While presenting a definition for EFFECTIVE university textbooks, this article introduces a selection of their most important FEATURES. There are 100 FEATURES in 10 categories, including: specialization and topic purposefulness, theoretical strength and functional argumentation, documentary quality and interdisciplinary nature, inclusiveness and updatability, qualifications and résumés of authors, writing and literary decency, coherent content and structure, compatibility with learning theories, interactive and motivating quality, as well as physical and visible FEATURES. Therefore, an EFFECTIVE book is a book that transmits the message and goals of its writer (s), has positive impact on readers so that it can improve their ideas. Thus, the presence of a proper portion of these FEATURES in a book leads to the fulfillment of its goals. In other words, an EFFECTIVE textbook is based on strong theoretical principles, has functional outcomes, is inclusive and up-to-date, reflects contemporary developments, properly communicates with readers and improves their motivation to follow the subjects. Both the scientific content, and the decent prose and structure of the book have a role in this communicative and motivating process.

Meet human social needs and supply needed opportunities in the case of his social experience, needs a space and physical setting. Public space considers huge capacity in order to meet this aspect of human life in civil societies. So, Because of this important aspect in one hand and to meet human social needs in such space on the other hand, this paper analyze important reasons and principles in order to keep and maintain public life and sociability in public spaces.

The selection of adequate FEATURES for studying and forecasting of each atmospheric quantity regarding the meteorological phenomena (for example, a tropical cyclone) is one of the basic challenges in system recognition and modeling concepts.This study uses various methods of feature extraction including sequential forward selection, sequential backward selection, mutual information, principal components analysis (PCA) and principal factor analysis (PFA) to extract quantities considering the wind speed at 10 meters above surface for a tropical cyclone activity period. This work studies 45 various quantities of a Guno tropical cyclone activity domain which occurred in early June 2007 over the Arabian Sea, the Gulf of Fars, and the Oman Sea regions which caused significant damage to the region due to storm surges. These quantities were initially selected from 286 quantities of the atmosphere which were determined in the global network of 1 by 1 degree under the standard conditions including standard pressure levels (1000 mb to 10 mb), surface land temperature (SLT), and surface sea temperature (SST). Due to the fact that the activity of the interesting phenomena (i.e. Guno tropical storm) concentrates at the low and medial troposphere, it is possible to exclude quantities of the upper atmosphere.As a rule, the employment of neural network and fuzzy logic methods requires that the data be tested and validated. As a result, the Yemyin tropical cyclone data was considered as testing data (it happened exactly 8 days after the Guno storm in the west of India, and, after traveling over India, then entered into the southeast of Iran and, subsequently, into the northwest of Pakistan). The Nargis tropical cyclone data which occurred in May 2008 at the Bay of Bangal was considered as validation data because there is currently no valid and acceptable data regarding the history of the old Guno tropical storm which occurred in this region approximately 35 years ago.The FEATURES extracted from the three first methods were tested using an adaptive network-based fuzzy inference system (ANFIS) approach. Of course, 44 FEATURES were considered as inputs and a speed of 10 meters of surface was considered as output. The Guno storm data was considered for training the network but the Yemyin storm data was used for testing the network and the Nargis storm data was used for the purpose of network validation.Due to the fact that the KMO parameter for all three storms was greater than 0.80, the PCA method was used with high confidence for data mining. The results show that the first 18 new components of the storms Guno and Yemyin are greater than the others and they include more than 95% of existing information regarding 45 variables; consequently, the remaining components can be eliminated. However, this is true for the Nargis storm with the first 20 new components.The results show that the computation time of the sequential backward selection method is very high in comparison with the sequential forward selection method. The results show that the corresponding accuracy for sequential backward selection is also high.The results of an ANFIS test for a different epoch show that the FEATURES extracted from the forward selection method have minimum errors within three epochs.The study of coefficient components shows the basic part of the second component is related to wind speeds of 10 meters height over the surface and wind speeds in the layer 30 mb above the surface. Therefore, these components can be utilized as output for neural networks and fuzzy systems such as the ones used in the ANFIS method.

In arid and semi-arid areas, water shortage leads people to the creation of traditional water management appropriate to local characteristics. Iran is one of the oldest civilizations of the world that has a unique water supply system according to social, cultural and climate condition. Indigenous knowledge-based management systems were replaced with modern management systems, and due to non-compliance modern technologies with social and environmental conditions of region, many problems have been created. The purpose of this paper was to combine traditional and modern governance rules for development of groundwater governance framework in order to achieve sustainable development. In fact, the use of culture and indigenous knowledge in the field of water resources management and retrieval new issues from new theories of governance can be the missing piece of groundwater management. In this research, the comparison of scientific documents and field observation were used to extract indigenous knowledge of sustainable groundwater governance from aqueducts management. For this purpose, at first we introduced modern system in water governance and then compared common factors with traditional governance to provide a groundwater governance framework adapted to arid and semi-arid regions. Finally, the gaps of traditional governance can be covered by combining with the principles of EFFECTIVE governance and then a management system adapted to climate condition. Dealing EFFECTIVEly with the challenges of the present era and providing appropriate development patterns will be achieved by combining indigenous knowledge with modern management.

The yield of the wheat crop is affected by the climate and soil parameters such as moisture and nutrients, plant pests and diseases. The main objective of this research is the feature level fusion of multiple EFFECTIVE criteria on the wheat yields using linear and machine learning regression models. The effects of vegetation condition, moisture, nutrients and pests on wheat yield are represented by spectral indices those are extracted from remotely sensed data. Optimum spectral indices are selected as the input FEATURES to each of the multiple linear and machine learning regression models such as decision tree, support vector regression and generalized regression neural network. The evaluation of the experimental results in eight wheat fields indicates that the wheat yield prediction based on spectral FEATURES fusion show the mean improvement of 0. 81 in RMSE comparing with considering only one vegetation index in all regression models. Moreover, all investigated machine learning regression models have about 0. 03 more performance than the multiple linear regression model as indicated by R2 coefficient. The generalized regression neural network model with the least RMSE error 0. 0063 has the best results compared with other machine learning regression models and MLR.

Geomorphology units always Dynamism dynamic natural environment is connected.Any move towards Development of cities and villages with a dynamic way and Dynamism mentioned phenomena and ultimately with crosses Geomorphology. Well. Origins exposure of the belt Alpin qualification special geographical This has led to the diversity of the land get a lot Geomorphology formation am able rural City Land Development has specific forms. Geo morphology studies of the peripheral organs can be considered as exploitation Planning and variety regardless of the damaging results of natural science and socio economic, will be followed.increasing urban and rural population in recent years, especially areas containing natural potentials redound excessive exploitation of natural resources user change been followed on for environmental hazards such as erosion and flood and mass movement is to follow has had. Organizing system population settlement and activity, nomination land use (industrial, tourism, Agriculture, Forestry Urbanization, to meadow, promenade and...), especially in areas with potential environmental or major, especially at national level on for a parallel population growth more and more important to make. Khalkhal Township with exposure to western slopes of the heights being Talesh mountainous and Topography specific Geomorphology and valleys deep ghzelozan and shahrood and waterfalls impassable by several passages capabilities and limitations of many Geomorphology has created. To achieve the development of these capabilities and limitations is necessary Geomorphology that the study is trying to develop capabilities and limitations city perspective Applied Geo morphology were studied in pragmatics solution necessary planning and development to be provided.

Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent FEATURES and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the previously presented works. The designed face detection algorithm is composed of two main stages, in the first stage of the algorithm, color based skin detection is performed using some selected color space components from the whole 15 color space components in order to reduce the search space and in the second one, verification of detected regions is done using some other kinds of different FEATURES including texture, gradient, image and geometric FEATURES. Unlike the other studied issues, in this paper, various types of FEATURES aren't evaluated in separated algorithms and systems, rather they compete all together in one competitive learning vector and after the training of the neural network the system participates in the process of feature selection. Using designed method and besides of dimensional reduction of input matrix extraordinarily, each chosen feature was ranked.

In this study, an EFFECTIVE and efficient algorithm for detection a saliency map is presented based on the modeling of the rapid response of the human visual system to changes in the intensity, texture and color. Some cases such as inspiration from performance of human visual system, requiring no training, reduce number of image colors, reduce color channels and Proper use of the least texture information in this algorithm have increased its efficiency. In the proposed method in the first step, Due to sensitivity of the human visual system to higher contrast signals, only higher contrast channel has been used to extract the color saliency map, Then the intensity saliency map as well as the texture saliency map are extracted using the intensity component in lab color space using Simple cell computational model of the visual cortex and finally, with the perfect combination of the saliency maps of the color, the intensity, and the texture, object saliency map is obtained. The proposed method and existing methods have been tested on MSRA10K and ECSSD databases. The results of the implementations show that the proposed hybrid algorithm for the detection saliency map using the dominant color and texture FEATURES, On the ECSSD database, the mean absolute error, F-measure score and the area under the ROC curve are 0. 173, 0. 789 and 0. 891, respectively, and on the MSRA10K database are 0. 178, 0. 790 and 0. 919, respectively, compared to other models, it indicates better performance of the proposed method than other methods.

This paper deals with evaluation of the mechanical properties of woven fabrics. Method of evaluation is concentrated loading system. A total of 17 pieces of woven fabric is used. The most related parts of the curve to the properties are shown and as a result, EFFECTIVE FEATURES are lightened up.