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The Sensitivity of Quantitative electroencephalogram (Brain MAPPING) was Compared with Transient ischemic Attack (TIA) and computerized tomography (C.T. Scan) Finding. Twenty Patients were tested with Quantitative EEG (Brain MAPPING) within 24h from the onset of neurological symptoms where as CT Scan was performed within 24h from the onset of attack. EEG was recorded from 16 electrodes placed upon the Scale to the international 10-20 system. Spectral analysis was carried out for each channel. Absolute and Relative power were calculated for Delta - Theta -Alpha and Beta frequency bands and such data were successively represented in color coded maps. The results indicated that EEG MAPPING showed focal abnormality in delta band in 17 of 20 cases.      

Introduction: Maps can create a deeper insight of our surrounding environment. The map can also be used in curriculum for its reforming, and developing and ultimately improving teaching-learning process. This systematic review aimed to identify applications and benefits of curriculum MAPPING.Methods: Seven search engine and websites were searched using the keywords including curriculum MAPPING, curriculum map (s), medical, curriculum development and planning.1100 article was found and 179 articles were enrolled in the study. Then the relevant article (i.e.36 articles) were selected and analyzed.Results: 112 advantage and 111 applications were found in reviewed articles.32 advantage and 29 applications were listed after removing repetitive cases and merging some cases. The most common mentioned advantage was developing a participation culture (students and teachers participation in curriculum). Most applications of these maps were curriculum monitoring and evaluation from different perspective regarding to the required competencies for students.Conclusion: Various users including curriculum planners, administrators, professors and students use curriculum MAPPING according to their needs. Regarding the benefits of curriculum MAPPING and its various applications in the educational system, it is suggested that teachers and administrators in each department, design a curriculum map in a project. In this regard, computer programmers can increase accuracy and speed in the project.

The present paper investigated the effectiveness of concept MAPPING as a learning strategy on EFL students’ self-regulation (metacognitive self-regulation, time and study environment, effort regulation, peer learning, and help seeking). Sixty university students participated in the study. They were randomly assigned to control and experimental groups, each including thirty students. They were at the intermediate level of English proficiency and studying English either Translation or Literature. Their language proficiency was determined by the Michigan Test of English Language Proficiency (MTELP) (Corrigan, 1979). The instrument to collect data on the students’ selfregulation was the Motivated Strategies for Learning Questionnaire (MSLQ) (Printrich et al., 1991). The findings revealed that students gained higher self-regulation in writing tasks as the result of the explicit instruction of the concept MAPPING strategy. The findings have implications for pedagogy as well as for research.

This paper is directed to the question of how to model and design an efficient tool for the intelligent MAPPING which is based on both dynamic and efficient storage of data and soft computing. The former is performed by our method that learns how to store, search and delete the data. After pointing out the limitation of the crisp evaluation of the distance between two points, we argue in favor of soft computing which is based on the extension of metric space to interval one and then to the fuzzy metric. A-Star algorithm is used to illustrate our model along with the injection of competitive data structures.

This study was an attempt to investigate the comparative impact of concept map and mind map instruc-tion on EFL learners’ descriptive and narrative writing. To fulfill this purpose, 60 intermediate EFL learners were selected from among a total number of 100 through their performance on a pretest, i. e., a piloted sample Cambridge Preliminary English Test (PET). Then, the students were randomly divided into two experimental groups of 30. The scores of the writing part of PET were analyzed separately and the mean scores of the two groups were compared through an independent samples t-test in order to as-sure that the writing ability of the students was homogeneous at the outset. Both groups underwent the same amount of treatment (14 sessions of 90 minutes) three days a week with one group undergoing the concept map treatment while the other the mind map treatment. A posttest comprising a descriptive and a narrative writing were administered at the end of the treatment to both groups. The result of the Pillai’ s Trace Test (F = 12. 73 and p = 0. 000 < 0. 05) indicated a statistically significant difference between the two experimental groups with the mind map group that gained a higher mean in both posttests outper-forming the concept map group.

While land resource management needs detailed and accurate information about soil properties and distribution, this kind of data is limited in Iran. In this research, we tested performance of three digital soil MAPPING (DSM) approaches including Multiple Linear Regression (MLR), Cubist (CU) and Random Forest (RF) to map the spatial 3D distribution of soil organic carbon (SOC) in Saadat Shahr plain in Fars Province. Latin hypercube sampling (LHS) was used to determine locations of soil profiles in the field. The soil profiles were sampled and SOC was measured. Different environmental covariates including terrain attributes, remote sensing auxiliary variables, and maps of soil, geoform and distance from rivers were used in this research as auxiliary data. According to the link of the environmental covariates and soil organic carbon contents in the framework of each model in combination with equal-area spline algorithm, soil organic carbon maps were produced at five standard depths of soils in the whole study area. Model performance was evaluated by root-mean-square error (RMSE), mean error (ME) and normalized root-meansquare error (NRMSE). Among the used models, RF model showed the highest performance to predict organic carbon in depths of 0-5 and 60-100 cm. Meanwhile, MLR and CU had the lowest error for prediction in depths of 5-15 and 15-30 cm, respectively. In spite of these results, RF model was considered as the best model for its power to explain the spatial distribution of soil organic carbon in all soil depths in the study area.