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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]

In this paper, an integrated workflow based on recent geoscience data is presented for assessing the reservoir characterization and structural interpretation of the Burgan formation, a highly productive formation in the Abadan plain, SW Iran. Utilizing newly acquired high-resolution SEM images, we evaluated the pore size, pore distribution, and pore aspect ratio of Burgan formation. The watershed segmentation algorithm is also capable of detecting throats and closed pores. The porosity fractions from SEM images are used for calibration of the porosity log at several well locations in order to perform petrophysical modeling. Due to the complexity of facies behavior in the study area, we utilized supervised Bayesian classifiers using P-wave velocity, density, and facies log data in order to incorporate spatial dependence into the model. The confusing matrix and machine learning metrics including Accuracy (97.01%), Precision (93.88%), F1 Score (94.16%), and False Positive Rate (2.52%), indicate that the classifier has been properly trained at well locations. A reasonable match is evident between the modeled petrophysical parameters and the true (core) porosity and water saturation at the location of the test well. Furthermore, we have demonstrated the validity of assumptions concerning the dominance of extensional structure in the Abadan plain by using interpreted seismic data. The presented workflow can be used to optimize drilling operations and reduce risks in similar geological settings in the studied formation. Highlights: • The vital pore properties have been evaluated using newly obtained SEM images. • Bayesian classification is a reliable classifier for complex facies modeling. • The dominant extensional structure can be observed in the Abadan plain. • The watershed segmentation algorithm can detect closed pores and throats. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a leading cause of cancer-related deaths, emphasizing the importance of timely diagnosis for effective treatment. Machine learning models have shown promise in assisting with GC diagnosis. Objectives: This study aimed at comparing the performance of various feature selection methods in identifying influential factors related to GC based on lifestyle using machine learning models. The ultimate goal was to enhance early detection and treatment of the disease. Methods: The data of patients from Shahid Ayatollah Modarres Hospital and Shohadaye Tajrish Hospital between 2013 and 2021 were utilized. Three feature selection methods (filter, wrapper, and filter-wrapper) were employed. The k-fold method validated each model. Four classifiers k Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) compared their outputs based on feature selection methods. Results: The filter-wrapper method outperformed others, achieving an area under the ROC curve and Fl score of 95.8% and 94.7%, respectively. GBDT also performed well. The wrapper and RF classifiers achieved an area under the ROC curve and Fl scores of 95.7% and 93.6%, respectively, after the filter-wrapper method. Without feature selection methods, the RF classifier had an area under the ROC curve and F1 scores of 95.6% and 91.7%, respectively, surpassing other classifiers. Conclusions: This study suggests that appropriate feature selection methods for identifying influential factors related to GC based on lifestyle can facilitate early diagnosis and treatment. The filter-wrapper method demonstrated the best performance in this regard. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran. In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar. [ABSTRACT FROM AUTHOR]

The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions. [ABSTRACT FROM AUTHOR]

The Cauchy problem for Helmholtz equation, for moderate wave number |$k^{2}$|⁠ , is considered. In the previous paper of Achieng et al. (2020, Analysis of Dirichlet–Robin iterations for solving the Cauchy problem for elliptic equations. Bull. Iran. Math. Soc.), a proof of convergence for the Dirichlet–Robin alternating algorithm was given for general elliptic operators of second order, provided that appropriate Robin parameters were used. Also, it has been noted that the rate of convergence for the alternating iterative algorithm is quite slow. Thus, we reformulate the Cauchy problem as an operator equation and implement iterative methods based on Krylov subspaces. The aim is to achieve faster convergence. In particular, we consider the Landweber method, the conjugate gradient method and the generalized minimal residual method. The numerical results show that all the methods work well. In this work, we discuss also how one can approach non-symmetric differential operators by using similar operator equations and model problems which are used for symmetric differential operators. [ABSTRACT FROM AUTHOR]

Ground vibration is one of the most significant issues resulting from the blasting operation. This paper presents two empirical relationships based on gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms for predicting blast-induced peak particle velocity (PPV) at Galali Iron Mine, western Iran. For this purpose, data on 13 parameters were collected from 34 blasting blocks in the studied mine before having the data processed using statistical methods. Eventually, four parameters, including burden, mean hole depth, charge per delay ratio, and distance to monitoring station, were identified as the most effective factors. PPV was also considered as the output parameter of the model. Then, exploring the best curve-fitting relationships between input and output data, an empirical relationship was developed by applying the GEP algorithm. Afterward, the TLBO algorithm was utilized to optimize the developed relationship. A comparative analysis based on statistical parameters such as correlation coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) indicated the superior accuracy of TLBO algorithm compared to the GEP method. Finally, a blasting pattern was formulated to attenuate the PPV at the center of the Galali Village from 10 mm/s to 1 mm/s while increasing the mine production from 5500 tons to 17,500 tons per blasting block. [ABSTRACT FROM AUTHOR]

Earthquakes have caused considerable economic damage and loss of lives. The ability to determine earthquake-prone areas has utmost importance to crisis management. Given the gravity of the issue, this research proposes to incorporate geographic information system-based fuzzy-analytic network process (FAHP) and fuzzy-analytic hierarchy process (FANP) methods to determine earthquake-prone areas in the southwest of Iran. In addition, this paper is to utilize the feature selection algorithm (FSA) to select the most relevant parameters influencing earthquakes and prepare seismic activity maps. The final maps obtained using the relevant data were compared with maps obtained using the entire data. For this aim, a fuzzy membership function was initially employed to generate a fuzzy map for different layers, after which the analytic network process and analytic hierarchy process methods were utilized for assigning proper weights to each layer and eventually procuring earthquake maps. The input information for the mentioned methods consisted of landscape, slope, elevation, lithology, land use, soil, road, and fault maps. According to the FSA approach, the most relevant parameters include distance to fault (DTF), lithology, slope, and landscape. The results of FANP and FAHP methods utilizing the receiver-operating characteristic curve showed that the FANP method has a higher accuracy for determining earthquake-prone areas. As the areas located in the northern parts of the region are more at risk. In addition, Finally, the results of earthquake maps obtained using solely the relevant data from FSA were compared with the maps obtained using the entire data. The results of FSA showed that the Best first and Greedy-Stepwise models have high accuracy (R = 0.99) and the most important data for determining earthquake-prone areas using these models are DTF, lithology, slope, aspect. Therefore, by saving time and money, it is possible to prepare maps of earthquake-prone areas with high accuracy. [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]

Purpose: In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate. Design/methodology/approach: The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran. Findings: Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately. Originality/value: In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors' knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services. [ABSTRACT FROM AUTHOR]

Reports of acute hepatitis with unknown origin in children have been published worldwide since April 2022. Due to the unfamiliar nature of the disease and the alarming trend of increasing reports worldwide, health workers must become aware of its diagnosis and treatment. Accordingly, the committee for developing guidelines for the Department of Pediatrics, Tehran University of Medical Sciences, prepared a clinical guideline for more efficient management of these patients. Due to the limited information about this disease, it has been tried to categorize and specify the required diagnostic and treatment measures based on the latest published data. Herein we express this algorithmic approach and diagnostic and therapeutic guidelines for acute hepatitis with unknown origin in children. [ABSTRACT FROM AUTHOR]

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]

In the real word, the decision maker may want to use revenue and cost eficiency analysis for existing units compared to virtual units, traditional DEA models can no longer be used and FDH models must be used to evaluate the efficiency of decision-making units. In this paper, we develop the revenue and cost efficiency models based on the FDH model and obtain the efficiency scores based on pair wise comparisons. In the following, we will develop the proposed models for the two-stage network structure and obtain the desired scores of inputs and outputs by considering the input and output prices. An algorithm for measuring the revenue and cost efficiency is presented based on the ratio of inputs and outputs. Finally, we use the proposed algorithm to evaluate the efficiency of 13 airports in Iran with a two-stage network structure without considering the constraint of convexity and present the results of the research. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]