A Validity and Reliability Assessment of the Persian Version of Maryland Physics Expectations Survey: Using a Polytomous Item Response Theory Model

This study was conducted to assess the validity and reliability of the Maryland Physics Expectations QuestionnaireSurvery, which is one of the most widely used instruments forto measuringe attitudes and expectations in the physics course. This is an applied research and its statistical population is consists of Iranian students who take had taken physics lessons in high school. The sample was included 423 high school students (197 girls males and 226 boysfemales) in from both the Experimentalthe science Sciences and mMathematics-Physicsal course fields of study in the academic yearduring 1399-13992019-20 . A cConfirmatory factor analysis was conducted to examine the factor structure, and while the IRT theory’s graded-response model in IRT theory was used to analyze the items of this questionnaire. The LISREL and IRTPro softwares  were used to analyze the data. The content validity of the scale was 0.93 and the reliability coefficient of each factor of the questionnaire was between 0.74 and 0.89. The fFactor analysis confirmed the structures predicted in the original questionnaire., In addition, and the questionnaire items, while having a suitable fit with the polytomous model of the graded response, the questionnaire items had the desired discriminant parameters and thresholds. The researcher recommends the use of the Persian version of this questionnaire to explore expectations and attitudes about physics

Land Change Detection and Identification of Effective Factors on Forest Land Use Changes: Application of Land Change Modeler and Multiple Linear Regression

Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year that it makes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, the multiple regression was used from 1995 to 2015 in the Mazandaran forests. A multiple regression perfectly enables to explain the relationship between reducing the forest area (dependent variable) and its influencing factors (independent variables). In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified to investigate the changes in forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable to estimating the reduction of forest area. The results of this study can be used as an efficient tool for managing and improving forests regarding to physiographical and human characteristics

Supervised saliency map driven segmentation of lesions in dermoscopic images

Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners, and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). A DRFI method incorporates multilevel segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and postprocessing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks

Establishment of a New Urban Solid Waste Management Programs in Mazandaran Province, North of Iran

This study reports residents’ preferences to establish a new urban solid waste management programs results from a double-bounded dichotomous choice contingent valuation method and choice experiment in Mazandaran province, north of Iran. In order to analysis the residents’ preferences, a dichotomous hypothetical market and a choice sets with different attributes and options were used For estimation of two mentioned methods, the normal logit and conditional logit were applied. In addition, an empirical comparison of the welfare measures derived from the doublebounded DC-CVM and CE is conducted. The main results show that there is no significant difference between the values derived from the two methods. The mean of WTP to establish a new solid waste management programs in CV and CE were estimated 2.45 and 2.61 US$, respectively, per a person per a month. Also the estimated marginal WTP for all attributes in CE was 8.1 US$ per a month. The results suggest that both double-bounded DC-CVM and CE can be successfully stablished for improvement environmental level quality in Mazandaran province. This paper could provide the basis for further development of other new programs on sustainable urban management of solid waste in Mazandaran province.Keywords: Dichotomous choice, Willingness to pay, Solid waste management, Mazandaran province, Ira

Land change detection and effective factors on forest land use changes: application of land change modeler and multiple linear regression

Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year and this causes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, multiple regression was used from 1995 to 2015 in Mazandaran forests. A Multiple regressions can link the decline in forest cover (dependent variable) and its effective factors (independent variable) are well explained. In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified in order to investigate the changes in the forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable of estimating the reduction of forest area. The results of this study can be used as an efficient tool to manage and improve forests regarding physiographical and human characteristics.Keywords: Land change Modeler, Multiple linear regression, remote sensing, Mazandaran forest

Segmentation of Lesions in Dermoscopy Images Using Saliency Map And Contour Propagation

Melanoma is one of the most dangerous types of skin cancer and causes thousands of deaths worldwide each year. Recently dermoscopic imaging systems have been widely used as a diagnostic tool for melanoma detection. The first step in the automatic analysis of dermoscopy images is the lesion segmentation. In this article, a novel method for skin lesion segmentation that could be applied to a variety of images with different properties and deficiencies is proposed. After a multi-step preprocessing phase (hair removal and illumination correction), a supervised saliency map construction method is used to obtain an initial guess of lesion location. The construction of the saliency map is based on a random forest regressor that takes a vector of regional image features and return a saliency score based on them. This regressor is trained in a multi-level manner based on 2000 training data provided in ISIC2017 melanoma recognition challenge. In addition to obtaining an initial contour of lesion, the output saliency map can be used as a speed function alongside with image gradient to derive the initial contour toward the lesion boundary using a propagation model. The proposed algorithm has been tested on the ISIC2017 training, validation and test datasets, and gained high values for evaluation metrics