Affecting daily lives of millions of people, Epilepsy is a common central nervous system (neurological) disorder where cell activity in the brain is disturbed, causing recurrent seizures. Epilepsy can be treated commonly by medications. Be that as it may, medications do not always work as one may hope, and thus, some patients tend to resort to surgeries. The primary challenge in such surgeries, and by extension any other surgery where some part of brain may need to be disabled, disconnected or removed, is managing to pose no threat to the critical healthy textures adjacent or close to the part being operated on.Therefore, the precise localization of epileptic focus is a matter of vital importance in treating this condition. Various algorithms have been proposed to localize the brain sources and thus to determine the epileptic focus; however, none has yet been able to offer a solution to effectively address this issue.With EEG signal containing temporal information and fMRI carrying spatial information, it is hoped that the combination of the two can yield optimal results. In this research, we first remove the artifacts caused by EEG gradients, and proceed to study the signal in and outside the scanner by localizing the brain sources. The simultaneous processing of EEG-fMRI enables us to make use of the temporal information in EEG to analyze fMRI. Epileptic foci are finally localized based on GLM method. This study has been conducted on 10 medication-resistant patients with epilepsy whose data was recorded in Iran National Brain Mapping Centre. The results suggest a significant improvement in localization accuracy compared to existing methods in the literature.

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long-range detectors or such smart encoder trailer. Our proposed approach uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%.

The neoclassical growth model is tested by use of panel data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Simultaneous localization and mapping (SLAM) is a fundamental problem in autonomous robotics. Many algorithms have been exploited to solve this problem, among these algorithms, Fast SLAM is one of the most widely used and Unscented Fast SLAM is one of the newest. Although in several scientific researches it is stated that Unscented Fast SLAM outperforms Fast SLAM, there are still unexamined potentials regarding Unscented Fast SLAM. Therefore, this paper seeks to improve the overall performance of Unscented Fast SLAM. Map accuracy and quality directly depend on the accuracy of localization and observations. In SLAM algorithms, robot pose is predicted using motion model, and then corrected using the difference between map features and recently observed features. Accuracy of pose estimation may improve by comparing two sequential observations and modifying robot pose to result in best match between them. This method is called scan matching and has been successfully combined with Fast SLAM algorithm and some other SLAM algorithms not including Unscented Fast SLAM. Therefore, this paper seeks to investigate the performance of Unscented Fast SLAM combined with scan matching. Simulation results show that combining Unscented Fast SLAM with scan match significantly improves accuracy of localization and mapping.

The health of structures, provision of safety, and the sense of security are among constant requirements and perpetual challenges of engineering and managers in the field of crisis management. Erosion and occurrence of minor local damage to structures and structural members in the early stages of construction or during operation, especially in critical structures such as power plants, tall buildings, stairs, dams, airports, and hospitals, have always been among major problems. As time passes, Structures are affected by a variety of natural and non-natural destructive factors such as earthquakes, non-systematic excavations, dynamic vibrations resulting from explosions and heavy vehicle traffic. In addition, factors such as serviceability expectation beyond the design capacity of structural elements and failure to meet the latest expectations imposed by regulations, use of poor-quality materials and execution problems will reduce efficiency and, consequently the service life of structures. Also, the spread of local damages in structures can impair the overall health of the structure. Undoubtedly, knowledge of structural health and safety is of vital importance and structural health monitoring is recognized as one of the most important subjects that has received a lot of attention from researchers. Plates are one of the most important structural elements that can, when damaged, progressively transfer damages to other elements and lead to overall structural damage incurring irreparable social and economic costs. Due to the increasing applications of steel plates, especially in building structures (as steel plate shear walls) in the present study attempts were made to focus on damage detection and localization as one of the most important steps of health monitoring using modal dynamic data (natural frequencies and mode shapes) and a proposed diagnostic method based on two-dimensional discrete wavelet analysis. To this end, the modeled steel plate was subjected to frequency analysis in ABAQUS finite element analysis software and the modal data associated with damaged and non-damaged states were extracted. The results showed differences between the frequencies and lack of correlation between primary and secondary vibration mode shapes based on the modal assurance criterion (MAC) and the angle between the primary and secondary mode shape vectors. Using a propoed damage localization index (DLI) based on the wavelet coefficients obtained from the diameter details of the two-dimensional wavelet analysis of the primary and secondary vibration mode shapes, the damage zones were detected by creating a maximum relative jumps in the DLI diagram. Studies showed that DLI values are sensitive to the damage severity of the damage zone and with increasing the damage severity, these values increase in fixed spatial coordinates in the damaged zone. Also, the DLI of one damaged zone is independent of the damage severity of the other damaged zones, and this is a positive advantage in the damage determination process. Otherwise, failure to detect one damaged zone may affect the detection of other damaged zones, and consequently pose problems in the process of damage detection and localization in cases where we are dealing with multiple damage zones. According to the results of the present study, DLI can be proposed as an efficient and effective index in detection and localization of damages in steel plate elements.

A sensitive modal data-based damage indicator is proposed to diagnose breathing cracks in beam-column structures subjected to axial load, as a percent of its critical value, and transverse harmonic load. The Newmark-Beta method is utilized to solve the equation of structural vibration, based on finite element method. The effect of Rayleigh-type damping is also examined. The indicator uses the modal deformations and their derivatives in healthy and damaged beam-column structures to identify the exact damage locations. The influence of some parameters such as noise effects and various axial loads on the efficiency of the method was also investigated. The results show the reliability of the approach in identifying the damage location for different scenarios, even in the presence of noise effect. Increasing the axial load, especially for values near to the critical load value, causes negative effects on the modal responses and their derivatives which are appropriately considered by the proposed index.

Extended Abstract Background: Honeybees have an important role in the pollination of plants. Apis florea Fabricius, 1787, has been described over the last two centuries.   A. florea is distributed in Vietnam, south-eastern China, India, Nepal, southern Thailand, Sri Lanka, Sudan, southern Iran, Pakistan, Saudi Arabia, and Oman. The climatic zones of A. florea change from tropical rainforest in the east to savannah in the west. Furthermore, westwards, the climatic zones change from tropical to subtropical steppe and semi-desert. Morphometric data were used in addition to analyses of molecular data to study the honeybees. Morphometric analyses are flexible tools employed by users in different areas of the world. The geometric morphometric method uses landmarks that can be located precisely on the vein junctions of wings. The geometric morphometric method has been used to compare the populations of A. florea. In addition, traditional or standard morphometric methods have been used to study the populations of A. florea in various areas of the world. The landmark configurations eliminate the effects of position, orientation, and size of shapes. Data on shapes are converted into Procrustes coordinates using the geometric morphometric method. Regression analysis between centroid size and Procrustes coordinates creates new data on the residuals, which can be applied in other analyses. No published research has utilized residual coordinates (residual data) to compare the populations of A. florea. Therefore, the current research aimed to compare the performance efficacy of residual and Procrustes coordinates in differentiation and relationships among the populations of A. florea in various areas of the world. Methods: Samples of the A. florea honeybee species were prepared from their distributed areas. The right forewings of honeybees were used to study the relationships among the various populations. Eighty samples were selected from each area. A tps file was built by importing the right forewing pictures into TpsUtil V. 1. 64 software. Then, the tps file was loaded into tpsDig V. 2. 18. Twenty landmarks were digitized in the vein junctions of the forewings. This was followed by the raw data obtained from landmarks, loaded into the MorphoJ software V. 1. 06d, and converted into Procrustes coordinates for use in future analyses, followed by analyzing the Procrustes coordinates. Mahalanobis distances and canonical variates were obtained using permutation tests. The regression between Procrustes data and centroid sizes was calculated, and an allometry test was performed afterward. The residual coordinates (residual data) were obtained after the removal of the size effect (size correction) from shape variables. The residual and Procrustes data were imported into PAST software v. 3. 19, and the populations of A. florea were compared using Canonical Variate Analysis (CVA). Moreover, clusters were drawn with the residual and Procrustes data using SAS v. 8 software. Results: Multivariate (MANOVA) and pair-wise analyses of residual and Procrustes data were tested for the populations of A. florea. The Procrustes and residual data of the populations showed statistically significant differences using MANOVA (p < 0. 001). Moreover, the tested pairwise comparisons indicated that all populations were significantly different in Procrustes and residual data (p < 0. 001). The landmark configurations of forewings were superimposed, and variations were obtained between the populations. The highest variation was found in the vein junction of R and Rs, landmark 19 (S2 = 0. 0000622). The lowest variation was observed in the vein junction of Cu and 1m-cu, landmark 8 (S2 = 0. 0000109). The populations of various areas were compared using Canonical Variate Analysis (CVA). In the CVA of Procrustes data (Procrustes coordinates), the first and second components included 75. 94% of all variation (CV1 = 28. 74% and CV2 = 47. 20%). In addition, in the CVA of residual data, the first and second components included 83. 06 % of all variation (CV1 = 31. 46 % and CV2 = 51. 60 %). The CVA results of Procrustes data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples overlapped with the Iranian samples of Bushehr, Shiraz, and Sistan and Balochestan. Additionally, Oman samples showed partial overlapping with South India and Kerman (Iran). The CVA results of residual data showed that the Pakistan samples overlapped with the Iranian samples, except for the Kerman population. Sudan samples were differentiated from Iranian samples. In addition, the Oman samples showed partial overlapping with South India. Both Procrustes and residual data differentiated the populations of Thailand and Vietnam from the other populations. Cluster analysis was used to compare the populations of A. florea in various areas. The cluster derived from Procrustes data indicated that Sri Lanka was closer to the populations of Iran, except for the Kerman samples. Furthermore, the Sudan and Pakistan populations were categorized under one group. In addition, the cluster derived from the residual data indicated that the Pakistan population was closer to the populations of Iran, except for the Kerman samples. The Sri Lankan population indicated a closer relationship with India, and the Sudanese population was differentiated from the other populations.    Conclusions: The recent findings showed that residual data revealed greater efficacy than Procrustes data in differentiation and relationships between the populations of A. florea. The results of the derived cluster from residual data indicated closer relationships of A. florea populations from Pakistan and Sri Lanka with Iran and South India, respectively.

Todays gamma knife radiosurgery is used widely for treatment of very small brain tumors. In order to investigate accuracy of dosimetry and treatment planning calculations, using Monte Carlo simulation with dedicated code named as beamnrc including non-CT data and CT data options is necessary. The aim of this study is choosing the best options in order to have an accurate tools based on their advantages and disadvantages. In this study, gamma knife unit 4C along with standard water equivalent phantom and EBT3 films were used to obtain dose distributions. Monte Carlo simulation was done with non-CT data and CT data options of the code and their resulting dose were compared. Comparison the calculated and measured dose distributions at X, Y and Z axis showed gamma value below 1 which verified Monte Carlo simulations. Also comparing the dose distributions from both non-CT data and CT data with each other implies that there is no significant difference between two methods. Based on the obtained results using non-CT data and CT data results in the same dose distribution. So for simplicity, using non CT data for regular phantom shapes is preferred.