Adaptive Neural Fuzzy Inference System

Water quality is one of the critical issues directly linked to public health and community hygiene. The necessity of monitoring water resources and ensuring sanitary and standard conditions for drinking has highlighted the importance of water quality control. In this study, using 19 years of common statistical data from 96 observation wells in Golestan Province, three key water quality parameters—Sodium Adsorption Ratio (SAR), Total Dissolved Solids (TDS), and Electrical Conductivity (EC)—were predicted through three approaches: Artificial Neural Network (ANN), Gene Expression Programming (GEP), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input data included sodium (Na⁺), pH, chloride (Cl⁻), sulfate (SO₄²⁻), calcium (Ca²⁺), and magnesium (Mg²⁺). Model calibration was performed using 70% of the dataset for training, and model performance was evaluated with the remaining 30% for testing. Evaluation metrics included Root Mean Square Error (RMSE), Correlation Coefficient (R), and Mean Absolute Error (MAE). Nine different input combinations were tested. Results indicated that, except for input combination 8, all models demonstrated high accuracy in estimating groundwater quality parameters. Moreover, Na⁺ and Cl⁻ had the most significant influence on prediction accuracy across all three models and parameters. The absence of these two parameters in input combination 8 led to poor model performance. Among the tested models, ANN generally outperformed the others in predicting groundwater quality, whereas ANFIS showed weaker performance during the testing phase. Despite slight differences in accuracy, all three models exhibited comparable capabilities, making them suitable tools for predicting groundwater quality parameters.

The importance of quality in the industry to obtain and produce high quality products has been known for a long time. Quality in the production environment improves reliability, increase production and attracts customer satisfaction. Classical control diagrams, using precise and definite data, place production processes in two groups, «rejection» or «acceptance». Descriptive characteristics are in fuzzy conditions due to ambiguity in the number of defects in the product and decision making by the inspector, and fuzzy sets by defining continuous membership functions and using ambiguous and indefinite data by using triangular and trapezoidal fuzzy numbers in the form of control categories are classified and express the quality level of the product more realistically. This research is an applied and descriptive research, which was carried out with the aim of designing model of Statistical Process Control through a fuzzy inference system to control descriptive characteristics in the food industry. Sampling system has been used in the inspection station to collect information and according to sensory and physical characteristics, the quality level of the produced chocolates was determined. In the Classical Method, 28 cases were identified «under control» and only 2 cases were «out of control». But in the investigation with the fuzzy designed model, 28 samples were «under control», 1 sample was «relatively under control» and 1 sample was «out of control»; Based on the research result, practical suggestions were recommended to the relevant industry.

Owing to the increasing deployment of vehicles in human societies and the necessity for smart traffic control, anomaly detection is among the various tasks widely employed in traffic monitoring. As the issue of urban traffic and their relative smart monitoring systems have gained popularity among researchers in recent years, there exist several studies in this regard in the literature. In most of these studies, classifications are based on the behavior of the drivers, where a set of default trajectories are used in order to learn the system and classify the related data. However, two less-studied challenges are the lack of access to sufficient data to provide an efficient model, along with the lack of access to anomaly data that covers all possible abnormal trajectories. While the former challenge can be fulfilled through long-term data recording, the latter requires appropriate considerations. To this aim, we have utilized a combination of optimized convolutional neural network and...

Accurate quantitative precipitation forecasts (QPFs) have been always a demanding and challenging job in numerical weather prediction (NWP). The outputs of ensemble prediction systems (EPSs) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (PQPFs). In this research, different configurations of WRF and MM5 meso-scale models form our eight member ensemble prediction system. The initial and boundary conditions come from the operational 1200 UTC runs of global forecasting system (GFS) of NCEP (National Center for Environmental Prediction). The integration period goes from first November 2008 to 30 April 2009 (182 days). Both WRF and MM5 are used with non-*

CHLFuzzy is a user-friendly, flexible, multiple-input single-output Takagi-Sugeno fuzzy rule based model developed in a MS-Excel Ò spreadsheet environment. The model receives a raw dataset consisting of four predictor variables, e.g., water temperature, dissolved oxygen content, dissolved inorganic nitrogen concentration, and solar radiation levels. It then defines fuzzy sets according to a collection of fuzzy membership functions, allowing for the establishment of fuzzy 'if-then' rules, and predicts chlorophyll-a concentrations, which highly compare to the measured ones. The performance of the model was tested against the Adaptive Neural Fuzzy Inference System (ANFIS), showing satisfactory results. An extensive dataset of environmental observations in Vassova Lagoon (Northern Greece), during the years 2001-2002, was used to train the model and an independent dataset collected during 2004 was used to validate CHLFuzzy and ANFIS models. Although both models showed a similar performance on the training dataset, with quite satisfactory agreement between observed and modeled chlorophyll-a values, the best results were obtained using the CHLfuzzy model. Similarly, the CHLfuzzy model depicted a fairly good ability to hindcast chlorophyll-a concentrations for the verification dataset, thus improving ANFIS model forecasts. Overall results suggest that CHLfuzzy can potentially be used as a lagoon water quality forecasting tool requiring limited computational cost.

Currently, investing in the stock market constitutes a significant portion of the country's economy. Securities are considered a reliable tool for gaining the trust of investors and are associated with various levels of risk. This approach can gather small and dispersed investments that, on their own, cannot be effectively utilized and transform them into substantial financial resources for economic development. In stock markets, price fluctuations are highly sensitive, leading to regular analysis and monitoring of these changes. As a result, stock price prediction has gained significant importance for investors, enabling them to maximize their returns and assisting them in making informed investment decisions. In recent years, modern time-series prediction methods based on artificial intelligence and machine learning have advanced rapidly. Given the high value of this data for investment and stock price prediction, traditional data analysis methods face limitations in effectively learning from it. With technological advancements and the introduction of new techniques, such as neural networks and metaheuristic algorithms, the use of these methods for stock price prediction has seen significant growth. In this study, the capability of various models based on neural networks optimized by two algorithms, Harris Hawk Optimization (HHO) and Honey Badger Algorithm (HBA), in predicting the stock price trends of two companies, Iran Khodro and Isfahan Oil Refinery, in the Tehran Stock Exchange over the next 10 days, is examined. The results of these two algorithms are compared using metrics such as MSE, RMSE, MAE, RSE, and EVS. The findings of this research indicate that the HBA algorithm outperforms the HHO algorithm, with accuracies of 75% and 76% in predicting the stock prices of Iran Khodro and Isfahan Oil Refinery, respectively, compared to the HHO algorithm's accuracies of 73% and 67%.

Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time-frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, VGG, and AlexNet networks. Finally, the proposed method is demonstrated to enjoy a high-generalization ability through a real-time monitoring experiment.

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. In this paper, we prepared 22 years' worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.

Cavitation is a phenomenon during which, with the movement of the flow and the reduction of the liquid pressure to the saturated vapor pressure of the liquid in susceptible areas, bubbles of air are formed during the flow. The microjet becomes full of energy with the fluid, which when these micro jets collide with the walls, causing vibration and noise and destructive effects such as structural erosion of ship propellers, pump blades, and dams, as well as reducing efficiency, and malfunction of hydraulic devices. This research aims to study different simulation models of the cavitation phenomenon and compare them in the way of cavitation cloud creation and expansion and the effect of this phenomenon on the flow. The present research has been analyzed numerically using ANSYS FLUENT software. In this research, an attempt has been made to study and compare different cavitation models in various geometries. Also, in one example, the results of the numerical model have been compared with the results of the laboratory model. The results showed that the formation of cavitation phenomenon in 10 cm throat compared to 5 cm by 5.88%, in 10 cm throat compared to without throat by 64.71%, and in 5 cm throat compared to without throat by 5.62% has changed.
Keywords
Cavitation  two-phase flow  computational fluid dynamics  saturated vapor pressure

This paper investigates the accuracy and convergence rate of different metaheuristic algorithms in determining the stiffness of structural elements using structural modal parameters and defining a suitable objective function. To achieve this purpose, three different structures, including a three-story one-dimensional frame, a six-story one-dimensional frame and a two-dimensional truss, were investigated. The metaheuristic algorithms, employed in this study, were Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization. The objective function utilized in this study consists of two terms; the first part involves the squared difference between the first frequency of the structure obtained from the responses of the investigated structure and the first frequency obtained from the hypothetical stiffness matrix in each generation of algorithms. The second part measures the norm of the difference between the first mode shape of the structure obtained from the responses of the investigated structure and the first mode shape obtained from the hypothetical stiffness matrix in each generation of algorithms. By minimizing the objective function, the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization determined the element stiffness of the three-story, six-story and truss structures, thus demonstrating the high efficiency of metaheuristic algorithms in resolving unknown parameters of structures. The average run time for the Genetic Algorithm was 3.38 seconds, 4.47 seconds, and 15.73 seconds for the three respective problems. For Particle Swarm Optimization, the times were 3.76 seconds, 6.47 seconds, and 16.76 seconds. The Teaching–learning-based Optimization achieved times of 1.92 seconds, 4.51 seconds, and 12.76 seconds. The Teaching–learning-based Optimization exhibited the highest convergence rate and the lowest error compared to the Genetic Algorithm and Particle Swarm Optimization. For example, in the two-dimensional truss, the values of the objective function in the last iteration of the Genetic Algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization were , and , respectively. The Particle Swarm Optimization demonstrated an acceptable convergence rate and error compared to the Genetic algorithm. The Genetic Algorithm, however, displayed a significant error rate in determining the stiffness of structural elements compared to the other two algorithms.
Keywords
Structural health monitoring  Genetic Algorithm  Particle Swarm Optimization algorithm  Teaching–learning-based Optimization algorithm

Objective Early diagnosis of hypertension in children is necessary to minimize the risks and consequences of this complication. This study aims to design and create a mobile application for diagnosing and predicting hypertension in children using machine learning methods so that the parents can continuously control and monitor their children's blood pressure. Methods In this applied-developmental study that was conducted in 2022, the values related to 19 factors affecting hypertension were collected from 1287 primary school children aged 7-13 years were first collected using questionnaires and measurements. Then, by combining the outputs of three machine learning methods (multi-layer perceptron, support vector machine (SVM) and random forest [RF]), a model was presented for a more accurate diagnosis of hypertension using Weka software, version 3.7.8 and Python programming language. To measure the effectiveness of the model, the 10-fold crossvalidation method was used, and the paired t-test was used to compare its performance with other methods. P<0.05 was considered statistically significant. Finally, the proposed model was implemented in an Android mobile application. Results The proposed model's precision, sensitivity and specificity were 91.74%, 83.5% and 94.49%, respectively, and had better performance in predicting hypertension in children than other methods. These values were significantly different from those of compared methods (P<0.05). Conclusion The proposed model performs better in diagnosing and predicting hypertension in children. The developed mobile application is thus useful for early diagnosis of hypertension in children. Parents can use this application to be informed about their children's blood pressure status and consult a doctor immediately if there is a danger.

As the science of Mechanics has been improved in recent years, different standards have been created in designing structures such as pressure vessels. Most of these standards have been developed by means of experience and examination. Today, most of researches, proceed the numerical analysis of the different elements of the pressure vessels and somehow they have put some questions on the previous standards. In this paper, offering an applicable model, it is shown that heuristic optimization methods such as genetic algorithm, particle swarm optimization and especially imperialist competitive algorithm can be used as a simple and fast method for designing the vessels without breaking any of the engineering conditions and criteria. Due to the present results of this research, these methods are caused a noticeable decrease of the vessel’s weight, with a high convergence speed, in a short time, compare to the design carried out in handbooks. You may observe that the Imperialist Competitive Algorithm has a better performance and convergence than the two other methods.

In the present study, the seismic behavior of hunchbacked block-type gravity quay walls rested on non-liquefiable dense seabed soil layer is investigated, and the optimal geometries for these wall types are proposed by performing non-linear time history dynamic analyses using Lagrangian explicit finite difference method. For this purpose, first, a reference numerical model of the hunchbacked quay wall is developed, and its seismic response is validated against the well-documented physical model tests. Then, the optimal hunch angles corresponding to the minimum horizontal displacement and zero rotation of the hunchbacked quay wall are estimated through the sensitivity analyses on the hunch angle of the wall, the friction angle of the backfill, and the ratio of hunch height to wall height. Subsequently, the statistical relationships are presented to predict the optimal hunch angle of the walls using multiple non-linear regressions based on the supervised machine learning technique. The results of non-linear dynamic analyses show that the deformation pattern, the movement mechanism, and, consequently, the seismic response of the hunchbacked quay wall change considerably with the variation of the hunch angle of the wall. In this regard, the rotation angle of the wall towards the seaside due to seismic loading decreases, and the deformation pattern and the movement mechanism of the hunchbacked quay wall alter from overturning towards the seaside to overturning towards the landside with an increase of the hunch angle. For all considered values of the ratio of hunch height to wall height and the backfill friction angle, increasing the hunch angle in the range of 25 to 35 degrees leads to a significant decrease in wall deformation. While increasing the hunch angle in the range of 35 to 50 degrees has less influence on reducing the wall deformation. For hunch angle values greater than 50 degrees, increasing the hunch angle has the opposite effect on improving the seismic performance of the hunchbacked quay wall and its seismic-induced deformations increase. Additionally, in the ratio of hunch height to wall height equal to 0.7, the optimal hunch angles corresponding to the zero wall rotation and the maximum reduction in the horizontal displacement of the wall decrease from 42.7 to 9. 23 degrees and from 53 to 34.5 degrees, respectively, with an increase of the friction angle of the backfill soil from 15 to 45 degrees.
Keywords
Gravity quay wall  broken-back geometry  geometrical optimization  non-linear dynamic analysis  supervised machine learning

Unlike serial manipulators, forward kinematic problem (FKP) of parallel robots is highly complicated and its analytical solution is not generally le. Therefore, in most cases numerical methods are used to solve this problem which are relatively time consuming. On the other hand, reducing the duration of FKP analysis is an essential task for applications like real-time control. In this paper, artificial neural networks and a 3rd-order numerical algorithm are combined and a new hybrid strategy is proposed for forward kinematics analysis of parallel manipulators which significantly increases the speed of the FKP solution. In the proposed method, an approximate solution of the FKP is first produced by the neural network. This solution is next considered as an initial guess for the 3rd-order numerical technique which solves the nonlinear forward kinematics equations and obtains the answer with a desired level of accuracy. The proposed method is applied to a spatial 3-PSP parallel manipulator. The results show that using this method will lead to a 35 percent reduction in number of iterations and a 12 percent reduction in the FKP analysis time, while maintaining high level of solution accuracy.

Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time-frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, VGG, and AlexNet networks. Finally, the proposed method is demonstrated to enjoy a high-generalization ability through a real-time monitoring experiment.

Due to the increasing deployment of vehicles in human societies and the necessity for smart traffic control, anomaly detection is among the various tasks widely employed in traffic monitoring. As the issue of urban traffic and their relative smart monitoring systems have gained popularity among researchers in recent years, there exist several studies in this regard. In most of these studies, classification is performed based on the behavior of drivers, where a set of default trajectories are used in order to learn the system and classify the related data. However, two understudied challenges are the lack of access to sufficient data to provide an efficient model, along with the lack of access to anomaly data that covers all possible abnormal trajectories. While the former challenge can be tackled through long-term data recording, the latter requires appropriate considerations. To this aim, we have utilized a combination of optimized convolutional neural network and fuzzy neural network classifiers, along with autoencoding neural networks. The final combination occurs at the decision level. First, the CNN-ANFIS classifier assigns the input trajectory to one of the predefined categories. Then, the trained autoencoder networks examine the result in order to find whether the trajectory is normal or abnormal. Obtaining 87.5% accuracy on QMUL and 99.5% on the T15 datasets confirms the superior performance of the proposed method.

Objective: The capability of intelligent systems in predicting economic and financial variables, particularly stock prices, has been confirmed in previous research in Iran and other countries. However, the valuation of block transactions is calculated for the first time in this study. The aim is to investigate the outcomes and information from the financial reports of listed companies on the Tehran Stock Exchange using 15 financial indices and determine the impact of these indices on the valuation of block transactions by employing the RMSE test on the Test dataset.Research Methodology: For this purpose, financial information from 64 companies within the accepted companies of the Tehran Stock Exchange for the period from 1390 to 1400 has been utilized. The research hypothesis is tested using the Long Short-Term Memory (LSTM) deep learning neural network model.Findings: The LSTM neural network, due to its high capability in training data, appropriate weights for these data, and creating a path that efficiently and accurately produces acceptable results for predicting the valuation of block transactions.Originality/Value: In the proposed model, by measuring the valuation of block transactions, we will scrutinize the prices of these transactions and the effects of information and liquidity in large-sized transactions.

T‌o‌d‌a‌y, t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t d‌a‌t‌a i‌s m‌o‌r‌e i‌m‌p‌o‌r‌t‌a‌n‌t t‌h‌a‌n e‌v‌e‌r. I‌t d‌e‌p‌e‌n‌d‌s o‌n t‌h‌e s‌t‌a‌t‌i‌s‌t‌i‌c‌a‌l c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s o‌f r‌e‌p‌e‌a‌t‌e‌d m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t‌s p‌e‌r‌f‌o‌r‌m‌e‌d u‌n‌d‌e‌r s‌t‌a‌b‌l‌e c‌o‌n‌d‌i‌t‌i‌o‌n‌s i‌n a m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m. I‌n o‌t‌h‌e‌r w‌o‌r‌d‌s, t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f t‌h‌e m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m i‌s o‌f p‌a‌r‌t‌i‌c‌u‌l‌a‌r i‌m‌p‌o‌r‌t‌a‌n‌c‌e b‌e‌c‌a‌u‌s‌e i‌t c‌a‌n e‌v‌e‌n o‌v‌e‌r‌s‌h‌a‌d‌o‌w t‌h‌e f‌l‌u‌c‌t‌u‌a‌t‌i‌o‌n‌s o‌f t‌h‌e p‌r‌o‌d‌u‌c‌t‌i‌o‌n p‌r‌o‌c‌e‌s‌s. I‌f t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f t‌h‌e m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m a‌n‌d t‌h‌e r‌e‌s‌u‌l‌t‌i‌n‌g s‌a‌m‌p‌l‌i‌n‌g f‌i‌g‌u‌r‌e‌s a‌r‌e l‌o‌w, t‌h‌e p‌r‌o‌c‌e‌s‌s a‌n‌a‌l‌y‌s‌i‌s w‌i‌l‌l n‌o‌t b‌e v‌a‌l‌i‌d a‌n‌d t‌h‌e c‌o‌r‌r‌e‌c‌t d‌e‌c‌i‌s‌i‌o‌n c‌a‌n‌n‌o‌t b‌e m‌a‌d‌e t‌o i‌n‌c‌r‌e‌a‌s‌e t‌h‌e q‌u‌a‌l‌i‌t‌y. I‌n t‌h‌i‌s r‌e‌s‌p‌e‌c‌t, t‌h‌e M‌u‌l‌t‌i-A‌t‌t‌r‌i‌b‌u‌t‌e D‌e‌c‌i‌s‌i‌o‌n M‌a‌k‌i‌n‌g (M‌A‌D‌M) t‌e‌c‌h‌n‌i‌q‌u‌e c‌a‌n b‌e u‌s‌e‌d t‌o m‌a‌k‌e p‌r‌o‌p‌e‌r d‌e‌c‌i‌s‌i‌o‌n a‌l‌o‌n‌g w‌i‌t‌h u‌s‌i‌n‌g t‌h‌e n‌e‌w a‌n‌d h‌y‌b‌r‌i‌d m‌e‌t‌h‌o‌d o‌f

a‌n‌a‌l‌y‌s‌i‌s a‌n‌d g‌u‌i‌d‌a‌n‌c‌e s‌y‌s‌t‌e‌m‌s. O‌n‌e o‌f t‌h‌e b‌a‌s‌i‌c p‌r‌i‌n‌c‌i‌p‌l‌e‌s o‌f a‌n‌y o‌r‌g‌a‌n‌i‌z‌a‌t‌i‌o‌n i‌s t‌o e‌n‌s‌u‌r‌e t‌h‌a‌t t‌h‌e d‌a‌t‌a a‌r‌e o‌b‌t‌a‌i‌n‌e‌d f‌r‌o‌m m‌e‌a‌s‌u‌r‌i‌n‌g i‌t‌s p‌r‌o‌d‌u‌c‌t‌s. I‌f t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f t‌h‌e m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m i‌s p‌o‌o‌r a‌n‌d, a‌s a r‌e‌s‌u‌l‌t, t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f t‌h‌e r‌e‌s‌u‌l‌t‌i‌n‌g d‌a‌t‌a i‌s p‌o‌o‌r, t‌h‌e a‌n‌a‌l‌y‌s‌i‌s o‌f t‌h‌e p‌r‌o‌c‌e‌s‌s w‌i‌l‌l n‌o‌t b‌e v‌a‌l‌i‌d a‌n‌d t‌h‌e‌r‌e m‌a‌y b‌e i‌n‌c‌o‌r‌r‌e‌c‌t d‌e‌c‌i‌s‌i‌o‌n‌s a‌b‌o‌u‌t w‌h‌e‌t‌h‌e‌r o‌r n‌o‌t t‌o a‌c‌c‌e‌p‌t i‌t a‌n‌d t‌h‌e c‌o‌s‌t‌s i‌n‌v‌o‌l‌v‌e‌d w‌i‌l‌l o‌b‌v‌i‌o‌u‌s‌l‌y b‌e h‌i‌g‌h. N‌u‌m‌e‌r‌o‌u‌s m‌o‌d‌e‌l‌s h‌a‌v‌e b‌e‌e‌n d‌e‌v‌e‌l‌o‌p‌e‌d t‌o s‌u‌p‌p‌o‌r‌t d‌e‌c‌i‌s‌i‌o‌n-m‌a‌k‌i‌n‌g p‌r‌o‌c‌e‌s‌s‌e‌s o‌v‌e‌r t‌h‌e y‌e‌a‌r‌s. A‌s o‌n‌e o‌f t‌h‌e r‌e‌s‌e‌a‌r‌c‌h a‌r‌e‌a‌s o‌f o‌p‌e‌r‌a‌t‌i‌o‌n‌s r‌e‌s‌e‌a‌r‌c‌h a‌n‌d m‌a‌n‌a‌g‌e‌m‌e‌n‌t, M‌u‌l‌t‌i-C‌r‌i‌t‌e‌r‌i‌a D‌e‌c‌i‌s‌i‌o‌n M‌a‌k‌i‌n‌g (M‌C‌D‌M) c‌a‌n b‌e a s‌o‌l‌u‌t‌i‌o‌n f‌o‌r e‌v‌a‌l‌u‌a‌t‌i‌n‌g a‌n‌d r‌a‌n‌k‌i‌n‌g o‌p‌t‌i‌o‌n‌s f‌r‌o‌m t‌h‌e b‌e‌s‌t t‌o t‌h‌e w‌o‌r‌s‌t.

T‌h‌e w‌o‌r‌s‌t-c‌a‌s‌e c‌o‌n‌d‌i‌t‌i‌o‌n‌s u‌n‌d‌e‌r c‌o‌n‌f‌l‌i‌c‌t‌i‌n‌g c‌r‌i‌t‌e‌r‌i‌a w‌i‌t‌h r‌e‌g‌a‌r‌d t‌o d‌e‌c‌i‌s‌i‌o‌n-m‌a‌k‌i‌n‌g p‌r‌i‌o‌r‌i‌t‌i‌e‌s s‌h‌o‌u‌l‌d b‌e f‌o‌u‌n‌d. D‌e‌c‌i‌s‌i‌o‌n-m‌a‌k‌i‌n‌g i‌s o‌n‌e o‌f t‌h‌e k‌e‌y t‌a‌s‌k‌s o‌f m‌a‌n‌a‌g‌e‌m‌e‌n‌t. C‌o‌m‌p‌a‌r‌i‌s‌o‌n o‌f d‌i‌f‌f‌e‌r‌e‌n‌t m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m‌s c‌a‌n a‌l‌s‌o b‌e v‌e‌r‌y h‌e‌l‌p‌f‌u‌l i‌n s‌e‌l‌e‌c‌t‌i‌n‌g t‌h‌e c‌o‌n‌t‌r‌a‌c‌t‌o‌r t‌o r‌e‌d‌u‌c‌e t‌h‌e r‌i‌s‌k o‌f p‌r‌o‌d‌u‌c‌t‌i‌o‌n o‌r d‌e‌c‌i‌s‌i‌o‌n-m‌a‌k‌i‌n‌g. F‌o‌r t‌h‌i‌s p‌u‌r‌p‌o‌s‌e, v‌a‌r‌i‌o‌u‌s M‌C‌D‌M m‌e‌t‌h‌o‌d‌s i‌n t‌h‌e a‌n‌a‌l‌y‌s‌i‌s o‌f m‌e‌a‌s‌u‌r‌e‌m‌e‌n‌t s‌y‌s‌t‌e‌m‌s h‌a‌v‌e b‌e‌e‌n u‌s‌e‌d. T‌h‌e m‌e‌t‌h‌o‌d o‌f t‌h‌i‌s r‌e‌s‌e‌a‌r‌c‌h i‌s d‌e‌s‌c‌r‌i‌p‌t‌i‌v‌e-l‌i‌b‌r‌a‌r‌y t‌y‌p‌e s‌u‌c‌h t‌h‌a‌t b‌y d‌e‌v‌e‌l‌o‌p‌i‌n‌g b‌a‌s‌i‌c m‌o‌d‌e‌l‌s c‌o‌l‌l‌e‌c‌t‌e‌d f‌r‌o‌m a‌r‌t‌i‌c‌l‌e‌s a‌n‌d r‌e‌f‌e‌r‌e‌n‌c‌e b‌o‌o‌k‌s, w‌e p‌r‌e‌s‌e‌n‌t a n‌e‌w m‌o‌d‌e‌l a‌n‌d s‌e‌l‌e‌c‌t t‌h‌e b‌e‌s‌t o‌p‌t‌i‌o‌n u‌s‌i‌n‌g M‌A‌D‌M t‌e‌c‌h‌n‌i‌q‌u‌e. T‌h‌e‌s‌e i‌n‌d‌i‌c‌e‌s w‌e‌r‌e o‌b‌t‌a‌i‌n‌e‌d b‌y C‌R‌I‌T‌I‌C m‌e‌t‌h‌o‌d a‌n‌d t‌h‌e‌n, t‌h‌e o‌p‌t‌i‌o‌n‌s w‌e‌r‌e r‌a‌n‌k‌e‌d u‌s‌i‌n‌g f‌u‌z‌z‌y s‌u‌m‌m‌i‌n‌g m‌e‌t‌h‌o‌d.

Complex systems design problems entail a suitable structure in which all disciplines, including their coupled relationships, have been considered and modeled at the same time. These types of design problems involve time and computational cost challenges. Multi-Disciplinary Design Optimization (MDO) methods have been developed to address these issues simultaneously. This article aims to provide a proper design structure for an uncertainty-based re-entry trajectory design optimization problem under the control restrictions and structural constraints of a Reusable Flexible Space Launch Vehicle (RFSLV) alongside the determination of optimal skin thickness and thermal protection system thickness concerning the design criteria of the flexible structure in such a way that the final design would meet the desired reliability. Trajectory, structure, aerodynamics, aeroelasticity, and thermal protection systems are considered to be involved disciplines in the design problem. The study's purpose will be to obtain an optimal trajectory to meet all the control and structure restrictions while estimating optimal body skin and thermal protection thicknesses based on structural design criteria evaluating the re-entry trajectory, which is in process. The flexible space launcher body has been considered as a free-free Bernoulli-Euler beam for bending variation and D’alembert’s principle for inertia force in static model with the aim of assessing structural design standards. The 3DOF longitudinal dynamic equations plus the first bending mode have been considered. By Chebyshev polynomial interpolation, the angle of attack scope has been achievable, and then the trajectory optimization problem has been transformed to a discrete nonlinear programming problem (NLP), which leads to numerical integration of state equation and satisfying all path constraints in Bolza optimal control problem. All highly nonlinear uncertainty-based constraints in the model have led to taking advantage of the evolutionary optimization algorithm that has been implemented here by Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, epistemic and aleatory uncertainties have been applied through Probability Theory to estimate the reliability of constraints that had been affected by uncertainties. The result shows a 75 percent decrease through utilizing the evolutionary multi-objective technique against the gradient-based algorithm in design space optimization regarding computational cost in recalling objective functions. The other conclusion is that the sequential reliability analysis structure modeling efficiency is much better compared to the parallel one.
Keywords:
Flexible Reusable Space Launch Vehicle, Uncertainty, Reliability Analysis, Multidisciplinary Design Optimization, Re-entry Trajectory, Aeroelasticity

Abstract
    Usually, in a high-altitude test facility, an exhaust diffuser is applied to create and maintain a vacuum condition in the motor test chamber utilizing the energy of the exhaust gases. In this system, the temperature of the exhaust gases, which directly hit the diffuser's inner walls, is much higher than the tolerable temperature of the diffuser metal body. In the current research, a new algorithm has been developed to design the cooling system to remove high heat fluxes from the vacuum simulator diffuser walls. In this algorithm, the three parameters of coolant mass flow rate, channel height, and cooling channel length are calculated based on the heat flux distribution along the diffuser, in such a way that, in addition to satisfying the temperature conditions of the metal body and maintaining the ease of implementation of the design, the total pressure drop also is in the desired range. Due to the error of empirical and semi-empirical relationships used to estimate convection heat transfer coefficients in concentric annular flows with large wall surfaces and high heat flux, a numerical simulation technique has been used to find suitable correlations and evaluate the design. The present studies show that the experimental correlations of Meyer and Kaneda are suitable for estimating the Nusselt number (with a maximum error of 3.81 %) and the friction coefficient (with a maximum error of 1.06 %) in the conditions of the present problem, respectively. Furthermore, the high capability of the algorithm is shown by presenting design results with different heat flux distributions. So, by distributed heat flux attributed to the stable working conditions of the vacuum simulator, a single cooling channel with a height of 3.2 mm and a mass flow rate of 8.025 kg/s has been designed, while for a critical heat flux of about 2.5 MW/m2, a two-channel cooling system with different mass flow rates and channel heights has been designed.

Keywords:
Metal diffuser, Multi-channel cooling, Water-jacket method, High heat flux

In order to transfer risks to the most capable party and provide a basis for project prot sharing, risk allocation has a strong in uence on the time and cost of construction projects. In this paper, for the rst time, allocation
of risk to the most deserving party is dened as an optimization problem, and a quantitative model for risk allocation optimization is introduced. Since the aim of risk allocation is dened as achieving project objectives
with maximum reliability and minimum cost, in the proposed model, an applicable and logical decision parameter is introduced and an optimization algorithm, based on the Ant Colony Optimization (ACO) method, is developed. The proposed model also provides a useful decision tool for the project owner to select the best insurance package by including a sensitivity analysis.
In order to design the ACO optimization algorithm of the risk allocation optimization model; initially, the objectives of owners within the allocation process are identied and the objective function is dened. With respect
to the objective function, the risk allocation problem is then restructured as an optimization problem, and the decision parameters, constraints and a owchart of the model structure are dened. These parameters and constraints are formulated in a mathematical equation; and an optimization algorithm is designed based on Ant Colony Optimization (ACO).
By receiving \prot requested for risk bearing by each participant" as input, the proposed model calculates the cost of risk management for the owner and then minimizes the objective function in an Ant Colony Optimization
algorithm. Two constraints are dened and formulated in the proposed model, in order to simulate the real decision process of risk allocation. As follows: 1) the maximum nancial credit of each party to compensate
the consequence of any risk and 2) the nancial ability of the owner to ensure against risk events. Varying this constraint, sensitivity analysis would be available in the model to optimize the guarantee package for the owner.
Therefore, this model could guide the decision maker towards addressing the most eective guarantees to the best party.
This model is applied in a case study to present its capability and usefulness. According to the ndings of the applied proposed model, it could be concluded that risk sharing in a project should be done based on party competency and willingness. It is also concluded that the owner should participate in the risk allocation process if he/she wishes to achieve the best reliability in project objectives with minimum cost.

Objective This paper seeks to employ fractional cointegration methodology to model high and low stock prices, as well as the range series, indicating the difference between high and low stock prices. Additionally, it tries to examine the regime-switching characteristic in the cointegrating relationship between price series. Conclusion The empirical results show that, unlike the return, which is stationary and unpredictable, the range prices have characteristics of the long-memory processes, falling into nonstationary and stationary levels with mean-reverting behavior. Accordingly, one can obtain a non-stationary range-based volatility estimator, which is more efficient than a stationary return-based realized volatility estimator. These results imply that traders, investors, and policymakers could predict the future extreme prices of the market indices from past values and exploit such predictions to design investment strategies. The cointegration between high and low prices of indices implies limited arbitrage opportunities for the investors and traders in the Tehran Stock Exchange and Iran Fara Bourse Co. Furthermore, the efficiency of these markets is shown to be unstable across indices. Still, there are robust long-run relationships between the high and low stock price indices. Thus, to design hedge fund strategies containing combinations of the stocks from these markets, the behavior and long-run relations of the indices must be considered. Moreover, to design portfolio allocation and diversification strategies, one should mix assets that haven't short-term behavior with indices that show long-term relationships. This conclusion is derived from the notion that mispricing and over-hedging may occur in the absence of cointegrating relationships between price series.

The understanding of architecture as an ecology of interactive systems moves past limitations and restricted tendencies toward spatial environments that are adaptive, perceptual, and behavioral. In this framework, the environment seeks to build interaction scenarios to activate relationships between components. In this case, architecture moves away from well-known models that always lead to disciplined responses and toward understanding adaptive ecologies that include active particles for communication and exploration. The current research investigated and discussed the design of a system that can replace the current methods of planning the construction of infrastructure units in the future. The result is the simulation of a cellular self-assembly system that can produce and rebuild its structure when needed. Therefore, there is a revolution in construction and a complete revision of architectural standards and construction planning, disremembering demolitions and laborious construction costs. This study aims to explore the function of quorum sensing, a mechanism of intra-species communication that serves as the central regulatory system in the formation and concurrent response to environmental changes. The algorithmic design features of the system's constituent units were also examined. During the assembly process, extracellular matrices act as the milieu in which individual cells interact with one another and the desired environment as constructed building blocks. This allows for parallel assembly and error correction at a general level, both automatically, and has significant implications for the field. The proposed model uses a two-level control system (micro-macro) to ensure that the interactions among the components, on the one hand, and between the components and their environment, on the other hand, are in line with the objectives of the plan and the balance of the whole system. This two-level approach primarily follows a bottom-up process at the scale of the interaction of its constituent particles and subsequently utilizes a top-down process during the overall regulation of the system through the extracellular matrix. Ultimately, the simulation results obtained using Grasshopper3d software were shown in three scales: small (chair), medium (shelter), and large. This approach has been demonstrated to be effective in previous research and offers a promising framework for further investigation in this field. This research can take essential steps in developing simulator machines to build a construction self-assembly system based on sequential configurations and numbers.
Keywords: Adaptive Ecologies  Self-assembly  Living Architecture  Diffusion Limited Aggregation  Reversible Fabrication

Traditional patterns could be an effective solution for water consumption and pollution-related problems in the construction industry. However, the water footprint of traditional buildings in Iran has not been investigated. Iran has rich experience in constructing traditional buildings. This paper presents a comprehensive analysis of the grey and blue water footprints of the construction of traditional buildings in Iran with emphasis on different climate zones. The results are compared with modern buildings (concrete and steel). High-quality data related to 11 materials factories and 34 traditional buildings (stone, wood, clay, and brick) are presented. Blue and grey water footprints of building materialization are calculated using the water footprint network and life cycle assessment methods. The focus is on the structures of buildings. The grey and blue water footprints of modern structures are 327 times and 1.5 times larger than the grey and blue water footprints of traditional structures, respectively. Steel and cement production are influential parameters in the greywater footprint of modern structures. Employee meals have the greatest impact on the water footprint of traditional structures. The blue water footprint dominates the water footprint of traditional structures, which is 2.26 times larger than the greywater footprint. Stone structures have a blue water footprint of 0.85 m3/m2, which is dominated by the blue water footprint of employees' food (38.82%) at construction sites. They have a smaller blue water footprint than adobe and brick structures (1.41 - 1.42 m3/m2) and are close to the water footprint of wooden structures. The water footprint of brick structures is mainly influenced by the energy used (57.04%) for brick production. On the other hand, the greywater footprint dominates the blue water footprint of modern structures, which is 99.61 times larger than the blue water footprint. Steel structures have a blue water footprint of 1.86 m3/m2 and a greywater footprint of 208 m3/m2, the main pollutant of which is cadmium. Concrete structures have a blue water footprint of 1.60 m3/m2 and a greywater footprint of 137 m3/m2, with mercury as the main pollutant. From the water footprint viewpoint, it is better to use concrete structures than steel structures if both have suitable properties for the conditions they are used in. According to the results, the non-use of traditional buildings leads to an increase in water consumption and pollution.
Keywords:
Blue water footprint  Greywater footprint  Traditional structures  life cycle assessment

Locating facilities in candidate nodes and allocating relief items to these facilities for emergency response before a disaster occurs, is a common approach to increasing the effectiveness of relief logistics. In this study, humanitarian logistics networks and network restoration are presented in the form of an integrated network, so that the damaged routes are repaired by crews using restoration equipment to distribute relief items. In this paper, a two-stage stochastic programming model is proposed in order to locate relief facilities and restoration equipment and distribute relief items to demand nodes as soon as possible.
The objective function minimizes the social costs of the problem such as deprivation cost (i.e., the cost imposed on survivors by the lack of access to critical supplies) and logistics costs under each scenario. Also, the flow of trucks carrying relief items and repair equipment on the routes is specified. In order to adapt the model to the real world, according to the nature of the effective parameters of the model, two types of structural and functional uncertainties have been considered. The first source is that some uncertain parameters may be based on future scenarios which are considered according to the probability of their occurrence. The second source is that the values of these parameters in each scenario are usually imprecise and can be specified by possibility distributions. In this regard, a robust fuzzy stochastic programming approach has been used to solve the model.  Possibility theory is used to choose a solution to such a problem and a robust fuzzy stochastic programming approach is proposed that has significant advantages. The proposed model has been implemented for a case study of 39 districts of Istanbul and the computational results show the effective efficiency of this model in reducing the social costs of the humanitarian logistics problem.

This paper presents a genetic algorithm (GA) for generating efficient rules for cost-sensitive misuse detection in intrusion detection systems. The GA employs only the five most relevant features for each attack category for rule generation. Furthermore, it incorporates the different costs of misclassifying attacks in itsfitnessfunction to yield rules that are cost sensitive. The generated rules signal an attack as well as its category. The GA is implemented and evaluated on the KDDCup 99 dataset. Its detection performance is comparable to the winners of the KDDCup 99 competition. However, the rules generated by our GA are short and amenablefor real time misuse detection.

T‌h‌e a‌i‌m o‌f t‌h‌i‌s p‌a‌p‌e‌r i‌s t‌o p‌r‌e‌s‌e‌n‌t a m‌e‌t‌h‌o‌d t‌o o‌p‌t‌i‌m‌i‌z‌e m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e p‌l‌a‌n‌n‌i‌n‌g f‌o‌r a f‌l‌e‌x‌i‌b‌l‌e m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌i‌n‌g s‌y‌s‌t‌e‌m. S‌u‌c‌h a s‌y‌s‌t‌e‌m c‌a‌n b‌e c‌o‌n‌s‌i‌d‌e‌r‌e‌d a‌s a m‌u‌l‌t‌i-c‌o‌m‌p‌o‌n‌e‌n‌t s‌y‌s‌t‌e‌m. T‌w‌o t‌y‌p‌e‌s o‌f m‌e‌t‌h‌o‌d‌s m‌a‌y b‌e u‌s‌e‌d i‌n t‌h‌e m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e o‌p‌t‌i‌m‌i‌z‌a‌t‌i‌o‌n o‌f m‌u‌l‌t‌i-c‌o‌m‌p‌o‌n‌e‌n‌t s‌y‌s‌t‌e‌m‌s, i.e., s‌t‌a‌t‌i‌c o‌r d‌y‌n‌a‌m‌i‌c m‌e‌t‌h‌o‌d‌s. S‌t‌a‌t‌i‌c m‌e‌t‌h‌o‌d‌s p‌r‌o‌v‌i‌d‌e a f‌i‌x‌e‌d m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e p‌l‌a‌n‌n‌i‌n‌g, w‌h‌e‌r‌e‌a‌s d‌y‌n‌a‌m‌i‌c m‌e‌t‌h‌o‌d‌s r‌e‌d‌e‌f‌i‌n‌e t‌h‌e g‌r‌o‌u‌p‌s o‌f m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e o‌p‌e‌r‌a‌t‌i‌o‌n‌s a‌t e‌a‌c‌h d‌e‌c‌i‌s‌i‌o‌n t‌i‌m‌e. D‌y‌n‌a‌m‌i‌c o‌r o‌p‌p‌o‌r‌t‌u‌n‌i‌s‌t‌i‌c m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e c‌a‌n i‌n‌c‌o‌r‌p‌o‌r‌a‌t‌e u‌p t‌o d‌a‌t‌e i‌n‌f‌o‌r‌m‌a‌t‌i‌o‌n s‌u‌c‌h a‌s 1) m‌a‌c‌h‌i‌n‌e‌s c‌o‌n‌d‌i‌t‌i‌o‌n, 2) t‌h‌e n‌u‌m‌b‌e‌r o‌f m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e t‌e‌a‌m‌s, a‌n‌d 3) p‌r‌o‌d‌u‌c‌t‌i‌o‌n-r‌e‌l‌a‌t‌e‌d c‌o‌n‌s‌t‌r‌a‌i‌n‌t‌s i‌n r‌e‌d‌e‌f‌i‌n‌i‌n‌g t‌h‌e g‌r‌o‌u‌p‌s o‌f m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e

o‌p‌e‌r‌a‌t‌i‌o‌n‌s. A‌s t‌h‌e l‌i‌t‌e‌r‌a‌t‌u‌r‌e r‌e‌v‌i‌e‌w s‌h‌o‌w‌s, t‌h‌e e‌x‌i‌s‌t‌i‌n‌g d‌y‌n‌a‌m‌i‌c o‌r o‌p‌p‌o‌r‌t‌u‌n‌i‌s‌t‌i‌c m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e m‌o‌d‌e‌l‌s a‌r‌e m‌a‌i‌n‌l‌y d‌e‌v‌e‌l‌o‌p‌e‌d t‌o s‌p‌e‌c‌i‌f‌y c‌l‌a‌s‌s‌e‌s o‌f m‌u‌l‌t‌i-c‌o‌m‌p‌o‌n‌e‌n‌t s‌y‌s‌t‌e‌m‌s t‌h‌a‌t a‌r‌e e‌x‌p‌e‌c‌t‌e‌d t‌o o‌p‌e‌r‌a‌t‌e c‌o‌n‌t‌i‌n‌u‌o‌u‌s‌l‌y w‌i‌t‌h‌o‌u‌t c‌o‌n‌s‌i‌d‌e‌r‌i‌n‌g t‌h‌e p‌r‌o‌d‌u‌c‌t‌i‌o‌n-r‌e‌l‌a‌t‌e‌d c‌o‌n‌s‌t‌r‌a‌i‌n‌t‌s a‌n‌d p‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e i‌n‌d‌i‌c‌a‌t‌o‌r‌s. T‌h‌e o‌b‌j‌e‌c‌t‌i‌v‌e o‌f t‌h‌i‌s p‌a‌p‌e‌r i‌s t‌o d‌e‌v‌e‌l‌o‌p t‌h‌e e‌x‌i‌s‌t‌i‌n‌g d‌y‌n‌a‌m‌i‌c o‌p‌p‌o‌r‌t‌u‌n‌i‌s‌t‌i‌c m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e a‌p‌p‌r‌o‌a‌c‌h‌e‌s f‌o‌r f‌l‌e‌x‌i‌b‌l‌e p‌r‌o‌d‌u‌c‌t‌i‌o‌n s‌y‌s‌t‌e‌m‌s t‌h‌a‌t o‌p‌e‌r‌a‌t‌e i‌n‌t‌e‌r‌m‌i‌t‌t‌e‌n‌t‌l‌y. T‌o t‌h‌i‌s e‌n‌d, a m‌i‌x‌e‌d-i‌n‌t‌e‌g‌e‌r n‌o‌n‌l‌i‌n‌e‌a‌r m‌a‌t‌h‌e‌m‌a‌t‌i‌c‌a‌l m‌o‌d‌e‌l i‌s d‌e‌v‌e‌l‌o‌p‌e‌d t‌o s‌i‌m‌u‌l‌t‌a‌n‌e‌o‌u‌s‌l‌y d‌e‌c‌i‌d‌e o‌n t‌h‌e m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e g‌r‌o‌u‌p‌i‌n‌g a‌s w‌e‌l‌l a‌s l‌o‌t s‌i‌z‌i‌n‌g a‌n‌d p‌r‌o‌d‌u‌c‌t‌i‌o‌n s‌c‌h‌e‌d‌u‌l‌e. M‌o‌r‌e‌o‌v‌e‌r, t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l c‌o‌n‌s‌i‌d‌e‌r‌s f‌u‌r‌t‌h‌e‌r u‌n‌d‌e‌r‌l‌y‌i‌n‌g a‌s‌s‌u‌m‌p‌t‌i‌o‌n‌s s‌u‌c‌h a‌s 1) t‌h‌e l‌i‌m‌i‌t‌e‌d n‌u‌m‌b‌e‌r o‌f m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e t‌e‌a‌m‌s, 2) i‌n‌i‌t‌i‌a‌l i‌n‌v‌e‌n‌t‌o‌r‌y, 3) a‌s‌s‌e‌m‌b‌l‌y o‌p‌e‌r‌a‌t‌i‌o‌n‌s, 4) l‌o‌t s‌i‌z‌i‌n‌g, 5) s‌e‌q‌u‌e‌n‌c‌e-d‌e‌p‌e‌n‌d‌e‌n‌t s‌e‌t‌u‌p t‌i‌m‌e‌s, 6) s‌a‌f‌e‌t‌y s‌t‌o‌c‌k l‌e‌v‌e‌l‌s, a‌n‌d 7) l‌o‌t‌s w‌i‌t‌h u‌n‌e‌q‌u‌a‌l a‌n‌d v‌a‌r‌i‌a‌b‌l‌e s‌i‌z‌e‌s. T‌h‌e o‌b‌j‌e‌c‌t‌i‌v‌e i‌n‌c‌l‌u‌d‌e‌s t‌h‌e c‌o‌s‌t‌s o‌f p‌r‌e‌v‌e‌n‌t‌i‌v‌e a‌n‌d c‌o‌r‌r‌e‌c‌t‌i‌v‌e r‌e‌p‌a‌i‌r a‌c‌t‌i‌v‌i‌t‌i‌e‌s a‌s w‌e‌l‌l a‌s v‌a‌r‌i‌o‌u‌s p‌r‌o‌d‌u‌c‌t‌i‌o‌n c‌o‌s‌t‌s c‌o‌n‌s‌i‌s‌t‌i‌n‌g o‌f p‌r‌o‌d‌u‌c‌t‌i‌o‌n a‌n‌d s‌e‌t‌u‌p c‌o‌s‌t‌s, t‌a‌r‌d‌i‌n‌e‌s‌s p‌e‌n‌a‌l‌t‌y c‌o‌s‌t‌s, a‌n‌d s‌a‌f‌e‌t‌y s‌t‌o‌c‌k p‌e‌n‌a‌l‌t‌y c‌o‌s‌t‌s. D‌u‌e t‌o t‌h‌e n‌o‌n‌l‌i‌n‌e‌a‌r n‌a‌t‌u‌r‌e o‌f t‌h‌e f‌a‌i‌l‌u‌r‌e r‌a‌t‌e o‌f t‌h‌e p‌r‌o‌d‌u‌c‌t‌i‌o‌n m‌a‌c‌h‌i‌n‌e‌s, t‌e‌c‌h‌n‌i‌q‌u‌e‌s f‌o‌r s‌o‌l‌v‌i‌n‌g l‌i‌n‌e‌a‌r m‌a‌t‌h‌e‌m‌a‌t‌i‌c‌a‌l m‌o‌d‌e‌l‌s c‌a‌n‌n‌o‌t b‌e u‌s‌e‌d. F‌r‌o‌m t‌h‌i‌s, a l‌i‌n‌e‌a‌r a‌p‌p‌r‌o‌x‌i‌m‌a‌t‌i‌o‌n o‌f t‌h‌e m‌o‌d‌e‌l i‌s p‌r‌e‌s‌e‌n‌t‌e‌d. T‌h‌e v‌a‌l‌i‌d‌i‌t‌y a‌n‌d e‌f‌f‌i‌c‌i‌e‌n‌c‌y o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l w‌e‌r‌e a‌n‌a‌l‌y‌z‌e‌d b‌y i‌m‌p‌l‌e‌m‌e‌n‌t‌a‌t‌i‌o‌n i‌n a n‌u‌m‌e‌r‌i‌c‌a‌l e‌x‌p‌e‌r‌i‌m‌e‌n‌t.

1- INTRODUCTION
Economic uncertainty is one of the important and influential factors on economic policies and their results, and in such a situation, rational decisions are replaced by other methods. Various studies has shown the effect of economic uncertainty on inflation, investment, economic growth, consumption and demand for money.
Uncertainty is difficult to measure due to its invisibility, and as the uncertainty measurement methods improve, the measurement of its effect on various economic variables and markets and the prediction of their behavior in response to the actions of economic agents will be more accurate.
The main aim of this article is to measure the economic uncertainty index by using news published in social networks. This method of measurement has become very important with the widespread use of social networks.

2- THEORETICAL FRAMEWORK
Uncertainty is one of the most controversial concepts in the philosophy and methodology of economics. The history of the concept of economic uncertainty goes back to David Hume. There are three categories of theories about economic uncertainty. The first group believes that the future reality is unchangeable and predetermined and economic decision makers have perfect information. In this view, there is no such thing as uncertainty and the world is in complete certainty. 18th century the economists of were the first group to present this theory. The second group believes that the reality of the future is unchangeable and predetermined and the decision makers are able to know the future. These economists use objective conditional probability functions to solve the future uncertainty problem. The third class considers the future reality to be changeable and unknown. The starting point of these theories started from the study of the Chicago school economist Frank Knight titled "Risk, Uncertainty and Profit". He clearly distinguished between the two concepts of risk and uncertainty. Keynes also reached the same results as Knight. In general, in a situation where the economy has a high level of uncertainty, the theories of the first and second category have a good explanation. But in confronting with exogenous shocks such as the corona virus epidemic, war and financial crisis, the concept of uncertainty will be more appropriate in the theories of the third category. This study will measure this index based on fundamental uncertainty (the third category).

3- METHODOLOGY
In this article, the economic uncertainty index in Iran was measured from January 2017 to December 2020 by monitoring and analyzing 3,117,960 news from 28 popular and influential Iranian Telegram channels. To analyze these news, we used "supervised machine learning" methods. In the first step, 13,404 news items were labeled by human evaluators according to their impact on uncertainty. The labels had two modes "affecting uncertainty" and "neutral". Then by using four algorithms ("C4.5" from decision tree methods, "Multilayer Perceptron" from artificial neural network methods, "Logistics" from function-oriented methods and "Simple Bayes" from Bayesian methods) labeling of the whole news was done. The economic uncertainty index was calculated numerically and based on the number of news items that affect economic uncertainty, the measurement and value of this index was standardized, and then the quality of the index was evaluated with historical evidence, relabeling and comparison with the index based on Google data.

4- RESULTS & DISCUSSION
Among the 4 media-based uncertainty indicators, 3 indicators can better explain the historical events of this period. Among them, the best performance is determined by C4.5 algorithm from the decision tree methods. After this algorithm, multilayer perceptron, logistic has the best performance and the weakest performance belongs to the simple Bayes method. Media-based economic uncertainty index trend with C4.5 method is consistent with the important events of the study period, in such a way that the highest level of uncertainty occurred during the period when Trump announced his withdrawal from the JCPOA until the official withdrawal of the United States from the JCPOA. In general, it can be said that the fluctuations of the economic uncertainty index have been limited and have several jumps, which are due to the withdrawal of the United States from the JCPOA, the oil embargo and the assassination of Sardar Soleimani.
In the logistic algorithm, the highest level of uncertainty dates back to the end of 2020. The period that coincides with Trump's presidential election. The level of economic uncertainty increases after Trump's official withdrawal from the JCPOA and reaches its peak with oil sanctions.
The output of the multilayer perceptron algorithm indicates that the average level of uncertainty has not changed significantly.  In the simple Bayes algorithm, the highest level was also reached during the period of the withdrawal of the United States from the JCPOA and the increase in enrichment.  The results of the regression showed that economic uncertainty has a positive and significant effect on the average logarithm of the exchange rate with multilayer perceptron, logistic and simple methods. This effect is larger in the multilayer perceptron model, which had better performance based on machine learning indicators.

5- CONCLUSIONS & SUGGESTIONS
The calculated economic uncertainty index is consistent with the important events of the study period, such as the US withdrawal from the JCPOA, Iran’soil sanctions, and the escalation of the US confrontation with Iran in the assassination of Sardar Soleimani. It is suggested that daily calculation of this index be used to reduce uncertainty in the managing future events. We employed GARCH model to test effect of Media-based Economic Uncertainty index on Iranian exchange rate. The results showed that Economic Uncertainty index has poisitve effect on exchange rate.

Small fraction of high conductivity BeO in UO2 fuel significantly improves thermal conductivity and also affects the overall performance of the fuel during steady state operation and during transients. In this study, performance of UO2-BeO composite under transient conditions such as loss of coolant accident (LOCA), using FRAPTRAN (fuel rod analysis program transient), was carried out. The subroutines in FRAPTRAN code that calculate key thermophysical properties such as thermal conductivity, specific heat capacity, and specific enthalpy were modified to account for the presence of the BeO in UO2. The fuel performance parameters like gas gap pressure, energy stored in fuel, and temperature profiles were studied. The simulation results showed reductions in fuel centerline temperatures and lower temperature drop across fuel rod cross-section under normal fuel operations. It was observed that there was reduction in gas gap pressure and energy stored in fuel. Transient conditions involvi...

Transformers are one of the most important and expensive equipments in industries whose optimal performance has been influenced by various parameters such as weather conditions (temperature, humidity, etc.) and consumption patterns of the region. Transformer oils are one of the most vital materials used in this equipment. The oil of the transformer coils has been controlled by two factors: First, it acts as an electrical insulator between the coils and the body.  Second, the heat created in the parts and the core of the transformer is transferred to the outside. In the electrical industry, many problems may be caused by transformers. Most of these cases have been affected by the performance of oils. In case of improper operation of the oil, it will cause disruption in electricity distribution and even damage and destruction. The oil should perform both tasks of cooling the transformer and insulating the body with electricity at the ideal level. However, due to the low thermal conductivity of such mineral oils, transformers cannot provide optimal performance.
In this research by the simulation study, sufficient and acceptable information has been obtained to create the most optimal nano-oil in the operational range with comprehensive library reviews. On the other hand, according to the theoretical analysis, it has been presented that metal oxide nanoparticles, such as  CuO, TiO2, Al2O3, SiO2, and MWCNT as hybrid NMs, have been used so far. The most important result of this study, in addition to the type of hybrid nanoparticles, is to find the best combination to increase thermal conductivity and the principles of electrical conductivity and standard indicators of transformer industries. To start the experimental design, the best and most optimal instruction is simulating the function of the transformer using different nano-oils by a computer. Then, in the next step, this simulation has been done by COMSOL software. So, first of all, precise prioritization for using different nanoparticles can be obtained.

Keywords: Transformer oil, Nanofluid based on oil, Hybrid nanoparticles, Heat transfer, Cooling

Abstract
The problem of tracking control is addressed for rigid bodies with interior shallow-water sloshing. The liquid motion is modeled by the Saint-Venant equations, coupled with the ODE of the rigid body, leading to a global system with an ODE-hyperbolic PDE cascade structure. The paper aims to design an innovative boundary feedback framework for a pre-specified position to deal with rigid body tracking errors. Using only one control force applied to the rigid body, the formulated strategy efficiently stabilizes both the finite- and infinite-dimensional states. The main complexity lies in the fact that no sensor can be implemented in the liquid domain. Indeed, the proposed stabilizing feedback law simply requires measurements of (i) the rigid body position error and velocity and (ii) the liquid pressure at the cavity walls (liquid boundary). The asymptotic stability of the closed-loop system is analyzed using the Lyapunov direct method and LaSalle’s invariance principle without any discretization, reduction, and linearization. Additional controller features are highlighted by simulation results, including its benefits in contrast to the corresponding PD controller and its robustness to time delay and system uncertainty.

Keywords: Rigid body; Shallow-water Sloshing; Saint-Venant model; Hyperbolic PDE-ODE cascade; Boundary feedback control

One of the most important security challenges in e-banking security centers is the inability of the internet to deal with attacks. These attacks are easily implemented and can be controlled locally or remotely. Most of these attacks are successful in reaching the main targets of the attack and bring the attacker to their desires. The reason for this is that there are many mechanisms for launching attacks based on the characteristics of the victim's server, which makes it impossible to provide a comprehensive defense solution against the attacks. Several strategies have been proposed to identify and deal with these attacks. In this paper, a combination of algorithm for selecting genetic features and machine learning methods, including decision tree algorithm, deep neural network and KNN, are presented. Provide guidelines for validation, the results obtained with other techniques such as machine learning techniques and combined with other optimization methods are compared and evaluated. In this research, 10% of KDD Cup 99 dataset for simulation has been used. First, in the preprocessing of data, the values of all attributes are converted to numbers, and the output characteristic values are changed to two values of zero and one. The results of the research indicate that the accuracy of the proposed strategy for detecting intruders compared to other recent methods is about 5%.

|@=} RQ= 'lU}Q X}YND 'BOT VwQ 'PPP |=yxSwQB %|O}rm u=oS=w "TOPSIS pOt 'xSwQB C}kiwt xtOkt "1 QO xm CU= p}rO u}ty x@ "Ovm p=@vO |HQ=N |=ys=w Q=Wi p}tLD =} w |twta u}= x@ = QH= |= Q@ |O=yvW}B |=yxSwQB R= Ovr@ |DUQyi xaUwDp=LQO |=yQwWm ?r=e |xSwQB 20 R= V}@ '2008 p=U u= Q}= QwWm QO \ki , qFt "Ci=} u=wD|t = Q VwQ ptL VN@ QO uy;x=Q Ovr@ Q}Ut 3 w '|y= QoQR@ |xSwQB 3 'jQ@ CavY QO |y=owQ}v "CU= xOW |R}Qxt=vQ@ VwQ u}= =@ = QH= |= Q@ pkv w R=}v xm Ov} wQ@wQ Cw=iDt |=yxSwQB R= |DUQyi =@ , =twta |DrwO uqwUt =Pr = Q =yu; |xaUwD |= Q@ |YwYN |x}=tQU ?PH w =yxSwQB u}= C=tON x@ O}OW |= Q@ |D} Q}Ot u=wD w x}=tQU , qta "Ovm|t s= Rr= BOT |=yVwQ R= |m} ?r=k QO ?=NDv= x@ Ovr@ CUQyi u}= u}@ R= O}=@ xmr@ 'OQ=Ov OwHw =yxSwQB u}= s=tD |= QH= |= QH= C}r@=k u}}aD "OQm s=Ok= BOT VwQ x@ = QH= |= Q@ =yxSwQB u} QD?U=vt xm CU= BOT VwQ QO xSwQB u; C}kiwt Tv=W |@=} RQ= |=vat x@ 'xSwQB l} p@k pt=wa R= l} Qy xm CU= |}xv} RyQB w pYit |HvUu=mt= C=ar=]t hOy C}kiwt Tv=W u}}aD 'C}kiwt |@=} RQ= "Ovvm|t s=Ok= u; x@ xSwQB l} x@ OwQw R= xm CU= xSwQB l} |= QH= R= OwN Q=_Dv= OQwt h=Oy= x@ |@=}CUO QO pt=a l} p=Ft= w '|O=YDk= Caivt '|U=}U-|a=tDH= C= Q}F-=D '|r=t OwU ?r=k QO Ov=wD|t "OW=@ u; C=ar=]t QDW}@ RmQtD xSwQB C}kiwt |xRwL QO xOQDUo C=k}kLD u}@ QO |}=U=vW =} w [53] 'u; |Q}oxR=Ov= |ovwoJ xSwQB C}kiwt O=a@= h} QaD Q@ =@ Q}N= |=yp=U QO [6] "CU= xOw@ CSF =yxSwQB C}kiwt |D=}L |=yQ=}at uO=Di=j}waDx@ ?Hwt RwQ |Qw=vi x@ |UQDUO sOa w |DrwO |xHOw@ Ow@tm xOW xaUwDp=LQO |=yQwWm R= |Q=}U@ QO =y=v@ Q} R QO xaUwD w |Q=Pox}=tQU |= Q@ |YwYN VN@ R= xaUwD p=LQO |=yQwWm |=yCrwO R= |Q=}U@ =Pr "CU= x@ CwaO CU= xOw@ CrwO Q=YLv= QO , q@k xm |}=v@ Q} R |=yxSwQB QO CmQ=Wt VN@ CmQ=Wt |=yVwQ u} QDR}t;C}kiwt w u} QDpw=ODt R= |m} "Ovvm|t |Q=mty 'VwQ u}= QO [2w1] "CU= BOT OQm}wQ ' 4 PPP |twta |=yxSwQB QO |YwYN u}t-=D |=v@t Q@ = Q xSwQB R= |Q=OQ@xQy@ w 'CN=U '|L= Q] |xi}_w |YwYN VN@ xSwQB R= |Q=OQ@xQy@ R=}Dt= w= x@ CrwO 'p@=kt QO "OQ}o|t xOyax@ |YwYN |r=t Ovv=t xSwQB Ot;QO R= CW=OQ@ =@ |YwYN VN@ =D OyO|t |v}at COt |= Q@ = Q w CN=OQB R=@ = Q xOWxDiQo |=ys=w w =yx}=tQU """w x=owQ}v jQ@ VwQi 'x= Q ZQ=wa QwWm x@ C=U}U-=D 'R=}Dt= COt uOW|[kvt R= TB "Ovm CW=OQ@ = Q |r=tDL= OwU = Q BOT |xSwQB l} |twta Q=DN=U 1 Q=Owtv xmu=vJu; 'OwW|t Q=Po=w u=@R}t [2] "CU= xO=O u=Wv xSwQB x@ \w@Qt |=ylU}Q R= |t}_a VN@ CrwO 'BOT |xSwQB |= QH= =@ sRq |=y|ovy=ty w C} Q}Ot u}ovU Q=@ R= = Q OwN w pkDvt |YwYN VN@ x@ = Q |xHOw@ x@ <=mD= uwO@ = Q OwN |}=v@ Q} R C=U}U-=D |xaUwD Ov=wD|t w Ovm|t =yQ

Airborne wind energy system (AWES) is a novel approach in wind energy harvesting. It has several advantages against conventional horizontal axis wind turbine (HAWT), like using less material and thus lower manufacturing cost, higher efficiency, stable electricity output and higher capacity for energy harvesting. It is obviously embedding a complex control system which makes the appropriate flight trajectory for the vehicle. These systems need to be carefully designed so using virtual flight simulators in design process is crucial. The main components of a typical AWES are: tether, flyer, and rotors. The flyer is designed to have a tether-constrained flight across the wind in a circular path. Consequently, the mounted rotors on the flyer’s wings will capture energy and this mechanical/electrical energy would be sent back to the ground via the same tether. It is notable that the flight path and the special design of the flyer, would make it capable to have a sustained motion in the circular loop with no energy consumption. A tethered drone equipped with several rotors is an example of such devices, already has been built and tested. In previous literature, the flight simulators usually contain some simple aerodynamic models for predicting the forces and moments generated by the rotors. It is derived by constant aerodynamic coefficients. In the current study, it has been developed a flight simulator for a typical AWES having onboard rotors. To make this flight simulator more accurate and to improve its fidelity in different environmental conditions, proper estimation of the external forces, particularly the aerodynamic forces and moments, seems to be necessary. Therefore, toward developing a high-fidelity simulator, Lagrangian dynamics and a new algorithm for estimation of the rotor aerodynamics, has been utilized. This new method is shown to have more accurate approximations of the system performance and also better description of the vehicle trajectory. By this framework, one could design optimized blades of the rotors and also the rotors arrangement. Implementing the new simulator, a single drone, as the flyer in AWES, having 3m wing span, would experience 40 percent improvement in the average power extracted which is near 2 KW.
Keywords: Airborne wind energy system (AWES), Lagrangian flight simulator, Tethered flight, Aerodynamic coefficients, Rotor section

The analysis of heat transfer in the channel in many types of heat exchangers, such as electric cooling equipment, solar collectors, heat exchanger systems, high-performance boilers, gas turbine blade coolers, etc., is the basis of the design, construction, and optimization. Controlling heat transfer to increase the rate of heat transfer in such systems by improving the cooling method is an effective energy engineering from the point of saving energy. Increasing the heat transfer performance in the scales of macro and microchannels is crucial. The use of expanded surfaces in the channel is a practical method to increase the heat transfer coefficient. In the upcoming article, the smart design of a two-dimensional nanofluid heat exchanger has been studied numerically in order to achieve optimal performance conditions in terms of heat transfer rate, the amount of deposition of nanoparticles in the structure of the exchanger, as well as the fluid pressure drop while passing through it. It can be seen that the geometric structure optimized by the combination of genetic algorithm and computational fluid dynamics of this channel causes an increase of 1.14% in the enthalpy of the passing nanofluid, a decrease of 11.21% in the pressure drop of the passing nanofluid, and a reduction of 8.44% percentage in the deposition of nanoparticles inside the channel and a total increase of 24.82% in the fitting function defined in terms of these three variables, compared to the channel designed in previous studies. Therefore, this optimal channel has a higher heat transfer rate with a pressure drop and a lower amount of nanoparticle deposition compared to the previous channel, which proves the ability of the genetic algorithm with computational fluid dynamics in the optimal design of all types of heat exchangers.
Keywords: Nanofluid heat exchanger design, expanded surfaces, computational fluid dynamics, genetic algorithm, Image processing.

Global population growth and industrialisation over the past two centuries have been increased the tendency toward increased use of fossil fuels. The rising trend in greenhouse gas emissions brought on by the usage of fossil fuels has contributed to global warming and, as a result, increased environmental hazards. Geothermal energy is a substantial source of clean, sustainable, and renewable energy that is utilized extensively for building heating and cooling and has a big impact on reducing greenhouse gas emissions. Energy piles as a convenient and efficient energy geo-structure receiving a lot of attention worldwide for use in building heating and cooling. Investigating the variables influencing the thermal interaction between the group of energy piles and its impact on reducing the extracted energy from the energy pile group is the goal of this study. The soil porosity, mass flow rate of the circulating fluid, pile diameter, pile length, and pile position have all been investigated using the COMSOL Multiphysics Software. The analysis of the simulation data reveals that as energy piles' diameters increase, so does the amount of extracted energy. The amount of energy extracted is significantly influenced by the soil's porosity, which causes thermal interaction to decrease and energy extraction to increase as porosity increases. It was found that the amount of energy extracted is not significantly affected by the mass flow rate of the fluid circulating in the pipe. If the amount of extracted energy calculated with respect to the pile length, as the length of the pile increases, the average energy extracted per meter of the pile decreases and tends to a constant value. When the pile diameter is kept constant, pile interaction tends to be reduced by increasing the ratio of the pile diameter to the pile spacing (s/D), and as a result, the amount of energy extracted increased. In the group of energy piles, it has also been found that the corner piles are the most and the center piles are the least effective piles.
Keywords: Geothermal energy, Energy pile group, Extracted energy, Interaction, Heat transfer in soil, Comsol

Financial health means the ability to profitability and continuity of activity of the economic unit, which is very important for all shareholders and stakeholders, and basically all stakeholders in economic units are interested in having appropriate tools that can assess and predict the profitability and continuity of activity. The purpose of this study is to identify the factors affecting financial health and predict the degree of financial health of companies listed on the Tehran Stock Exchange. For this purpose, the data of 138 companies (1104 companies-years) during the years 2011 to 2018 and Matlab software were used to test the research hypotheses. Variable selection test performed using artificial intelligence algorithm to analyze neighboring components showed that among the criteria of the management system, the ratio of non-executive managers, the ratio of institutional owners, the size of the audit committee and the independence of the audit committee and change of CEO to explain the financial health of companies. Also, in order to predict the degree of financial health of companies listed on the Tehran Stock Exchange, the decision-oriented algorithm of the decision tree was used. The financial health of companies listed on the Tehran Stock Exchange.

Nuclear industry is in crisis and innovation is the central theme of its survival in future. Artificial intelligence has made a quantum leap in last few years. This paper comprehensively analyses recent advancement in artificial intelligence for its applications in nuclear power industry. A brief background of machine learning techniques researched and proposed in this domain is outlined. A critical assessment of various nuances of artificial intelligence for nuclear industry is provided. Lack of operational data from real power plant especially for transients and accident scenario is a major concern regarding the accuracy of intelligent systems. There is no universally agreed opinion among researchers for selecting the best artificial intelligence techniques for a specific purpose as intelligent systems developed by various researchers are based on different data set. Interlaboratory work frame or round-robin programme to develop the artificial intelligent tool for any specific purpose, based on the same data base, can be crucial in claiming the accuracy and thus the best technique. The black box nature of artificial techniques also poses a serious challenge for its implementation in nuclear industry, as it makes them prone to fooling.

OWQ= |U=vWQ |wHWv=O |OtLtR} Ra |Oyt u}wRk OL=w |tqU= O=R; x=oWv=O 'l}v t w`}=vY |UOvyt |xOmWv=O u= Q}Ot |=yC}r=ai u} QDsyt R= |m} 'u=oOvvmu}t-=D x@ O} QN C=WQ=iU X}YND |xrUt |xQ}HvR QO |rOt |x= Q= =@ Q=DWwv u}= QO "OwW|t ?wULt u}t-=D |xQ}HvR l} QO O} QN xOW xHwD R}v`kwt x@ p} wLD w C}i}m x@ 'Q=O}QN |=yxv}Ry pm uOQmxv}tm Q@ xwqa u}t-=D 'u}= Q@ xwqa "OwW|t xO=iDU= xOvvmu}t-=D ?=NDv= |xrUt pL |= Q@ wDQ=B OQm}wQ R= w "CU= xOW xDiQo Q_v QO R}v q=m Ow@tm |xv}Ry u}vJty w |H} QOD h}iND |SD= QDU= Q@ |vD@t |xiOyOvJ sD} Qwor= wO R= xOvvmu}t-=D ?=NDv= |O=yvW}B pOt pL Qw_vtx@ sD} Qwor= w NSGA-II ?wret=v |R=U?DQt l}DvS sD} Qwor= |=ys=vx@ wDQ=B OQm}wQ OQmrta C=@F= Qw_vtx@ C}=yv QO "s}=xDiQo xQy@ NRGA ?wret=v |Ov@x@DQ l}DvS |W}=tR; p=Ut |wQ =yVwQ u}= '|O=yvW}B pOt QO xOW x=Q= pL |=yVwQ ?U=vt =@ R}v xOt; CUO x@ |=yMU=B w xDiQo Q= Qk p}rLD w x} RHD OQwt hrDNt O=a@= =@ xOWO}rwD "Ov=xDiQo Q= Qk p}rLD OQwt |Q=t; |=yVwQ R= xO=iDU=

C}iQ_ 'pUwS %|O}rm u=oS=w "Twm=@; Q= Ri=sQv '|OOa |xar=]t 'KrUt l=N xtOkt "1 'O}QowS |x}q |wQ xDiQo Q= Qk l=N |=yxv=O =@ =yxm@W pN=O |=yxv=O CU@ w pik |=yxv=O |Q}oQO =yO} QowS Q=m T=U= u}=Q@=v@ 'O@=}|t V}=Ri= l=N |Q@Q=@ C}iQ_ =ypUwS [1] "CU= ha[ l} 'xv=OR}Q |=yl=N QO`w[wt u}= xm 'CU= l=N u}@ VvmQOv= R= |W=v xm '|@v=H Ctw=kt uDW=O w s=k VvD`} RwD |x]U=w x@ R}v QO xOt; OwHw x@ |}=We QF= u}vJty 'CU= pwrU |xOvvmQ) B l=N w pUwS K]U C}iQ_ V}=Ri= ?Hwt 'CU= KrUt l=N |WtN Ctw=kt R= |W=v xm 'pUwS |= Q@ pUwS w O} QowS OQmrta x@ xHwD =@ 'T=U= u}= Q@ [42] "OvwW|t l=N |Q@Q=@ Q=}U@ pUwS R= xO=iDU= 'CU= |UQ sQv |=yl=N R= xm '`=@W= uQ=t l=N K}rUD x@ =ypUwS Ovv=t '|Qt}rB Kr=Yt R= xO=iDU= "OUQ|t Q_v x@ O} QowS R= QD?U=vt w u=UOvyt xHwD OQwt Q}N= |xyO OvJ QO l=N |Q@Q=@ C}iQ_ V}=Ri= Qw_vt =@ |Q=}U@ u= QoWywSB '|Oq}t 1970 |xyO p}=w= R= "CU= xDiQo Q= Qk u= QoWywSB C}iQ_ |@=} RQ= x@ |}=QLY w |r}rLD '|OOa '|y=oW}=tR; C=ar=]t R= xO=iDU= =} pUwS |=yx}q \UwD u; Q} R l=N xm '|L]U |=y|B CUWv w |}=yv |Q@Q=@ R= |NQ@ x@ xt=O= QO xm 'Ov=xDN=OQB 'CU= xOW KrUt =yl}DDvUwS R= Qo}O`wv Qy [5] %CU= xOW xQ=W= =yv; u} QDsyt l=N sDU}U l} QO = Q pUwS pwrU`=iDQ= w xR=Ov= QF= '1994 1 =DB wo w p=Ovt |=y?}mQD 'OvwW|t Ci=} Qwiw x@ u=yH hrDNt \=kv w u= Q}= QO xm |vQ=t |=yl=N xm CU= |Q=OxrUt |=yl=N xrtH R= uQ=tl=N "OvQ=O C=v@Qm s}Urm w |UQ "OQ=O Ctw=kt w |Q@Q=@ C}iQ_ Q_v R= |@U=vt C=}YwYN 'lWN Cr=L QO uDiQ u}@ R= w C=v@Qm uOW pL p}rO x@ ?; =@ T=tD QO C=}YwYN u}= =t= |=y|B |= QH= u}=Q@=v@ "Ovvm|t =O}B Ci= COW x@ uQ=t |xOvyOp}mWD C= QP w |Q@Q=@ C}iQ_^=Lr R= |DqmWt RwQ@ ?Hwt '|vQ=t |=yl=N |wQ |L]U |= Q@ u=UOvyt "OwW|t xR=U ?} QND =} w uO}O ?}U; Ea=@ 'C}=yv QO w CUWv |v} Ro}=H |=yVwQ R= |OQ=wt QO |vQ=t |=yl=N QDU@ Q@ O}OH |=yxR=U E=OL= =yxSwQB R= |Q=}U@ QO xm 'Ovvm|t xO=iDU= sm = QDt w ?U=vt l=N =@ uQ=t l=N w |ovU uwDU =@ |R=Uy@ uwJ |}=yVwQ R= u}=Q@=v@ "OQ=Ov |O=YDk= |xiQY w =yO} QowS pt=W , =DOta xm '=yl}DDvUwS R= xO=iDU= =@ l=N |R=UKrUt u}vJty |=yxv=O uDiQo Q= Qk '=yO}QowS =@ l=N K}rUD OvwQ QO "OvQ@|t xQy@ 'CU=ypUwS =@ w OwW|t O} QowS w l=N |=yxv=O |Q}oQO Ea=@ 'O}QowS |=yxm@W u}@ QO l=N

A need exists in the nuclear industry for higher-fidelity tools for light water reactor (LWR) analysis, due to increasing core heterogeneity and higher burnup of fuels. In order to address this need, a high-fidelity multi-physics (HFMP) system has been developed at the Pennsylvania State University (PSU). It consists of three codes-CTF for thermal hydraulics, TORT-TD for neutron kinetics, and FRAPTRAN for fuel performance. FRAPCON, which is applied to long-term steady-state fuel performance, is left separate and not modified, but is relevant to the system because it generates the initial conditions used in FRAPTRAN. These codes have been combined into a system in which they are coupled by means of serial integration. FRAPTRAN is the latest addition to the system while the initial coupling of TORT-TD and CTF was verified in different applications. Recent efforts have been directed at the design of an object-oriented system of interfaces for the coupled codes, by which the main program may control them in terms of high-level functionality. Further modifications to the system include the ability to use coolant-centered rather than fuel-centered channels, and the ability for TORT-TD to use a time step size that differs from that of CTF. The obtained results verify this new coupling, as well as demonstrate the advantages of using a fuelperformance code for modeling fuel rod feedback.

Objective Financial markets are considered to be a kind of complex network due to the interaction and interrelationship of its various actors. Therefore, the development of stock communication networks is an important issue for discovering potential connections between different companies. Therefore, the purpose of this research is to investigate and analyze the communication structure of companies in the capital market based on price, return, and trading volume of stock with a network approach. Methods This study employs a quantitative research methodology, specifically focusing on network analysis. The data were collected in the period between 2017 and 2021 and analyzed using the network analysis method and with the help of Excel 2021, SPSS 26, UCINET 6, and its complementary package NetDraw. In addition to the general analysis and review of the price, returns, and trading volume of the stock correlation network, the performance of each node in the network was also investigated using micro-indices. Centrality is one of the most important micro-concepts of network analysis, which examines the influence and importance of people in the network. Also, it was studied with three indices of degree, betweenness, and closeness of centrality of network nodes. Results The results indicated that as the correlation basis expands, there is a decrease in communication between different companies along with an increase in irregularity. The higher the correlation basis, the less likely it is to be affected by a common variable or interaction, which will reduce the correlation between stocks. In addition, there are companies in the communicative network that are in a better position than the rest. They may have higher connections, faster access to information, and greater influence over other units' price changes. As a result, these companies can play a decisive and leading role, or in other words, the role of key and influential actors in the governing structure. Also, the results showed that in the second period of 2021 compared to the first period, communication networks were much more erratic and scattered. Conclusion Considering the importance of capital market communication networks and the decisive role of key companies, relationship pattern analysis helps to increase transparency, reduce risk, and, consequently, improve decision-making, policy-making, portfolio optimization, etc by revealing the network structure of the capital market. The results of this research can lead to a better understanding of the forces that create the communication structure of companies and enable policymakers to make better decisions. Therefore, all users are suggested to pay attention to the communication structure governing the companies in the capital market and the role and impact of their position on the advancement of the company's goals in their decisions and to consider this communication structure and the way power is distributed.

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Brain-computer interfacing is an emerging field of research where signals extracted from the human brain are used for decision making and generation of control signals. Selection of the right classifier to detect the mental states from electroencephalography (EEG) signal is an open area of research because of the signal's non-stationary and Ergodic nature. Though neural network based classifiers, like Adaptive Neural Fuzzy Inference System (ANFIS), act efficiently, to deal with the uncertainties involved in EEG signals, we have introduced interval type-2 fuzzy system in the fray to improve its uncertainty handling. Also, real-time scenarios require a classifier to detect more than two mental states. Thus, a multi-class discriminating algorithm based on the fusion of interval type-2 fuzzy logic and ANFIS, is introduced in this paper. Two variants of this algorithm have been developed on the basis of One-Vs-All and One-Vs-One methods. Both the variants have been tested on an experiment involving the real-time control of robot arm, where both the variants of the proposed classifier, produces an average success rate of reaching a target to 65% and 70% respectively. The result shows the competitiveness of our algorithm over other standard ones in the domain of non-stationary and uncertain signal data classification.

The price forecasting and its changes trend is one of the most important factors in decision making and formulating strategies related to agricultural products. This study aimed at presenting a hybrid data mining model for accurate price forecasting of agricultural products, including nonlinear models of wavelet transform, genetic algorithm, deep neural network and Monte Carlo technique. This proposed model involved a two-stage hybrid model and the base model of nonlinear-nonlinear. In this proposed model, the genetic algorithm for determining the optimal lag of price time series, the wavelet function for the de-noising of price data, the deep neural network for price forecasting, the Monte Carlo method for simulating the most probable price probability and finally, the complex soft calculations for "out-of-sample forecasting with new data set" were used. Results of comparison of the proposed model including "Genetic Algorithm-Wavelet Transform-Deep Neural Network-Monte Carlo", through evaluation criteria, with three competing models of "Genetic Algorithm-Deep Neural Network-Monte Carlo", "Genetic Algorithm-Wavelet Transform-Neural Network-Monte Carlo" and "Genetic Algorithm-Neural Network-Monte Carlo" showed that the proposed model had the better performance in forecasting of future price of saffron compared to the three competing models. Also, the use of deep neural network compared to neural network, the application of wavelet theory for de-noising and also the use of Monte Carlo technique to simulate the predicted prices, increase the forecasting accuracy of future price of saffron. In addition, the use of soft calculations showed that the proposed model had the necessary efficiency and high accuracy for short-term forecasting of the future price of saffron. Therefore, the present study has a good position in achieving the index of maximum accuracy, scenario making of future price trends, sensitivity analysis of components affecting the price and finally, forecasting the future price. Accordingly, the use of the proposed model to forecast the price of agricultural products is recommended.

Nowadays, due to the high uncertainty in estimating precipitation in different geographical areas, the use of computational intelligence methods based on optimization algorithms to accurately estimate daily precipitation has been considered by water engineers. In the present study, the combined Adaptive Neuro Fuzzy Inference System and Wavelet transform (W-ANFIS) method was used as a pre-processor for daily rainfall data to estimate precipitation values. The structure of the W-ANFIS hybrid model was developed using the Fuzzy Clustering Means (FCM) method in the training phase. Moreover, constant coefficients of membership functions applied in the ANFIS model were optimized using four optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony community (ACO). In the present study, rainfall statistics of Izmir basin in the western part of Turkey were used. Through applying five-time delays in daily rainfall statistics as well as decomposing each time delay in the three levels of wavelet transform, each of the W-ANFIS optimal models had twenty input variables. The results of the statistical analysis for both training and testing stages by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) showed that the application of the DE algorithm into W-ANFIS structure had the best performance (RMSE = 22.22 and MAE = 17.11mm) than other combined models with PSO (RMSE = 28.11 and MAE = 24.11 mm), ACO (RMSE = 30.41 and MAE = 26.50 mm), and GA (RMSE = 25.70 and MAE = 18.11 mm).

Nowadays, the threats caused by aerial targets are among the most critical challenges. Two main components in threat management are visualization and decision-making based on compiled pictures. In this process, the decisions are made based on threat evaluation and resource allocation modelling. To do so, the sequential nature of resource allocation is considered to provide a more accurately visualized situation. Here, a multi-stage multi-objective model is constructed based on the fuzzy inference system and the decision tree. Also, NSGA-II and SPEA-II are used to find Pareto solutions. Both Algorithms have been compared based on generational distance as a convergence measure, spread as a diversity measure, and the actual computational time. Ultimately, the TOPSIS method is used to make the final decision while the results are reported using a simulated scenario. The simulation results show that the SPEA-II has better convergence and spread, while NSGA-II is faster and has less standard deviation in the execution time. We believe that the fuzzy inference system is more suitable than the decision tree in practical applications. Nevertheless, when facing data shortage or incompatibility, the decision tree would be the preferred choice.

The performance of an intelligent model largely depends on the selection of the most relevant and most influential input variables and the lowest complexity of the learning model. Therefore, in the present study, to predict the index of Tehran Stock Exchange based on financial and economic variables, first prioritize features With MID algorithm, then 4 different neural network models (MLP, SVR, RBF, DNN) are used, which are the most important and innovative prediction models. According to the results of the analysis of the studied models, an algorithm is proposed to select the appropriate features on the index, as ISF-MID, and are compared with several similar methods. The data used in this study were collected daily in the period of 18/01/2014 to 21/08/2018. Evaluation of the models was performed by K-fold cross validation method. The MAE, MSE, and RMSE criteria are also used to evaluate the performance of the mentioned models. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange.