 21 to April 19, 2020 evaluated. The prevalence, demographic, clinical, laboratory, and radiological presentations and outcomes and their correlation were studied.Results: The prevalence of COVID-19 in HCWs was 5.62%, and the total mortality rate was 0.2%. The mortality rates were different between genders (P=0.002) and in general ward compared to intensive care unit (P=0.001). In the multivariate analysis, age (OR:1.12, 95%CI 1.02–1.23, P=0.014), diabetes mellitus (DM) (OR:10.73, 95%CI 1.91–60.3, P=0.007), blood group B (OR:19.2, 95%CI 1.8–199.984, P=0.013), the presence of peribronchovascular involvement (OR:1.1, 95% CI OR:1.02–1.2, P=0.019), dyspnea on admission (OR:1.05, 95%CI 1.01–1.09, P=0.013), higher neutrophil count (OR:1.09, 95%CI 1.04–1.14, PP=0.001), and longer prothrombin time (OR:1.027, 95%CI 1.008–1.046, P=0.005) increase the risk of mortality. The cutoff of 90% for oxygen saturation on admission (sensitivity=91.9%, specificity=88.9%) and 1004 for absolute lymphocyte count (sensitivity=81.8%, specificity=66.6%) were estimated as predictors of mortality.Conclusions: Old age, male sex, underlying disease of DM and hypertension, O2 saturation less than 90%, and absolute lymphocyte count less than 1004/mL in HCWs are prone to adverse outcomes such as the need for mechanical ventilation or death.

Background and Aim: Auditory-verbal mem­ory (AVM) is the ability to learn, retain, and recall syllables and words. Memory has a strong relationship with the nervous and endocrine systems in humans. Changes in estrogen levels occur naturally at short (menstrual period) and long (pregnancy, menopause, and maturity) peri­ods. Changes in estrogen levels are likely to affect memory function. This study aimed to evaluate the effects of hormone fluctuations on the AVM of women. Methods: This cross-sectional study with a pretest/posttest design was conducted on 25 women aged 40−49 years with normal hearing and normal menstrual cycles of 28 ± 4 days, who were selected using a convenience sampling method. They were evaluated using two Persian versions of Rey Auditory-Verbal Learning Test (RAVLT). This test was performed twice and at two different menstrual periods (2-5 and 14−16 days). Results: A significant correlation was observed between the mean of total recall score, recall score after interference, and delayed recall score at two different periods (p < 0.01). The scores of women in three areas on days 14−16 of the menstrual cycle were higher than on days 2−5. Conclusion: Women’s RAVLT scores on days 14−16 of the menstrual cycle are higher than on days 2−5, indicating the effect of hormonal fluc­tuations on their AVM function. Therefore, it is necessary to consider the changes in women’s AVM in different days of the menstrual cycle. Keywords: Auditory-verbal working memory; Rey learning test; menstrual cycle

Background: Despite the lack of conclusive and proven supporting epidemiologic data about the association between cell phone radiofrequency electromagnetic fields (RF-EMF) and the developing fetus complications, there are frequent discussions about this matter. Methods: A medical student interviewed eligible pregnant women admitted to Al Zahra Hospital, an academic referral hospital in the North of Iran, to fill questionnaires intended to assess maternal demographic data and their attitude towards cell phone use during pregnancy in 2020. Results: A total of 322 pregnant women with the mean age of 30.12 ± 6.81 years enrolled in the survey, in which 3.1% of them were non-cell phone users, and 6.052% of all did not use a cell phone during the first trimester. Also, 38.81% of them mentioned healthcare providers as the main source of obtaining information, while 40.6% did receive any information. The mean years of cell phone use and the daily hours were 8.26 ± 4.1 and 2.66 ± 2.02, respectively. Also, 64.3% believed that cell phone use poses risks to the fetus, while 26.7% had no idea. A positive association was also found concerning maternal awareness from cell phones harmfulness to the fetus and maternal education, residency, and employment (P < 0.0001). Conclusions: In this study, the majority of pregnant women believed that cell phone use during pregnancy could be harmful to the fetus; however, most of them were cell phone users with no special consideration even during the first trimester. It seems that the importance of the issue must be revealed to them by more healthcare providers’ interventions.

Background: Shivering frequently occurs in cesarean section (CS) under spinal anesthesia (SA), resulting in several complications. To date, pethidine has been considered as the gold standard for post-SA shivering control, but it is contraindicated in breastfeeding women. Methods: This randomized, double-blind study was conducted at Alzahra hospital in Guilan, Iran, From January 2019 to November 2020. A total of 508 eligible term parturient women were enrolled and randomly divided into four groups of low dose ketamine (K), tramadol (T), ondansetron (O), and placebo (P). The incidence and severity of shivering and patients’ complications were recorded and compared among the groups. Results: The patients were homogenous in terms of demographic variables. Shivering was witnessed in 68 (53.5%), 26 (20.5%), 75 (59.1%), and 82 (64.6%) patients in K, T, O, and P groups, respectively (P = 0.0001). Regarding shivering severity, there was a significant difference among the four groups (P = 0.0001). In addition, a significant difference was seen regarding Apgar scores at the first minute, but not at the fifth minute (P = 0.168). Conclusions: Considering the high incidence of shivering in placebo group, prophylactic intervention in CS under SA seems to be necessary. Among the studied drugs, tramadol was the most effective one, followed by a low dose of ketamine and ondansetron.

Background: Recently, concerns about general anesthesia (GA)-related neurotoxicity has been growing in societies. Parents’ information obviously plays an important role to make right decision for elective surgeries on children aged under three years old. Objectives: The aim of this survey was to evaluate the knowledge, attitude, and performance of pregnant women about the GA-related neurotoxicity in children aged under three years old. Methods: This descriptive study was conducted at Alzahra Teaching Hospital in Guilan, Iran, during 2020. The eligible pregnant women admitted to this center were interviewed, and a questionnaire containing 10 items was filled out by the responsible resident of anesthesiology. Results: In this research, a total of 361 pregnant women were enrolled and underwent a face-to-face interview. The mean age of the participants was 31.4 ± 7 years, 64.5% were living in urban areas, 82.5% were housewives, and 65.7% were multipara. Moreover, 83.7% of participants believed that receiving information in this regard was crucial, and 81.7% preferred physicians as the source of information. Only 8% of mothers had received information regarding the issue. A significant correlation was observed between the habitat, employment, the level of education, knowledge, and attitude status, and the source of receiving information. Conclusions: According to our results, the knowledge, attitude, and performance of pregnant women were not optimal and needed to be improved through practical strategies.

Background: Although individuals who experience childhood emotional maltreatment (CEM) are more likely to use maladaptive emotion regulation strategies, resulting in vulnerability to depression, no research has examined whether emotion dysregulation may explain the association between CEM and current depressive symptoms in a clinical sample of heroin-dependent individuals. Objectives: The current study aimed to assess the direct effect of CEM on current depressive symptoms and its indirect effect via emotion dysregulation in a treatment-seeking sample of males with heroin dependence. In a cross-sectional design, participants (N = 350) completed the Childhood Trauma Questionnaire-Short Form (CTQ-SF), the Difficulties in Emotion Regulation Scale (DERS), the Obsessive-Compulsive Drug Use Scale (OCDUS), and the Beck Depression Inventory-II (BDI-II). Results: Emotional abuse and neglect severity had significant direct effects on current depressive symptoms and significant indirect effects through emotion dysregulation after controlling for clinical factors related to heroin use. Limitations: Study limitations include the cross-sectional design and use of self-report scales. Conclusions: Findings suggest emotion dysregulation may increase depressive symptoms in heroin users who experienced CEM. Training in emotion regulation strategies may decrease depressive symptoms in heroin-dependent individuals with CEM. Additional research with a longitudinal design to confirm these results is warranted.

The energy performance gap is one of the most discussed issues in the design community since energy modeling became an integral part of the building design process. Different limitations apply to almost every building performance simulation (BPS) software available today and to have confidence in the predictions of whole-building energy models it is necessary to have a thorough understanding of various features, specific capabilities, and disadvantages of BPS tools. The main contribution of this work is the use of three methods of evaluation with different levels of complexity, free-floating, ideal air load system, and detailed method to analyze and compare the capabilities of EnergyPlus and IES to model complex LEED silver multi-purpose university building . Consequently, this study provides new information regarding capabilities of two extensively used BPS tools to model advanced and innovative buildings, and in particular HVAC systems that are increasingly becoming used in high-performance commercial and institutional buildings worldwide. The difference between the total energy consumptions of the EnergyPlus and IES models developed according to the final construction drawings was approximately 2.1%, whereas differences between the heating and cooling loads of the EnergyPlus and IES models with ideal air load systems were 6 MWh (1.7%) and 0.86 MWh (7.8%), respectively. The high consistency between the models’ aggregated predictions are particularly pertinent and encouraging for certification programs such as LEED, which take into account aggregated energy consumption data in their assessments. The findings also emphasize the importance of the modeler's assumptions regarding the modeling of HVAC systems and their impact on the model's predictions.

The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold. Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.e., seven B-mode and five Doppler features) were extracted for each nodule type. The isthmus thickness, nodule volume, echogenicity, margin, internal component, microcalcification, and halo sign features were obtained in the B-mode, while the vascularity pattern, resistive index (RI), peak systolic velocity, end diastolic velocity, and peak systolic/end diastolic velocity ratio (SDR) were determined, based on Doppler ultrasounds. All significant features were incorporated in the computer-aided diagnosis (CAD) system to classify hot and cold nodules. Among all sonographic features, only isthmus thickness, nodule volume, echogenicity, RI, and SDR were significantly different between hot and cold nodules. Based on these features in the training dataset, the CAD system could classify hot and cold nodules with an area under the curve (AUC) of 0.898. Also, in the test dataset, hot and cold nodules were classified with an AUC of 0.833. 2D sonographic features could differentiate hot and cold thyroid nodules. The CAD system showed a great potential to achieve it automatically. • Cold nodules represent higher volume (p = 0.005), isthmus thickness (p = 0.035), RI (p = 0.020), and SDR (p = 0.044) and appear hypoechogenic (p = 0.010) in US. • Nodule volume with an AUC of 0.685 and resistive index with an AUC of 0.628 showed the highest classification potential among all B-mode and Doppler features respectively. • The proposed CAD system could distinguish hot nodules from cold ones with an AUC of 0.833 (sensitivity 90.00%, specificity 70.00%, accuracy 80.00%, PPV 87.50%, and NPV 75.00%).

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 < 0.001 ). The regional longitudinal and circumferential strain parameters showed significant associations with LGE ( P < 0.001 ), while regional circumferential strain was the only independent predictor of LGE in logistic regression models (OR: 1.140, 95% CI: 1.073 to 1.207, P < 0.001 ). A comparison of global strains between patients with LGE percentages of below 15% and above 15% demonstrated that global circumferential strain was the only parameter to show impairment in the group with marked myocardial fibrosis, with borderline significance ( P = 0.09 ). A review of 212 segments demonstrated a qualitative visual agreement between DWI and LGE in 193 segments (91%). The mean apparent diffusion coefficient was comparable between LGE-positive and LGE-negative segments ( P = 0.51 ). Conclusions. FT-CMR, especially regional circumferential strain, can reliably show fibrosis-containing segments in HCM. Further, DWI can function as an efficient qualitative method for the estimation of the fibrosis extent in HCM.

A multi-objective optimization approach is suggested here for scientific workflow task-scheduling problems in cloud computing. More frequently, scientific workflow involves a large number of tasks. It requires more resources to perform all these tasks. Such a large amount of computing power can be supported only by cloud infrastructure. To implement complex science applications, more computing energy is expended, so the use of cloud virtual machines in an energy-saving way is essential. However, even today, it is a difficult challenge to conduct a scientific workflow in an energy-aware cloud platform. The hardness of this problem increases even more with several contradictory goals. Most of the existing research does not consider the essential characteristic of cloud and significant issues, such as energy variation and throughput besides makespan and cost. Therefore, a hybridization of the Antlion Optimization (ALO) algorithm with the Grasshopper Optimization Algorithm (GOA) was proposed and used multi-objectively to solve the scheduling problems. The novelty of the proposed algorithm was enhancing the search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud environment. The purpose was to minimize the makespan, cost of performing tasks, energy consumption, and increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 algorithm. Experimental results indicate that based on these metrics, a proposed multi-objective optimization algorithm is better than other similar methods.

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., Stanford Chemistry, Engineering and Medicine for Human Health (ChEM-H); RISE Program; Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI)

The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.

A particle detection system that exploits the scintillation light produced by ionizing particles in liquid argon (LAr) has been assembled at CERN. The system is based on 10 large-area photomultiplier tubes (PMT) immersed in a 1500-liter dewar filled with liquid argon and equipped with an extendible feed-through and mechanical support for an alpha source (241Am). The position of the source can be changed with respect to the PMT plane in vertical and horizontal directions. Arrays of silicon photomultiplier (SiPM) photodetectors, integrated in the source support, are used for the data acquisition trigger and to define the t0 of the light generation. PMT and SiPM signals can be recorded at different distances and different positions allowing the measurement of some of the LAr scintillation light properties. The system was studied and characterized in detail, and physics results on LAr scintillation properties are expected soon., Contribution to: IPRD19

Background: Tetralogy of Fallot (TOF) is considered as the most frequent cyanotic congenital heart disorder. Right ventricular (RV) dysfunction is possible to be observed in patients with repaired TOF. Delayed enhancement (DE) is one of the recommended findings for RV dysfunction. Objectives: This study aimed at investigating the DE and its probable relationship with RV function through cardiac magnetic resonance (CMR). Patients and Methods: In this cross-sectional study, the values of cardiovascular magnetic resonance of 110 symptomatic patients, who had repaired TOF for 35 years, were gathered. We compared cardiac function indices (CFI) in patients with and without DE. Results: The patients had an average age of 21.93 ± 6.94 years (59.12% were male and 40.94% were female). 93.66% of the subjects showed DE and 6.44% of them did not have DE. 78.61% of the samples had DE of the right ventricular outflow tract (RVOT); whereas, 21.39% showed DE of the other sites. The differences between mean regurgitation fraction, average RV end-diastolic volume (RVEDV), average RV end systolic volume (ESV), and average RV ejection fraction in DE positive and negative patients were statistically significant (P values = 0.01, 0.04, 0.04, and 0.01, respectively.) Conclusion: DE commonly occurs as a complication of surgery to repair TOF and could be used as a factor for impaired RV function and other complications. Using CMR imaging for follow up of these patients could lead to diagnosis of these complications.

Objectives Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. Methods Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. Results The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. Conclusions Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.

Available data indicate a possible beneficial effect of flaxseed on cardiovascular disease, but limited studies have evaluated the effects of flaxseed on endothelial dysfunction and biomarkers of inflammation in patients with coronary artery disease (CAD). The purpose of the present study was to examine the effect of flaxseed consumption on flow-mediated dilation (FMD) and inflammatory markers in CAD patients. In this randomized controlled parallel trial, 50 patients with CAD of both genders were randomly allocated to 12 weeks consumption of flaxseed (30 g/day) or usual care control. Before and after the intervention, changes in brachial FMD and plasma high-sensitivity C-reactive protein (hs-CRP), interleukine-6 (IL-6), and tumor necrosis factor-α (TNF-α) were measured. Forty-four participants (aged 56.43 ± 8.21 years; BMI 26.65 ± 2.44 kg/m2) completed the study. No significant weight change was observed in either group. Compared to control (n = 23), flaxseed consumption (n = 21) was associated with improved FMD (mean of change from baseline was 5.1 vs −0.55%; p = 0.001 for the flaxseed and control, respectively). When compared to control, flaxseed consumption was associated with reduced inflammatory markers (mean of change from baseline for hs-CRP was −1.18 and −0.3 mg/L, p = 0.003; for IL-6 was −7.65 and −0.77 pg/mL, p = 0.017; for TNF-α was −34.73 and −2.18 pg/mL p = 0.001 in flaxseed and control, respectively). The results of this study indicate that by adding flaxseed to diet of CAD patients, it is possible to improve FMD and plasma levels of inflammatory markers.

Recent years have witnessed a huge upsurge of interest in internet of things (IoT) throughout the world. This shift has led to the emergence of new challenges associated with this novel paradigm. They all need to be addressed properly by experts and scholars in the field. In this paper, an integrated approach using fuzzy analytic network process (FANP) was applied to carry out the following tasks: first, to identify the most important IoT technology development challenges in Iran and secondly, to prioritize the aforementioned factors. The implemented approach took into account that technological factors, privacy and security issues, business related factors, legal and regulatory challenges, and cultural elements are the main factors which have an impact on IoT technology development. Besides, it was also taken into consideration that several correlations among the aforementioned classes exist. The results indicate that “technological” and “privacy and security” challenges are the most significant factors which affect IoT. Furthermore, “business model”, “architecture and design” and “education and training” were ranked as the most considerable sub-factors respectively.

We report the first sensing film-coated quartz crystal microbalance (QCM) as a coupled resonating sensor (CRS). QCM-CRSs are known for high sensitivity characteristics and, to date, they have been constructed by attaching high aspect ratio resonant micropillar structures on electrode surfaces. These pillars are composed of photoresist-based materials and do not accommodate exclusive sensing material in the form of films or other structures. This property restricts the sensing applications to pillar material properties. Here, we overcame this limitation by utilizing both sides of the QCM, i.e., one side is coated with a sensing film and the other side of the QCM is attached to resonating SU-8 micropillars. To show a potential application of the proposed sensor design, a zinc oxide (ZnO) thin film was chosen as an example sensing medium tosense the relative humidity (RH), and the enhanced mass sensitivity of a sensing film-coated QCM was demonstrated. Optimized micro-fabrication steps were used to construct the QCM-CRS, and the fabrication was finalized when the ZnO-coated QCM-CRS exhibited typical characteristics of a coupled resonator system Experiments were conducted to measure the resonance frequency shift (Δf0) for different values of RH. The QCM-CRS showed extraordinary sensitivity for changes in RH. It was observed that the Δf0 was 10 times higher than that of an identical QCM-based sensor without any coupled resonance features. The proposed QCM-CRS also showed the best time response and repeatability and had a tolerance in Δf0 of ±0.4%, indicating it had a high reliability and stability. The proposed technique can be applied to designing a variety of sensors by replacing the ZnO film with suitable sensing films.

A note from Melanie Ooi: In this issue on polymer sensors, I am proud to introduce two guest columnists who are actively working on acoustic wave sensors. Narayanan Ramakrishnan is the recipient of the prestigious Asian Invention Excellence award from the 30th International Invention, Innovation and Technology Exhibition which was held in May 2019. His invention was chosen from over 1000 different exhibits from more than 20 participating countries. He wrote this column with his research partner and former Ph.D. student Mohammad Kashan, who is currently working at Piezo Parts Co., Ltd. on the design and manufacture of quartz crystal microbalance sensors.

ICARUS T600 is the far detector of the Short Baseline Neutrino program at Fermilab (U.S.A.), which foresees three Liquid Argon Time Projection Chambers along the Booster Neutrino Beam line to search for LSND-like sterile neutrino signal. The T600 detector underwent a significant overhauling process at CERN, introducing new technological developments while maintaining the already achieved performances. The realization of a new liquid argon scintillation light detection system is a primary task of the detector overhaul. As the detector will be subject to a huge flux of cosmic rays, the light detection system should allow the 3D reconstruction of events contributing to the identification of neutrino interactions in the beam spill gate. The design and implementation of the new scintillation light detection system of ICARUS T600 is described. ICARUS T600 is the far detector of the Short Baseline Neutrino program at Fermilab(USA), which foresees three Liquid Argon Time Projection Chambers along the Booster Neutrino Beam line to search for LSND-like sterile neutrino signal. The T600 detector underwent a significant overhauling process at CERN, introducing new technological developments while maintaining the already achieved performances. The realization of a new liquid argon scintillation light detection system is a primary task of the detector overhaul. As the detector will be subject to a huge flux of cosmic rays, the light detection system should allow the 3D reconstruction of events contributing to the identification of neutrino interactions in the beam spill gate. The design and implementationof the new scintillation light detection system of ICARUS T600 is described.

The aim of this study was to compare the severity of impulsivity and craving among stimulant-dependent (e.g. methamphetamine and crack) and opiate-dependent (e.g. opium and heroin) patients as well as normal subjects. Forty stimulant-dependent patients, 40 opiate-dependent patients, and 40 normal controls completed the Barratt impulsiveness scale-II (BIS-II) and the craving beliefs questionnaire (CBQ). The results showed that there were significant differences concerning the impulsivity and craving among the three groups. Stimulant-dependent and opiate-dependent patients had more impulsivity and craving than normal controls. Stimulant-dependent patients exceeded opiate-dependent ones on total impulsivity and its subscales except for cognitive impulsivity as well as craving. These findings indicated that increased impulsivity and craving in substance-dependent patients compared with normal community suggest the role of cognitive impairments in substance dependency. Also, impulsivity and craving may...

We present the underlying physics in the resonance frequency characteristics of a bare quartz crystal microbalance (QCM) and a QCM-micro pillar based coupled resonator sensor (QCM-CRS) when they were subjected to pressure changes in a vacuum chamber. The QCMs experienced an increase in resonance frequency with decrease in pressure due to an inverse mass loading effect introduced on the QCM surface. However, when the pressure (below atmospheric pressure) dominated the mass loading, the resonance frequency of QCMs decreased. A sensitivity increase of 11 times to the pressure changes was observed for the case of QCM-CRS made of resonating micropillars attached around the central region of the electrode. In addition, the resonance frequency shift was linear for pressure changes between 31 and 71 kPa, demonstrating the possibility of employing the QCM-CRS as a vacuum detector element. The proposed sensor element is envisioned to have wide industrial applications ranging from detecting vacuum in chambers used in coating systems to crack detection in closed chambers.

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 ...

The temporal evolution of the polarimetric SAR data provides an extremely valuable information source for the various applications of this data. In this paper, the evolution of the agriculture crops in the time dimension is employed for multitemporal multidimensional speckle filtering. The proposed filtering method is based on a temporal statistical criterion dependent on all elements of the covariance matrix. Also, this method adaptively uses the pixels with the same scattering characteristics in the spatial and temporal space. The experimental results on time series of Radarsat-2 images show that the proposed filter preserves the polarimetric information and also has a better performance compared to the other used filters, based on different criteria such as the equivalent number of looks and edge-preservation parameter.

Tracking damaged roads and damage level assessment after earthquake is vital in finding optimal paths and conducting rescue missions. In this study, a new approach is proposed for the semi-automatic detection and assessment of damaged roads in urban areas using pre-event vector map and both pre and post-earthquake QuickBird images. In this research, damage is defined as debris of damaged buildings, presence of parked cars and collapsed limbs of trees on the road surface. Various texture and spectral features are considered and a genetic algorithm is used to find the optimal features. Subsequently, a support vector machine classification is applied to the optimal features to detect damages. The proposed method was tested on QuickBird pan-sharpened images from the Bam earthquake and the results indicate that an overall accuracy of 93% and a kappa coefficient of 0.91 were achieved for the damage detection step. Finally, an appropriate fuzzy inference system (FIS) and also an “Adaptive Neuro-Fuzzy Inference System” are proposed for the road damage level assessment. These results show that ANFIS has achieved overall accuracy of 94% in comparison with 88% of FIS. The obtained results indicate the efficiency and accuracy of the Neuro-Fuzzy systems for road damage assessment.

We present experimental evidence of electron-induced upsets in a reference European Space Agency (ESA) single event upset (SEU) monitor, induced by a 200-MeV electron beam at the Very energetic Electronic facility for Space Planetary Exploration in harsh Radiation environments facility at CERN. Comparison of experimental cross sections and simulated cross sections is shown and the differences are analyzed. Possible secondary contributions to the upset rate by neutrons, flash effects, and cumulative dose effects are discussed, showing that electronuclear reactions are the expected SEU mechanism. The ESA Jupiter Icy Moons Explorer mission, to be launched in 2022, presents a challenging radiation environment due to the intense high-energy electron flux in the trapped radiation belts. Insight is given to the possible contribution of electrons to the overall upset rates in the Jovian radiation environment. Relative contributions of both typical electron and proton spectra created when the environmental spectra are transported through a typical spacecraft shielding are shown and the different mission phases are discussed.

BACKGROUND: Lifestyle is one factor that forms the nurses’ health, particularly those who work in shiftwork schedules. AIMS: The aim of this study was to design and test a model for health promotion of Iranian nurses. In this model, nurses’ lifestyle was considered as the precedent, physical and mental health as the outcomes, and sleep disturbance and chronic fatigue as the mediators. METHODS: A cross-sectional study using structural equation modeling was conducted among 240 shiftworker nurses in Iran. The data collection was performed using the Persian versions of the Survey of Shiftworkers Questionnaire and Life Style Questionnaire. Bootstrap in Preacher and Hayes’ Macro program was used for testing mediation. RESULTS: Lifestyle had a weak significant direct effect on physical (β = 0.13, p < .04) and mental health (β = 0.12, p < .02), and it had a significant indirect effect on physical health via chronic fatigue (β = −0.11, p < .001) and sleep disturbance (β = −0.05, p < .01). This variable only had a significant indirect effect on mental health via chronic fatigue (β = −0.19, p < .001). The final model proposed a new significant path between sleep disturbances and chronic fatigue (β = 0.22, p < .001). CONCLUSIONS: Therefore, the hospital officials can enhance the nurses’ physical and mental well-being by providing interventions and training courses on aspects of healthy lifestyles, such as physical activity, avoidance of smoking, and maintenance of body weight.

International audience; Space applications frequently use flash memories for mass storage data. However, the technology applied in the memory array and peripheral circuity are not inherently radiation tolerant. This work introduces the results of radiation test campaigns with heavy ions and protons on a SLC NAND Flash. Static tests showed different failures types. Single events upsets and raw error cross sections were presented, as well as an evaluation of the occurrences of the events. Characterization of effects on the embedded data registers was also performed.

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease (6% - 35%) and is associated with cardiovascular risk factors and metabolic syndromes (age, diabetes, hyperlipidemia, hypertension and smoking). Hereby, we evaluated the association of increased carotid intima-media thickness (IMT) with NAFLD considering other cardiovascular risk factors to see if NAFLD is independently associated with increased carotid IMT. Patients and Methods: Totally 300 patients [150 NAFLD and 150 normal] who were referred to four academic referral hospitals were enrolled. Patients with a history of alcohol use, hepatitis, HIV, chemotherapy and other organ failures were excluded. A single radiologist performed abdominal sonographies [3 - 5 MHz probe] and carotid sonography [7.5 MHz probe] (measured the posterior wall of both common carotids and calculated the mean value). NAFLD was diagnosed based on sonographic findings in the absence of acute or chronic liver and kidney diseases and malignancies. Results: The mean age was 51.2 ± 14.8 [20 - 97] and 184 [61.3%] were male. Mean IMT was greater in patients with diabetes, hypertension, hyperlipidemia and NAFLD [all Ps < 0.001] and these variables with age were statistically significant in univariate models for estimating IMT. Among NAFLD patients, 38 [25.3%] had increased IMT [unilateral or bilateral; considering 0.8 as cutoff point] while this frequency was 8 [%5.3] among normal subjects [P < 0.001, odds ratio = 6, 95% confidence interval (CI) = 2.7 - 13.4]. In multivariate regression models [IMT as dependent variable], NAFLD, age and hyperlipoproteinemia (HLP) were independent significant variables in linear model [R2 = 0.41] and NAFLD showed highest odds ratio [16.4] among significant variables [age, body mass index (BMI), NAFLD and HLP] in the logistic model. Conclusion: Increase carotid IMT is highly associated with NAFLD independent of other cardiovascular risk factors and should be considered in these patients.

Background: Coronary artery bypass graft surgery (CABG) with cardiopulmonary bypass (CPB) triggers a profound systemic inflammatory response and sympathetic nervous system activation translated into post-operative adverse outcomes. Objectives: The current study aimed at determining whether preparative use of selenium could reduce the severity of inflammatory reaction to surgery. Methods: The current randomized, double-blind clinical trial was conducted at the Department of Cardiac Surgery of Dr. Heshmat Hospital; a referral center affiliated to Guilan University of Medical Sciences (GUMS), Rasht, Iran. From May 2015 to September 2015 one hundred and fourteen patients who were candidates for elective CABG were randomly recruited to receive either an intravenous bolus of 600 µg Se before induction of anesthesia in the group S (n = 56) or normal saline as placebo in the group C (n = 58). To measure white blood cells (WBC) serum levels, blood samples were collected preoperatively (T0) and three times after CPB initiation immediately after the end of CPB (T1), as well as 24 (T2) and 48 hours (T3) after surgery. Statistical tests including chi-square, the Kruskal-Wallis, and independent t tests, as well as repeated measurement ANOVA were used to analyze the data. Results: There was no significant difference between the two groups regarding the baseline characteristics (P = 0.0512). There was no significant difference between the two groups in other four time points; T0 (P = 0.0512), T1 (P = 0.571), T2 (P = 0.974), and T3 (P = 0.215), although the trend of changes was statistically significant in each group (P = 0.0001), the intergroup comparisons showed no significant changes (P = 0.166). Conclusions: There was no strong evidence that a single-dose of Se could reduce inflammation following CABG surgery.

Glioma is considered the most common type of primary central nervous system (CNS) tumor. Imaging is crucial for diagnosis, characterization, grading, and therapeutic planning of CNS gliomas. Along with a brief description of conventional computed tomography and magnetic resonance imaging techniques, this article reviews the ever-developing role of modern imaging techniques in preoperative management of CNS gliomas. It discusses current clinical applications, promising features, and limitations of each imaging method.

In recent decades, building and tree detection from LiDAR data and aerial imagery with high automation and accuracy level has been the focus of many researchers which was selected as the purpose of our research. At first, after data preprocessing, off-terrain objects (OTO) including trees and buildings were extracted from LiDAR data. Second, a number of features were produced as inputs of support vector machines (SVMs) to separate buildings from trees. In the SVM, an automatic procedure was used for selecting the training data. After separating the trees, mathematical morphology operations were used to eliminate small objects and fill small holes in the detected buildings and trees. Finally, k-means clustering algorithm was used to separate buildings with different heights. The obtained results for detected buildings and trees were evaluated by working group III/4 of ISPRS, which demonstrated a high rate of success. For completeness, correctness, and quality metrics in per area mode, average values of 88.70%, 95.60%, and 85.30% for buildings and 74.30%, 63.50%, and 52.10% for trees were obtained, respectively.

Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel- and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit ( $\text {rect\_fit}$ ), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.