hojjat emami

According to the report released by world health organization (WHO), the ST-segment elevation myocardial infarction- cardiogenic shock (STEMI-CS) is one of the important factors in patient mortality within hospitals (1), (2), (3), (4). CS and its related complications need a huge financial and medical burden. Some researchers stated that high mortality and complication rates of STEMI-CS patients are associated with the lack of effective early preventive treatments. Given the risk of CS and the different risk factors associated with it, accurate clinical risk prediction tools need to be developed to accurately predict the onset of CS. Recently, researchers have been used various machine learning methods to predict the risk of mortality in STEMI-CS patients. Recently, machine learning (ML) methods were developed to establish predictive models to identify the in-hospital mortality risk of STEMI-CS patients. The existing methods achieved encouraging results; however, their performance is not ideal, and more effort is needed to improve the performance. The aim of this study is to present a hybrid machine learning method for predicting the risk of mortality in STEMI-CS patients. Our proposed method combines a powerful swarm intelligence strategy, anti-coronavirus optimization algorithm (ACVO) with support vector machine (SVM) in risk prediction phase. The proposed model is compared with standard support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and adaptive neuro fuzzy inference system (ANFIS) on a real-world benchmark dataset.

To predict the mortality status of STEMI-CS patients, we proposed the ACVO-SVM algorithm. The proposed method is a hybrid machine learning algorithm that combines the SVM with ACVO algorithm to identify the most effective parameters on the death of patients. The incentive mechanism of using ACVO is to optimally configure the parameters of SVM to improve its prediction performance. The proposed ACO-SVM is also useful in determining the optimal subset of features and treatment strategies that have the greatest impact in predicting the status of STEMI-CS patients. The proposed approach models the problem of predicting the status of patients as an optimization problem. In order to determine the most effective features in predicting the survival or death of STEMI-CS patients, the proposed ACVO-SVM model is trained with different combinations of patient characteristics and adopted treatment strategies. Then the best combination of features that provides the highest performance is considered as the superior combination. To select the most effective features, first all the features are considered for training the SVM model, then the remaining features are ignored one by one and the model with the same structure is trained. The models were compared based on accuracy, recall rate, F1 criterion. Finally, the best model is used to predict the status of patients in test dataset. The data set used to evaluate the proposed method includes 410 records of patients hospitalized due to STEMI-CS complications in Shahid Madani Hospital of Tabriz University of Medical Sciences. The collected data is related to a 10-year period from 2009 to 2018. This data set includes five categories of main characteristics, which are demographic characteristics, type of myocardial infarction, risk factors, clinical symptoms, and type of treatment used. It should be noted that 80% of the records of the data set are considered as training data, and 20% of the records are considered as the test data set. The proposed method is implemented in MATLAB software.

Among M1 to M5 feature combination models, the experimental results show that the M1 model has higher performance on the training and test dataset in terms of predicting the patient's condition compared to other combination models. Model M1 includes the combination of characteristics of age, sex, type of myocardial infarction, smoking, percutaneous vascular interventions and coronary artery bypass surgery. This shows that considering the mentioned features has the greatest effect on the final condition of STEMI-CS patients. The results are in line with previous studies (2), (3) in this field, which stated that age, gender, smoking, coronary artery bypass surgery and percutaneous vascular interventions have the greatest effect on the mortality rate of patients. The M2 model ranks second in terms of efficiency in determining the status of patients, which shows that smoking also has a greater effect on the mortality of patients with STEMI-CS. Also, the M3 model indicates that the use of the balloon pump treatment strategy, along with other demographic symptoms of the patient, history of heart infarction and smoking have a great effect on the mortality rate of patients. In summary, it can be concluded that the demographic characteristics of the patient such as age and gender, smoking, history of illness and the use of coronary bypass surgery and percutaneous vascular interventions have a great impact on the mortality of STEMI-CS patients. The proposed ACVO-SVM approach is compared with several other popular approaches, which include: standard SVM model, LASSO regression, ANFIS, and XGBoost. The experimental results justify that the proposed ACVO-SVM outperformed its counterparts.

In this study, a hybrid supervised machine learning model was presented to determine the status of patients with cardiogenic shock due to ST-segment elevation myocardial infarction. The proposed ACVO-SVM model uses an ACVO optimization algorithm to estimate the optimal parameters of the SVM model, making the SVM training process more efficient. The proposed model was evaluated using a dataset of patients with cardiogenic shock and the results were compared with the LASSO, ANFIS, XGBoost, and SVM models. The results showed that the proposed method worked well compared to other proposed classification models. We also found that age, gender, type of myocardial infarction, smoking, percutaneous vascular surgery, and coronary bypass transplantation surgery are the most effective factors for survival in STEMI-CS patients. In this research, the models were evaluated on a small dataset. Therefore, one of the necessary tasks to improve this research is to evaluate the proposed method and other counterpart models on large datasets to determine their strengths and weaknesses. Another limitation of this research is the lack of examination of all factors affecting the survival of STEMI-CS patients, such as blood sugar level and duration of ischemia. For this reason, it is necessary to investigate all factors affecting the mortality of STEMI-CS patients to improve the quality of classification and prediction of the final status of patients.

Orthogonal Frequency Division Multiplexing (OFDM) is a useful technology in wireless communications that provides high-rate data transmission in multipath fading channels. The advantages of OFDM systems are the high spectral efficiency and strong resistance to frequency selective fading. In OFDM systems, a large number of sub-carriers are used to modulate the symbols causing the time-domain OFDM signal to have a large dynamic range, or a high peak-to-average power ratio (PAPR). When the signals are applied to a nonlinear power amplifier, the OFDM systems’ performance is degraded by the high PAPR. In recent years, several works have been done to reduce the PAPR of OFDM systems. One of the most well-known methods is a partial transmit sequence (PTS). Regardless of the PTS advantages, it suffers from a high computational complexity. Because it requires an exhaustive search over all possible combinations of phase factors. The computational complexity of the PTS increases with increasing the number of phase factors and sub-blocks. There are several approaches to overcome the computation complexity issue of the PTS technique. The majority of these methods mainly employed swarm intelligence and evolutionary optimization algorithms to resolve the PTS shortcoming. These methods report encouraging results, however, their performance is far from the ideal state. This highlights that improving the performance of PTS is an open problem and there is room for more improvement. As an element of research, we propose an optimization approach based on the election algorithm (EA) to overcome the computational complexity of the PTS technique. To realize this goal, we improve the EA algorithm by introducing a new version of positive advertisements operator. The new operator efficiently improves the search capability of the EA through balancing between the exploration and exploitation power of the algorithm. The proposed EA based PTS (EA-PTS) approach, by searching the optimal phase factors, imposes less computational complexity on the system and reduces the PAPR to an acceptable level. The proposed method is compared with the optimal PTS (O-PTS), genetic algorithm-based PTS (GA-PTS) and imperialism competition algorithm based PTS (ICA-PTS) techniques. Simulation results show that the proposed EA-PTS has better performance in simultaneously reducing the PAPR and computational complexity.

Extracting structured information about entities from web texts is an important task in web mining, natural language processing, and information extraction. Information extraction is useful in many applications including search engines, question-answering systems, recommender systems, machine translation, and etc. An information extraction system aims to identify the entities from the text and extract their related information to form a profile of the target entity. In recent years, several methods have been proposed for extracting structured information from web text. The majority of existing methods for extracting entity-centric information require a predefined ontology. The ontology includes the complete knowledge of the entities and their attributes. The main challenge of these methods is their inability to extract information about entities that are not already defined in the ontology. Besides, the existing methods have ignored semantic information extraction and have not linked the extracted information to the general ontology entries. This highlights that introducing new methods for semantic information extraction is an open problem and there is room for more efforts in this field. As an element of research, we proposed a new method for the automatic extraction of semantically structured information from Farsi web text. The proposed method does not require background knowledge about the entities and their properties. The proposed method consists of three main phases including pre-processing, semantic analysis and frame extraction. To fulfill these phases, we use a combination of language resources, text processing tools, and distant ontologies. The main focuses of the proposed method are to enrich the predicate-argument frames with the semantic information extracted from distant ontologies, extract the entity-related information from predicate-argument frames, and link the extracted information with their corresponding sense in DBPedia ontology. The issue facilitates the processing of Farsi texts by computers. To evaluate the proposed method, we created a small Farsi dataset containing 100 complete sentences. Then, the proposed method is compared with three information extraction methods on this dataset. The results of experiments show the superiority of the proposed method compared to counterpart methods in terms of precision and F1 measures.

Labyrinth weirs are considered as an appropriate choice to correct the weirs that are having difficulty in passing the maximum possible flow. For this purpose, in the present study, a new weir was introduced called the of pseudo-Cosine labyrinth weir. First, models with different widths and heights were built in FLUENT software as a virtual laboratory. The anti-corona algorithm and adaptive neuro-fuzzy inference system (ACVO-ANFIS) were used for predicting discharge coefficient. of pseudo-Cosine labyrinth weir. Validation of the proposed method was performed using Experimental data. Then, to identify the superior model and determine the parameters affecting the discharge coefficient of pseudo-cosine labyrinth weir, the combination of different dimensionless parameters was evaluated. The performance of the proposed method was evaluated with five statistics, including determination coefficient (R2), root means squared error (RMSE), mean absolute percentage error (MAPE), nash-sutcliffe (NSE), and relative root mean square error (RRMSE). The results showed that in low hydraulic heads, the discharge coefficient has its highest value. As the radius increases, the discharge coefficient increases due to the increase in the effective length of the labyrinths. At a constant H/W, with increasing weir height, the discharge coefficient increased. The results of the ACVO-ANFIS model showed that the input variables are the ratio of the radius to the weir height (R/W), the ratio of the length of the weir to weir height (L/W), and the ratio of the hydraulic head to the weir height (H/W), with error values R2=0.971, RMSE=00.9, MAPE=0.006, RRMSE=0.010, and NSE=0.977, the most effective parameters in determining the discharge coefficient of pseudo-cosine labyrinth weirs.

Necessity to a complete and accurate analysis of the crack behavior in concrete dams using new methods is felt due to the sensitivity of the cracking problem in these dams. Meanwhile, meta-heuristic algorithms have a very good performance and accuracy in evaluating and predicting problems rather than other methods. In this study, Zayandehrood arch concrete dam has been chosen as the case study and the displacements in the cracks of this dam have been investigated by using election algorithm (EA). Water level and concrete temperature from 2000 to 2013 were considered as input parameters and also horizontal and vertical displacement of cracks were selected as output parameters. The results were compared with genetic algorithm (GA) and artificial neural networks (ANN). To evaluate the performance of the proposed method, three statistical criteria including correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) were utilized. The results show that EA has a higher efficiency with R2 = 0.96, RMSE = 0.022 and NSE = 0.74, compared to GA and ANN. However, due to the lack of sufficient data, the amount of regression coefficient for spillway cracks was lower than the dam cracks. It is concluded that for evaluating the displacements of cracks in concrete dams and predicting their variations in future, meta-heuristic algorithms can be utilized as a very exact and powerful method. These methods can help dam managers and decision-makers in monitoring and vulnerability analysis of dams during their operation.

Labyrinth weirs are important hydraulic structures for water level regulation and flow control in canals, rivers, and reservoirs. Due to the uneven distribution of hydraulic head on the weir crest, the discharge coefficient changes along the labyrinth weirs are noticeable. For optimal use of this type of weir, it is necessary to estimate the discharge coefficient. In this regard, in this study, using a data set including 120 experimental data collected by Kumar et al. (2011) and numerical (simulated by FLUENT software using k-ε RNG turbulence model) to optimally estimate the discharge coefficient of triangular-duckbill labyrinth weir embedded in a rectangular channel 0.28 m wide, 12 m long and 0.41 m high was addressed using modern gray wolf meta-heuristic (GWO) and election (EA) algorithms. To investigate the effect of discharge coefficient, angles of 30, 60, 90, 120, 150 and 180 degrees with weir height of 10 cm were selected and the flow conditions in all cases were considered as subcritical, turbulent and falling flow. The objective function is the sum of the squares of the difference between the computational flow and the observations defined as the minimum. Comparison of the results of GWO and EA algorithms and FLUENT software with values of R2=0.96 and NRMSE=0.052 in comparison with the observed values, shows a good agreement between the observed and computational values.

During recent decades, the excessive use of water has led to the scarcity of the available surface and groundwater resources. Quantitative and qualitative surveys of groundwater resources indicate that accurate and efficient optimization methods can help to overcome the numerous challenges in assessment of groundwater quality. For this purpose, three optimization meta-heuristic algorithms, including imperialist competitive (ICA), election (EA), and grey wolf (GWO), as well as the support vector regression method (SVR), were used to simulate the groundwater quality of the Salmas Plain. To achieve this goal, the data of the groundwater quality for the Salmas plain were utilized in a statistical period of 10 years (2002-2011). The results were evaluated according to Wilcox, Schuler, and Piper standards. The results indicated higher accuracy of the GWO-SVR method compared to the other two methods with values of R2=0.981, RMSE=0.020 and NSE=0.975. In general, a comparison of the results obtained from the hybrid methods and different diagrams showed that the samples had low hardness and corrosion. Also, the results indicated the high capability and accuracy of the GWO-SVR method in estimating and simulating the groundwater quality.

The use of labyrinth weirs is one of the most effective and economical ways to increase weir efficiency by enhancing weir crown length, so that at a specified width and hydraulic height compared to other weirs, more discharge passes. In this study, the hybrid method of the Whale optimization algorithm and the fuzzy neural- inference system to estimate the optimal discharge coefficient of labyrinth weirs with the curved plan is introduced. In the proposed method, the efficiency and convergence of the Whale algorithm have been measured. The experimental data were used to validate the proposed method. The performance of the proposed method was evaluated with four statistics, including the correlation coefficient (R2), the root means square error (RMSE), the mean absolute error (MAE) and the efficiency criterion (NSE). The results indicate a good match between the observed and estimated values. The results of the WOA-ANFIS method with (H /P) and (θ), showed that the discharge coefficient of labyrinth weirs with the curved plan (Cd=0.786) in compared to labyrinth weirs with a triangular plan (Cd=0.682), it increases by 10%. Also, statistical analyzes performed on the obtained results showed that the WOA-ANFIS method with high values of RMSE = 0.021, R2 = 0.981, MAE = 0.010 and NSE = 0.976 has high efficiency in estimating the discharge coefficient of this type of weirs.

In order to achieve an effective approach to improve nitrogen use efficiency in rice, a field experiment was conducted based on randomized complete block design with three replications and 13 treatments on a local rice variety, namely, Hashemi, in Rice Research Institute of Iran (Amol) during 2015 and 2016. The treatments included: N0: no N, N1: split application of 55.2 kg ha-1 N from source of urea (recommended for Hashemi variety), (N2, N3), (N4, N5) and (N6, N7) application of 55.2 and 27.6 kg ha-1 N from SCU, USG and AS sources, respectively; N8: application of nitrogen  nanofertilizer; N9, N10 and N11: application of Azospirillium, Azotobacter, and Azospirillium + Azotobacter. In addition to the mentioned sources, in N8 to N11 treatments, 27.6 kg ha-1 N from urea was used. N12: Farmers’ fertilizer practice (Pre planting application of 55.2 kg ha-1 N from urea). Results of the experiment showed that the effect of treatments on the measured traits, except nitrogen content of straw, caused a significant (p<0.05) difference. The highest grain yield was obtained from N6 (4542 kg ha-1). The performance of N5 (deep placement of 27.6 kg ha-1 Nfrom source of USG) was not significantly different from the treatment with the highest grain yield (N6). The highest amount of PFP, AEN and REN was obtained from N5 treatment and the highest PEN and IEN[H1]  was obtained from N10. N5 increased grain yield by 4.6% and recovery efficiency by 59.4% in compared to N12. The highest amount of N uptake (119.5 kg ha-1) was obtained from N4 treatment. Based on the results, it could be concluded that the deep placement of USG fertilizer has relative advantage compared to other N supplying sources because of enhanced amount of N uptake and reduced N losses.



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In many areas of Iran, there is no detailed information on the amount of erosion, sediment transport and sedimentation of rivers, and in many cases, there are many difference between measurements. Due to the fact that the flow regime and consequently the sediment regime in the watersheds are not constant, the prediction of sediment rate helps to estimate the sediment accumulated behind the structures, specially the dams, and determine the dead volume of reservoirs in the future months, and by adopting timely arrangements facilitate the deposition management to a certain extent. In this research, three optimization algorithms including Genetic Algorithm (GA), Gray Wolf Optimizer (GWO) and Election Algorithm (EA) were used to predict the suspended sediment load of the rivers. In order to evaluate the performance of the algorithms, three statistics consists of R2, RMSE and NSE were used. The suspended sediment load of sedimentary station located in the Zarrineh-Rood river during the 2005-2015 are used as a case study. The results show GWO algorithm with values R2=0.96, RMSE=0.022 and NSE=0.74 has a very high accuracy compared to other algorithms used.

In recent years, increasing salinity and reducing the quality of groundwater have become one of the environmental challenges due to the penetration and mixing of pollutants. These challenges have created serious risks for the development of human societies and health. In order to prevent future risks and appropriate planing for preserving water resources, a qualitative study of groundwater resources is an essential requirement. In this research, a swarm intelligence approach based on election algorithm and gray wolf optimization algorithm is presented to determine the optimal values ​​for water quality parameters such as TDS, EC and SAR. In order to evaluate the proposed method, data on the plain of Bostanabad city in the 10 years period (2003-2013) were used and the results were evaluated based on Wilcox, Schuler and Piper measures. The results of the experiments show that the underground water of Bostanabad city is modest to acceptable for agriculture and drinking and is suitable for industry due to corrosion and hardness. Most of the data were in the C2S2 class, which is suitable for agriculture. The correlation coefficient above 95% indicates the acceptable accuracy of the gray wolf optimization algorithm in comparison with the election algorithm in estimating the groundwater quality parameters.

Zinc is one of the essential micronutrients for plants, mining and industrial activities leading to pollution of heavy metals, including zinc metal contamination in soils. In addition to the total concentration, knowledge of the Zinc fractions is necessary to assess the mobility of zinc in the soils. One of the sequential extraction methods is Tessier method. sequential extraction with plant cultivation simultaneously, is the appropriate approach for assessing the mobility of toxic metals. Therefore this study was conducted to evaluate the chemical forms and determine their relationship to the physical and chemical properties of soils in some fields under cultivation in Khorasan Razavi province.
The experiment was conducted in a completely randomized design with factorial arrangement includes 4 levels of contamination (0, 500, 1000 and 1500 (mg/kg)) and 10 soil types from different regions of Khorasan Razavi province of 0-30 cm depth in the range of electrical conductivity 1 up to 15 ds/m, with three replications at Research greenhouses of Ferdowsi University of Mashhad. An example of mining waste was prepared as a source of pollution. The soil samples were kept at field capacity moisture for 6 months. Then air-dried soil samples were used for planting borage and determine the Zinc fractions. Then soil samples were air dried and used for planting borage and determining the Zinc fractions. Texture, cation exchange capacity, organic carbon, electrical conductivity, pH and Available phosphorus and potassium were measured in the saturation extract. DTPA-extractable Zinc was measured by atomic absorption spectrometry. Borage was planted in greenhouses in 3 kg pots with three replications. During flowering, the plants were harvested and dry digestion method was used to measure the concentration of Zinc. Chemical forms and Pseudo total concentration of zinc in the samples were determined using Tessier and digestion by HCl and HNO3 acids (3:1) methods respectively. The concentration of the extracts was measured by atomic absorption spectrometry. Statistical analysis was done using Minitab and Excel softwares.
Chemical Forms Average of zinc in soils was as follows:Exchangeable In this study, the residual, carbonate and organic fractions are dominant form of zinc in soils, respectively. With increasing level of contamination, percentage of residual zinc decreased and percentage of other fractions increased, particularly organic and carbonate. Increasing the availability of zinc, is a threat to the environment. There is a high correlation coefficient between different fractions of Zinc with each other and with the pseudo total, amount of plant and available plant zinc showed that there is a dynamic relationship in the soil systems. There is a higher correlation coefficient between the available zinc and carbonate and organic fractions of soils, which indicate available plant zinc, are mainly derived from carbonate and organic fractions.

Increased generation of pollutants such as heavy metals is one of the serious and developing environmental issues threatening human society. Heavy metal pollution not only affects the physical and chemical properties of the soil but also it is dangerous to human health through entering into the food chain and finding its way into the groundwater. The present study was conducted to predict soil lead concentration, as one of the most important heavy metals, using readily available soil properties based on artificial neural network model. For this purpose, 63 soil samples were collected from 60-cm depth of the land surrounding Kashafrud River located in Mashhad City. Measured parameters included pH, electrical conductivity, particle size distribution, organic carbon, and Pb content in soil. The multilayer perceptron (MLP) as an artificial neural network model was used to predict the Pb concentration in soil. The performance of this model was assessed by the coefficient of determination (R2), mean absolute error (MAE), and also root mean square error (RMSE). The results showed that artificial neural network model is a suitable method to determine Pb concentration in soil rather than the direct laboratory measurement, which is an expensive and time-consuming method.

Entity profiling (EP) as an important task of Web mining and information extraction (IE) is the process of extracting entities in question and their related information from given text resources. From computational viewpoint, the Farsi language is one of the less-studied and less-resourced languages, and suffers from the lack of high quality language processing tools. This problem emphasizes the necessity of developing Farsi text processing systems. As an element of EP research, we present a semantic approach to extract profile of person entities from Farsi Web documents. Our approach includes three major components: (i) pre-processing, (ii) semantic analysis and (iii) attribute extraction. First, our system takes as input the raw text, and annotates the text using existing pre-processing tools. In semantic analysis stage, we analyze the pre-processed text syntactically and semantically and enrich the local processed information with semantic information obtained from a distant knowledge base. We then use a semantic rule-based approach to extract the related information of the persons in question. We show the effectiveness of our approach by testing it on a small Farsi corpus. The experimental results are encouraging and show that the proposed method outperforms baseline methods.

In current air traffic control system and especially in free flight method, the resolution of conflicts between different aircrafts is a critical problem. In recent years, conflict detection and resolution problem has been an active and hot research topic in the aviation industry. In this paper, we mapped the aircrafts’ conflict resolution process to graph coloring problem, then we used a prioritization method to solve this problem. Valid and optimal solutions for corresponding graph are equivalent to free conflict flight plans for aircrafts in airspace. The proposed prioritization method is based on some score allocation metrics. After score allocation process, how much the score of an aircraft be higher its priority will be higher and vice versa how much the score of an aircraft be lower its priority will be lower. We implemented and tested our proposed method by different test cases and test results indicate high efficiency of this method.