Showing papers by "Graduate University of Advanced Technology published in 2019"

Ridge Gap Waveguide Multilevel Sequential Feeding Network for High-Gain Circularly Polarized Array Antenna

Abstract: This paper proposed a wideband, efficient, and high gain circularly polarized (CP) array antenna based on the gap waveguide technology for 30 GHz frequency band applications. The antenna subarray is a $2 \times 2$ slot groove gap waveguide-based cavity-backed antenna. The antenna is fed by a ridge gap waveguide (RGW). Two corners of the radiating slots are truncated to obtain CP radiation, and some corrugations are introduced to the radiating layer to suppress the surface waves between neighbor subarrays. The planar 16-way feeding network based on RGW is utilized to feed the subarrays. The feeding network is multilevel sequential phased with a quadrature phase shift. To validate the array antenna performance, a prototype of the antenna has been manufactured using standard milling technology. The measured reflection coefficient bandwidth is about 22% covering from 28.3 to 35.3 GHz, and a measured 3 dB axial ratio bandwidth of about 21.8% is achieved from 27.8 to 34.6 GHz. The maximum measured gain is 23.5 dBi which is occurred at 30.75 GHz.

Evaluation of Carum-loaded Niosomes on Breast Cancer Cells:Physicochemical Properties, In Vitro Cytotoxicity, Flow Cytometric, DNA Fragmentation and Cell Migration Assay.

Abstract: Thymoquinone (TQ), a phytochemical compound found in Carum carvil seeds (C. carvil), has a lot of applications in medical especially cancer therapy. However, TQ has a hydrophobic nature, and because of that, its solubility, permeability and its bioavailability in biological mediums are poor. To diminish these drawbacks, we have designed a herbal carrier composed of Ergosterol (herbal lipid), Carum carvil extract (Carum) and nonionic surfactants for herbal cancer treatment. C. carvil was extracted and characterized by GC/Mass. Two different formulations containing TQ and Carum were encapsulated into niosomes (Nio/TQ and Nio/Carum, respectively) and their properties were compared together. Morphology, size, zeta potential, encapsulation efficiency (EE%), profile release rate, in vitro cytotoxicity, flow cytometric, DNA fragmentation and cell migration assay of formulations were evaluated. Results show that both loaded formulations have a spherical morphology, nanometric size and negative zeta potential. EE% of TQ and Carum loaded niosomes was about 92.32% ± 2.32 and 86.25% ± 1.85, respectively. Both loaded formulations provided a controlled release compared with free TQ. MTT assay showed that loaded niosomes have more anti-cancer activity compared with Free TQ and free Carum against MCF-7 cancer cell line and these results were confirmed by flow cytometric analysis. Cell cycle analysis showed G2/M arrest in TQ, Nio/TQ and Nio/Carum formulations. TQ, Nio/TQ and Nio/Carum decreased the migration of MCF7 cells remarkedly. These results show that the TQ and Carum loaded niosomes are novel carriers with high efficiency for encapsulation of low soluble phytochemicals and also would be favourable systems for breast cancer treatment.

In vitro cytotoxicity assay of D-limonene niosomes: an efficient nano-carrier for enhancing solubility of plant-extracted agents.

Abstract: The low solubility of the plant-extracted agent like D-limonene in cancer therapy is a critical problem. In this study, we prepared D-limonene-loaded niosomes (D-limonene/Nio) for cancer therapy through in vitro cytotoxicity assay of HepG2, MCF-7, and A549 cell lines. The niosomal formulation was prepared by film hydration technique with Span® 40: Tween® 40: cholesterol (35:35:30 molar ratio) and characterized for vesicle distribution size, morphology, entrapment efficiency (EE%), and in vitro release behaviour. The obtained niosomes showed a nanometric size and spherical morphology with EE% about 87 ± 1.8%. Remarkably prolonged release of D-limonene from niosomes compared to free D-limonene observed. The loaded formulation showed significantly enhanced cytotoxic activity with all three cancer cell lines (HepG2, Macf-7 and A549) at the concentration of 20 μM. These results indicated that niosome loaded with phytochemicals can be a promising nano-carrier for cancer therapy applications

Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.

Abstract: Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a ferocious demand for obtaining an accurate prediction of WQIs is of high importance for perception of pollutant patterns in natural streams. Field studies conducted on different rivers indicated that there is no general relationship to yield water quality parameters with a permissible level of accuracy. Over the past decades, several artificial intelligence (AI) models have been employed to predict more precise estimation of WQIs rather than conventional models. In this way, through the current study, multivariate adaptive regression spline (MARS) and least square-support vector machine (LS-SVM), as machine learning methods, were used to predict indices of the five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). To improve the proposed approaches, 200 series of field data, collected from Karoun River southwest of Iran, pertain to the nine independent input parameters, namely electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), orthophosphate ([Formula: see text]), nitrite ([Formula: see text]), nitrate nitrogen ([Formula: see text]), turbidity, and pH. The performances of the LS-SVM and MARS techniques were quantified in both training and testing stages by means of several statistical parameters. Furthermore, the results of the proposed AI models were compared with those obtained using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression equations. Results of the present research work indicated that the proposed artificial intelligence techniques, as machine learning classifiers, were found to be efficient in order to predict water quality parameters.

Voltammetric Determination of Bisphenol A in Water and Juice Using a Lanthanum (III)-Doped Cobalt (II,III) Nanocube Modified Carbon Screen-Printed Electrode

Abstract: A La3+ doped Co3O4 nanocube modified graphite screen-printed electrode (La3+-doped Co3O4 nanocube/SPE) was prepared and utilized for the sensitive voltammetric determination of bisphenol A. In comp...

Experimental study of the effects of curing time on geotechnical properties of stabilized clay with lime and geogrid

Abstract: Lime treatment is extensively used to improve the properties of soft and weak soils One of the most important reasons for using lime is to reduce the swelling behaviour of the clay soil The react

Prediction of water quality parameters using evolutionary computing-based formulations

Abstract: Rivers, as one of the crucial components of water resources, play a substantial role in human life. Due to a wide range of water consumptions, water quality is emphatically recommended assigning at a permissible level. Enormous field investigations have been carried out to quantify indicators of water quality parameters which have a great impact on the water quality. In this way, there are not explicit equations to predict WQPs with an allowable degree of accuracy. In the present work, gene expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) have been employed to estimate three indices including biochemical oxygen demand (BOD), dissolved oxygen, and chemical oxygen demand (COD). To develop the proposed models, nine input parameters $${\text{Ca}}^{2 + }$$ , $${\text{Na}}^{ + }$$ , $${\text{Mg}}^{2 + }$$ , $${\text{NO}}_{2}^{ - }$$ , $${\text{NO}}_{3}^{ - }$$ , $${\text{PO}}_{4}^{3 - }$$ , $${\text{EC}}$$ , $${\text{PH}}$$ , and $${\text{turbidity}}$$ were selected as effective variables. Results of training and testing stages for these approaches have been investigated. Performance of the models indicated that the relative superiority of the EPR approach compared to the GEP and MT models. Gamma test was applied to determine important parameters for predicting BOD, COD, and COD indices. It was found that $${\text{PO}}_{4}^{3 - }$$ , $${\text{Ca}}^{2 + }$$ , and pH have the most significant effect on BOD, COD, and COD, respectively.

A Framework on the Performance Analysis of Dual-Hop Mixed FSO-RF Cooperative Systems

Abstract: Free space optical (FSO) communication systems provide line-of-sight wireless connectivity in the unlicensed optical spectrum and achieve higher data rates in comparison to their radio frequency (RF) counterparts. FSO systems are particularly attractive for the last mile access problem by establishing a connectivity bridge between optical fiber-based backbone and RF access networks. To address this practical deployment scenario, there has been an increasing attention on the so-called mixed (dual-hop) FSO-RF systems where FSO transmission is utilized at one hop followed by RF transmission at the other one. In this paper, we investigate the performance of mixed FSO-RF transmission systems where the FSO link is modeled by the double generalized Gamma distribution with generalized pointing error impairments and the RF link experiences the extended generalized-K shadowed fading. For both amplify-and-forward and decode-and-forward relaying, we derive closed-form expressions for outage probability, bit error probability, and ergodic capacity in terms of bivariate Fox-H function. Monte Carlo simulation results are provided to verify the accuracy of derived expressions. We further provide an asymptotic analysis and discuss the achievable diversity orders of mixed FSO-RF systems.

Synthesis of novel sepiolite-iron oxide-manganese dioxide nanocomposite and application for lead(II) removal from aqueous solutions.

Abstract: In this study, the sepiolite-iron oxide-manganese dioxide (Sep-Fe3O4-MnO2) nanocomposite was synthesized and applied as a magnetically separable adsorbent for removal of Pb(II) ions from water in a batch system. The effects of initial Pb(II) concentration, adsorbent dosage, contact time, pH value, and temperature were investigated to optimize the conditions for maximum adsorption. The equilibrium adsorption data were analyzed with the Langmuir, Freundlich, and Temkin models. The adsorption process closely agreed with the Langmuir adsorption isotherm, and the monolayer saturation adsorption value was achieved as 131.58 mg g-1. The adsorption kinetics follow the pseudo-second-order (PSO) model that illustrated the rate controlling step might be chemisorption. Thermodynamic investigations for the removal process were conducted by determining the values of ∆G°, ∆H°, and ∆S°. The adsorption behavior of Pb(II) on the Sep-Fe3O4-MnO2 was a spontaneous and endothermic process. Several consecutive adsorption-desorption cycles confirmed that the proposed Sep-Fe3O4-MnO2 nanocomposite could be reused after successive lead removal. Furthermore, the practical application of the adsorbent was successfully realized by the treatment of real Pb-contaminated water samples.