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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]

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 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]

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]

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]

We will refer to the paper "Convergence rates for Kaczmarz-type algorithms", published in Numerical Algorithms, 79(1)(2018), 1-17. It was observed by Dr. Mokhtar Abbasi (Department of Mathematics, University of Qom, Iran) that in the proof of Theorem 7, related to the linear convergence rate of the MREK algorithm it exists an inequality which is not always true. [ABSTRACT FROM AUTHOR]

In ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization–maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

Grouping of earthquakes into distinct clusters is applied to improve mechanism identification and pattern recognition for active seismicity in a region. One of the important issues concerning earthquake data clustering is determining the optimum number of clusters (ONC) at the early stages of algorithms. In this paper a robust method based on K-nearest neighbor search (KNNS) is presented to achieve three goals: improving output accuracy, improving output stability, and adding the ability to weight the features used in ONC determination. By introducing a new formula, the proposed method utilizes the error calculated for clustered data based on the similarity between the members in each cluster. An outlier attenuation algorithm is also used to improve the performance of the method. Both the Krzanowski–Lai Index (KLI) and the silhouette coefficient (SC), as two conventional methods, were used to compare the results and evaluate the performance. Experiments on synthetic data sets verified the effectiveness of the method, with considerable differences found. The clustering of a real earthquake catalogue related to the seismogenic province of Zagros in Persia using our proposed methodology suggests using 13-cluster analysis for clustering based on the spatiotemporal features with the same weights, and seven-cluster analysis for a case where priority is given only to the spatial parameters of the epicenters. Under the same circumstances, the KLI and SC methods suggest three and 18 clusters, respectively. The results of the experiments on synthetic data sets indicate that the proposed method is quantitatively more stable and more accurate than the other two methods. [ABSTRACT FROM AUTHOR]

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]

This paper presents an improved nature-based algorithm, namely multivariable modified teaching learning based optimization (MM-TLBO) algorithm, as in an iterative process can estimates the best values for the model parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MM-TLBO algorithm is to improve 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 radius (R), depth (h), shape factor (q), density contrast (q) and axis location (x0) parameters. We apply MM-TLBO and TLBO methods for the residual gravity anomalies caused by the buried masses with a simple geometry such as spheres, horizontal and vertical cylinders. The efficiency of these methods are also tested by noise corruption synthetic data, as the acceptable results were obtained. The obtained results indicate the better performance the MM-TLBO algorithm than the TLBO algorithm. We have utilized the MM-TLBO for the interpretation of the six residual gravity anomaly profiles from Iran, USA, Sweden and Senegal. The advantage of the MM-TLBO inversion is that it can estimates the best solutions very fast without falling into local minimum and reaches to a premature convergence. The considered primary population for the synthetic and real gravity data are thirty and fifty models. The results show which this method is able to achieve the optimal responses even if a small population of learners had been considered. [ABSTRACT FROM AUTHOR]

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]

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]

In this paper optimal designing of two hybrid photovoltaic/wind turbine (PV/WT) systems with different storage include battery and hydrogen is presented with objective of minimising cost of energy generation (COEG) and considering reliability constraint as deficit power probability of the system using improved salp swarm optimisation algorithm (ISSOA) for commercial centre area, for Iran country. Different hybrid systems are compared in view of cost and reliability and the optimal size is determined. The results show that the PV/WT with battery storage has lower COEG than PV/WT with hydrogen storage to supply the commercial centre and also its reliability is better than the system integrated with hydrogen. The average cost of per kW of load supply for the PV/WT system with battery and hydrogen storage is 0.216$ and 0.610$, respectively. Superiority of ISSOA is confirmed in comparison with conventional SSOA and particle swarm optimisation in hybrid systems designing. [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 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]

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]

This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility. [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]

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts. [ABSTRACT FROM PUBLISHER]

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]

Daily increasing demand of electrical energy and also recent developments in technology of distributed generations (DGs) have created special interest in using these units. On the other hand, capacitor insertion in the distribution feeders has been implemented by distribution companies for several years to reduce power loss and improve the voltage profile. Hence, according to the joint role of distributed generation units and capacitors, their optimal allocation will be influenced by each other. Therefore, in order to achieve the maximum benefits, this paper has discussed the optimal siting and sizing of DG resources and capacitors. One of the characteristics of the proposed method is that all technical and economical factors associated with installation of DGs and capacitors will be priced. For this purpose, all effective factors in optimal allocation and sizing of DG resources and capacitors are extracted and priced. For example, for the first time, the factor of voltage profile has been priced by the number of tapping. Using this approach, weighting the objective function parameters would be no more required. One of the other features of this paper is presenting an algorithm to determine the optimum number of DGs and capacitors. With this algorithm, power companies can find the optimum number of DGs and capacitors in the network they cover. The proposed method in this paper is tested on the 97-bus network of Bakhtar Regional Electrical Company in Iran Country. [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]

Purpose The purpose of this paper is to present an integrated framework for performance evaluation and analysis of human resource (HR) with respect to the factors of health, safety, environment and ergonomics (HSEE) management system, and also the criteria of European federation for quality management (EFQM) as one of the well-known business excellence models. Design/methodology/approach In this study, an intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy data envelopment analysis (FDEA) are developed and employed to assess the performance of the company. Furthermore, the impact of the factors on the company's performance as well as their strengths and weaknesses are identified by conducting a sensitivity analysis on the results. Similarly, a design of experiment is performed to prioritize the factors in the order of importance. Findings The results show that EFQM model has a far greater impact upon the company's performance than HSEE management system. According to the obtained results, it can be argued that integration of HSEE and EFQM leads to the performance improvement in the company. Practical implications In current study, the required data for executing the proposed framework are collected via valid questionnaires which are filled in by the staff of an aviation industry located in Tehran, Iran. Originality/value Managing HR performance results in improving usability, maintainability and reliability and finally in a significant reduction in the commercial aviation accident rate. Also, study of factors affecting HR performance authorities participate in developing systems in order to help operators better manage human error. This paper for the first time presents an intelligent framework based on ANFIS, FDEA and statistical tests for HR performance assessment and analysis with the ability of handling uncertainty and vagueness existing in real world environment. [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]

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