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In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]

The ever-increasing of renewable distributed generation sources in distribution networks, as well as increasing network size, have faced agent-based protection schemes with a heavy communicational load. Accordingly, despite the fast and reliable nature of multi-agent systems, they have the possibility of improper performance, especially in centralized protection systems. This paper presents an intelligent self-healing method that has the ability to replace common multi-agent systems during fault conditions. Therefore, protection tasks are performed in a single control level, without dependence on higher communicational levels, to clear the fault. Decentralized operation of this structure is provided by using intelligent electronic devices and distributed communications. In this way, the proposed scheme is described with high-speed peer-to-peer communication capability using the IEC-61850 GOOSE protocol. Then, a penetration-free algorithm, without the help of a central controller, is provided by using GOOSE message capabilities, to prevent any electricity interruption due to insufficient protection settings. Finally, by planning different scenarios and simulating a practical distribution network via ETAP software, the accuracy of the proposed algorithm has been proven. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The accurate and scientific assessment of the risk to issue an insurance policy is one of the most critical and important stages of risk assessment frameworks. This leads companies to identify high-risk customers and determine the policy rates in accordance with their risks, and as a result, the claims will be covered appropriately through the insurance premiums. In this paper, a new method is presented to define the concept of risk factor in more practical, flexible and accurate way. In this method, which is based on an unsupervised clustering algorithm, initially, every single factor is examined based on different ranges and their corresponding impact on customer loss levels. Then, considering their connection with the ranges of other factors in terms of creating similar levels of customer loss, they are combined to form a package. Thus, different packages are created, each of which is considered a risk factor and comprise the ranges of factors affecting different levels of loss. METHODS: The k-means clustering method was used to divide insurers into clusters with similar risks, which correspond to the risk packages associated with the customers’ risk level. The number of desired clusters should be determined in advance, which is the main challenge of using this algorithm. Two main approaches for validation, namely the silhouette score and the elbow method, were presented. FINDINGS: Based on the elbow plot and silhouette coefficient, as well as considering the practical and realistic evaluation needed by insurance companies, four clusters were obtained. Cluster 2 and 3 are similar and can be merged to form a cluster of medium risk level. Therefore, three clusters were considered the best outcome for categorizing insurance policyholders. CONCLUSION: The risk packages can be introduced from the examination of the 3 clusters including People with high, medium and low age (confidence interval) with low price car whose gender is male can be introduced as the highest level of risk; People with medium and high ages (confidence interval) with medium and high car prices can be considered as medium risks, and Middle-aged and older people (confidence interval) with expensive cars were considered the lowest level of risk. From the results of these risk packages, it can be concluded that although a significant population of older policyholders falls into the first package (first cluster), they have the highest level of risk. On the other hand, the older people in the third package (even though their average age is the highest among the clusters) have the lowest level of risk. Another important point is that the risk level decreases as income increases simultaneously with age. [ABSTRACT FROM AUTHOR]

In this paper, the problem of real-time scheduling of multi-factury production network in the smart manufacturing system with job shop environment is studied. In this smart manufacturing system, a number of independently owned factories are joined together to form a multi-agent production network, which is also called a virtual production network. In such a network, each factory focuses on its interests and communicates with each other by sharing information such as machine breakdowns and job transfers. Therefore, it can be stated that studying the problem of distributed scheduling in the environment of smart factories is important and will have a significant effect on obtaining desirable and ideal results. At first, a biobjective mixed integer linear programming model is presented; then an approach to solve the dynamic real-time scheduling problem is proposed . Considering the successful applications of the Lagrangian relaxation algorithm in solving scheduling problems, in this research, the improved Lagrangian relaxation algorithm is used to solve the problem. To examine the performance of the proposed algorithm, its results were compared with solving the original model that was solved by the augmented epsilon constraint method. The obtained results showed that the proposed Lagrangian relaxation algorithm has a better performance than the augmented epsilon constraint method. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

Background & Aims: Cancer is one of the great human challenges in all countries, both advanced and developing. Cancer treatment management can include surgery, chemotherapy, or radiation therapy (1). Radiation therapy is done in two ways: Teletherapy and Brachytherapy. Brachytherapy involves the use of radiation sources to treat cancer by irradiating cancerous tissue from within the patient’s body (2). But the dose and how to use this method has always been questionable for researchers. Therefore, this study creates a new fuzzy approach to high-dose brachytherapy by optimizing the distribution of double roughness based on dosimetric criteria. The use of fuzzy logic itself has increased the accuracy of the mathematical model of the problem. This fuzzy model is a new study and innovation used in this paper. Due to the fuzzy nature of this method and its limitations, it is considered fuzzy. This makes the method more accurate and includes parameters such as the patient’s physical ability and age in the problem, which in itself increases the accuracy of the method for each patient. As a result, the obtained answer is improved and the executive program of brachytherapy method is more accurate. Methods: In the present study, the dose prescribed for an organ was evaluated by dosimetric indices listed in Table 1 (18). For the present study, data from 20 patients in the age range of 50 to 74 and mean age 62 years with a wide range of prostate volume between 23 and 103 cubic centimeters, and for the treatment of prostate cancer by brachytherapy from the Academic Medical Center (AMC, Amsterdam, the Netherlands) had participated. To compare brachytherapy programs with high interstitial dose, the dose rate was calculated with 192Ir beam with a radiation dose of 13 Gy, according to the standard protocol TG-43. To begin with, computed tomography (CT) scans or magnetic resonance imaging (MRI) were taken from the patients pelvis, and entered into the treatment planning software for use in treatment planning sessions. BT treatment planners and specialists then determined the input catheters, target volumes, and OARs obtained from the medical images. Depending on the size and exact location of the target volumes, between 14 and 20 catheters entered the patient’s body, reaching the target volumes. After designing and approving an acceptable treatment plan, the catheters inserted into the patient’s body were connected to a retractor 4 that controls the movement of the radiation source. After the treatment program, the source was returned to the retractor (8, 14). Then, an integer program model with fuzzy constraints was proposed for programming on high-dose brachytherapy. The description of infrastructures, parameters and variables in this model are in Table 2 (15 ). Finally, the dosimeter index is equal to the sum of all the index variables, as it turned out: The proposed model is a correct programming model called IP. In general, three evolutionary algorithms (EP), (GA) and (ES) were used in this research. The algorithms stopped after creating 20 generations of desirable answers and the best answer of each generation was determined on the chart as a point. The resulting set of answers were connected in the form of graphs, which are used to analyze the results. Each point on the graph identifies the best answer from each of the generations generated by the respective algorithms. These three algorithms were performed independently for each patient and the obtained answers were identified as dots on the chart. The graph obtained for each patient indicates the capability of each of these algorithms. Results: According to this study, each of the three algorithms (EP), (GA) and (ES) are each run independently for each of the patients with prostate cancer. At first, the genetic algorithm showed the ability to get closer to the desired answer sooner, but as the optimization process continues, the rate of convergence to the desired answer decreases, so if the time parameter is very important, the genetic algorithm can be useful. Especially for patients whose prostate volume was larger than other patients. In patients 15, 12, 8 and 9, due to their younger age than other patients and better physical condition, as well as prostate volume less than 80 ml, they had much more promising results than other patients. In contrast, for patients 4 and 19 with an age of over 70, the results were not as favorable as for other patients. The * sign in the table indicates that there is no answer through that patient-specific algorithm that covers 95 or even more of the tumor volume. Therefore, according to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. According to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. Therefore, according to Table 3, the ES algorithm has better answers than the other two algorithms for the case in which the largest volume of the tumor is covered. According to the results, the ES algorithm has obtained the best results for patients under 60 years of age and normal prostate volume. Conclusion: According to the obtained results, it can be stated that whether the main goal is the maximum coverage or the goal is the shortest possible time to reach the coverage above 95%, the best algorithm that can get a good answer for each patient is the evolutionary strategy algorithm. [ABSTRACT FROM AUTHOR]

This paper uses the unique biometric features of fingerprints to generate random cryptographic keys. The main aspects of the security of the generated key include the privacy of the fingerprint and the randomness and complexity of the key generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be random from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated keys show the acceptable random nature of the keys. Therefore, the proposed structure for generating a random key can be used in encrypting digital signals with large volumes of data such as image and sound. [ABSTRACT FROM AUTHOR]

In this paper, an asymmetric breakup of non-Newtonian droplet (with power law behavior) in a new geometry (network junction) has been investigated. The geometry can break an initial droplet into six unequal size droplets. The research method is numerical simulation with Volume of Fluid (VOF) algorithm. The numerical results are compared with the results of a benchmark problem and a very good agreement is seen. The results showed that in areas close to the wall, mixing of materials of inside droplet is performed better, which is important in industrial applications of droplet based flows, especially in pharmaceutical and chemical industries. The results showed that the maximum vorticity magnitude in the K1 branch (the lowest output branch in the system) is 26, 44 and 28 % more than the maximum vorticity magnitude of the branches of K2, K3 and K4 (K4 is the highest output branch is in system). Also, maximum effective viscosity in the K1 branch is 27, 29 and 24 % less than the maximum effective viscosity in the K2, K3 and K4 branches, respectively. Therefore, K1 branch has the best performance in mixing of the material of inside droplet among the output branches. It was also revealed that the pressure of inside of droplet (both before and after breakup) is constant along the channel width [ABSTRACT FROM AUTHOR]

The growing use of internet and the existence of vulnerable points in networks have made the use of intrusion detection systems as one of the most important security elements. Intrusion detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network intrusion detection based on an estimation of distribution algorithm that uses a probabilistic dependency tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL- KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other feature selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on intrusion detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different feature selection methods studied in this paper, an effective subset of features for intrusion detection is identified. [ABSTRACT FROM AUTHOR]

Numerical solution of conjugate heat transfer (CHT) problems in multi-region domains is a challenging issue in terms of efficiency and stability due to inherent coupling between governing equations in separate sub-domains. This paper aims at comparing two distinct strategies for coupling energy equations at interface of regions: partitioned and monolithic. While the former enforces strong coupling via iterative procedures, the latter suggests simultaneous solution of energy equation throughout all regions at once. More precisely, the primary objective of this study is to compare computational costs for three finite volume- based CHT solvers which share a same method for handling pressure-velocity coupling in the fluid, i.e. semi-implicit projection method, and are distinguished from the coupling strategy for energy equation at interface of interacting sub-domains. The first method applies simultaneous solution of energy equation across all regions while second and third ones use separate solver for each region with difference in handling iterative loops. The accuracy and convergence rate of three algorithms are assessed via transient solution of conjugate free convection in fluid and conduction in vertical wall. Finally, it is concluded that, in contrast to partitioned solver in OpenFOAM for CHT problems, employing a separate loop to enforce energy-energy coupling per iteration, can be much more beneficial in terms of computational costs. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the result of a numerical study on the natural convection in an inclined T shap cavity filled with Water-Cu nanofluid with the presence of a constant magnetic field was investigated. A heat source embedded on the bottom wall of enclosure, the upper wall is cold and the other walls are adiabatic. Discretization of the governing equations are achieved through a finite volume method and solved with SIMPLE algorithm. The Hartmann number has been varied from 0 to 80 and the cavity has been twisted under the angles between 0 to 90 degrees. The findings of study show that the effect magnetic field on the average Nusselt number is higher in high Reyleigh number. In Ra=105, the increase in nanofluid, to the Hartman number 20, contributes to decrease of the average number and in the Hartman number 40 and more, causes the average Nusselt number to increase. In Ra=106, the increase in nanofluid, to the Hartman number 20, contributes to increase of the average number and in the Hartman number 40 and more, causes the average Nusselt number to decrease. The results also indicate that, the maximum heat transfer, in Ra=105 and Ra=106 accurse at 67.5° angle. The minimum heat transfer, in Ra=105 and Ra=106 accurse at 0° and 22.5° angle respectively. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the navigation error effect for the optimal and constraint Trajectory de of the UAVs that are required to fly at low altitude over terrains has been discussed. Due to the increasing deviation problem of inertial navigation systems in terms of time, having a safe flight and collision avoidance with terrain at low altitude is the main point in the trajectory design of this type of the vehicles. On the other hand, some of these vehicles use Terrain Contour Matching (TERCOM) as a navigation aiding system. This system is more efficient in rough terrains, and providing the requirements of this system beside other constraints is a complex task. In this paper is tried to meet these constraints in the trajectory design process. For this purpose, an algorithm based on the layered network flow on the digital terrain maps used in a manner that has a high potential in adoption of various constraints and optimal trajectory is generated. Then, using equations of motion on a terrain digital data in 3D space with the dynamical constraints and different optimality criteria, a complete model of navigation error and also parameters affecting TERCOM has been developed to generate feasible path reducing terrain collision probability to zero. The resulting algorithm applying initial conditions and in the least time possible, produces the trajectory. Numerical results show validity of this issue. [ABSTRACT FROM AUTHOR]

An alarm threshold plays an important role in an industrial fault detection system and directly contributes the False Alarm Rate (FAR) and Missed Alarm Rate (MAR). A crucial consideration for designing a threshold is estimating the Probability Density Function (PDF) of both normal and abnormal state based on recorded samples. The existence of measurement error in samples will be the contributors to an inaccurate estimation, following that, the alarm threshold will also be inaccurate. Therefore, grasping and recognizing measurement errors is highly important; in this paper, this problem will be investigated. For this purpose, firstly, a mathematical closedform of statistical parameters will be estimated, and, then, based on error propagation rule, the computation error of estimated parameters will be explored. It is assumed the high limit and low limit values of the measurement error are known or computable. Secondly, an approach is introduced to design a varying alarm threshold adapting to the current value of measurement error computing based on change detection algorithm. The proposed method is confirmed via a Monte Carlo simulation and it is finally applied to an industrial benchmark, Gas Turbine V94.2, experiencing fouling fault. [ABSTRACT FROM AUTHOR]

Organizations should consider process improvement for promoting their performance. The main problem is the high volume of process dataset along with the wide variety of their features that leads to increase the complexity in process improvement methods. Previous methods cannot improve processes in a high volume of process dataset. Data mining approach can support improvement methods by identifying valuable hidden patterns in high volume of process dataset. In this paper a framework of using data mining techniques for extracting valuable hidden patterns in high volume of process dataset is developed for presenting the improvement suggestions. In order to evaluate the proposed framework, a real set of processes along with their features were gathered. After that, using classification, clustering, and features selection algorithms, valuable patterns in high volume of process dataset were identified. After evaluating these patterns, improvement suggestions were recommended by the identified patterns. The results showed that identified patterns can support processes improvement activities with recommending the improvement suggestions. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive algorithm for spectral unmixing in hyperspectral images (HSIs) is proposed. Many of the existing spectral unmixing algorithms, under the assumption of the linear model for the spectral mixing phenomenon, attempt to estimate the signatures of available materials in the observed HSI image. Then, based on the similarity between the estimated spectral signatures and the available spectral signatures in the spectral library, they identify the materials in the HSI and estimate their relative abundances. While the spectral library, as prior knowledge, has not been directly considered in the founding of existing algorithms, the proposed method is directly concentrated on the spectral signatures library. Assuming the linear spectral mixing model, the proposed method takes a set of spectral signatures which are probably present in the observed HSI. Then, based on a non-statistical approach, the normalized least mean square (NLMS) adaptive algorithm is engaged to estimate a weight vector for each spectral signature in the selected set in such a way that each weight vector and its corresponding spectral signature are non-orthogonal whereas the weight vector of each spectral signature is almost orthogonal to the other spectral signatures. A synthetic dataset of hyperspectral images is considered to evaluate the performance of the proposed method. The evaluation results show that the proposed method outperforms its counterparts in low signal to noise ratio (SNR). [ABSTRACT FROM AUTHOR]

Due to the increasing load growth and expansion and significant changes in structure of electricity distribution networks, the issues related to the optimization of these networks face many challenges. Optimal placement and capacity of Distribution substations and their feeder networks are among the important issues of optimization of distribution networks from technical and economic viewpoints. In this paper, considering two important factors including loss reduction and improvement of reliability in distribution networks, a new multi-objective function was presented for optimal placement of location and capacity of Distribution substations based on their service area. The proposed objective function includes all construction and operation costs of equipment, loss costs, and reliability index (Energy Not Supplied). The Artificial Bee Colony (ABC) algorithm was applied to optimize the proposed objective function with the purpose of determining the optimal placement and capacity of substations of two sample distribution networks. A comparison of results obtained from optimization of the proposed objective function by the Artificial Bee Colony (ABC) algorithm with results of other algorithms in two sample networks indicates the efficiency and effectiveness of the proposed algorithm and objective function. [ABSTRACT FROM AUTHOR]

Network on-chip is a communication subsystem within an integrated circuit that provides communication between processors in the on-chip system. There are several different ways to get from one node to another. Therefore, there must be a routing algorithm to find the route to the destination. This paper presents an algorithm based on the reduction of the passing path to reach a packet from origin to destination which is able to increase the reliability, reduce latency, power consumption and increase network efficiency on the chip. And this is when most of the fault-tolerant networks presented in this field increase parameters such as delay, power consumption and circuit complexity in order to achieve higher reliability. The proposed method improves network performance with minimal hardware changes and circuit complexity. The path passed by the packet is reduced to reach the destination, which means passing through fewer links and routers and less chance of encountering faulty links and routers and increasing network reliability. Also, passing fewer links and routers will reduce network latency and power consumption. [ABSTRACT FROM AUTHOR]

One of the important issues in coordination of protective relays is minimization of the time interval between the operation of main and backup overcurrent (OC) relays. Overcurrent relays coordination problem due to the large number of variables and the nature of the objective functions can be introduced as a complex optimization problem that necessitates the need for an efficient optimization method with appropriate accuracy and speed. Given that for the realization of protective purposes including increased relays operation speed, selectivity, support, reliability and stability, different objective functions can be described, therefore, providing a multi-objective mathematical problem in optimizing protective problems may be necessary. In this regard, this article due to the capabilities of the MOPSO, proposes a multi-objective optimization structure, in which the SQP by increasing the speed and not increasing the search space is added to MOPSO. In this paper, several objective functions are proposed based on protection goals and optimal adjustment points have been extracted using the proposed multi-objective MOPSO-SQP. The simulations have been implemented and carefully analyzed on several typical power systems. The simulation results confirm the efficiency of the proposed algorithm in ensuring the optimal coordination of protective overcurrent relays in power system. [ABSTRACT FROM AUTHOR]

Background and Aim: The substantial changes in the COVID-19 epidemic around the world and its impact have repeatedly changed the management, diagnostic, and even treatment protocols. The meaning of changing the concept of triage is adjusting the process of ordinary hospital triage to a gray and two-layer triage. In this study, the field evaluation of the strengths and weaknesses of the management and triage of COVID-19 patients and the design of a biological triage model in a selected military hospital has been conducted. Methods: This research was a descriptive study in terms of its purpose and method; it is a qualitative study. The Delphi method was used. The studied community, specialists, and elites related to the treatment of COVID-19 included emergency, infectious, internal medicine, lung, and anesthesia specialists, as well as experts and senior hospital managers in the scientific committee of the university. Sampling was done using a targeted and available method. Focused group discussions, expert panels, and qualitative interviews people used to obtain were optional. Delphi results were analyzed in three rounds with content analysis to identify the main topics and use collective agreement indicators. Results: By holding a panel of experts, the results of research findings in qualitative panels were presented in three categories. First, the weaknesses and strengths of the existing and notified protocols of the Ministry of Health were identified. Then, the experts in the scientific committee of Corona presented the implementation solutions to improve and enhance the treatment protocol in the hospital. Finally, the implementation algorithm agreed upon by most experts was presented. The maximum isolation of patients from the beginning of entering the gray triage unit, the use of the maximum emergency capacity in a pandemic, and the use of home care methods to accelerate the discharge process of recovered patients were among the most important features of the proposed algorithm. Conclusion: Proper and continued training based on the latest findings, complete isolation of the triage area until hospitalization of COVID-19 patients, use of resistant structures, development of support beds in the emergency room, and conducting preparation maneuvers in biological pandemics based on an algorithm are of particular importance. [ABSTRACT FROM AUTHOR]

In this paper spacecraft formation flying control in leader -- follower structure is investigated. A non-linear PID controller is designed based on predictive control. The formation relative equation is obtained from nonlinear Hill's equation. The control input is achieved using predictive control algorithm. In controller design, disturbances have been replaced by disturbance observer. Equations are rewritten in the form of PID gains. Stability of the closed-loop system is proven by closed-loop error dynamics. Nonlinear PID controller performance in the pursuit of desired arrangement has been tested in simulations. Also, effects of various factors on the quality of response are studied. It is proven that predictive horizon time and disturbance observer gains have the most effect on system response. It is shown that if predictive time increases the settling time increases and control effort decreases if disturbance observer gain increases from a limit, it has no effect on settling time but control effort increases. The simulation shows the ability of this nonlinear control method in tracking. [ABSTRACT FROM AUTHOR]

Optimal trajectory planning is an important task which is required in most of guidance missions. This paper introduces a new method that utilizes the most important characteristics of global optimization methods along with a new gradient-based method in a two layered scheme for the trajectory planning. In the first layer to construct a convenient shooting method based algorithm, some of the most important global methods of optimization are used in an information transform structure. Exchanging the information between selected algorithms helps for increasing the efficiency of problem solving. To do this, a comprehensive model for parameterization of the control history is introduced which allows the method to search for the best profile in a variety of different profiles. Results of this layer are transformed to the second layer that uses one of direct methods of solving the optimal control problems. This gradient based method named Radau pseudospectral method using of the results of global methods, completes the optimization process. Finally, developed algorithm is used to find the optimal trajectory of a reentry capsule and effects of the path constraint values on the total heat absorbed is investigated. [ABSTRACT FROM AUTHOR]

In this paper, trajectory tracking control of a wheeled mobile robot is analyzed. Wheeled mobile robot is a nonlinear system. This system including three generalized coordinates (x, y, ∅), and a nonholonomic constraint. First, system kinematic and dynamic equations are obtained. A non-model-based control algorithm using PD-action filtered errors has been used in order to control the wheeled mobile robot. Non-model-based controllers are always more appropriate than model-based algorithms due to independency from dynamic models, lower computational costs and also robustness to uncertainties. Asymptotic stability of the closed loop system for trajectory tracking control of wheeled mobile robot has been investigated using appropriate Lyapunov function and also Barbalat's lemma method. Finally, in order to show the effectiveness of the proposed approach simulation and experimental results have been presented. Obtained results show that without requiring a priori knowledge of plant dynamics, and with reduced computational burden, the tracking performance of the presented algorithm is quite satisfactory. Therefore, the proposed control algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to examine the impact of economic growth on coefficient of cardinal welfare in Iran's economy. Hence, we employ the Bayesian approach and the estimation of forward and backward density functions in order to measure the effect of economic growth spillovers on the social welfare. The paper has applied the Gibbs sampling algorithm which is a rigorous tool for forward simulation so as the results rising from this simulation indicate that there is a positive relationship between economic growth and welfare variables in Iran. It means that the flow of economic growth has had a positive impact on the rise of welfare in the country, such that the average of Bayesian coefficient is near to 0.17 per cent for the change of welfare during the period 19852011. Accordingly, it is recommended that: 1-the policy makers should follow the growth-based strategies 2-the respective officials should identify the effective variables on growth in order to increase growth rate for the economy and eventually, 3-design more efficient institutions for the poor so as they enjoy greater gaining from the growth. [ABSTRACT FROM AUTHOR]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

Nowadays, determining the contribution of the individual consumers to the harmonic contamination is required for improving power quality. Due to changes occurred in lines, transformers, loads and generators over time, network operating conditions and impedance of different sectors are frequently varied resulting in the change in the contribution of harmonic sources. In this paper, an intelligent-based three-step method is developed to continuously determine the contribution of each harmonic source to the harmonic voltage of different buses without any access to the voltage phase data at different buses in the network. In this algorithm, firstly, the K-means clustering method is used for pre-processing of the measured data in order to reduce the background harmonic destructive effect on accuracy of the harmonic contribution determination. After calculating the harmonic contribution, the K-nearest neighbor method is used to generalize the results, and subsequently to create a continuous harmonic contribution matrix (HCM). Finally, the method is applied to a standard power network-based calculation example. The results demonstrate the capability of the proposed algorithm to evaluate the effects of harmonic sources in power networks. [ABSTRACT FROM AUTHOR]

Background and Objectives Corona is caused by a viral infection called acute respiratory syndrome corona virus 2 which spreads rapidly, causing irreparable damage and death. In order to predict and develop treatment algorithms, the study of biomarkers is important in determining the severity of the disease. Therefore, we investigated the correlation of Interleukin 6 IL-6, Lactate dehydrogenase LDH, C reactive protein CRP and disease severity in COVID-19 patients. Materials and Methods In this descriptive and analytical cross-sectional study, the information of 365 coronavirus hospitalized patients randomly selected in Sina Hamadan hospital during 2019 was entered into SPSS 26. The severity of the disease was also measured according to the defined reference and clinical symptoms and classified into two levels. The data were analyzed with gamma correlation instability test, Somers' D at significance level of p value <0.05. Results It was found that IL-6 with a correlation coefficient of 0.506, LDH with a correlation coefficient of 0.291 and CRP with a correlation coefficient of 0.23, all at the level of 0.01 (p value = 0.01), have a positive effect on the severity of Corona disease and the amount of this positive effect is based on Somers' effect coefficient d 0.839, 0.458 and 0.358, respectively. Conclusions Patients with severe COVID-19 have significantly higher serum levels of CRP, LDH and IL-6. Baseline levels of IL-6, CRP, and LDH were closely related to the severity of COVID-19 disease. These laboratory markers can be used as clinical indicators of disease exacerbation and prognosis of COVID-19. [ABSTRACT FROM AUTHOR]

Economic vulnerability, is a country's structural feature, making it exposed by out-of-control economic exogenous variables. On the other hand, resilience stems from how macroeconomic policies are adopted. The purpose of this article is to analyze the relationship between sustainable development, vulnerability and resilience in the Iran's economy in the time period of 1391-1398.1 this way, sustainable development consists of 5 Dimensions: Economic, Social, Environmental, Governance, Peripheral and also includes 44 variables as a sub-indicator of each development's dimension. It uses graph theory and Tarjan Algorithm for study the network structure of relationships between variables. The Algorithm identifies34 variables as the fundamental variables affecting economic resilience and vulnerability and introduces economic and governance Dimensions as control (major) Dimensions; Social, Environmental and Peripheral Dimensions as Contingent (minor) Dimensions. The results show that weakness of Iran's economy in this period was due to vulnerability and resilience in governance and economy Dimensions. Among the contingent Dimensions, the Peripheral dimension has the worst situation in terms of vulnerability and resilience. The innovation of this research is in studying the Dimensions of sustainable development and its effect on vulnerability and resilience, moreover graph theory has been used in order to study the influential variables for the first time in Iran. In this algorithm, without limiting the number of input variables, the phenomenon can be analyzed comprehensively. furthermore, a holistic table has been prepared, by focusing on the variables affecting the state of vulnerability and resilience in the theoretical foundations of the subject, that has not been shown in any other studies. [ABSTRACT FROM AUTHOR]

The accurate awareness of the users as the most vital need for media has always been a concern for communication researchers. Given the emergence of new media, traditional techniques no longer have the ability to meet this need, and the use of modern methods is essential in understanding users' behavior. The present study used the Social Network Analysis (SNA) technique to investigate the user's behavior of VOD platforms. In this research, the site of CinemaMarket selected as the sample, and a dataset of its user’s purchases formed. Using Gephi software, the dataset was visualized and the members were clustered based on logical algorithms. By analyzing three detected communities, two different strategies were founded for the development of the users of this platform. Based on these strategies, platform managers can adapt their media policies to load new content on the platform and offer targeted content; as a result, they can meet their user's needs. [ABSTRACT FROM AUTHOR]

Role and Reference Grammar (RRG) provides a workable mechanism, namely “interclausal relations hierarchy”, for the analysis of complex constructions. It is used as a general rule for interlinguistic studies. This hierarchy consists of "semantic continuum" and "syntactic continuum" and is based on iconicity, which means that constructions with high semantic correlations are expressed in the form of stronger syntactic relations and vice versa. In this hierarchy, causative constructions are also included, which were the focus of the present study. In this research, causative constructions were divided into 3 types: causative [type 1] or direct causatives, causative [type 2] or indirect causatives, and jussives. We first examined their syntactic semantic forms and interactions in causative constructions. The juncture-nexus relations and semantic hierarchy were discussed in the syntactic and semantic dimensions, respectively. The findings indicated that the direct causatives were in nuclear co-subordination relations, while the indirect causatives and jussives were in core co-subordination relations. We also proposed a constructional scheme for direct causation in Persian. The interaction of the two levels of syntax and semantics in causative constructions was also explained in the form of linking algorithms. Finally, it was found that Persian language acts in a way that it confirms the "interclausal relation hierarchy" and thus, its explanatory adequacy could be corroborated. [ABSTRACT FROM AUTHOR]

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

Background and Aim Breast cancer is the abnormal cell growth in the breast. In both benign and malignant masses, there is rapid and high cell growth. Nowadays, due to the development of technologies, the diagnosis of diseases has become non-invasive and physicians attempts to diagnose the disease without surgery and based on internal organ images. Methods & Materials In this study, by using images prepared from the Digital Database for Screening Mammography (DDSM), a new method is proposed for detecting cancerous masses in the mammographic images using geometric features extraction and optimization of Support Vector Machine (SVM) parameters to classify breast cancer masses automatically. First, images were pre-processed and then boundaries were determined using threshold method. Next, morphological operators were used to improve these boundaries and the segmentation of images was carried out to classify cancerous masses. Finally, by using the SVM parameter optimization method, Grasshopper Optimization Algorithm (GOA), and 4-fold crossvalidation method, data were classified into two groups of benign and malignant (cancer) masses. Ethical Considerations Images from DDSM database were used in this research, all images are open access in this database. Results The accuracy, sensitivity and specificity values for applying the Radial Basis Function (RBF) kernel in SVM classifier (before optimization process) were obtained 97%, 100% and 96, respectively. After optimization of SVM parameters by the GOA, it was reported 100% for all accuracy, sensitivity and specificity indices for applying linear kernel function, indicating the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for applying all three SVM kernel functions after optimization were 95.83, 100 and 94.81%, respectively. Conclusion The extracted geometrical features from breast cancer masses are highly efficient for model training and the diagnosis of breast cancer. The GOA could improve the overall accuracy of the proposed method by optimizing the SVM parameters. The results showed the higher performance of the proposed method compared to other methods. [ABSTRACT FROM AUTHOR]

Background and Aim Chronic Lymphocytic Leukemia (CLL) is the most commonly occurring leukemia in adults, accounting for about 30-25% of total leukemia. One of the important etiological causes of this leukemia is the disruption of the Nuclear Factor Kappa B (NF-kB) signaling pathway. The two proteins of Apoptosis-Inducing Ligand (APRIL) and B-Cell Activating Factor (BAFF) play a role in the pathogenesis of this leukemia by affecting the NF-kB signaling pathway. In this study, due to the effect of miRNAs in regulating many cellular processes, the prediction of the prominent miRNAs targeting APRIL and BAFF transcripts in B-cell CLL patients was evaluated using specific and different bioinformatics programs. Methods & Materials Afterwards retrieving the sequences of APRIL and BAFF proteins from the NCBI website, by using several programs including miRanda, TargetScan, miRWalk, DIANA and miRDB with different algorithms, the prediction of miRNAs targeting these genes was investigated. Ethical Considerations This study was approved by the Research Ethics Committee of Arak University of Medical Sciences. Results Based on the scoring system of bioinformatics programs, “hsa-miR-145-5p” and “hsa-miR-185-5p” were identified as miRNAs targeting APRIL gene, while “hsa-miR-424” and “hsa-miR-497”were miRNAs targeting BAFF gene. They were suggested for the practical studies in future. Conclusion Based on the important role of APRIL and BAFF genes in the normal process of cell death and B-cell evolution, it seems that the mi-RNAs predicted by bioinformatics programs using different algorithms can be used as a diagnostic molecular biomarker to identify B-cell CLL patients. [ABSTRACT FROM AUTHOR]

The need for complex surfaces in CAD motivates researchers for methods which can produce smooth and visually pleasing surfaces. In this research, a new method is presented for creating compatible cross-sectional curves for surface fitting to certain sections or lofting. In this method, the distribution of sections' data points along with basis knot vectors are improved in order to reach a desired smooth surface. In compatibility process, the section curves' degrees and their knot vectors must be set equal before implementing lofting process. Based on proposed algorithm, in this research, the constructed smooth and faired surfaces can be used in many engineering applications such as reverse engineering, biomedical engineering, quality control, etc. The main focus of the method is improvement of data points' distributions and their assigned parameters in a way that by a few iterations, data points' distribution are improved in order to reach a common knot vector for all cross-sectional curves. The method is implemented on some benchmarking examples and its efficiency are confirmed. In addition, the amount of final data points' deviation from the initial section curve is analyzed using the vigorous Hausdorff method. It is worth mentioning that the quality of obtained final surface is visually pleasing. In order to quantitatively confirm that the proposed method will result in smooth and fair surfaces, MVS is used. Finally the application of the method in modeling the rootjoint zone of a wind turbine blade is presented. [ABSTRACT FROM AUTHOR]

Background: Liver cirrhosis was one of the most important liver diseases. Other chronic liver diseases could be lead to liver cirrhosis. Liver cirrhosis could be lead one kind of liver cancers named hepatocellular carcinoma. Cirrhosis in the early stages just by laboratory and imaging testes could be diagnosed. In this study cirrhotic patients were classified based on laboratory symptoms. For this purpose data mining approach has been used in this research. Data mining was an interdisciplinary science that discovers the hidden knowledge in the data. Materials and Methods: We used K-Means algorithm to categorize the statues of cirrhotic patients. In order to determine the quality of clustering results and to find the best number of clusters, we have used silhouette indices. Results: Our data consists of 410 records which have been collected from Dr. Shariati hospital The number of features in this study are .1 items and sampling were divided into two main groups. Conclusion: At first, we have done clustering based on 21 attributes and the average silhouette was 41 percent. We improved the model, in order to reach a reasonable structure. Finally, based on 7 attributes, a reasonable clustering model was derived. The new model provides 64 percent average silhouette, and based on patients' status, patients are divided into 2 main categories. The risk of HCC in the first cluster is 23 percent and in the second cluster is 14 percent. [ABSTRACT FROM AUTHOR]

Background and Objective: Meditation is commonly perceived as an alternative medicine management tool for psychological diseases such as depression and anxiety disorders. To our knowledge, there is no published study providing an index for estimating meditation's depth from biological signals. Estimating the depth of meditation can be useful in controlling its different levels, and it can be used as a biofeedback technique to help a person achieve the desired state of meditation. In this study, an index for meditation depth is offered using the features of electroencephalogram and heart rate signals. Material and Methods: For this purpose, EEG signals in Fz, Cz, and Pz channels, and the heart rate time series of 25 healthy women were collected both before and during the meditations. The algorithm is suggested based on the rational alpha power of EEG signals and the time domain feature of the heart rate to estimate the depth of meditation. Results: The analysis of biological signals using this method suggests that 22 of the 25 participants have experienced the deepest meditation state. Interestingly, 2 of the beginners as well as one expert-mediators could not reach the deepest state by following up the master mediator. These results were in line with the evaluation of the questionnaire. Conclusions: The suggested algorithm has some practical characteristics including: the option of being calibrated for each subject; not requiring high-volume calculations; and it does not take much time. [ABSTRACT FROM AUTHOR]