In this paper, a code for 3D forward modeling of ELECTROMAGNETIC low INDUCTION number (EM-LIN) data is developed based on the linear integral equations (IE). At first, the code is manipulated for a resistive block immersed in a homogenous background, and the obtained results have RMS errors of 2% comparing with the previously standard published results, which demonstrates the productivity of the 3D forward modeling code. Then, a model composed of two conductive anomalies with different depth ranges and conductivities in a resistive background is considered. IE Forward reposes shows that the shallower block produce larger values in spite of having less conductivity. Since the forward modeling is linear, the productivity of the forward modeling code depends on the efficiency of the forward operator. Furthermore, linear forward operator plays the key role in the linear inversion procedure, therefore, a real data set of a thick dyke in Bloemfontein Nature Reserve region in South Africa is manipulated. Weighted damped minimum length solution is utilized for the inversion procedure and the inverted model is demonstrative of the forward operator efficiency in practical applications.

ELECTROMAGNETIC INDUCTION tools are extensively applied worldwide for rapid evaluation of spatial variability in soil salinity. This study was conducted in pistachio orchards of Chah Afzal area of Yazd Province for assessment of soil salinity using ELECTROMAGNETIC INDUCTION (EM38) device in two plots of 45 and 40 ha with clay loam soils and average moisture contents of 25 and 35 percents, respectively. A systematic sampling grid with the size of 20×80m was established. At each grid point two EM38 readings were taken in horizontal (EMh) and vertical (EMv) orientations. Different calibration equations were employed for conversion of the readings to electrical conductivity of the saturation paste and the best method was selected.Results of this study showed that the best calibration method was multiple linear regression equations fitted to the field data. It was also found that more reliable regression equations could be derived at 35% moisture content in comparison with 25% moisture content between soil salinity at different depths of soil and horizontal/vertical readings, with correlation coefficients (R2) of 0.67 to 0.85, respectively. Consequently, soil salinity maps of different depth intervals and also soil salinity profile maps at upstream and downstream of the field borders were generated and interpreted.

Identifying, detecting, classifying and separating unexploded ordnance (UXO) is one of the essential needs of countries and societies that have been involved in war. Marine mines submerged in seawater or buried in land are a common danger in many areas of the world. The majority of mines are composed of metal and explosive materials. There are many non-destructive schemes for detecting the location, orientation and depth of a metallic mine (modeled as a perfectly conducting sphere and spheroid). These methods of exploration of such materials, which are mainly based on geophysical methods, have a special importance and capability in terms of economy and safety. A major challenge of detecting a metallic object is to discriminate the object, such as an unexploded ordnance, from the noisy environment. It takes time and resources to identify the object, especially due to false signals from other metal objects and cultural features such as metal buildings, pipelines, and oil well casings. By measuring the secondary response of an electrically conductive object placed in a low frequency primary magnetic field, distinct spectral characteristics such as electrical conductivity, magnetic permeability, object geometry, and size can be obtained. Being one of the frequency domain methods, the ELECTROMAGNETIC INDUCTION method (EMI) is an efficient geophysical method that is used to identify land mines and sea mines. This technique takes into account Eddy-Current Response (ECR) induced on the conducting marine mines as well as Current-Channeling Response (CCR) associated with the perturbation of currents induced in the conductive marine environment. In the processing of the data obtained from geophysical surveys, especially in the EMI, such anomalies are modeled with simple geometric objects such as spheres or spheroids. In the first step of data interpretation and to determine the dimensions of the model that indicates the type of mine or bomb and also to determine its location, it is necessary for the interpreter to have a clear view of how the ELECTROMAGNETIC INDUCTION response depends on the geometrical and physical parameters of such materials. For this purpose, in this article, the changes and behavior of the received ELECTROMAGNETIC INDUCTION response in the receiver coil are investigated according to the depth, dimension and orientation of the abnormality. The graphs obtained are examined and analyzed using the mentioned variables. While qualitatively determining the state of abnormality to a large extent, the obtained results can be useful in numerical methods of calculation of geometrical and physical quantities of anomalies.

Issue of the ELECTROMAGNETIC launch technology in recent decades has been considered seriously. Power supply makes up sensitive and significant part of this launchers. in the previous power supplies that designed for launchers, the energy storage such as inductors and capacitors are used, that the major disadvantage of this power supplies is great weight and size of them and this problem is a serious limitation for implementation and expansion of these launchers. Also, in some of them, is used indirect method for frequency conversion (AC-DC-AC). In this paper a scheme is proposed that by using the matrix converters, obtains the specifications required for the supply of coil-INDUCTION launcher directly, that not required to any energy storages and uses power of electricity network instead of using the energy storage resources.

Precise mapping of the spatial distribution of salt-affected soils is prerequisite for effective management of these soils. This study was carried out for mapping soil salinity of 78000 hectares of Ardakan soils (0-30 and 0-100 cm) using 151soil samples which were taken based on hyper cube method. The secondary variables used in co-kriging method were ETM data, terrain analysis and EM38 readings. The best model was selected by means of cross validation and error evaluation methods, such as RMSE and ME methods. Results showed that cokriging method with EM38 data as a secondary variable was the best method for prediction of soil salinity (69.1, 30.55, 48.8 and 20.41, respectively). Results recommended EM38 as secondary data for mapping soil salinity. Additionally, results showed the largest amount of soil salinity in the north of the area and the smallest values in the areas with more elevation. The concavely shaped plain could help to move soluble salts toward the north of area in which the soils with highest electrical conductivity are found.

In this study, a method has been proposed for energy harvesting from waste heat. A magnet was floated on the liquid in the coiled container and the system was placed on the heat source. By pool boiling of the liquid and according to Faraday's INDUCTION law, the voltage was induced in the coil by the movement of the magnet. Excess temperature, dimensions of the container, liquid height in the container, and the frame shape and diameter have been selected as effective parameters. Effects of these parameters on peak-to-peak voltage and root mean square voltage have been investigated experimentally. Obtained results showed that the maximum energy was harvested at higher values of excess temperature, liquid height, coil turn, and frame diameter with a spherical frame shape. The highest measured parameters were 532 mV and 95.65 mV for Vpp and Vrms, respectively. In the second part, the numerical method is used to simulate the proposed system. The effect of various parameters on interface characteristics has been investigated. The results showed that the trend of changes in the interface parameters, including its pressure and height, were consistent with experimental data. Therefore, this method can be used to design and predict the performance of the energy harvester.

Background and Objectives: Soil salinity is one of the most important soil properties and it's variability investigation is essential to crop management, land degradation and environmental studies. Soil salinity is measured using electrical conductivity (EC) and estimation of soil salinity contents using experimental methods is expensive and time consuming. Therefore, the collection of information on the spatial distribution of soil salinity in n vast areas requires new inexpensive techniques. Recently, new techniques such as ELECTROMAGNETIC INDUCTION, visible-near infrared spectroscopy and remote sensing were applied to measure soil salinity. The purpose of this study is the estimation of soil salinity using visible-near infrared spectroscopy, ELECTROMAGNETIC INDUCTION and remote sensing methods. Materials and Methods: The study area is located 20 km northeast of Ghorveh city in Kurdistan Province and covers a surface of 26000 hectares. 100 soil samples (0-30 cm depth) were collected and. soil electrical conductivity was measured in a saturated extract. Applied auxiliary data in this study were spectral information of visible-near infrared spectroscopy method, reading of ELECTROMAGNETIC INDUCTION method and ETM+ data of Landsat 8. In the 100 sampling sits, horizontal and vertical readings were read using EM38 and salinity index (SI) and normalized difference vegetative index (NDVI), bright index and Bands 1, 2, 3, 4, 5, 6 and 7were computed and extracted using Landsat 8 ETM+ data and Arc GIS software. Moreover, the 100 samples were scanned using spectrometer (model of FieldSpec® 3, ASD, FR, USA) with a spectral range of 350 to 2500 nm. To make a relationship between soil salinity and auxiliary data of the three methods, artificial neural network (ANN) model were applied. Finally, soil salinity was estimated using ANN and were validated using cross validation method. Results: Soil salinity contents were low to high (0. 23 to 14. 47 dSm-1). The highest contents of soil salinity were observed in central regions (low and bare land) and the lowest contents of soil salinity were located in high and range land. Based on sensitive analysis of artificial neural network model, in remote sensing methods salinity index, NDVI index, band 7 and band 3 were the most variables to predict soil salinity. In general, the results showed the most important auxiliary variables to predict soil salinity were spectral information of visible-near infrared range, vertical reading and remote sensing data, respectively. Soil visible-near infrared spectroscopy method to predict soil salinity had 0. 94, 0. 27 and 0. 64, respectively for determination of coefficient (R2), mean error (ME) and root mean square root (RMSE) and was better compared to the ELECTROMAGNETIC INDUCTION, remote sensing although combination of three methods together had the best results to estimate soil salinity. Conclusion: The most important auxiliary data to predict soil salinity in the study area was spectral information of visible-near infrared range. ELECTROMAGNETIC INDUCTION method also is suitable auxiliary data to predict soil salinity and it can recommend as speed, accurate and cheap method to predict soil salinity. Combination of three methods together (ELECTROMAGNETIC INDUCTION, visible-near infrared spectroscopy and remote sensing) had the best results to estimate soil salinity. Therefore, it is suggested to predict soil salinity, ANN model and auxiliary data such as spectral information of visible-near infrared spectroscopy method and ELECTROMAGNETIC INDUCTION will be applied in the future studies.

Saltwater intrusion is as an environmental hazard in coastal lines if not appropriately managed. The over-exploitation, over-population and climate change have invited and pushed the saltwater landwards and polluted the freshwater aquifers. This research studies the results of the implemented project at the coast of Saint Andre' located in Koksijde, Belgium, to study this phenomenon through near-surface geophysics. Two geophysical methods, including Electrical Resistivity Tomography (ERT) and ELECTROMAGNETIC INDUCTION (EMI) were used to identify the saltwater intrusion. The present study aimed to investigate the possibility of saltwater intrusion, its extension and assess the government reclamation attempts to push back the saltwater. In the inversions, the Depth of Investigation Index (DOI) and the topography effect were evaluated. The subsurface conductivity of both methods was compared. The reliability of both methods to identify the saltwater intrusion has been established,however, the ERT survey provided a more comprehensive visualization than the EMI. The saltwater intrusion was found in the first 80 m of the coastal line with resistivity values of 2 to 5 Ohm. m,however, the infiltration of freshwater and the reclamation operation have stopped the further progress salinity into the dunes. Local possibilities of brackish water or clay lenses were identified with 7 to 25 Ohm. m resistivity values. The freshwater body was observed at distances between 120 and 220 m of the ERT line with values between 46 and 136 Ohm. m. The results were correlated with other studies, proving the reliability of the models.

various methods have so far been applied to calibrate ELECTROMAGNETIC INDUCTION data. Throughout the present research, Multi-Linear Regression (MLR) as well as artificial intelligence techniques (i.e. ANFIS, GA, ANNs) were applied to calibrate the apparent Electrical Conductivity (ECa)- measured using an ELECTROMAGNETIC INDUCTION instrument and Electrical Conductivity (ECe)- as measured in saturation paste. A number of 600 soil samples were collected from Ardakan (Central Iran), divided into two subsets for calibration (80%) and testing (20%) of the models. To evaluate models, some such evaluation parameters as root mean square, average error, and coefficient of determination were applied. Results indicated that ANFIS model yields a more accurate estimate than the other techniques where this model increased accuracy of predictions for about 9, 9, 5 and 2% for EC15, EC30, EC60, and EC100, respectively. Higher performance of ANFIS to predict soil salinity might be because of somehow compensation for the uncertainties. Following ANFIS model, GA and ANN resulted in better accuracies in comparison with multivariate regression. As a whole, results indicated that artificial intelligence methods were of a higher performance than the regression techniques in calibrating ELECTROMAGNETIC INDUCTION data.

Background: ELECTROMAGNETIC INDUCTION hyperthermia is a promising method to treat the deep-seated tumors such as brain and prostatic tumors. This technique is performed using the INDUCTION of ELECTROMAGNETIC waves in the ferromagnetic cores implanted at the solid tumor.Objective: This study aims at determining the conditions of the optimal thermal distribution in the different frequencies before performing the in vitro cellular study.Material and Methods: In this experimental study, the i-Cu alloy (70.4-29.6; wt%) was prepared and characterized and then the parameters, affecting the amount of INDUCTION heating in the ferromagnetic core, were investigated. Self-regulating cores in 1, 3, 6, and 9 arrangements in the water phantom with a volume of 2 cm3 were used as a replacement for solid tumor.Results: Inductively Coupled Plasma (ICP) analysis and Energy Dispersive X-ray Spectroscopy (EDS) show the uniformity of the alloy after 4 times remeling by vacuum arc remelting furnace. The Vibrating Sample Magnetometer (VSM) shows that the Curie temperature (TC) of the ferromagnetic core is less than 50 °C. Temperature profile with a frequency of 100-400 kHz for 30 min, was extracted by infrared imaging camera, indicating the increase temperature in the range of 42 °C to 46 °C. Conclusion: The optimum conditions with used hyperthermia system are supplied in the frequency of 100 kHz, 200 kHz and 400 kHz with 6, 3 and 1 seeds, respectively. It is also possible to induce a temperature up to 50 °C by increasing the number of seeds at a constant frequency and power, or by increasing the applied frequency at a constant number of seeds.