Evaluation of thermal stability of confectionary sunflower protein isolate and its effect on nanoparticulation and particle size of the produced nanoparticles

In this study, the effect of different defatting conditions on heat stability of confectionary sunflower protein isolate (SnPI) and the particle size of the produced nanoparticles was investigated. The evaluated factors included temperatures of defatting (40, 50, and 60 °C), time of defatting (2, 6, and 10 h), and the amount of activated carbon (0, 25, and 50% of sample weight). The results of the central composite design showed a significant effect (P < 0.05) among the studied factors, where denaturation temperature and particle size of SnPI nanoparticles were found to be in the ranges of 75.05–89.12 °C and 268–1594 nm, respectively. Moreover, the interaction of activated carbon with temperature and time of defatting proved to be influential factors for the heat stability of confectionary SnPI.     

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg–Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.     

Optimal Design of Check Dams in Mountainous Watersheds for Flood Mitigation

One of the measures for flood control is to construct a series of small barriers, known also as check dams, on tributaries of watershed stream network. Check dams are generally used in mountainous areas in order to control sediment transport and attenuate flood peak. In this paper, a simulation-based optimization model is developed to determine size, shape and the number of check dams for flood mitigation. HEC-HMS model is used to simulate watershed rainfall-runoff process considering various check dam designs. The model is coupled with a multi-objective evolutionary algorithm, called non-dominated sorting differential evolution (NSDE), to find the trade-off solutions considering three objective functions: 1) minimizing the investment cost, 2) minimizing the flood peak discharge and 3) maximizing the time to peak discharge. The proposed model is applied to a mountainous watershed in Iran and (near) optimal strategies, including the suitable number of check dams in each sub-watershed, and optimal dam size (e.g. optimal height, bottom width and side angles) in each sub-watershed are obtained. The results show that cost-effective designs can decrease peak discharge up to 53%, 54 and 54% corresponding to 2-yr, 5-yr and 10-yr flood return period scenarios, respectively. In addition, the check dams can also increase the time to peak for up to 88%, 81 and 77%, corresponding to 2-yr, 5-yr and 10-yr flood scenarios, respectively.     

Electromagnetic scattering from a buried cylinder in layered media with rough interfaces

A solution to scattering from a cylinder buried arbitrarily in layered media with rough interfaces based on extended boundary condition method (EBCM) and scattering matrix technique is developed. The reflection and transmission matrices of arbitrary rough interfaces as well as an isolated single cylinder are constructed using EBCM and recursive T-matrix algorithm, respectively. The cylinder/rough surface interactions are taken into account by applying the generalized scattering matrix technique. The scattering matrix technique is used to cascade reflection and transmission matrices from individual systems (i.e., rough surfaces or cylinders) in order to obtain the scattering pattern from the overall system. Bistatic scattering coefficients are then obtained by incoherently averaging the power computed from the resulting Floquet modes of the overall system. In numerical simulations, the bistatic scattering coefficients are first validated by comparing the simulation results with the existing solutions which are the limiting cases including scattering from two-interface rough surfaces without any buried object and from a buried cylinder beneath a single rough surface. Subsequently, the numerical simulations of scattering from a buried cylinder in layered rough surfaces are performed to investigate the relative importance and sensitivity of various physical parameters of layered rough surfaces to incoherent scattering coefficients. Results show layered rough interfaces can significantly alter the scattering behaviors of a buried cylinder.     

Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network – AutoRegressive eXtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the “Ward” method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R ²?=?0.97, RMSE?=?0.03 m, ME?=?--0.07 m and R ²?=?0.81, RMSE?=?0.35 m, ME?=?0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN.     

Forest variable estimation from fusion of SAR and multispectral optical data

Radar and optical remote sensing data are used in a unified algorithm to estimate forest variables. The study site is the H. J. Andrews experimental forest in Oregon, which has significant topography and several mature and old-growth conifer stands with biomass values sometimes exceeding 1000 tons/ha. Polarimetric multifrequency Airborne Synthetic Aperture Radar (AIRSAR) backscatter, interferometric C-band Topographic Synthetic Aperture Radar (TOPSAR) coherence, and multispectral Landsat Thematic Mapper (TM) digital numbers are used in a regression analysis that relates them to forest variable measurements on the ground. Parametric expressions are derived and used to estimate the same variables(s) at other locations from the combination of AIRSAR and TM data. It is shown that the estimation accuracy is significantly improved when the radar and optical data are used in combination compared to estimating the same variable from a single data type alone.     

An Ultra-Low-Power 9T SRAM Cell Based on Threshold Voltage Techniques

This paper presents a new nine-transistor (9T) SRAM cell operating in the subthreshold region. In the proposed 9T SRAM cell, a suitable read operation is provided by suppressing the drain-induced barrier lowering effect and controlling the body–source voltage dynamically. Proper usage of low-threshold voltage (L-\(V_{\mathrm{t}}\)) transistors in the proposed design helps to reduce the read access time and enhance the reliability in the subthreshold region. In the proposed cell, a common bit-line is used in the read and write operations. This design leads to a larger write margin without using extra circuits. The simulation results at 90 nm CMOS technology demonstrate a qualified performance of the proposed SRAM cell in terms of power dissipation, power–delay product, write margin, read access time and sensitivity to process, voltage and temperature variations as compared to the other most efficient low-voltage SRAM cells previously presented in the literature.     

Printed Wide-Slot Antenna for Wideband Applications

The design and analysis of a novel printed wide-slot antenna, fed by a microstrip line, for wideband communication systems is presented. Detailed simulation and experimental investigations are conducted to understand its behavior and optimize for broadband operation. The designed antenna has a wide operating bandwidth of over 120% (2.8-11.4 GHz) for S11 <-10 dB. In addition to being small in size, the antenna exhibits stable far-field radiation characteristics in the entire operating bandwidth, relatively high gain, and low cross polarization. By properly choosing the suitable slot shape, selecting similar feed shape and tuning their dimensions, the design with very wide operating bandwidth, relatively small size and improved radiation pattern is obtained. A comprehensive parametric study has been carried out to understand the effects of various dimensional parameters and to optimize the performance of the designed antenna. Results show that the impedance matching of this kind of antenna is greatly affected by the feed-slot combination and feed gap width, with the slot shape being a main contributor of the radiation characteristics. The simulated and measured Results for return loss, far-field E and H-plane radiation patterns, and gain of designed antenna are presented and discussed.     

A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images

A new framework for model-based lung tissue segmentation in three-dimensional thoracic CT images is proposed. In the first stage, a parametric model for lung segmenting surface is created using shape representation based on level sets method. This model is constituted by the sum of a mean distance function and a number of weighted eigenshapes. Consequently, unlike the other model-based segmentation methods, there is no need to specify any marker point in this model. In the second stage, the segmenting surface is varied so as to be matched with the binarized input image. For this purpose, a region-based energy function is minimized with respect to the parameters including the weights of eigenshapes and coefficients of a three-dimensional similarity transform. Finally, the resulted segmenting surface is post-processed in order to improve its fitness with the lung borders of the input image. The experimental results demonstrated the outperformance of the proposed framework over its model-based counterparts in model matching stage. Moreover, it performed slightly better in terms of final segmentation results.     

A preclinical system prototype for focused microwave thermal therapy of the breast

A preclinical prototype of a transcutaneous thermal therapy system has been developed for the targeted treatment of breast cancer cells using focused microwaves as an adjuvant to radiation, chemotherapy, and high-intensity-focused ultrasound. The prototype system employs a 2-D array of tapered microstrip patch antennas operating at 915?MHz to focus continuous-wave microwave energy transcutaneously into the pendent breast suspended in a coupling medium. Prior imaging studies are used to ascertain the material properties of the breast tissue, and these data are incorporated into a multiphysics model. Time-reversal techniques are employed to find a solution (relative amplitudes and phase) for focusing at a given location. Modeling tests of this time-reversal focusing method have been performed, which demonstrate good targeting accuracy within heterogeneous breast tissue. Experimental results using the laboratory prototype to perform focused heating in tissue-mimicking gelatin phantoms have demonstrated 1.5-cm-diameter focal spot sizes and differential heating at the desired focus sufficient to achieve an antitumor effect confined to the target region.