فهرست مطالب sajad yousefi

In addition to all the advantages of renewable energies, their use in the form of wind turbines and solar cells in power grids to provide the required power can be considered a negative factor from the point of view of stability in power systems. The main aim of this paper is design of wide-area damping controller (WADC) in converter of battery energy storage system connected to renewable energy sources including photovoltaic and permanent magnet synchronous generator (PMSG) in large-scale power system. The proposed approach is based on Free-Weighting Matrices(FWM). At first, by using optimal control theory based on delay-dependent feedback, a set of constraints related to linear matrix inequality(LMI) are formulated. Then, for obtaining parameters of optimal control that can tolerate maximum time delay, the FWM approach is employed to solve time dependent on the time delay. In this regard, an iterative algorithm based on conical complement linearization has been also presented to search for parameters of optimal control. Finally, non-linear simulation is implemented on 5-area 16-machine power system in MATLAB. The simulation results clearly demonstrate the desirability of the proposed controller under existing uncertainties.

Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning (ML) techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using ML models performed on multidimensional dataset, this article aims to find the most efficient and accurate ML models for tumor classification prediction.

Several supervised ML algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this research, ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.

As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. Therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.

The technique of ML can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of ML to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

Heart disease is, for the most part, alluding to conditions that include limited or blocked veins that can prompt a heart attack, chest torment or stroke. Earlier identification of heart disease may reduce the death rate. The cost of medical diagnosis makes it perverse to cure it for the large amount of people early. Using machine learning models performed on dataset. This article aims to find the most efficient and accurate machine learning models for disease prediction.

Several supervised machine learning algorithms were utilized to diagnosis and prediction of heart disease such as logistic regression, decision tree, random forest and KNN. The algorithms are applied to a dataset taken from the Kaggle site including 70000 samples. In algorithms, methods such as the importance of features, hold out validation, 10-fold cross-validation, stratified 10-fold cross-validation, leave one out cross-validation are the result of effective performance and increase accuracy. In addition, feature importance scores was estimated for each feature in some algorithms. These features were ranked based on feature importance score. All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.

The algorithms performance is compared to each other so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, random forest algorithm with F1 score 92%, accuracy 92% and AUC ROC 95%, has better performance than other algorithms.

The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the diagnosis and prediction of heart disease is compared to determine the most appropriate classifier.

Most synthetic dyes used in the textile industry have environmental hazards. Therefore, the interest in using biodegradable natural dyes prepared from plants is the main goal of this article.

 Raw wool fibers were dyed using natural dye and the amount of dye extraction was investigated. Therefore, different percentages of dyes with three different pH values ​​were added to dye bath. The extraction parameter was measured using a UV-visible method. To investigate the effect of changes in dye concentration and pH on extraction, a new neural network model was developed. By development of new neural network model, the concentration of dye spray with three different pH values was considered as the input of the network and the extraction value of wool fibers was considered as the output of the model. In neural network training, 80% of the data were considered for training and the remaining 20% for testing.

Fidings: 

In this paper, the experimental results are based on the values obtained from the UV-visible method and the modeling results are a prediction values of the developed neural network model as the extraction parameter.

Based on the experimental and modeling results, with increasing the dye concentration, the extraction value of wool fibers increases and changing the pH of the dye bath has practically no effect on the salinization of wool fibers. Finally, the neural network prediction model can be used as an alternative to repeated experiments with toxic substances to study the dyeing behavior of fibers and thus save human lives from death.

Superimposition of left and right structures is an issue in lateral cephalometric radiographs in dentistry, making it challenging to measure structures, including the gonial angle. Panoramic radiography contains fewer superimposing structures; therefore, it could measure the gonial angle. This study aimed to compare the gonial angle in panoramic and lateral cephalometric radiographs in different skeletal patterns.

In this cross-sectional study, 240 panoramic and lateral cephalometric radiographs were collected from the archives of Sari Dental School. The gonial angle of each radiograph was traced and measured with a conveyor. Data analysis was performed using ANOVA and paired t-test, and P-value <0.05 were considered significant.

The paired t-test of the gonial angle did not show a statistically significant difference between different skeletal groups when measured using panoramic and lateral cephalometric radiographs. According to the results, the p-value of the difference between the right and left gonial angles in skeletal class III patients (p=0.019) was obtained larger than the test level (p=0.05).  Therefore, it can be concluded that there is a statistically significant difference between the size of the left and right gonial angles in panoramic radiographs of skeletal class III patients. 

Panoramic radiography can be an accurate alternative to lateral cephalometric radiographs for measuring the gonial angle in different skeletal patterns due to the lower radiation it exposes to the patient.

Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually . Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning m odels for disease prediction.

Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.

The algorithm perf ormance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.

Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying alg orithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classifi cation . As a result of evaluation, better performance was observed in both Decision Tree and Logistic Re gression models.

Today, tourism activity as one of the fastest growing economic sectors has doubled in importance and in many cities has become one of the pillars and drivers of socio-economic development as well as a way to achieve sustainable employment and improve the quality of urban life. . One of the 14 goals of the World Tourism Organization, which was presented at the Summit on Sustainable Development in Rio 20, is to focus on tourism as an activity that can help the economies of developing countries and their cities. The World Tourism Organization reports that in 2017, the tourism sector accounted for 10.4% of world GDP and accounted for 313 million jobs or 9.9% of total employment. Shabab city is one of the cities of Chardavol city in Ilam province of Iran. This city is located in Shabab section and according to the general population and housing census in 2016, its population is 3063 people (in 852 households). This city is one of the cities between Arbaeen pilgrims and it can be said that a very high percentage of Arbaeen tourists pass through this city. And the commercial sector, restaurants and transportation are strongly affected, and this tourism population has a very high potential for the development of the city. The purpose of this study is to investigate the role of Atbat-e-Aliat tourism on the development of intermediate cities. In terms of the nature of the data, it is quantitative, in terms of the research problem, it is descriptive-analytical. The result of this research also shows the impact of religious tourism on the physical dimension of this city.

The elongation of styloid process can result in Eagle syndrome. The purpose of this study was to investigate the prevalence and pattern of ossification of elongated styloid process (ESP) in digital panoramic radiography and their association with gender and age.

In this retrospective study, 400 digital panoramic radiographs of patients attending Oral and Maxillofacial Radiology Department in Mazandaran University of Medical Sciences were examined. The length of the styloid process was measured and classified into elongated, pseudoarticular, and segmental types. Length equal to or greater than 30 mm was considered as an elongated styloid process. Data were analyzed in SPSS using Chi-square, t-test, and correlation test.

Radiographs of 400 patients including 167 (42%) males and 233 (58%) females aged 11-79 (38.13 ± 48.43) years were investigated. ESP was observed in 118 (29.5%) patients. The prevalence of ESP was significantly higher in males (50.8%) than females (49.1%) (P=0.016). Also, the prevalence of ESP was significantly higher in older adults (P<0.001). Among the types of ESP, type I was found to be more prevalent.

Elongated styloid process was relatively common in the population studied and was significantly correlated with age and sex (more prevalent in males).

Temporomandibular joint (TMJ) disorders are abnormalities that interfere with the shape or normal functioning of the joint, characterized by a group of clinical symptoms including pain, joint sounds, and limitation or deviation when opening the mouth. Considering the effect of skeletal relationships of the jaw on the shape, size, and morphologic variations of the condyle and its changes in TMJ, current study aimed at investigating radiographic changes in TMJ in patients with skeletal class II and III in Sari, Iran 2015-2018.

In a retrospective cross-sectional study, 249 angle class II and 66 class III subjects were studied. After determining the skeletal relationship class using cephalometric radiographs, TMJ bone changes were investigated in panoramic images. Data analysis was done applying Chi square, T test, and logistic regression in SPSS V16.

There was a significant difference in flattening distribution between class II and III subjects in patients aged 26-30 years old (P=0.036) and the prevalence of this disorder was higher in patients with skeletal class III. There were no significant differences between patients with skeletal class II and III in the prevalence of flattening, erosion, hypoplasia, and hyperplasia. TMJ disorders were significantly more frequent in females (P=0.035).
Conclusion In this study, the most and least frequent disorders were erosion after flattening and hyperplasia, respectively.

According to high prevalence of depression in armed forces personnel and lack of researchs on their mood status, study of mental health in soldiers and military personnels is very important. This study was conducted to compare the frequency and severity of depression in Bandar Lengeh air defense forces.
Methods and Materials: A population of military forces of Bandar-e Lengeh air defense group were included in the study, including personnel, military students and soldiers. Beck depression inventory was used in this study. The severity of depression was compared among the personnel group, students and soldiers. In addition in the personnel group, the relation between severity of depression and military rank, marital states, years of service and age was evaluated.
The mean and standard deviation of depression score were 12.19 and 11.25 respectively. In total, 53.7% were normal, 16.4% had mild depression, 22.4% had moderate depression and 7.5% had severe depression. The percentage of people with severe and moderate depression in the conscript group was higher than the personnel, and the percentage of healthy and mild depressed people in the personnel group was more than the conscripts. Mean intensity of depression in the conscript group was higher than personnel group, but there was no significant difference between the student and the soldier groups. There was no significant relationship between the severity of depression, military rank, and years of service in the personnel group.
Discussion and Depression in the conscripted forces is more common and more severe than permanent military personnel. Therefore, more considerations should be given to the mood of the forces and other aspects of mental health dimensions should be considered in personnel.

Leadership as one of the important and influencing organizations factors has been studied extensive. The extent of organization success in achieving pre-determined goals, the amount of employee efforts for organization success and the influence of leader on staffs depend on leader behaviors and style. Theorists have stated numerous leadership styles that have been investigated in this study. Also Full Range Leadership Model that involve Transformational and Transactional leadership styles and their relative behaviors and the influence of these styles on three leadership outcomes such as the amount of employee effort, their perception about leader effectiveness and employee satisfaction rate has been studied. The results of this study showed that Transformational and Transactional leadership behaviors that followed by exception based management (active) are most influential leadership behaviors. Reverse passive/avoidant and Laissez-faire leadership behaviors are least influential leadership behaviors. Also, a leader can use both Transformational and Transactional leaderships, especially conditional reward that is one of the Transactional leadership behaviors. The most influencing leaders are those who regularly use both Transformational leadership (Individualized Consideration, Inspiration, Intellectual, Stimulation, and Charisma) and conditional reward of Transactional leaderships. In this case, Transformational leadership is complement for Transactional leadership.