Graduate University of Advanced Technology

Eslaminejad S., Rahimi R., Fayazi M.

The widespread use of fluoroquinolone antibiotics, such as ofloxacin (OFL), has led to their unintended presence in aquatic environments. The removal of OFL from water bodies is crucial to mitigate the spread of antibiotic resistance. In this work, palladium nanoparticles supported on MXene/metal organic framework (Pd/MXOF) nanocomposite was successfully prepared via a green approach and then employed as a novel catalyst material for the photocatalytic degradation of OFL. The Pd/MXOF sample demonstrates improved absorption in the visible region in contrast to MXOF samples, possibly attributed to better electronic transfer at catalyst surface. According to experimental results, a higher photocatalytic activity was obtained for Pd/MXOF catalyst in comparison with MXene, MIL-101(Fe), and MXOF substances. Excellent photodegradation efficiency (∼100 %) of OFL after 30 min irradiation of visible light was obtained using Pd/MXOF. The effectiveness degradation of OFL through the suggested photocatalysis process was dependent on the initial concentration of OFL, catalyst dosage, and solution pH value. Following four cycles, the photocatalyst exhibited acceptable stability and reusability. The key roles of hole (h+) and •O2− radical in the photocatalytic reaction were elucidated by the active species trapping studies. This work may provide a very potent strategy to photodegrade antibiotic pollutants in contaminated waters.

Rajaee K., Pourabbas Bilondi M., Barimani M.H., Amiri Daluee M., Zaresefat M.

While fine glass powder is a promising precursor for geopolymer production, its preparation is time-consuming and expensive. This study investigates using six different particle sizes of recycled glass – three uniform (fine and coarse) and three hybrid (combinations) – to reduce processing costs and optimize geotechnical and microstructural properties of clay-based geopolymers. Glass content varied from 0 % to 30 % by dry weight of soil, and a sodium hydroxide (NaOH) solution as an alkaline activator was used at three molar concentrations (2 M, 4 M, 6 M). A series of unconfined compressive strength (UCS), direct shear, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR) tests were conducted on samples. Results demonstrate that hybrid glass gradations containing both large (0.075–0.3 mm) and small (

Dargah M.M., Youseftabar-Miri L., Divsar F., Hosseinjani H., Mahani M., Bakhtiari S., Montazar L.

In this study, a new triplex hairpin oligosensor was developed for the determination of a breast cancer biomarker using silicon quantum dots (Si QD) (λex = 370 nm, λem = 482 nm) as donor and gold nanoparticles (GNP) as an acceptor in a FRET (fluorescence resonance energy transfer) mechanism. In the triplex hairpin oligosensor, a triplex-forming oligonucleotide (TFO) labeled with Si QD and a single-strand DNA labeled with GNP form a hairpin shape with a triplex structure at the hairpin stem. In a turn-on mechanism, the triplex hairpin stem is opened in the presence of sequence-specific miRNA-155 which leads to the release of the Si QD-labeled TFO probe and recovery of the fluorescence signal. About 80 % of the fluorescence intensity of the Si QD-TFO is quenched in the triplex hairpin structure of the oligosensor and in the presence of 800 pM miRNA-155, the fluorescence signal recovered to 57.7 % of its initial value. The LOD of about 10 pM was obtained. The designed triplex-based biosensor can discriminate concentrations of breast cancer biomarkers with high selectivity.

Motallebi H.

Clustering is the process of grouping similar data objects into different subsets based on their similarities. Inspired by the concept of the popularity of individuals in a community, we rate the popularity of each sample which reflects the centrality of that sample in the dataset. With the aim of identifying clusters with arbitrary shapes and varying densities, we propose a clustering approach that divides samples into separate population groups. This approach is based on identifying the backbone of data, characterized by a set of popular points surrounded by less popular points. To distinguish poorly separated clusters, a proximity measure is defined based on the popularity of samples. We also use the popularity of samples to assign halo points to clusters and calculate cohesion between clusters. The proposed clustering method can detect arbitrary-shaped clusters with varying densities without requiring to specify the number of clusters. Outliers are also identified according to popularity. We demonstrate the effectiveness of the approach on synthetic and real-world datasets.

Ghojoghi E., Ebrahimi Farsangi M.A., Mansouri H., Rashedi E.

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA Algorithm, Multi-Verse Optimization Algorithm, and Gravitational Search Algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances.

Khoshhal A.R., Bagheri Khatibani A., Tirehdast Z., Shaddoust M., Niroei M.

Pristine zinc cobaltite (ZnCo2O4) and graphene zinc cobaltite nanoparticles (NPs) have been successfully synthesized by hydrothermal method. The physical properties of both structures were studied using field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). XRD results indicated a formation of spinel structure. The elemental composition of the samples has been determined through EDX analysis. From morphological investigation, the aggregation of grains and nano-sized quasi-spherical grains was observed. Nuclear radiation protection investigation for gamma-ray has been studied for both samples. The protection factors against gamma rays, including linear attenuation coefficients (LAC), mass attenuation coefficients (MAC), transmission Factor (TF), radiation protection efficiency (RPE) mean free path (MFP), half value layer (HVL) and tenth value layer (TVL) were calculated. In addition to the experimental examination, simulation with GEANT4 simulation code was also used for both samples to examine the shielding parameters. The most essential parameters of gamma shielding factors such as mass attenuation coefficients (MAC), mean free path (MFP), half value layer (HVL), tenth value layer (TVL) and linear attenuation coefficients (LAC) were evaluated both practically and by simulation. Practically, these values were: 0.75/0.78 cm2/g, 0.22/0.20 cm, 0.15/0.14 cm, 0.50/0.47 cm, and 4.55/4.86 cm−1, for ZnCo2O4 and ZnCo2O4/graphene samples, respectively. By simulation, these values were: 0.65/0.70 cm2/g, 0.25/0.22 cm, 0.17/0.16 cm, 0.59/0.52 cm, and 3.91/4.38 cm−1, for ZnCo2O4 and ZnCo2O4/graphene samples, respectively. The results show that the presence of graphene has affected all the protection parameters. Keywords: Zinc cobaltite, structural properties, gamma shielding, hydrothermal method.

Shirkavand A., Farrahi-Moghaddam K.

In the current study, a non-hydrostatic two-dimensional vertical (2DV) numerical model with a shock-capturing technique is developed to simulate irregular wave breaking on a barred beach. The complete form of the 2DV Reynolds-averaged Navier-Stokes (RANS) equations is discretized using the finite volume approach. In the spatial discretization phase, a flux limiter function is employed for the velocity advection terms to prevent non-physical oscillations in regions with steep gradients. A novel method has been introduced during the discretization stage, leading to a significant reduction in computational expenses. For temporal discretization, the Leapfrog method, known for its second-order accuracy in time, is employed to address wave-damping issues. Model validation involves a comparison between simulation results and experimental data pertaining to irregular wave breaking, affirming the satisfactory performance of the model. To evaluate the accuracy of the model, the Root Mean Square Error (RMSE) was calculated. The assessment showed that the model is capable of estimating the significant wave height reported in the experiments used with an RMSE between 0.002 and 0.089, and the mean wave periods with an RMSE between 0.061 and 0.252.

Najafzadeh M., Mahmoudi-Rad M.

Vortex structures are widely employed for energy dissipation in urban surface water conveyance systems. When transporting wastewater through these networks, a substantial amount of water energy is dissipated. The effectiveness of these structures is usually evaluated by their efficiency in dissipating energy. Recent literature reviews on vortex structures have emphasized that, despite numerous experimental studies aimed at assessing their hydraulic performance, a reliable mathematical model to predict the residual energy head ratio remains elusive. In this study, resilient numerical models employing Artificial Intelligence (AI) methodologies (such as non-parametric regression, decision trees, and ensemble learning) have been structured by reliable experimental tests. By analyzing the experiments, three primary factors, referred to as flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the residual energy head ratio. Through experimental study, the residual energy head ratio is computed as a ratio of downstream flow energy (E2) to upstream flow energy (E1) at vortex structure. During the training and testing phases of AI models, the results of statistical tests, serving as quantitative evaluations, have shown that ensemble learning models namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost) models had higher level of efficiency in the E2/E1 predictions and followed by Model Tree (MT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Multivariate Adaptive Regression Spline (MARS). Additionally, the second-order regression-based equation was obtained from Fully Factorial Method (FFM) which had lower level of precision (R = 0.8275, RMSE = 0.1156, and MAE = 0.0846) in the residual energy head ratio predictions when compared with all predictive AI models. Variations of three effective factors (i.e., Fr, L/D, Hs/D) versus the predicted E2/E1 ratios were in well agreement with observational tests. Moreover, the results of Sobol's index indicated that Fr number was determined as the most effective parameter in the evaluation of residual energy head ratio in the vortex structure.

Bagheri Khatibani A., Khoshhal A.R., Basiri Tochaee E., Rasouli Jamnani S., Milani Moghaddam H.

Nowadays, utilizing of alternative materials instead of lead in the environmentally sensitive and nontoxic material research in radiation shielding has attracted a lot of attention. Within this study, pure samarium oxide and samarium oxide/graphene nanostructures were successfully synthesized consecutively by facile hydrothermal method. The various physical properties of these materials were investigated using the conventional methods of energy dispersive X-ray spectroscopy (EDX), UV–Visible spectrophotometry, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and field emission scanning electron microscopy (FESEM). XRD result verified cubic phase of Sm2O3; however, the addition of graphene modified slightly the main structure. The result of FESEM showed the connection between the graphene nanoplates and samarium oxide nanoroads. The verification of the elemental presence for the both samples was performed through EDX analysis. The main factors of gamma protection properties such as mass attenuation coefficients (MAC), mean free path (MFP), half value layer (HVL), tenth value layer (TVL) and linear attenuation coefficients (LAC) have been measured for Sm2O3/graphene sample. They were 0.59 cm2/g, 0.23 cm, 0.16 cm, 0.54 cm, and 4.2 cm−1, respectively. According to these factors, it can be concluded that utilizing of graphene/Sm2O3 as a gamma ray protector can be useful. In addition to the experimental examination, simulation with GEANT4 simulation code was also used for both samples to examine the shielding parameters. The experimental and simulation attenuation for Sm2O3/graphene was about 19.2 % and 18.6 %, respectively.

Rahmati M.

Mercaptopurine (6-MP), a cancer medication limited by its hydrophobic properties, was studied using molecular dynamics simulations with biodegradable copolymers (PLA-PEG, PLA-PEG-PLA, PEG-PLA-PEG) as drug carriers. The influence of PEG concentration in copolymers on drug release was examined. Results revealed that blending PEG with PLA or copolymerizing them affected hydrogen bond numbers and mixing energy in the drug carrier, dependent on copolymer type and PEG concentration. Introducing PEG into PLA chains increased hydrogen bonds and energy, but copolymerizing PLA and PEG did not consistently enhance hydrogen bond energy (PLA-PEG-PLA > PEG-PLA-PEG > PLA-PEG). Mixing energy was consistently elevated by PEG-PLA copolymerization, ranked PEG-PLA-PEG > PLA-PEG > PLA-PEG-PLA. The fastest drug release was observed with the PLA&PEG-10.9 carrier, attributed to weak PLA-PEG chain connections facilitating water access to PEG. Increasing PEG content reduced chain mixing energy, slowed drug release via stronger chain hydrogen bonding. Notably, PEG-PLA-PEG-8.7 exhibited the highest drug release rate due to quicker water penetration. The copolymer type and chain length were found to impact water molecule access to PEG and drug release rate significantly.

Zandi R., Najafzadeh M., Lari K., Ghazanfari-Moghaddam M.S.

This research aimed to identify the optimal geometry for maximizing wave energy extraction from wave energy converters (WECs) by employing different primary geometries of floating bodies (specifically, cube, cylinder, and sphere). Laboratory experiments were conducted using a wave tank to generate waves with varying characteristics. The floats were subjected to waves of different wave conditions, including height (4 cm, 6 cm, and 9 cm) and frequency (0.9 Hz and 0.83 Hz). Multiple probes and sensors were used to collect experimental data. The results revealed that when hit by a wave with a height of 9 cm and a frequency of 0.9 Hz, the rectangular cube experienced an average displacement of 12 cm and 14 cm along its length, with the same weight and draft, respectively. Furthermore, comparisons of the experimental data across different scenarios indicated that the rectangular cube, elongated perpendicular to the incident waves, performed well in most of the test cases. The efficiency of primary wave energy conversion was evaluated for all floated bodies and wave conditions, encompassing a range of values from 0.02 to 0.34. Additionally, numerical simulations of wave and float displacement demonstrated good agreement with the experimental observations.

Yoosefian M., Sabaghian H., Kermanshahaninezhad S.O.

The COVID-19 pandemic posed unprecedented challenges to global healthcare, particularly affecting respiratory systems and impacting individuals with pre-existing conditions, including those with HIV. HIV's impact on clinical outcomes was assessed in four Statistical Population, synchronized with control groups. The study also explored the influence of SARS-CoV-2 and COVID-19 treatments. Ultimately, a comparison was drawn between patients with and without HIV. In the first Statistical Population of COVID-19 patients with HIV, predominantly African-American men with risk factors such as obesity, hypertension, and diabetes were present. Diagnostic results showed no significant differences between the two groups. In the second Statistical Population, half of the patients were asymptomatic, with diagnoses mostly based on clinical symptoms; 6 individuals developed severe respiratory illness. In the third Statistical Population, 81 % of patients were treated at home, and all hospitalized patients had CD4+ lymphocyte counts above 350 cells/mm³. Most patients improved, with fatalities attributed to comorbid conditions. In the fourth Statistical Population, HIV patients were less likely to benefit from antimicrobial drugs, and mortality was higher, though synchronized analysis did not reveal significant differences. HIV patients are more susceptible to COVID-19, but the direct impact is less significant than other factors. Additional factors contribute to increased risk, while early improvement, accurate diagnosis, and intensive care reduce fatalities.

Barshan A., Mohammadi S.M., Abdollahi F., Davarani R.Z., Esmaeili S.

This work proposes an approach based on critical dynamic modes to detect four types of phasor measurement unit (PMU) data anomalies in smart power grids. Smart power grids as modern power systems involve new information and communication technologies (ICTs), relying on real-time data, like data of PMUs. Due to the dependence of PMUs on communication technology, PMUs are prone to different data anomalies and false data injection attacks, such as replay attack, which has been rarely considered in previous works. Since detecting abnormal data is necessary for the proper function of the power system, most detection methods have been data-based approaches that may need a large amount of data and lead to more complexity. This paper proposes an online detection approach based on the minimum number of dynamic modes for the local detection of a replay attack and three types of bad data injection on PMU measurements. For this purpose, a distributed modeling of the power system is considered. Then, a replay attack and bad data injections are detected locally by tracking critical dynamic modes. The proposed approach can detect simultaneous data anomalies on more than one PMU. The effectiveness and accuracy of this approach are evaluated through simulations on the 10-machine New England 39-bus power system.

Es'haghi P., Safaei A.

Abstract A plasmonic electro-optical modulator, which is based on the free carrier dispersion effect, has been introduced here. The structure of the proposed modulator is a substrate of fused silica/gold/aluminum doped zinc oxide/hafnium dioxide/gold/air. The free carrier dispersion effect occurs in the aluminum doped zinc oxide (AZO) layer. As the electrical permittivity of the AZO layer is near zero (the epsilon near zero effect) at a wavelength of 1.55 μm, the amplitude of an electrical field is high in this layer, therefore, the modulator is highly sensitive to the varying refractive index of this layer. By applying a voltage to two gold layers, the electrical charge density changes in the AZO layer. Therefore, the real and imaginary parts of the refractive index have been changed, which led to a change in the absorption of the modulator. In order to obtain the charge density distribution in the AZO layer, the Poisson equation is solved by using the finite difference method. To investigate modulator absorption, the Nelder-Mead method is implemented in order to solve the dispersion equation numerically. Finally, the magnetic field, the electric field, and the time average of the Poynting vector have been given by using the least squares approximation method.

Gonouiezadeh R., Safari H.

Abstract In this paper, the time evolution of a two-level atom in the presence of a medium-assisted thermal field is explored through which the formula for the decay rate of an excited atom is generalized in two aspects. The obtained formula applies to a thermal electromagnetic field as well as to the presence of an arbitrary arrangement of magnetoelectric media. In order to be general with respect to the material environment, the Green’s function approach is used. It is shown that the non-zero temperature contributes to the decay rate via an additive term that is equal to the zero-temperature result multiplied by two times the photon number at the atomic transition frequency.