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The past five decades have witnessed satellite remote sensing become one of the most efficient tools for fire detection and estimating total burned areas. Not all algorithms are appropriate for detecting high-temperature events on a global scale. It is difficult for traditional fire detection algorithms to capture small and low-intensity fires with significant accuracy. For this, we propose an improved fire detection algorithm by considering channel 4 and 11 µm of MODIS data with two different thresholds for hot and cold seasons. Moreover, false alarm rejections caused by the edges of clouds are designed for this algorithm. The validation against 231 reference fires showed good performance of our algorithm over the northern forests of Iran. This algorithm detected 72 fires, while the MODIS fire product, a widely used source for fire detection, detected only 26 fires. The results indicate an outperformance of 19.91%. The results show that our algorithm for the fire detection method overperforms the traditional methods and can be particularly useful for fire detection in the northern forests of Iran and can be applied in similar forests worldwide. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users' point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of "customization" has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. Results: After preprocessing, clustering was performed on the data with the dimension (4710 x 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets. Conclusion: According to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent. [ABSTRACT FROM AUTHOR]

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg–Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC. [ABSTRACT FROM AUTHOR]

Economic vulnerability, is a country's structural feature, making it exposed by out-of-control economic exogenous variables. On the other hand, resilience stems from how macroeconomic policies are adopted. The purpose of this article is to analyze the relationship between sustainable development, vulnerability and resilience in the Iran's economy in the time period of 1391-1398.1 this way, sustainable development consists of 5 Dimensions: Economic, Social, Environmental, Governance, Peripheral and also includes 44 variables as a sub-indicator of each development's dimension. It uses graph theory and Tarjan Algorithm for study the network structure of relationships between variables. The Algorithm identifies34 variables as the fundamental variables affecting economic resilience and vulnerability and introduces economic and governance Dimensions as control (major) Dimensions; Social, Environmental and Peripheral Dimensions as Contingent (minor) Dimensions. The results show that weakness of Iran's economy in this period was due to vulnerability and resilience in governance and economy Dimensions. Among the contingent Dimensions, the Peripheral dimension has the worst situation in terms of vulnerability and resilience. The innovation of this research is in studying the Dimensions of sustainable development and its effect on vulnerability and resilience, moreover graph theory has been used in order to study the influential variables for the first time in Iran. In this algorithm, without limiting the number of input variables, the phenomenon can be analyzed comprehensively. furthermore, a holistic table has been prepared, by focusing on the variables affecting the state of vulnerability and resilience in the theoretical foundations of the subject, that has not been shown in any other studies. [ABSTRACT FROM AUTHOR]

In this research, three- dimension finite difference method was applied to simulate the tunnel excavation by earth pressure balance (EPB) boring machine of Tehran Metro Line 7 tunnel in Iran. Sensitivity analysis and parameter identification was carried out by Morris Elementary Effect method to evaluate the relative sensitivity of model for each input parameter. Then, artificial neural network (ANN) was used as a meta model to predict model response for the dataset of selected sensitive parameters. For this purpose, 100 numerical simulation has carried that a database including 1500 data was compiled for using in ANN. Thereafter, PSO algorithm was employed as a parameter identification technique to find the optimized values of the parameters according to monitoring surface settlement. Also, after material identification, the same PSO-ANN algorithm was used to optimize operation parameter (face pressure). The results showed that ANN prediction and numerical simulation with initial set of parameters has more than 98% concordance with about 2.4% RMSE and 0.014 MAE for all dataset. The optimized parameters gained via back analysis enable the meta model to well predict the ground settlement in the about 15 seconds. [ABSTRACT FROM AUTHOR]

Depletion of groundwater resources is nowadays being discussed as one of the most significant challenges in this field. Thus, simulation of the available groundwater resources seems necessary for taking reliable management measures for the plains. In the present study, a combination of various metaheuristic algorithms including feed-forward artificial neural network (ANN), the algorithm of innovative gunner (AIG), and black widow optimization (BWO) algorithm was employed to simulate the groundwater of the Selseleh plain located in the southwest of Iran during the period 2008–2018 on a monthly time scale. Furthermore, chicken swarm optimization (CSO) was adopted to optimize the weight coefficients. The data were divided with 70% being used for training purpose and the remaining 30% for test validation. Different statistical indices including the coefficient of determination (R2), root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) coefficient, and percentage of bias (PBIAS) were utilized for evaluation of the efficiency of modeling. The results illustrated that all the three hybrid models had better results in combinatory patterns. Moreover, the evaluation criteria suggested that among all the applied models, the ANN-AIG model was the only one possessing all statistical indices (e.g., R2 = 0.964–0.995, RMSE = 0.273–0.71 m, MAE = 0.219–0.059 m, NSE = 0.886–0.978, and PBIAS = 0.002–0.001) in the validation stage. In a nutshell, this study suggested that among all the recommended hybrid models, ANN-AIG, ANN-BWO, and ANN-CSO reduced the simulation error compared to the standalone ANN model with (12.78–20.26)%, (10.13–17.61)%, and (9.27–1675)%, respectively. [ABSTRACT FROM AUTHOR]

Inverters play a significant role in the configuration of grid-connected photovoltaic (PV) systems. The perturb-and-observe (P&O) algorithm is a common method to derive the maximum power from grid-connected inverters; however, the possibility of losing maximum power due to sudden changes in radiation is a significant drawback of this control strategy. To overcome this barrier, the two-stage multi-string inverter using the ZETA DC–DC converter and a novel P&O algorithm has been proposed to increase the efficiency of these systems. The proposed inverter has been simulated in MATLAB/SIMULINK software. To investigate the performance of the proposed inverter, technical, environmental and economic feasibility studies have been performed for the construction of a 5-kW PV power plant in a northern city of Iran (Sari) using the RETScreen software developed by Natural Resources Canada. On the other hand, most feasibility studies for power-plant construction are based on the concept of inverter peak efficiency, which leads to non-optimal system design due to the short operation duration of the inverter at this value. However, the weighted European efficiency has been used in the feasibility study for more accurate computations. Moreover, the performance of the proposed inverter is compared to that of a two-stage multi-string inverter using a conventional P&O algorithm and the single-stage (central) inverter. The simulation results indicated that the proposed inverter injects 7.6 MW of power into the grid per year. Moreover, it prevents the emission of 88 tons of CO2 (over 20 years), which is equivalent to saving 1883.5 litres of gasoline per year. [ABSTRACT FROM AUTHOR]

In this paper a new meta-heuristic approach based on the nature of honey bees mating has been used for the calibration of a real city in the north of Iran named Langarud. This city has a population of nearly 68,000 people and about 43,000 water consumers. Langarud's area is about 900 km². The method was used to determine the Hazen-Williams roughness factor of the main pipes of the town and a correction factor for the nodal demands in the main nodes of the network. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon that occurs in many places on the planet and may cause considerable damage. Selection of an integrated index for quantifying drought severity is a challenge for decision makers in developing water resources and operation management policies. In this study, the standardized precipitation index, water surface supply index, and Palmer drought severity index have been combined to develop an integrated index, called the hybrid drought index (HDI), using associated damage of drought events. Application of the HDI in drought severity prediction has been examined using two different types of artificial neural networks, namely, a probabilistic neural network and a multilayer perceptron network. These models have been selected due to their special characteristics that are suitable for prediction schemes. The proposed algorithm for developing HDI and drought prediction has been applied to the “Gavkhooni/Zayandeh-rud” basin in the central part of Iran. The results show the merits of each model in prediction of drought severity and model adaptation. The results also show the significant value of the proposed algorithm in formulation of a combined index for drought prediction. [ABSTRACT FROM AUTHOR]

Background. Depression, as the most common psychological problem in hemodialysis patients, can be alleviated by spiritual care and belief in God’s mercy and power. Objectives. The aim of this study was “evaluation of the effect of spiritual care based on the Sound Heart Model on depression in hemodialysis patients”. Material and methods. This clinical trial study was conducted in 2018 on hemodialysis patients at the Kamkar hospital in Qom. Sampling was done in an accessible form and according to inclusion criteria. Due to the patients’ self-care ability, spiritual care was provided in the supportive-educational system by 12 individual educational sessions, 45–35 minutes long, for patients and families, based on the Sound Heart Algorithm after donating educational booklet. The Goldberg Depression Questionnaire was completed by both control and intervention groups before and after the intervention. Results. There was no significant difference in mean scores of depression between the intervention and control groups before intervention (p = 0.604). After the intervention, the difference was significant (p = 0.000). There was no statistically significant difference in mean scores of depression in the control group before and after the intervention (p = 0.259), while this difference was significant in the intervention group (p < 0.001). Conclusions. The Sound Heart Model, with a holistic, community-based approach by creating courage in the face of disease crises, hope, optimism, positive thinking, development of the family’s and patient’s relationship with God, self, people and nature, was able to reduce depression in hemodialysis patients, and thus the use of its strategy is recommended. [ABSTRACT FROM AUTHOR]

Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models. Fire effective factors, including elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance to river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined using a random-forest algorithm. Fire-susceptibility maps were produced in R 3.0.2 software using GAM, MARS, SVM algorithms, and a new ensemble of the three models: GAM-MARS-SVM. The four fire-susceptibility maps were validated using the area under the curve. The results show that the distance to the village, annual mean rainfall, and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest precision of fire-susceptibility mapping. The fire-susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province. The fire-susceptibility map produced by the ensemble model can be very useful for creating and enhancing management strategies for preventing fires, particularly in the higher-risk portions of Golestan Province. [ABSTRACT FROM AUTHOR]

The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR. [ABSTRACT FROM AUTHOR]

Many scholars mentioned various dimensions and complex relations for manufacturing flexibility that implementing all of them is not possible for a manufacturing firm. On the other hand, today's dynamic environment convinces flexible organizations to create capabilities in order to take over competitors. Hence, the aim of this study is to determine dominant groups of flexibility for manufacturing firms and to prioritize them under the influence of dynamic organizational capabilities. In this research, firstly components of dynamic organizational capabilities and flexibility of manufacturing are identified by content analysis method. Then, based on that, taxonomy for Iran's manufacturing firms was determined using support vector machine algorithm. In the next step, based on experts' view using FDEMATEL method, conceptual model for relationship of manufacturing flexibility dimension as well as dynamic capabilities are provided. Conceptual model in three flexibility clusters was tested by structural equation modeling, and dominant groups were prioritized based on dynamic organizational capabilities. Results show that the best number of clusters is three. Results also show that dynamic organizational capabilities not only affect flexibility dimensions of manufacturing but also this influence is different in manufacturing firms with various industry fields. [ABSTRACT FROM AUTHOR]

Management areas in the country are mainly identified and protected to conserve genetic resources, protect natural landscapes and support the origin of ecosystem services. These areas would not be much effective without a proper management plan based on their zoning. Due to lack of appropriate criteria with ground check capability, zoning has always been a major challenge for planners. Therefore, this study was carried out to develop a decision algorithm for zoning protection zones 1 and 2 of Hara protected area. This region, with an area of 86,258 hectares, covers a vast range of Mangrove forests and is located between Khamir Port and Qeshm Island in Hormozgan province, Iran. Zoning of the protected areas in this region was performed through identifying the ecologic-based criteria for Restricted Nature Reserve Zone (zone 1) and Protection Zone (zone 2), finalizing and screening them by Delphi questionnaire and weighting those criteria using Entropy Shannon methods. Moreover, the linear model for spatial criteria combination was developed, and layering and criteria combination were performed in Geographic Information System using the designed decision algorithm. The results from zoning the protected areas indicated that zone 1 and zone 2 constituted 5% and 11.38% of the total area, respectively. In other words, the protection zones covered over 16% of the study area. The results also indicated that the criteria linear combination model and the decision algorithm could facilitate accurate zoning of the areas while maintaining their ecologic and human values. [ABSTRACT FROM AUTHOR]

Bed sills are a kind of grade control structure built at the same level as the river bed to protect it against erosion and to reduce the longitudinal slope of the channel. The aim of the present research was to determine the optimal dimensions of the structure by devising a new methodology to minimize cost and maximize stability, based on the optimization model of the multi-objective non-dominated sorting genetic algorithm (NSGA-II). This is achieved by calculating the width, height and distance between bed sills with these stability and cost objectives in mind. The NSGA-II algorithm is applied to solve the problem, and a set of Pareto points is obtained. The results are verified by considering the bed sills of the River Madarsoo in Iran and the River Plima in Italy, and comparing the results with those obtained using the Branch-and-Bound (B-and-B) algorithm. The results indicate that the cost and stability could respectively be improved by up to 18 and 20% in the River Madarsoo, and up to 18 and 26% in the River Plima, if the NSGA-II algorithm is used. [ABSTRACT FROM AUTHOR]

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration. [ABSTRACT FROM AUTHOR]

Inversion of magnetic data has been an important tool for the interpretation of the data. The conventional inversion approaches cannot recover geologically acceptable models in the presence of remanence. Hence, some techniques have been developed for the inversion of magnetic amplitude data which is independent of magnetization direction. The conventional methods in the inversion of magnetic amplitude data give rise to blurred models with long tails. In this study, a new algorithm has been developed in favor of sparse inversion of the data that generates more focused models using the Cauchy norm. The algorithm applies a regularized conjugate gradient (RCG) method to acquire the inverse model. The new algorithm has been employed for the inversion of two data sets from synthetic examples and magnetic data sets over the Allahabad iron deposit in Iran and the Osborne copper–gold deposit in Australia. All data sets exhibit remanent magnetization. The results showed that the new algorithm generates robust solutions that are geologically acceptable. [ABSTRACT FROM AUTHOR]

Background and Objectives Change in the age structure of the population affects many activities and processes, and most countries are expected to enter the old population phase by 2050. Today, for various reasons, the consumption of blood and blood products has increased. The aim of this study was to investigate the age change in donor population during the years 1390 to 1397 and compare it with the society's age change algorithm to determine if the latter affects the former. Materials and Methods In this cross-sectional descriptive study, demographic information of 329631 blood donors from 1390 to 1397 from Blood Transfusion Software (Negareh software version 5/9) was extracted and the latest demographic information of the province was achieved using the website of the Statistics Center of Iran. Results Of the population of Yazd and Ardakan cities, 64.7% and 65.38% are within the appropriate age range for blood donation. The donor population of 18-30 years decreased by 30.7% in 2016 and 32.3% in 2018 compared with the year 2011 while other age groups almost increased. Mean age of donors in 2014 and 2017 were 44.2 and 40.05 years, respectively. Conclusions Due to the aging population, and the reduction of the population under the age of 30, it is necessary to take effective programs to maintain and attract 18-30 year old population for blood donation. [ABSTRACT FROM AUTHOR]

This research specifically addresses the prediction of dairy product demand (DPD). Since dairy products have a short consumption period, it is important to have accurate information about their future demand. The main contribution of this research is to provide an integrated framework based on statistical tests, time-series neural networks, and improved MLP, ANFIS, and SVR with novel meta-heuristic algorithms in order to obtain the best prediction of DPD in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using the Pearson correlation coefficient, and statistically significant variables are determined. Since the regression relation is not able to predict this demand properly, the artificial intelligence tools including MLP, ANFIS, and SVR are implemented and improved with the help of novel meta-heuristic algorithms such as grey wolf optimization (GWO), invasive weed optimization (IWO), cultural algorithm (CA), and particle swarm optimization (PSO). The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The high accurate results confirm that the proposed hybrid methods have the ability to improve the prediction of the demand for various products. [ABSTRACT FROM AUTHOR]

Summary: People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m3/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance. [ABSTRACT FROM AUTHOR]

Due to the recent climate changes and their consequences such as flash floods and droughts, there is a need for Land Use Land Cover mapping to monitor environmental changes which have effects on ecology, policy management, health, and disaster management. It should be noticed that recent droughts caused by climate change, and on the other hand, population growth has increased the rate of urbanization in Iran, where people are moving from rural areas to urban areas. In this study, two well-known machine learning classifiers, including Support Vector Machine (SVM) and Complex Tree (CTree), are used for land cover mapping. An advanced supervised algorithm, namely the Derivative-free Multi-layer Perceptron (FDMLP), which is based on the Multi-layer Perceptron (MLP) function, is developed in MATLAB programming language. The FDMLP uses a derivative-free function for the optimization of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted Land Use Land Cover (LULC) maps. A Deep Neural Network (DNN) is used for LULC mapping as well. The FDMLP classifier has outperformed the other two well-known algorithms of the SVM and the CTree for the classification of the pixel-based Landsat-8 imagery and the object-based Landsat-8 imagery with a spatial resolution of 15 m in terms of the overall accuracy and index of kappa. Based on the test data, the DNN classifier for the object-based Landsat-8 imagery with a spatial resolution of 15 m with values of 91.28 and 88.57 percent for the OA and Kappa index has outperformed the other supervised classifiers. The worst results of classification are for the DNN algorithm for the pixel-based Landsat-8 imagery. [ABSTRACT FROM AUTHOR]

Hydropower is essential for global electricity production, but it consumes water by evaporation from the reservoir surface. Here, a new approach is introduced in relation to modelling the water footprint of electricity (WFe) from hydropower. Two of the most important variables in calculating the WFe are volume of evaporation (EV) and electricity production (EP). In this study, the random forest (RF) model was used to predict both EV and EP. For analysing hybrid models, wavelet transform was used and wavelet RF (WRF) models were developed. After decomposing the input variables by wavelet transform, the relief algorithm (RA) was used to recognize important components and inserted into the RF model. The proposed approach was applied at Mahabad Hydropower in Iran. The results suggest that applying the wavelet transform on input data and using algorithms such as RA can be regarded as a good approach for modelling of EV and EP. [ABSTRACT FROM AUTHOR]

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions. [ABSTRACT FROM AUTHOR]

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. [ABSTRACT FROM AUTHOR]

COVID-19 as a viral infection is usually asymptomatic in children, but complication of this disease in children is not rare and may be fatal. Hyper inflammation of COVID-19 is a potential fatal in undiagnosed children. It is very similar to Kawasaki disease but with higher mortality and morbidity. Multisystem inflammatory syndrome in children (MIS-C) and multi-organ involvement was reported in hyper inflammation syndrome following COVID19. Herein we report our algorithmic approach to Kawasaki-like syndromes due to COVID-19 in our center. Based-on this approach we had no mortality during the last 5 months. [ABSTRACT FROM AUTHOR]

In the current highly competitive airline market, many companies have failed due to their low revenue rates. For this reason, many of them have to develop strategies to increase their revenue. In this study, we develop revenue management (RM) strategy for the Iranian airline industry. More specifically, we present a mathematical model that considers some conditions not studied in previous research in order to provide a more realistic RM modeling of airlines that fits well for the special characteristics of Iranian Airways. A binary differential evolution algorithm is employed to solve the model due to the stochastic nature of data and the NP-hardness of the considered problem. To generate maximum revenue among the six types of airplanes that fly the four capital cities of Iran, the airline under investigation is advised to operate only 21 flights to those cities and cancel the rest of the flights. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle. [ABSTRACT FROM AUTHOR]

This study seeks to achieve three main goals with respect to customer‐relationship management (CRM): (1) analysis of the relationship between different factors of CRM, (2) performance assessment of an organization from a CRM viewpoint, and (3) analysis of how each factor of CRM impacts an organization's performance. First, fuzzy DEMATEL is used to identify potential relationships between the factors. Subsequently, these relationships are examined by path analysis to find out which ones are supported. To accomplish the second goal, data‐envelopement analysis (DEA) is used to calculate the efficiency scores of the decision‐making units (DMUs), and an intelligent algorithm based on genetic programming (GP) and artificial neural networks (ANNs) is employed to determine the effectiveness scores. Finally, how each factor of CRM influences the performance is analyzed statistically. To demonstrate the applicability of the framework in the real world, it was used to study the household‐appliances industry in Iran. [ABSTRACT FROM AUTHOR]

Aims: Classification is an appropriate tool for the summarizing of species data in community ecology. Researchers need to select the effective classification method(s) and the optimum number of clusters to perform a reasonable classification. The aims of the present research are to assess the efficacy of various classification algorithms and to select the optimum number of clusters. Study area: We used a dataset of 197 400 m2 relevés recorded from Tarbiat Modares University research forest located in the north of Iran. Methods: For each relevé, a species list and the canopy cover were recorded by using Braun-Blanquet cover-abundance scale modified by van der Maarel. We considered seven classification methods: flexible-β linkage (β = -0.25), Ward's linkage, complete linkage, average linkage, Modified TWINSPAN, k-means, and PAM. Using each of these algorithms, data were classified into 2-21 cluster levels. Then, values of eight internal evaluators viz. ASW, 1-C.index, PARTANA, PBC, 1-ISA.pval, ISA.sig.inds, ISAMIC, 1-Morisita as well as mean lambda index were calculated for each classification level resulted from algorithms. These values were applied in three methods to select the appropriate classification algorithm(s). Also, we used those values to choose the optimum number of clusters in the selected algorithm(s). A discriminate analysis opted for the verification of the selected optimums. Results: Our results revealed that, for our data, flexible-β linkage was the proper classification algorithm with 12 the optimum number of clusters. Despite the vast number of available classification algorithms, there is no ultimate best one for all vegetation datasets. Therefore scientists need using multiple criteria to choose their specific appropriate method. With respect to this, our methods and findings could provide a generalized framework for choosing the effective method(s) for the subsequent classification analyses. [ABSTRACT FROM AUTHOR]

Background and Aim Chronic Lymphocytic Leukemia (CLL) is the most commonly occurring leukemia in adults, accounting for about 30-25% of total leukemia. One of the important etiological causes of this leukemia is the disruption of the Nuclear Factor Kappa B (NF-kB) signaling pathway. The two proteins of Apoptosis-Inducing Ligand (APRIL) and B-Cell Activating Factor (BAFF) play a role in the pathogenesis of this leukemia by affecting the NF-kB signaling pathway. In this study, due to the effect of miRNAs in regulating many cellular processes, the prediction of the prominent miRNAs targeting APRIL and BAFF transcripts in B-cell CLL patients was evaluated using specific and different bioinformatics programs. Methods & Materials Afterwards retrieving the sequences of APRIL and BAFF proteins from the NCBI website, by using several programs including miRanda, TargetScan, miRWalk, DIANA and miRDB with different algorithms, the prediction of miRNAs targeting these genes was investigated. Ethical Considerations This study was approved by the Research Ethics Committee of Arak University of Medical Sciences. Results Based on the scoring system of bioinformatics programs, “hsa-miR-145-5p” and “hsa-miR-185-5p” were identified as miRNAs targeting APRIL gene, while “hsa-miR-424” and “hsa-miR-497”were miRNAs targeting BAFF gene. They were suggested for the practical studies in future. Conclusion Based on the important role of APRIL and BAFF genes in the normal process of cell death and B-cell evolution, it seems that the mi-RNAs predicted by bioinformatics programs using different algorithms can be used as a diagnostic molecular biomarker to identify B-cell CLL patients. [ABSTRACT FROM AUTHOR]

Background: Drowning is one of the leading causes of death from unwanted injuries in the world. The highest death rate from drowning is among children. Pools are one of these places that have significant problems in current systems due to technical and methodological aspects. Using an automated visual monitoring system can help reduce drowning. The main objective of the project is to minimize the death of children from drowning. Method: This research was performed using applied research and content analysis methods and through up-to-date international studies. In this study, cameras were installed around swimming pools. A threshold surface is defined for the depth of water. Images of children are taken every time they enter the pool and stored in databases. If a child reaches below the depth of the threshold, the child is identified and a warning message is sent to the rescuer. The name of the drowned child will be announced and action can be taken to save the child. Result: The presented algorithm can detect drowning conditions along with the exact position of the person drowning in the pool and provide the result in the best possible time, which is relatively low compared to the rescue time required for the drowning rescue operation. Conclusion: In order to solve the problem of insufficient monitoring, the child drowning prevention system was examined. Implementing the algorithm under study makes rapid decision-making possible. The proposed system has shown that it can be implemented in domestic swimming pools and public pools to prevent unintentional drowning deaths. The survey is a method of tracking humans within the framework of a video surveillance system capable of automatically detecting drowning in a pool. [ABSTRACT FROM AUTHOR]

Background: Ischemic Heart Disease (IHD) is the leading cause of mortality in both developed and developing countries. It accounts for more than 15% of the total mortality worldwide. At the global scale, the massive occurrence of this disease can have tremendously negative effects on economy, especially among young people. Objective: The present study aimed to investigate the relationship between IHD and several important risk factors involved. It also looked into the prevalence of IHD among patients between 20 and 40 years of age. Methods: The present cross-sectional, retrospective survey was conducted in three referral heart hospitals affiliated to Mashhad University of Medical Sciences, Iran. The required data were extracted from integrated Hospital Information System (HIS) from 2010 to 2012. The data included clinical and demographic information, such as age, gender, marital status, occupation, diabetes, blood pressure, blood cholesterol, cigarette smoking, and family history. In the next phase, clustering technique and k-means algorithm were applied using the WEKA (3-6-9) software. Results: Totally, 88623 patients suffered from heart diseases between 2010 and 2012. When the specific inclusion and exclusion criteria were considered, the number of records was restricted to 776, which included 548 males. The clustering technique was done in two phases. Firstly, there were four clusters extracted and secondly, cluster analysis was done in terms of age and gender. According to the findings, cigarette smoking in males aged between 20 and 40 years was the main risk factor. Conclusion: The present research aimed to investigate the risk factors of heart diseases among patients between 20 and 40 years of age. Those below 40 years old were known as the main human resource in the community. The early prevalence of IHD in this population disabled them for the rest of their lives. This disability could also lead to irreparable physiological effects along with financial costs. It could also impose high costs on the society. Recognition of the risk factors of heart diseases at younger ages could contribute to healthcare policies. [ABSTRACT FROM AUTHOR]

The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide. [ABSTRACT FROM AUTHOR]

Similar to any other geophysical method, seismic refraction method faces non-uniqueness in the estimation of model parameters. Recently, different nonlinear seismic processing techniques have been introduced, particularly for seismic inversion. One of the recently developed metaheuristic algorithms is bat optimization algorithm (BA). Standard BA is usually quick at the exploitation of the solution, while its exploration ability is relatively poor. In order to improve exploration ability of BA, in the current study, a hybrid metaheuristic algorithm by inclusion a mutation operator into BA, socalled mutation based bat algorithm (MBA), is introduced to inversion of seismic refraction data. The efficiency and stability of the proposed inversion algorithm were tested on different synthetic cases. Finally, the MBA inversion algorithm was applied to a real dataset acquired from Leylanchay dam site at East-Azerbaijan province, Iran, to determine alluvium depth. Then, the performance of MBA on both synthetic and real datasets was compared with standard BA. Moreover, the dataset was further processed following a tomographic approach and the results were compared to the results of the proposed MBA inversion method. In general, the MBA inversion results were superior to standard BA inversion and results of MBA were in good agreement with available boreholes data and geological sections at the dam site. The analysis of the seismic data showed that the studied site comprises three distinct layers: a saturated alluvial, an unsaturated alluvial, and a dolomite bedrock. The measured seismic velocity across the dam site has a range of 400 to 3,500 m/s, with alluvium thickness ranging from 5 to 19 m. Findings showed that the proposed metaheuristic inversion framework is a simple, fast, and powerful tool for seismic data processing. [ABSTRACT FROM AUTHOR]

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient's control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians' knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician's therapeutic actions would be based on control level. [ABSTRACT FROM AUTHOR]

A robust methodology to determine geological facies in oil and gas fields is the integration of seismic attributes and well data to estimate flow zone indicator (FZI). Recently, the application of fuzzy and Neuro-Fuzzy approach regarding this purpose has enjoyed an increasing attention. The current study was carried out in the Surmeh (Arab) formation at Persian Gulf basin, Southern Iran. A Nero-fuzzy system was applied to estimate FZI cube from seismic attributes. To do so, core data and seismic data from four wells were imported to ANFIS system. Subsequently, the outcomes were compared with those of probabilistic neural network (PNN). Finally, a fuzzy C-Means clustering (FCM) technique was applied to characterize different hydraulic flow units (HFUs). The results of this study demonstrate that adaptive neuro-fuzzy inference systems (ANFIS) turn out to be successful in modeling FZI from seismic attributes and well data for a faraway well location. Moreover, the results achieved suggest that using the FCM technique is an efficient methodology to determine different HFUs from FZI cube. [ABSTRACT FROM AUTHOR]

Introduction: Economic evaluation of healthcare interventions usually needs accurate data on utility and health-related quality-of-life scores. The aim of this study is to map QLQ-C30 scale score onto EQ-5D-5L and SF-6D utility values in colorectal cancer (CRC) patients.Methods: EQ-5D-5L, SF-6D, and QLQ-C30 were completed by 252 patients with CRC who were referred to three cancer centers in Tehran between May and September 2017. Moreover, OLS, Tobit, and CLAD models were used to predict EQ-5D-5L and SF-6D values. The goodness of fit of models was evaluated using Pred R2 and Adj R2. In addition, their predictive performance was assessed by MAE, RMSE, ICC, MID, and Spearman's correlation coefficients between observed and predicted EQ-5D-5L and SF-6D values. Models were validated using a 10-fold cross-validation method.Results: Considering the goodness of fit and predictive ability of models, the OLS Model 2 performed best for EQ-5D-5L (Adj R2 = 58.09%, Pred R2 = 58.93%, MAE = 0.0932, RMSE = 0.129) and the OLS Model 3 performed best for SF-6D (Adj R2 = 54.90%, Pred R2 = 55.62%, MAE = 0.0485, RMSE = 0.0634).Conclusion: Our results demonstrated that algorithms developed based on OLS Models 1 and 2 are the best for predicted EQ-5D-5L and SF-6D values, respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patient safety culture (PSC) as a main component of the organizational culture plays a key role in providing safe, effective and economic cares and services in healthcare organizations. PSC provides a way to assist hospitals in order to improve patient safety and prevent medical errors. OBJECTIVE: The present study aimed to measure PSC and healthcare professionals' attitude towards voluntary reporting of adverse events in two hospitals in Iran and to develop a hybrid intelligent approach for modeling PSC grades. METHODS: The Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire and a two-part questionnaire were used for examining the PSC and healthcare professionals' attitude towards voluntary reporting of adverse events, respectively. Principal component analysis (PCA) was applied to extract of the main components in the HSOPSC questionnaire and to construct 12 dimensions of patient safety culture. The overall grade of patient safety culture was modeled using adaptive neuro-fuzzy inference systems (ANFIS) as a classification problem. RESULTS: Almost half of the participants have experienced a medical error and adverse events. The PSC grade was acceptable from the point of view of 55.5% and 50% of participants in hospital No.1 and hospital No.2, respectively. The overall accuracy of ANFIS in modeling overall grades of patient safety culture in both study hospitals was 0.84. Of those individuals gave an acceptable grade on patient safety culture in both study hospitals, more than 50% believed that all medical errors and near misses should be reported. CONCLUSIONS: The ANFIS algorithm was proposed for modeling and predicting of PSC for healthcare organizations. The results confirm the capability of the proposed model to predict patient safety grades in healthcare settings. [ABSTRACT FROM AUTHOR]

This study aimed to explore the combination of factors and circumstances that could increase the likelihood of a pedestrian crash to be fatal. Six pedestrian crash clusters identified in our previous study on 34,178 crashes occurred in Iran, were employed in the current work and the association rules technique was used as the analysis tool. Based on the results, jaywalking or waiting beside the road on poorly illuminated locations of those segments of rural roads that pass through residential/agricultural districts substantially increased the fatality risk of pedestrian (Lift = 3.4). Therefore, appropriately illuminated locations need to be designed on these segments of rural roads for pedestrians to wait beside or cross the road safely. In case of motorcycle-pedestrian crashes, inattention to yield sign, especially in residential areas, might significantly increase the fatality risk of pedestrian (by a factor of 8.1). Also, in those crashes where the motorcyclist is under 19 years old (i.e. unlicensed) and the pedestrian is over 60, the crash could be 5.3 times more likely to be fatal. More strict law enforcement over unlicensed motorcyclists is required as they were found to involve in more severe pedestrian crashes. The combined use of clustering analysis and association rules discovery should not be considered as an alternative to other modelling techniques, but as a complementary descriptive method to be used in road safety research. [ABSTRACT FROM AUTHOR]

Background: Utilizing multidimensional item response theory (MIRT) and genetic algorithm (GA) we aimed to design and test the psychometric properties of the short form Postpartum Quality of Life Questionnaire (PQOL). Methods: In this methodological study, 500 women aged 18 to 42 were enrolled through a multistage random sampling scheme in Tabriz, Iran. We used MIRT model and GA to identify a short form of the 40-item PQOL measure (SF-PQOL). Construct and criterion validity of the SF-PQOL was assessed by confirmatory factor analysis (CFA) and the correlation between SF-PQOL scores with a 12-item short form of QOL (SF-12) and Edinburg Postnatal Depression Scale (EPDS) scores, respectively. The internal consistency, test-retest reliability and feasibility of the measure were evaluated. Results: sixteen- and 13-item SF-PQOL were identified based on MIRT and GA, respectively. The results indicate the better performance of the MIRT based 13-item SF-PQOL; Construct and criterion validity, the test-retest and internal consistency reliability, and the feasibility were confirmed in the MIRT based SF-PQOL, but not in the GA-based SF-PQOL. Conclusion: The MIRT suggests a 13-item SF-PQOL with adequate content which demonstrated satisfactory validity, reliability, and feasibility. SF-PQOL could be used across the population for both research and clinical objectives. [ABSTRACT FROM AUTHOR]

Abstract: Aim: Personality disorders (PD) have been deemed the most problematic diagnostic grouping in terms of validity and utility, which particularly applies to non‐Western societies. The current study evaluated the structural validity of PD trait domains in the Diagnostic and Statistical Manual of Mental Disorders‐5 (DSM‐5) Section III and the proposed International Classification of Diseases‐11 (ICD‐11) in the Iranian population. Methods: Community‐dwelling adults (n = 285; 66% women) were administered the Personality Inventory for DSM‐5, whereas the proposed ICD‐11 trait domains were delineated using an algorithm for Personality Inventory for DSM‐5 trait facets. The five‐factor organization and higher‐order hierarchical structure of both models were examined using exploratory structural equation modelling analyses. Results: The five‐factor exploratory structural equation modelling loadings overall resembled international findings on DSM‐5 Section III traits (including Psychoticism), whereas the ICD‐11 five‐factor structure aligned with the proposed ICD‐11 domain features (including Anankastia). Additionally, we identified a hierarchical structure from one to five factors for both models that conceptually aligned with established models of personality and psychopathology. Conclusions: This study provided initial support for the structural validity of DSM‐5 and ICD‐11 PD trait models in Iranian culture. Future research warrants replication in larger samples and clinical populations. Copyright © 2018 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Community Based Organizations (CBOs) are important health system stakeholders with the mission of addressing the social and economic needs of individuals and groups in a defined geographic area, usually no larger than a county. The access and success efforts of CBOs vary, depending on the integration between health care providers and CBOs but also in relation to the community participation level. To achieve widespread results, it is important to carefully design an efficient network which can serve as a bridge between the community and the health care system. This study addresses this challenge through a location-allocation model that deals with the hierarchical nature of the system explicitly. To reflect social welfare concerns of equity, local accessibility, and efficiency, we develop the model in a multi-objective framework, capturing the ambiguity in the decision makers' aspiration levels through a fuzzy goal programming approach. This study reports the findings for the real case of Shiraz city, Fars province, Iran, obtained by a thorough analysis of the results. [ABSTRACT FROM AUTHOR]

Background: Panoramic radiography is a standard diagnostic imaging method for dentists. However, it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the superimposed facial skeleton structures. The objective of this study was to develop a deep learning algorithm that is capable of detecting mandibular fractures and trauma automatically and compare its performance with general dentists. Materials and Methods: This is a retrospective diagnostic test accuracy study. This study used a two-stage deep learning framework. To train the model, 190 panoramic images were collected from four different sources. The mandible was first segmented using a U-net model. Then, to detect fractures, a model named Faster region-based convolutional neural network was applied. In the end, a comparison was made between the accuracy, specificity, and sensitivity of artificial intelligence and general dentists in trauma diagnosis. Results: The mAP50 and mAP75 for object detection were 98.66% and 57.90%, respectively. The classification accuracy of the model was 91.67%. The sensitivity and specificity of the model were 100% and 83.33%, respectively. On the other hand, human-level diagnostic accuracy, sensitivity, and specificity were 87.22 ± 8.91, 82.22 ± 16.39, and 92.22 ± 6.33, respectively. Conclusion: Our framework can provide a level of performance better than general dentists when it comes to diagnosing trauma or fractures. [ABSTRACT FROM AUTHOR]

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called 'LLE'(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately. [ABSTRACT FROM AUTHOR]