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Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the attentive interpretable tabular learning (TabNet) model through three swarm-based metaheuristic algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and hunger games search (HGS). A spatial database incorporating dust occurrence areas was created using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2022, including 15 influential criteria related to climate, soil, topography, and land cover. Four models were employed for modeling and DSM generation: TabNet, TabNet-PSO, TabNet-GWO, and TabNet-HGS. Evaluation of the modeling results using performance metrics indicated that the TabNet-HGS model outperformed the other models in both training (mean absolute error (MAE) = 0.055, root-mean-square error (RMSE) = 0.1, coefficient of determination (R 2 ) = 0.959), and testing (MAE = 0.063, RMSE = 0.114, R 2  = 0.947) data. Following TabNet-HGS, the TabNet-PSO, TabNet-GWO, and TabNet models demonstrated progressively lower accuracy. The validation of the DSM was performed by assessing receiver operating characteristic (ROC) curves, revealing that the TabNet-HGS, TabNet-PSO, TabNet-GWO, and TabNet models exhibited the highest modeling accuracy, with corresponding area under the curve (AUC) values of 0.994, 0.986, 0.98, and 0.832, respectively. These results highlight the enhanced accuracy of dust susceptibility modeling achieved by integrating swarm-based metaheuristic algorithms with the TabNet model. The dust susceptibility map provides valuable insights into the sources, pathways, and impacts of dust particles on the environment and human health in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Yazdi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. [ABSTRACT FROM AUTHOR]

The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels-Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Nasrin Bagheri Mazraeh et al.)

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.

This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features. Therefore, it can be utilized to detect either soft or hard onsets. Through this combination, we obtained a more precise separation between two adjacent notes. This ability is especially useful to detect the reaz, repeatedly played notes with the same frequency and short durations. For the evaluation of the method, a data set with predetermined onsets was produced and the results were compared with an energy-based method explained in terms of F-measure. [ABSTRACT FROM AUTHOR]

Improving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14‐bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs. [ABSTRACT FROM AUTHOR]

This paper presents a mixed integer non-linear programming (MINLP) model for a bi-objective and multi-depot vehicle routing problem with time windows. The main goals of the paper are minimization of total cost and equitable distribution of commodities between vehicles. Two types of vehicle including delivery and installation vehicles are utilized in the network regarding customers' needs. Satisfying all demands of the customers is not obligatory and unmet demands are permitted which leads to extra cost. A presented model is applied for real life case study in different provinces of Iran. To tackle the small-size problems, the augmented E-constraint method is utilized by linearization of the model. Because of the NP-hard nature of the problem, as the size of the problem increases, so does the complexity. As such, we develop multi-objective simulated annealing (MOSA meta-heuristic) algorithm for large scale problems. Then, several numerical experiments and sensitivity analyses are conducted to validate the presented model and the solution method, which indicate the efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

In this paper, a new version of the multi-objective particle swarm optimizer named the Diversity-enhanced fuzzy multi-objective particle swarm optimization (f-MOPSO/Div) algorithm is proposed. This algorithm is an improved version of our recently proposed f-MOPSO. In the proposed algorithm, a new characteristic of the particles in the objective space, which we named the "extremity," is also evaluated, along with the Pareto dominance, to appoint proper guides for the particles in the search space. Three improvements are applied to the f-MOPSO to mitigate its shortcomings, generating f-MOPSO/Div: (1) selecting the global best solution based on the diversity of the extreme solutions, (2) impeding the particles to be trapped in the local optima using a mutation scheme based on the dynamic probability, and (3) removing the pre-optimization process. To validate f-MOPSO/Div, it was compared with some other popular multi-objective algorithms on 14 standard low- and high-dimensional test problem suites. After the comparative results indicated the outperformance of the proposal, the f-MOPSO/Div was applied to solve an optimal conjunctive water use management problem, in a semi-arid study area in west-central Iran, over a 13-year long-term planning period with two main objectives: (1) maximizing the aquifer sustainability as an environmental goal, and (2) maximizing the crop yields as a socio-economic goal. As the results suggest, the cumulative groundwater level drawdown is considerably decreased over the whole planning period to make the aquifer sustainable, while the water productivity is held at a desirable level, demonstrating the superiority of the f-MOPSO/Div when also applied to solve a large-scale real-world optimization problem. [ABSTRACT FROM AUTHOR]

Stormwater management in an urban environment is beset by uncertainties about future development. Dynamic strategies must be devised to cope with such uncertain environment. This work proposes a simulation-optimization model that minimizes the costs of low-impact development (LID) measures for mitigating impacts of future urban development on runoff. This paper's methodology is tested in an urban watershed in Tehran, Iran, relying on the stormwater management model (SWMM) coupled with the genetic algorithm (GA) to function as a simulation-optimization method for urban-runoff control by means of LID stormwater control measures. A sensitivity analysis of the calculated optimal solution revealed the impacts the most sensitive LIDs would have on runoff considering a set of plausible future development scenarios in the urban catchment. A comparison of the results from two different scenarios of future development with the existing stormwater system's performance shows the cost increase in redesigning the existing system to make it LID sensitive would equal 20% of the existing system's cost. The additional cost of redesigning the existing system without LID features would be 45% of the existing system's cost. These results demonstrate the importance of assessing the sensitivity of designed units in a stormwater management system and studying the trade-offs between possible decisions and future uncertainties concerning development in the watershed.

Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ensemble methodology based on evidential belief function and tree-based models (EBFTM) for preparing groundwater potential maps (GPM). The performance of these new models is then compared with three previously implemented models, i.e., boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). For this purpose, spring locations in the Meshgin Shahr in Iran were detected. The spring locations were randomly categorized into training (70% of the locations) and validation (30% of the locations) datasets. Furthermore, several groundwater conditioning factors (GCFs) such as hydrogeological, topographical, and land use factors were mapped and regarded as input variables. The tree-based algorithms (i.e., BRT, CART, RF, and RTF) were applied by implementing the input variables and training dataset. The groundwater potential values (i.e., spring occurrence probability) obtained by the BRT, CART, RF, and RTF models for all the pixels of the study area were classified into four potential classes and then used as inputs of the EBF model to construct the new ensemble model (i.e., EBFTM). At last, this paper implemented a receiver operating characteristics (ROC) curve for determining the efficiency of the EBFTM, RTF, BRT, CART, and RF methods. The findings illustrated that the EBFTM had the highest efficacy with an area under the ROC curve (AUC) of 90.4%, followed by the RF, BRT, CART, and RTF models with AUC-ROC values of 90.1, 89.8, 86.9, and 86.2%, respectively. Thus, it could be inferred that the ensemble approach is capable of improving the efficacy of the single tree-based models in GPM production.

This paper is aimed to develop and verify a novel hybrid intelligent model, which is indeed a new version of GMDH algorithm and named generalized structure of GMDH (GS-GMDH) to solve engineering problems. The proposed GS-GMDH model was validated its capability of predicting blast-induced ground vibration, a very important safety issues in the mining industry. For this regard, a data set with a totally of 96 samples was gathered from a blasting site in Shur River Dam region, Iran. Among them, 67 and 29 samples were used for constructing and testing the model, respectively. To check the accuracy and robustness of the proposed algorithm, the values of the performance evaluation measures, i.e., R2, variance accounted for (VAF), mean absolute error, root mean square error, scatter index, and Nash–Sutcliffe model efficiency (EN–S), were used. The results showed the efficiency of the GS-GMDH algorithm in the prediction of the blast-induced ground vibration. It was also confirmed that the proposed algorithm can be applied effectively to solving/predicting the engineering problems and it has also the potential to be generalized to other fields. [ABSTRACT FROM AUTHOR]

This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran's wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%. [ABSTRACT FROM AUTHOR]

The special features of some old residential complexes make reconstructing a better option than rebuilding. The complexity of redesign issues, the ability of computers to solve problems, and the capability of evolutionary optimization techniques in architecture make the use of these methods a priority over traditional processes. This paper, while introducing Particle Swarm Optimization (PSO) as one of the swarm intelligence based algorithms, answers the question of how this algorithm can be used to redesign residential complexes. To achieve this, after reviewing the application of PSO algorithms in architectural space planning, the redesign criteria and constraints was divided into four categories; including primary design specifications, contact-related criteria, designer-related criteria and redesign-related constraints. After the quantification process of these criteria, how to implement the algorithm to achieve optimal plans for each of the five plan types available in high-rise apartment complex in Mashhad, Iran, was shown as a case study. [ABSTRACT FROM AUTHOR]

Abstract: Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks. [ABSTRACT FROM AUTHOR]

Background: In medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. Due to the limitations of usual statistical models, other methods such as Artificial Neural Network (ANN) and hybrid models could be used. In this paper, we propose a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to prediction the occurrence of heart block and death in myocardial infarction (MI) patients simultaneously., Methods: For fitting and comparing the models, 263 new patients with definite diagnosis of MI hospitalized in Cardiology Ward of Hajar Hospital, Shahrekord, Iran, from March, 2014 to March, 2016 were enrolled. Occurrence of heart block and death were employed as bivariate binary outcomes. Bivariate Logistic Regression (BLR), ANN and hybrid ANN-GA models were fitted to data. Prediction accuracy was used to compare the models. The codes were written in Matlab 2013a and Zelig package in R3.2.2., Results: The prediction accuracy of BLR, ANN and hybrid ANN-GA models was obtained 77.7%, 83.69% and 93.85% for the training and 78.48%, 84.81% and 96.2% for the test data, respectively. In both training and test data set, hybrid ANN-GA model had better accuracy., Conclusions: ANN model could be a suitable alternative for modeling and predicting bivariate binary responses when the presuppositions of statistical models are not met in actual data. In addition, using optimization methods, such as hybrid ANN-GA model, could improve precision of ANN model.

COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic "pandemic status," with 22.47% in the prevention category, and the lowest number of publications is related to the topic "environment," with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers. [ABSTRACT FROM AUTHOR]

Introduction: Since the beginning of the COVID-19 epidemic in Iran, several electronic registration systems have been created to record the data of infected patients. This narrative review aimed to investigate the articles that described the COVID-19 electronic registry systems designed and implemented in Iran. Methods: In this review, four electronic databases [Medline (accessed from PubMed), Scopus, Science Direct, and Web of Science] were searched till June 22, 2020, using specific MeSH terms and related keywords in English language. Considering the titles and abstracts, unrelated studies were excluded. The full texts of the remained studies were evaluated by authors, independently. Then, their findings were assessed and reported. Results: Finally, four articles were enrolled, introducing four COVID-19 registries. These registries were designed and launched by Isfahan, Shiraz, Tehran, and Ilam Universities of Medical Sciences. They were different in design, the used algorithms for patients’ management, recorded data, and methods of quality assurance. Conclusion: Considering the differences between various registry systems designed for COVID-19 in Iran, it is recommended to develop a single web-based registry system by the Iran Ministry of Health and Medical Education to register and follow up the patients with COVID-19. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

Figures in scientific documents are rich sources of information. The first step in retrieving information from such figures is to build a valid figure database. To this end, we developed a system for generating figure database from scholarly Persian documents, in large scale. The first step is to parse files and extract figures and their corresponding descriptions. There are two general approaches for extracting figures from documents. One is based on image processing methods and another is based on processing the file primitives. The focus of this paper is on latter one. This approach is shown to be a better choice for the search engines because of its speed and scalability properties. We propose a structure based method that extracts the figures and their descriptions by analyzing the file layout. This information is saved in a database with a specific structure and is indexed for retrieval in the search engine. The proposed algorithm was implemented in Python programming language. As a benchmark we used the basic method in the literature which is based on the processing PDF file. We employed the proposed method in a case study on Iran scientific information database (Ganj). In this regard, 150 scientific documents were randomly chosen from Ganj database and analyzed using two mentioned methods. Based on our experimental results, the proposed method is more efficient than the basic method especially for Persian documents. There are many unanswered challenges for Persian documents when using the basic method. The number of noise images resulted from the basic method is high and Persian text extracted is not well organized. Our proposed method overcomes some of these drawbacks and is recommended for generating figure database from scientific Persian documents. The proposed method is able to correctly extract about 40% of the images with their corresponding descriptions which is 10% better than the basic method. [ABSTRACT FROM AUTHOR]

Muscle biopsy has been practiced as a well-established part of paraclinical workup in patients with neuromuscular diseases since late 1960s. In this narrative review paper, the role of some pre and post muscle biopsy factors and their importance in achieving the best diagnostic results will be explained. Considering the new advances in diagnostic molecular techniques and the availability of local standard myopathology laboratory as well as the presence of a dedicated myopathologist, the indications of muscle biopsy in four major types of neuromuscular diseases will be shortly reviewed. Moreover, indications of muscle biopsy in four major types of neuromuscular diseases will be shortly discussed based on literature review of recent published diagnostic algorithms and our 11 years' experience of performing about 4000 muscle biopsies cases in the only standard referral diagnostic center for muscle biopsy in Iran. Although diagnostic algorithms of some muscle diseases have been changed based on recent advances in biochemical and molecular diagnostic techniques, still muscle biopsy continues to play a major role in the diagnosis and management of variety of neuromuscular disorders and has proved to be a preferable diagnostic procedure by some neuromuscular specialists for the cases who can benefit from rapid therapeutic management. The application of diagnostic algorithms should be practiced in accordance with geographic distribution of the diseases, the availability of diagnostic techniques and the presence of specialists in each center considering the local insurance coverage and the cost to be paid by the patient in every center. [ABSTRACT FROM AUTHOR]

The article aims to introduce a gold-standard Pashto dataset and a segmentation app. The Pashto dataset consists of 300 line images and corresponding Pashto text from three selected books. A line image is simply an image consisting of one text line from a scanned page. To our knowledge, this is one of the first open access datasets which directly maps line images to their corresponding text in the Pashto language. We also introduce the development of a segmentation app using textbox expanding algorithms, a different approach to OCR segmentation. The authors discuss the steps to build a Pashto dataset and develop our unique approach to segmentation. The article starts with the nature of the Pashto alphabet and its unique diacritics which require special considerations for segmentation. Needs for datasets and a few available Pashto datasets are reviewed. Criteria of selection of data sources are discussed and three books were selected by our language specialist from the Afghan Digital Repository. The authors review previous segmentation methods and introduce a new approach to segmentation for Pashto content. The segmentation app and results are discussed to show readers how to adjust variables for different books. Our unique segmentation approach uses an expanding textbox method which performs very well given the nature of the Pashto scripts. The app can also be used for Persian and other languages using the Arabic writing system. The dataset can be used for OCR training, OCR testing, and machine learning applications related to content in Pashto. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge. [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 optimal allocation and sizing of distributed generation (DG) resources are important in installing these resources, to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level, as well as to increase economic factors. In this paper, a new multi-criteria algorithm and objective function are proposed for the optimal sizing and allocation of renewable and non-renewable DG resources simultaneously. The proposed algorithm is implemented on 63/20 kV substations at 20 kV levels. In the proposed objective function, all important technical and economic factors as well as important constraints, such as penetration level of DGs and budget constraint, are considered in a way that all factors are assigned to monetary values. Moreover, a new mathematical formulation is introduced for the allocation of renewable DG resources to reduce run-time optimization. The genetic algorithm (GA) is employed in the proposed algorithm to minimize the objective function. For renewable DG resources, photovoltaic panels and wind turbines, and for non-renewable DG resources, gas turbines are considered. The 115 buses network of Bakhtar Regional Electric Company (BREC) in Iran is used to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm improves technical factors efficiently and maximizes the profitability of the investment. [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]

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]

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]

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]

In this paper optimal and cost-effective design of a photovoltaic/wind turbine/fuel cell (PV/WT/FC), the renewable energy system is proposed with the objective of lifespan cost of hybrid system (LSCS) minimisation and considering reliability constraint as a maximum of load interruption probability (LIP) as LIPmax to supply the demand of residential-commercial centres (RCC) located in Iran based on real data of irradiance and wind speed of this region. A new hybrid meta-heuristic algorithm named hybrid grey wolf optimiser-sine cosine algorithm (HGWOSCA) based on the exponential decreasing function (EDF) with high accuracy and speed of optimisation in achieving to the global solution is applied to determine the optimal size of system components. The superiority of the proposed HGWOSCA is compared with SCA, GWO and particle swarm optimisation (PSO) methods in designing of different combinations of the hybrid system and under varying reliability constraint. The results showed that the PV/WT/FC combination is the best combination in view of LSCS and LIPmax for supplying the RCC as a cost-effective and reliable combination. The superiority of the HGWOSCA in designing different combinations of the hybrid system is proved with fewer LSCS and LIP than the other methods with more convergence speed and accuracy. The results clearly showed that the use of hydrogen storage as a reserve power has well compensated the fluctuations in renewable sources production for meeting the RCC load to achieve optimal reliability. The results cleared that the cost of per kW of RCC supply for LIPmax = 1% and LIPmax = 5% is 0.853 and 0.764 $. The results indicated that increasing the failure rate of the components increases the system cost and weakens the reliability. In addition, the results showed that increased FC efficiency has reduced the level of reserve hydrogen, reduced the LSCS, and improved reliability. [ABSTRACT FROM AUTHOR]

In this paper, optimal designing and energy management of hybrid photovoltaic/wind/fuel cell (PV/WT/FC) system is presented with cost of hybrid system life span (CHSLS) minimizing and considering loss of load interruption probability (LOLIP) for Recreational Center of Gonbad (RCOG), a remote area region in Iran country using actual irradiance and wind speed data of this region. A meta-heuristic improved sine–cosine algorithm (ISCA) is used based on nonlinearly decreasing inertia weight strategy (NDIWS) for improving global and local search of the conventional SCA to find the optimal combination of hybrid system. In designing problem the optimal size of hybrid system components is determined using ISCA considering CHLS and LOLIP to satisfy the RCOG load demand in most cost-effective way. The performance of the proposed ISCA is compared with conventional SCA and well-known PSO methods in different combinations and under LOLIP changing conditions. Simulation results showed that the ISCA finds easily the optimal combination as PV/WT/FC system with lower CHSLS and better LOLIP. Also WT/FC system is not economical due to high CHSLS for RCOG. The results demonstrated that the average cost of per kW RCOG load supply using the PV/WT/FC for maximum LOLIP of 1% is equal to 0.87$ and for maximum LOLIP of 5% is 0.76$. The results showed the contribution and management of PV, WT and fuel cell together as PV/WT/FC is made a reliable and cost-effective energy system to supply the remote area application like RCOG. Moreover superiority of the ISCA than the SCA and PSO is proved with better convergence speed and accuracy. Also, the effect of some effective factors on the system designing is evaluated. • Hybrid system designing for RCOG load supply using ISCA, case study for Iran. • Optimal sizing of PV/WT/FC with minimizing the CHSLS and respect to the LOLIP. • Determination of reliable and cost-effective system combination to supply RCOG load. • Superiority of the ISCA performance compared to SCA and well-known PSO method. • Investigating the effect of some economic and technical factors on system designing. [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]

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]

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: 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]

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]

This study focuses on development of equilibrium strategy based on simulated annealing (SA) algorithm for balancing economic and environmental concerns in waste load allocation (WLA) problem. To resolve conflicts among various stakeholders, including Iran Department of Environment (DoE) as governmental authority and industrial and municipal dischargers, Stackelberg and Nash bargaining games have been applied in this WLA problem and the results have been compared. SA algorithm has been coupled to QUAL2Kw model to derive optimal WLA program and the environmental penalty tariff (EPT) in Nash bargaining and Stackelberg games. The proposed tools and methodologies were illustrated in a case study of multi-stakeholders WLA problem in Gheshlagh River, Sanandaj, Kordestan, Iran. The results indicate that lower BOD removal rates are allocated to the pollutant dischargers in the Stackelberg game compared to the Nash bargaining game. Furthermore, the EPT assigned by Iran DoE in Stackelberg and Nash bargaining games are 11.25 and 3.6 Rials/(gr/month), respectively. The estimated EPT in the Stackelberg game is close to the current tariff (10 Rials/(gr/month)) specified by Iran DoE on impermissible BOD discharges. [ABSTRACT FROM AUTHOR]