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Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology-, climatology- and solar-energy-related applications. Since the shape and appearance of clouds is variable, and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy and cloud-free pixels without taking into account the cloud type. On the other hand, cloud classification is typically determined separately at the image level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks; however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit many more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300 000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky or a low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights using the same training and validation sets. Achieving 85.8 % pixel accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) of the respective cloud classes, where the improvement is between 5 and 20 percentage points. Furthermore, we compare the performance of our best model with regards to binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 percentage points, reaching a pixel accuracy of 95 %. [ABSTRACT FROM AUTHOR]

Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance for the operation of distribution grids and photovoltaic power plants, energy storage systems and flexible consumers. To nowcast solar irradiance, systems based on all-sky imagers (ASIs), cameras monitoring the entire sky dome above their point of installation, have been demonstrated. Accurate knowledge of the CBH is required to nowcast the spatial distribution of solar irradiance around the ASI's location at a resolution down to 5 m. To measure the CBH, two ASIs located at a distance of usually less than 6 km can be combined into an ASI pair. However, the accuracy of such systems is limited. We present and validate a method to measure the CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first method to measure the CBH with a network of ASIs which is demonstrated experimentally. In this study, the deviations of 42 ASI pairs are studied in comparison to a ceilometer and are characterized by camera distance. The ASI pairs are formed from seven ASIs and feature camera distances of 0.8...5.7 km. Each of the 21 tuples of two ASIs formed from seven ASIs yields two independent ASI pairs as the ASI used as the main and auxiliary camera, respectively, is swapped. Deviations found are compiled into conditional probabilities that tell how probable it is to receive a certain reading of the CBH from an ASI pair given that the true CBH takes on some specific value. Based on such statistical knowledge, in the inference, the likeliest actual CBH is estimated from the readings of all 42 ASI pairs. Based on the validation results, ASI pairs with a small camera distance (especially if <1.2 km) are accurate for low clouds (CBH<4 km). In contrast, ASI pairs with a camera distance of more than 3 km provide smaller deviations for greater CBH. No ASI pair provides the most accurate measurements under all conditions. The presented network of ASIs at different distances proves that, under all cloud conditions, the measurements of the CBH are more accurate than using a single ASI pair. [ABSTRACT FROM AUTHOR]

Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology, climatology and solar energy-related applications. Since the shape and appearance of clouds is variable and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy- and cloudfree-pixels, without taking into account the cloud type. On the other hand, cloud classification is typically determined separately on image-level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks, however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit much more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300,000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky, low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights, using the same training and validation sets. Achieving 85.8 % pixel-accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) on the respective cloud classes, where the improvement is between 5 and 20 % points. Furthermore, we compare the performance of our best model on binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 % points, reaching a pixel-accuracy of 95 %. [ABSTRACT FROM AUTHOR]

Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance to operate distribution grids as well as photovoltaic power plants, energy storage systems and flexible consumers. To nowcast solar irradiance, systems based on all-sky-imagers (ASIs), cameras monitoring the entire sky dome above their point of installation, have been demonstrated. Accurate knowledge of CBH is required to nowcast the spatial distribution of solar irradiance around the ASI's location at a resolution down to 5 m. Two ASIs located at a distance of usually less than 6 km can be combined into an ASI-pair to measure CBH. However, the accuracy of such systems is limited. We present and validate a method to measure CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first method to measure CBH by a network of ASIs which is demonstrated experimentally. In this study, the deviations of 42 ASI-pairs are studied in comparison to a ceilometer and characterized by camera distance. The ASI-pairs are formed from seven ASIs and feature camera distances of 0.8…5.7 km. Each of the 21 ASI-tuples formed from seven ASIs yields two independent ASI-pairs as the ASI used as main and auxiliary camera respectively is swapped. Deviations found are compiled into conditional probabilities telling how probable it is to receive a certain reading of CBH from an ASI-pair given that true CBH takes on some specific value. Based on such statistical knowledge, in the inference the likeliest actual CBH is estimated from the readings of all 42 ASI -pairs. Based on the validation results, ASI-pairs with small camera distance (especially if < 1.2 km) are accurate for low clouds (CBH < 4 km). In contrast, ASI-pairs with camera distance of more than 3 km provide smaller deviations for greater CBH. No ASI-pair provides most accurate measurements under all conditions. The presented network of ASIs at different distances proves that, under all cloud conditions, the measurements of CBH are more accurate than using a single ASI-pair. [ABSTRACT FROM AUTHOR]

Background: Time to first pregnancy (TTFP) has never been studied in an Iranian setting. Lifestyle, occupational and environmental factors have been suggested to affect the female reproduction. Objective: This study was conducted to measure TTFP in the south of Iran and survey the effects of several similar factors on TTFP by frailty models. Materials and Methods: The data on TTFP were available for 882 women who were randomly selected from the rural population (the south of Iran). Only the first and the planned pregnancies of every woman were included. The data were collected retrospectively by using self-administered questionnaires. Frailty and shared frailty models were used to determine which factors had the highest impact on TTFP. Results: The median TTFP was 6.4 months and several factors were surveyed. However, only the age of marriage, height, maternal education and regularity of menstruation prior to conception were selected in the multivariable models. Conclusion: Among the several factors which were included in the study, the result of frailty model showed that the height, age of marriage and regular menstruation seemed more notable predictors of TTFP. [ABSTRACT FROM AUTHOR]

Resource assessment for concentrated solar power (CSP) needs accurate direct normal irradiance (DNI) measurements. An option for such measurement campaigns is the use of thoroughly calibrated rotating shadowband irradiometers (RSIs). Calibration of RSIs and Si-sensors is complex because of the inhomogeneous spectral response of these sensors and incorporates the use of several correction functions. One calibration for a given atmospheric condition and air mass might not be suitable under different conditions. This paper covers procedures and requirements of two calibration methods for the calibration of rotating shadowband irradiometers. The necessary duration of acquisition of test measurements is examined with regard to the site-specific conditions at Plataforma Solar de Almería (PSA) in Spain. Seven data sets of long-term test measurements were collected. For each data set, calibration results of varying durations were compared to its respective long-term result. Our findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Calibration results within certain periods (i.e. November to January and April to May) show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods. Consequently, the duration of calibrations at PSA can now be selected depending on the time of year in which measurements commence. [ABSTRACT FROM AUTHOR]

Background. Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and 2×2×𝐽 tables. Methods. Simple estimates for predictive values when misclassification is nondifferential are presented. Using them, we estimated the corrected log odds ratio and its variance for 2 × 2 × 𝐽 tables, using the inverse matrix method. A two-step weighted likelihood method was also developed. Moreover, we compared the matrix and inverse matrix methods to the maximum likelihood (MLE) method using a simulation study. Results. In all situations, the inverse matrix method proved to be more efficient than the matrix method. Matrix and inverse matrix methods for nondifferential situations are more efficient than differential misclassification. Conclusions. Although MLE is optimal among all of the methods, it is computationally difficult and requires programming. On the other hand, the inverse matrix method with a simple closed-form presents acceptable efficiency. [ABSTRACT FROM AUTHOR]

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation's local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network's estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m 2 , GHI 98 W/m 2 ) compared to the ASI pair (RMSD for DNI 173 W/m 2 , GHI 119 W/m 2 and radiometer alone (RMSD for DNI 213 W/m 2 ), GHI 140 W/m 2 ). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions. [ABSTRACT FROM AUTHOR]

Emergency Department (ED) triage in hospitals is part of their emergency structure carried out by nurses in emergency units. There have not been many studies available on nurses' performance in triage based on the Emergency Severity Index (ESI). This present study aimed to investigate the nurses' performance in triage with regard to the ESI and its relation to clinical outcome measures in the emergency department. This was a cross-sectional study. The hospitalization record of 600 patients who arrived at the emergency department of Sanandaj Social Security Hospital was randomly assessed based on the accuracy of triage performed by nurses. The data analysis procedure was done by employing STATA software version 12, as well as Fisher's exact test, independent t-test, and one-way ANOVA. Findings of the study revealed that nurses' overall performance in triage showed that 82.67% of nurses had perfect triage accuracy, 12.17% had lowlevel triage accuracy and 5.17% had high-level triage accuracy. There was an association between nurses' performance in triage with disposition of patients (p=0.029) and length of stay (p=0.009). Results of the study showed the acceptable performance of emergency nurses in triage using the ESI and provided a foundation for identifying effective factors for decreasing the length of stay and disposition of patients in emergency care units. [ABSTRACT FROM AUTHOR]

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts. [ABSTRACT FROM AUTHOR]

The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts. [ABSTRACT FROM AUTHOR]

Background: Bacterial infections are among the most serious infections worldwide. They can cause miscarriage, premature birth, stillbirth, and ectopic pregnancy in pregnant women. The aim of this study was to investigate the relationship between bacterial infections and pregnancy outcomes through a systematic review and meta-analysis. Materials and Methods: PubMed, Scopus, Web of Science, and Embase databases were searched from January 2000 to December 2018 using appropriate keywords to identify related articles. The final related studies were selected and evaluated using the Newcastle-Ottawa Scale (NOS). Results: Results of this meta-analysis based on combining case-control studies showed that the presence of bacterial infections could lead increase in the odds of all pregnancy outcomes like premature infant birth (odd ratio [OR]: 1.50; 95% Confidence Interval [CI], 1.39-1.61), preterm delivery (OR: 1.54; 95% CI, 1.39-1.70), abortion (OR: 1.16; 95% CI, 1.04-1.29), stillbirth (OR, 1.29; 95% CI, 1.12-1.49), and ectopic pregnancy (OR: 1.12; 95% CI, 1.05-1.19). The results showed that the Risk Ratio (RR) of preterm delivery in pregnant women with vaginal infections was 1.57 (95% CI, 1.46-1.67), whereas the RR of abortion was 2.02 (95% CI, 1.72-2.38). Conclusions: Based on the results of this meta-analysis, the presence of bacterial infections in pregnant women can lead increase in the risk of pregnancy outcomes especially, preterm delivery, abortion, stillbirth, and ectopic pregnancy. Therefore, it is necessary for obstetricians and gynecologists to pay attention to the diagnosis of these infections in women before pregnancy and during pregnancy in order to prevent the consequences of these infections. [ABSTRACT FROM AUTHOR]

Background: Ethambutol (EMB) resistance is a major concern in patients with tuberculosis (TB). The aim of this study was to determine the frequency rate of mutations in the embB306 gene of Mycobacterium tuberculosis (M. tuberculosis) resistant to EMB, based on a systematic review and meta-analysis. Methods: Thirty-seven original articles (1997-2015) that have been published in valid databases were considered for this research. The articles were systematically reviewed for the prevalence and rate of mutations in embB306 in EMB-resistant M. tuberculosis. Data were analyzed using meta-analysis and random effects models (CI 95%, P < 0.10). Results: With a 6,931 sample size in 37 original articles, the lowest rate was related to EMB resistance that was observed in 2014 with 0.05 (95% CI: 0.04-0.07) and the highest prevalence rate was 0.84 (95% CI: 0.68-1.01), observed in 1997. Lowest and highest prevalence rates of embB306 gene mutation in M. tuberculosis were 0.03 (95% CI: 0.01-0.07) in 2014 and 0.78 (95% CI: 0.71-1.84) in 2005, in the USA, respectively. Conclusions: The present study revealed the prevalence and association of mutations in the embB306 gene of M. tuberculosis with resistance to EMB. Detecting EMB-resistant M. tuberculosis can help in controlling and correcting the administration of drugs for patients with TB. [ABSTRACT FROM AUTHOR]

Due to the need to know the availability of solar resources for the solar renewable technologies in advance, this paper presents a new methodology based on computer vision and the object detection technique that uses convolutional neural networks (EfficientDet-D2 model) to detect clouds in image series. This methodology also calculates the speed and direction of cloud motion, which allows the prediction of transients in the available solar radiation due to clouds. The convolutional neural network model retraining and validation process finished successfully, which gave accurate cloud detection results in the test. Also, during the test, the estimation of the remaining time for a transient due to a cloud was accurate, mainly due to the precise cloud detection and the accuracy of the remaining time algorithm. [ABSTRACT FROM AUTHOR]

Objectives: Resistance to multiple antibiotic classes is called multidrug resistance (MDR), extremely drug resistance (XDR), and pandrug resistance (PDR). In addition, the genotyping survey of resistant bacteria is a prominent factor in epidemiological surveying. Therefore, the present study aimed to conduct an epidemiological survey in order to detect XDR, MDR, and PDR of Pseudomonas aeruginosa isolated from clinical specimens in Kurdistan province, Iran. Materials and Methods: During (December) 2015-(August) 2017, a total of 134 strains of P. aeruginosa were detected by the polymerase chain reaction and were tested for antibiotic resistance, MDR, XDR, and PDR. The related specimens of the patients with nosocomial infections were subjected to BOX-PCR analysis. Finally, the data were analyzed using Stata software with frequency determination, Fisher exact test, and Logistic regression (P = 0.05). BOX-PCR analysis was performed by GelJ version 1.13 software. Results: Based on the results, the lowest and highest resistance and susceptibility rates in 134 isolates of P. aeruginosa were related to cefpodoxime (93.28%), imipenem (27.61%), colistin (69.40%), and cefpodoxime (6.71%), respectively. In addition, MDR and XDR isolates were observed in 97.76% and 15.67% of P. aeruginosa isolates, respectively. However, PDR was detected in none of the isolates while BOX-PCR demonstrated four main clusters with 56 unique patterns in nosocomial infection isolates. Further, a significant relationship was observed between MDR and XDR isolates and nosocomial infection (P = 0.05). Eventually, there was a significant association between ward, the type of specimens, and hospitals with nosocomial infection (P = 0.05). Conclusions: In general, a high frequency of antibiotic resistance was observed in this study whereas no genetic correlation was observed between P. aeruginosa strains. In sum, selecting the most effective antibiotics and devising an efficient way for controlling the antibiotic resistance should be taken into consideration. [ABSTRACT FROM AUTHOR]

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system's accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system's accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich. [ABSTRACT FROM AUTHOR]

Calcium hydroxide Ca(OH)2 has been used as an intracanal medicament to targets microbial biofilms and avert secondary infection in the root canal system. This study evaluated the effects of this material on the morphology and physicochemical properties of an established in-vitro biofilm of Enterococcus faecalis. A biofilm of E. faecalis was grown in multichannel plates. The chemicals including Ca2+, OH−, and saturated Ca(OH)2 (ie 21.6 mM) were prepared in order to evaluate which component eradicated or amplified biofilm structure. Various biochemical and microscopic methods were used to investigate the properties of the biofilm. Biofilms treated with Ca(OH)2 absorbed more Ca2+ because of the alkaline pH of the environment and the ions affected the physicochemical properties of the E. faecalis biofilm. A denser biofilm with more cavities and a granular surface was observed in the presence of Ca2+ ions. This resulted in a decrease in the surface-to-biofilm ratio with increases in its biomass, thickness, colony size, and volume. Calcium hydroxide did not destroy E. faecalis biofilms but rather contributed to the biofilm structure. This in-vitro study sheds light on a missing link in the formation of E. faecalis biofilm in which the Ca2+ in Ca(OH)2 [ABSTRACT FROM AUTHOR]

Background: Methamphetamine use in men who have sex with men population is significantly higher than that in the general population. Meth use can cause high-risk sexual behaviors, such as having sex with a variety of sexual partners. The aim of this study was to determine the association between meth use and the number of sexual partners in MSM.Methods: Searching international databases (PubMed (Medline), Scopus, Web of Sciences, Embase (Elsevier), PsycInfo (Ovid), Cochrane CENTRAL (Ovid)) until March 2021 was performed in this meta-analysis using appropriate keywords terms to identify related articles. After retrieving articles in these databases, screening was performed based on the title, abstract and full text of the articles, and the final related studies were selected and evaluated using the Newcastle Ottawa scale checklist.Results: The sample size consisted 18,455 people in this study, including four cohort studies with a sample size of 15,026 MSM and four case-control studies with a sample size of 3429 MSM. The results of meta-analysis showed that meth use increased the number of sexual partners in MSM (RR: 3.70; % 95 CI: 2.04-6.70). The results of subgroup analyze based on the number of sexual partners showed that in MSM taking meth, the risks of having one to three, four to five, and six or more than six sexual partners were respectively 2.82, 2.98 and 5.89 times higher than those in MSM who did not take meth.Conclusion: The results showed that meth uses in MSM increased the number of their sexual partners. Due to the fact that increasing the number of sexual partners and high-risk sexual behaviors increase the risk of contracting sexually transmitted diseases such as HIV, it is necessary to adopt control programs to prevent meth use by this group, or to implement programs of reduction in the risk of STIs for this group. [ABSTRACT FROM AUTHOR]

Solar field developers include innovative solutions to optimize the energy production of their plants. Simulation tools play a significant role in the design and testing phases as they provide estimations of this yield in different conditions. Transient processes, like passing clouds and solar field start-up, are specifically challenging to optimize and estimate using such simulation tools. Solar fields are subject to high degree of both temporal and spatial variability in the energy input and a detailed estimation can be achieved by simulating subsystems within acceptable time and computational power. Hence, such simulation tools cannot be utilized for tests under realistic operation conditions. The Virtual Solar Field is a computationally efficient simulation tool that allows a detailed transient simulation of parabolic trough solar fields based on single-phase fluids. Using this tool, developers could reproduce a transient test case with exactly the same disturbances to provide fair comparisons between different configurations. In this paper, an evaluation process based on numerical simulations using the Virtual Solar Field is presented. The economic benefit of novel innovative control concepts can be assessed according to the presented scheme. This is demonstrated by evaluating the potential benefit of availability of spatial DNI nowcasts on the control of parabolic trough solar fields. Results show that nowcasting can increase the economic revenue of commercial power plants by up to 2.5% per day. This proves the feasibility of installing such systems. [ABSTRACT FROM AUTHOR]

Background and Aim: Although laparoscopic cholecystectomy causes less pain than open cholecystectomy, it is still not completely painless. Several methods have been used to relieve the pain of laparoscopic surgery. The aim of this research was to compare the effect of gabapentin and hydrocortisone on pain control after laparoscopic cholecystectomy. Materials and Methods: In this double-blind clinical trial, a total of 60 adult patients aged 18–70 years from both sexes American Society of Anesthesiologists Classification (ASA Classification 1 and 2) who were selected for laparoscopic cholecystectomy were divided into two groups of 30 subjects to be studied. 150 mg gabapentin and 100 mg hydrocortisone were administered to the first and second groups before the operation, respectively. Pain score and vital signs (systolic blood pressure and heart rate) were recorded. Data were fed into SPSS 23 software and analyzed using Fisher-test, independent t-test, and repeated measurement. P < 0.05 was considered as significance level. Results: Patients were similar in terms of age and sex. Mean score of visual analog scale (VAS) in the first 4 h after operation was 5.84 ± 2.33 and 5.20 ± 1.74 in the gabapentin group and was 7.03 ± 1.23 and 6.50 ± 1.30 in the hydrocortisone group (P < 0.05), respectively. Although mean VAS scores at 6, 12, and 18 h after operation showed no significant differences between gabapentin and hydrocortisone groups (P > 0.05), VAS score 24 hours after operation was 2.87 ± 1.57 and 3.92 ± 1.28 in gabapentin and hydrocortisone groups, respectively (P < 0.05), indicating a significant difference in VAS score between the two groups 2 and 24 h postoperation. Conclusion: The results of this study showed that gabapentin was more effective than hydrocortisone within the first 4 h of laparoscopic cholecystectomy. In addition, gabapentin was shown to be a better pain controller 24 h postoperation. [ABSTRACT FROM AUTHOR]

Objectives: To provide more accurate estimates of the prevalence of Hepatitis B (HBV) and Hepatitis C (HCV) and their contributing factors among prisoners in Iran.Methods: Cross-sectional study of 6200 Iranian prisoners in 2015. Data were collected through questionnaires and interviews. HBV infection and HCV exposure status of the participants was determined by HBsAg and HCV antibodies blood tests using enzyme-linked immunosorbent assay (ELISA). Data were analysed in STATA-12.Result: Prevalence of HCV exposure was 9.48% (95% CI: 8.73-10.27), and prevalence of HBV was 2.48% (95% CI: 2.07-2.89) in the general prison population. In multivariate analysis, the most important risk factor for HBV was a history of drug use in lifetime (adjusted odds ratio, AOR: 1.8, 95% CI: 1.17-3.02). The main risk factors for HCV exposure were a history of drug use in lifetime (AOR: 4.08, CI: 2.56-6.27), age over 30 (AOR: 2.68, CI: 2.01-3.56), and having tattoos (AOR = 1.67, CI: 1.35-2.07).Conclusion: Although vaccination is used to control HBV among prisoners, prevalence of HCV exposure is alarming in the prison population of Iran, especially among people who inject drugs. Eliminating viral hepatitis in Iran by 2030 requires a national commitment and rapid measures for targeting this high-risk group. Given the increased efficiency of HCV treatment in recent years, prisons provide an opportunity to access patients for treatment. [ABSTRACT FROM AUTHOR]

Background: Screen time (ST), including watching television and playing electronic games are the leading cause of a growing obesity epidemic. This study aimed to evaluate ST and its association with physical activity, overweight and socioeconomic status (SES) in children 10 to 12 years of age in Sanandaj. Methods: This study was designed as a cross-sectional study, which was conducted in Sanandaj in 2015. ST and physical activity data were collected using the Modifiable Activity Questionnaire (MAQ). Overweight indices are defined based on BMI age- and gender-specific percentiles, as proposed by WHO criteria. Considering household assets data, SES was calculated using principal component analysis (PCA). The relationship between ST and different determinants was assessed using logistic regression analysis. Results: Based on the results obtained in our study, 47.28% (95% CI: 45.33-49.24) of the participants spent more than two hours a day on television and video watching and electronic games playing. People who spend greater time on ST activities, independent of their physical activities, are more susceptible to overweight and obesity (p=0.002). People in higher socioeconomic groups spent more time on watching TV and video and playing electronic games (p=0.001). There was a direct relationship between the residential area and ST (P=0.052). ST in male was found to be greater (p=0.033). In addition, ST was also lower in school-aged children whose mothers had a greater education (p=0.56). Conclusion: Based on the results of this study, it is recommended to increase the education level and knowledge of mothers and design interventions consistent with children gender and residential location so that to reduce ST and its associated outcomes in children. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to directly compare efficacy of atomoxetine and methylphenidate in treatment of children and adolescents 6- 18 years. Methods: All published, randomized, open label or double blind trials, comparing the efficacy of methylphenidate with atomoxetine in treatment of children diagnosed with ADHD, using DSM-IV criteria were included in this study; ADHD Rating Scale-IV-Parent Version: Investigator Administered and Scored (ADHDRS) scores was used. The standardized mean difference (SMD) was used as a measure of effect size. Results: Eleven studies were included with a total of 2,772 participants. The meta-analysis did not find a significant difference in the efficacy between methylphenidate and atomoxetine (SMD= 0.09, 95% CI -0.06, 0.25) (Z= 1.18, p= 0.24). Sub group analysis showed a significant standardized mean difference favoring OROS methylphenidate (SMD= 0.31, 95% CI 0.16, 0.47 (Z= 3.91, p< 0.0001); immediate release methylphenidate was not superior to atomoxetine (SMD= -0.05, 95% CI -0.20, 0.10) (Z= 0.68, p= 0.49). Open label trials did not make a difference in the standardized mean difference (SMD= 0.10, 95% CI -0.02, 0.23) (Z= 1.17, p= 0.09). There was significant heterogeneity among the studies (p= 0.003, I2= 63%). Subgroup analysis demonstrated that heterogeneity was because of the open label trials (p= 0.009, I2= 79%). Conclusion: Atomoxetine and methylphenidate showed comparable efficacy in the treatment of children and adolescents with ADHD. However, Osmotic (Controlled) Release Oral (Delivery) System (OROS) methylphenidate is more effective than atomoxetine in treatment of ADHD in children and adolescents that is suggested as a first-line treatment in ADHD. Moreover, comparing the immediate release (IR) methylphenidate to atomoxetine did not lead to the benefit of IR methylphenidate. [ABSTRACT FROM AUTHOR]