Showing papers by "Islamic Azad University published in 2021"

An improved grey wolf optimizer for solving engineering problems

Abstract: In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems

Abstract: Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.

C-, N-Vacancy defect engineered polymeric carbon nitride towards photocatalysis: viewpoints and challenges

Abstract: As an alluring metal-free polymeric semiconductor material, graphite-like carbon nitride (g-C3N4; abbreviated as GCN) has triggered a new impetus in the field of photocatalysis, mainly favoured from its fascinating physicochemical and photoelectronic structural features. However, certain inherent drawbacks, involving rapid reassembly of photocarriers, low specific surface area and insufficient optical absorption, limit the wide-range applicability of GCN. Generation of 0D point defects mainly by introducing vacancies (C and/or N) into the framework of GCN has spurred extensive consideration owing to their distinctive qualities to manoeuvre substantially, the optical absorption, radiative carrier isolation, and surface photoreactions. The present review endeavours to summarise a comprehensive study on vacancy defect engineered GCN. Starting from the basic introduction of defects and C/N vacancy modulated GCN, numerous advanced strategies for the controlled designing of vacancy rich GCN have been explored and discussed. Afterwards, light was thrown on the various substantial technologies which are useful for characterising and identifying the introduction of defects in GCN. The salient significance of defect engineering in GCN has been reviewed concerning its impact on optical absorption, charge isolation and surface photoreaction ability. Typically, the achievement of defect engineered GCN has been scrutinised toward various applications like photocatalytic water splitting, CO2 conversion, N2 fixation, pollutant degradation, and H2O2 production. Finally, the review ends with conclusions and vouchsafing future challenges and opportunities on the intriguing and emerging area of vacancy defect engineered GCN photocatalysts.

The impact of COVID-19 pandemic on physical and mental health of Asians: A study of seven middle-income countries in Asia.

Abstract: The coronavirus disease (COVID-19) pandemic has impacted the economy, livelihood, and physical and mental well-being of people worldwide This study aimed to compare the mental health status during the pandemic in the general population of seven middle income countries (MICs) in Asia (China, Iran, Malaysia, Pakistan, Philippines, Thailand, and Vietnam) All the countries used the Impact of Event Scale-Revised (IES-R) and Depression, Anxiety and Stress Scale (DASS-21) to measure mental health There were 4479 Asians completed the questionnaire with demographic characteristics, physical symptoms and health service utilization, contact history, knowledge and concern, precautionary measure, and rated their mental health with the IES-R and DASS-21 Descriptive statistics, One-Way analysis of variance (ANOVA), and linear regression were used to identify protective and risk factors associated with mental health parameters There were significant differences in IES-R and DASS-21 scores between 7 MICs (p<005) Thailand had all the highest scores of IES-R, DASS-21 stress, anxiety, and depression scores whereas Vietnam had all the lowest scores The risk factors for adverse mental health during the COVID-19 pandemic include age <30 years, high education background, single and separated status, discrimination by other countries and contact with people with COVID-19 (p<005) The protective factors for mental health include male gender, staying with children or more than 6 people in the same household, employment, confidence in doctors, high perceived likelihood of survival, and spending less time on health information (p<005) This comparative study among 7 MICs enhanced the understanding of metal health in the general population during the COVID-19 pandemic

A theoretical study of the Caputo-Fabrizio fractional modeling for hearing loss due to Mumps virus with optimal control

Abstract: Mumps is the most common cause of acquired unilateral deafness in children, in which hearing loss occurs at all auditory frequencies. We use a box model to model hearing loss in children caused by the mumps virus, and since the fractional-order derivative retains the effect of system memory, we use the Caputo–Fabrizio fractional derivative in this modeling. In the beginning, we compute the basic reproduction number R 0 and equilibrium points of the system and investigate the stability of the system at the equilibrium point. By utilizing the Picard–Lindelof technique, we prove the existence an unique solution for given fractional CF -system of hearing loss model and investigate the stability of iterative method by fixed point theory. The optimal control of the system is determined by considering the treatment as a control strategy to reduce the number of infected people. Using the Euler method for the fractional-order Caputo–Fabrizio derivative, the approximate solution of the system is calculated. We present a numerical simulation for the transmission of disease with respect to the transmission rate and the basic reproduction number in two cases R 0 1 and R 0 > 1 . To investigate the effect of fractional order derivative on the behavior and value of each of the variables in Model 2, we calculate the results for several fractional order derivatives and compare the results. Also, considering the importance of reproduction number in the continuation of disease transmission, we analyze the sensitivity of R 0 respect to each of the model parameters and determine the impact of each parameter.

The role of hypoxia in the tumor microenvironment and development of cancer stem cell: a novel approach to developing treatment.

Abstract: Hypoxia is a common feature of solid tumors, and develops because of the rapid growth of the tumor that outstrips the oxygen supply, and impaired blood flow due to the formation of abnormal blood vessels supplying the tumor. It has been reported that tumor hypoxia can: activate angiogenesis, thereby enhancing invasiveness and risk of metastasis; increase survival of tumor, as well as suppress anti-tumor immunity and hamper the therapeutic response. Hypoxia mediates these effects by several potential mechanisms: altering gene expression, the activation of oncogenes, inactivation of suppressor genes, reducing genomic stability and clonal selection. We have reviewed the effects of hypoxia on tumor biology and the possible strategiesto manage the hypoxic tumor microenvironment (TME), highlighting the potential use of cancer stem cells in tumor treatment.

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

Abstract: Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the e-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS.

Eco-Friendly Cutting Fluids in Minimum Quantity Lubrication Assisted Machining: A Review on the Perception of Sustainable Manufacturing

Abstract: In modern days, the conception of sustainability has progressively advanced and has begun receiving global interest. Thus, sustainability is an imperative idea in modern research. Considering the recent trend, this review paper presents a summary of the previously published research articles on minimum quantity lubrication (MQL) assisted machining. The requirement to stir towards sustainability motivated the researchers to revise the effects of substitute lubrication methods on the machining. Conventional lubri-cooling agents are still extensively employed when machining of engineering alloys, but the majority of the recent papers have depicted that the utilization of vegetable oil, nanofluids, and nanoplatelets in MQL system confers superior machining performances as compared to conventional lubrication technology. In actual, the definite principle of this manuscript is to re-examine modern advancements in the MQL technique and also explore the benefits of the vegetable oil and nanofluid as a lubricant. In brief, this paper is a testimony to the advancing capabilities of eco-friendly MQL technique which is a viable alternative to the flood lubrication technology, and the outcomes of this review work can be contemplated as a movement towards sustainable machining.

Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network.

Abstract: Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

Oil-in-water nanoemulsions comprising Berberine in olive oil: biological activities, binding mechanisms to human serum albumin or holo-transferrin and QMMD simulations.

Abstract: Berberine is widely used in traditional Iranian medicine to treat diabetes and inflammatory conditions. This study was aimed at developing a method for the preparation of Berberine nanoparticles (N...

A new optimization method based on COOT bird natural life model

Abstract: Recently, many intelligent algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search volatile and multi-dimensional solution spaces and find optimal answers timely. In this paper, a new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot. The Coot algorithm imitates two different modes of movement of birds on the water surface: in the first phase, the movement of birds is irregular, and in the second phase, the movements are regular. The swarm moves towards a group of leading leaders to reach a food supply; the movement of the end of the swarm is in the form of a chain of coots, each of coot which moves behind its front coots. The algorithm then runs on a number of test functions, and the results are compared with well-known optimization algorithms. In addition, to solve several real problems, such as Tension/Compression spring, Pressure vessel design, Welded Beam Design, Multi-plate disc clutch brake, Step-cone pulley problem, Cantilever beam design, reducer design problem, and Rolling element bearing problem this algorithm is used to confirm the applicability of this algorithm. The results show that this algorithm is capable to outperform most of the other optimization methods. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm .

A chain mediation model on COVID-19 symptoms and mental health outcomes in Americans, Asians and Europeans.

Abstract: The novel Coronavirus-2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) in March 2020, impacting the lifestyles, economy, physical and mental health of individuals globally. This study aimed to test the model triggered by physical symptoms resembling COVID-19 infection, in which the need for health information and perceived impact of the pandemic mediated the path sequentially, leading to adverse mental health outcomes. A cross-sectional research design with chain mediation model involving 4612 participants from participating 8 countries selected via a respondent-driven sampling strategy was used. Participants completed online questionnaires on physical symptoms, the need for health information, the Impact of Event Scale-Revised (IES-R) questionnaire and Depression, Anxiety and Stress Scale (DASS-21). The results showed that Poland and the Philippines were the two countries with the highest levels of anxiety, depression and stress; conversely, Vietnam had the lowest mean scores in these areas. Chain mediation model showed the need for health information, and the perceived impact of the pandemic were sequential mediators between physical symptoms resembling COVID-19 infection (predictor) and consequent mental health status (outcome). Excessive and contradictory health information might increase the perceived impact of the pandemic. Rapid COVID-19 testing should be implemented to minimize the psychological burden associated with physical symptoms, whilst public mental health interventions could target adverse mental outcomes associated with the pandemic.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

Abstract: A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

COVID-19 and cardiac injury: clinical manifestations, biomarkers, mechanisms, diagnosis, treatment, and follow up.

Abstract: Introduction Coronavirus disease 2019 (COVID-19) has the characteristics of high transmission, diverse clinical manifestations, and a long incubation period. In addition to infecting the respiratory system, COVID-19 also has adverse effects on the cardiovascular system. COVID-19 causes acute myocardial injuries, as well as chronic damage to the cardiovascular system. Areas covered The present review is aimed at providing current information on COVID-19 and the cardiovascular system. PubMed, Scopus, Science direct, and Google Scholar were searched. Expert opinion It is suggested that heart injury caused by COVID-19 infection might be an important cause of severe clinical phenotypes or adverse events in affected patients. Myocardial damage is closely related to the severity of the disease and even the prognosis in patients with COVID-19. In addition to disorders that are caused by COVID-19 on the cardiovascular system, more protection should be employed for patients with preexisting cardiovascular disease (CVD). Hence, it is very important that once relevant symptoms appear, patients with COVID-19 be rapidly treated to reduce mortality. Thus, early measurements of cardiac damage via biomarkers following hospitalization for COVID-19 infections in a patient with preexisting CVD are recommended, together with careful monitoring of any myocardial injury that might be caused by the infection.Abbreviations: ICU: An intensive care unit; 2019-nCoV: 2019 novel coronavirus; ACEI: ACE inhibitor; ACS: Acute coronary syndrome; ARDS: Acute respiratory distress syndrome; AT1R: Ang II type 1 receptor; ATP: Adenosine triphosphate; ACC: American College of Cardiology; ACE: Angiotensin converting enzyme; Ang II: Angiotensin II; ARB: Angiotensin II receptor blocker; AV block: Atrioventricular block; CAD: Coronary artery disease; CVD: Cardiovascular disease; CT: Computerized tomography; CHF: Congestive heart failure; CHD: Coronary heart disease; CK-MB: Creatine kinase isoenzyme-MB; CRP: C-reactive protein; cTnI: Cardiac troponin I; EAT: Epicardial adipose tissue; ECMO: Extracorporeal membrane oxygenation; FDA: Food and Drug Administration; G-CSF: Granulocyte colony-stimulating factor; HFrEF: HF with a reduced ejection fraction; synhACE2: Human isoform of ACE2; IL: Interleukin; IABP: Intra-aortic balloon counterpulsation; IP10: Interferon γ-induced protein 10 kDa; LPC: Lysophosphatidylcholine; Mas: Mitochondrial assembly receptor; MCP1: Monocyte chemoattractant protein-1; MERS: Middle East respiratory syndrome; MIP1a: macrophage inflammatory protein 1a: MOF: Multiple organ failure; MI: Myocardial infarction; MRI: Magnetic resonance imaging; MYO: Myohe-moglobin; NT-proBNP: N-terminal pro-brain natriuretic peptide; PCPS: Percutaneous cardiopulmonary assistance; rhACE2: Recombinant human ACE2; SARS: Severe acute respiratory syndrome; Th: T helper; RAS: Renin-angiotensin system; TNF-α: Tumor necrosis factor-α; WHO: World Health Organization.

Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs)

Abstract: In this paper, artificial neural networks (ANNs) are developed to predict the thermal conductivity ( $$k_{\text{nf}}$$ ) of multi-walled carbon nanotubes (MWCNTs)-CuO/water nanofluid. After generating experimental data points, an algorithm is proposed to find the optimum ANN regarding the best performance. Three different states including ANN, experimental, and fitting method have been evaluated, and their errors in $$k_{\text{nf}}$$ prediction have been investigated. Regarding the obtained results, it can be seen that the best and worst neuron numbers are 8 and 31, respectively. Then, using curve fitting method, the behavior of nanofluid is predicted by a surface equation with third order. Finally, the ANN results and fitting results have been compared. Finally, it is found that the ability of the ANN to predict the $$k_{\text{nf}}$$ is greater. It was also found that the ANN has better performance and correlation and thus less error in the predicted data. On the other hand, comparing methods in predicting the $$k_{\text{nf}}$$ is an important issue. The use of ANNs in predicting the $$k_{\text{nf}}$$ as a new approach can lead to a great contribution in determining the most desirable performance and achieving the best and most accurate state. In addition, mean squared error (MSE) has obtained 2.4451e−05 for fitting method. According to the experimental data, it can be seen that in φ = 0.6% and T = 50 °C, an increase of more than 30.38% has occurred in the $$k_{\text{nf}}$$ compared to the ambient temperature.

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Abstract: The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO 3 -N), Nitrite-Nitrogen ( NO 2 -N), Phosphate ( P O 4 3 - ), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance ( R 2 t e s t = 0.98 , R M S E t e s t = 2.99 ), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy ( R 2 t e s t = 0.97 , R M S E t e s t = 3.74 ). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

Is one performing the treatment data of adsorption kinetics correctly

Abstract: In the literature, the linear form of the pseudo-first-order (PFO) and pseudo-second-order (PSO) models are often applied for fitting the data of adsorption kinetics. Many authors have applied the linear form of the PSO model and concluded that such a kinetics is better fitted, based on the values of adsorption capacity at the equilibrium (qe) and the high value (which should be close to 1.0) of the coefficient of determination (R2). The linearized PFO model is usually ruled-out because the values of qe and R2 are worse than those obtained by the linearized PSO. On the other hand, the nonlinear fitting of data is highly recommended for the use of equations that are not typically linear such as kinetics data. In this communication, the data of 52 articles (containing 225 experiments of adsorption kinetics) were collected, and the kinetic data were treated using the linear and nonlinear PFO and PSO models. Results indicated that the values of k2 (the rate constant of the PSO model) calculated from the nonlinear fitting method were quite different from those acquired from the linear one. However, the values of qe2 (adsorption capacity at the equilibrium of the PSO model) are in complete agreement, which induces users to an erroneous decision. Using a linearized kinetic model, all the 225 values of R2 of the PSO model were closer to 1.0 than PFO. However, when nonlinearized fitting of the data was used, 122 out of 225 cases (54.22%) showed that the nonlinear PFO is better fitted than the PSO kinetic model.

Tailoring cadmium sulfide-based photocatalytic nanomaterials for water decontamination: a review

Abstract: Global energy demand and pollution are calling for advanced materials such as visible light semiconductor photocatalysts. In particular, cadmium sulfide (CdS) appears promising due to its tunable bandgap, high absorption of visible light and excellent optical properties. Here we review the photocatalytic mechanism, properties, synthesis and application to wastewater treatment of CdS photocatalysts. Strategies to improve photocatalytic performance include heteroatom doping, heterojunction formation, morphology and crystallinity modification, hybridization with co-catalysts and the use of carbon materials.