Showing papers by "Islamic Azad University published in 2020"

Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis.

Abstract: The COVID-19 pandemic has had a significant impact on public mental health Therefore, monitoring and oversight of the population mental health during crises such as a panedmic is an immediate priority The aim of this study is to analyze the existing research works and findings in relation to the prevalence of stress, anxiety and depression in the general population during the COVID-19 pandemic In this systematic review and meta-analysis, articles that have focused on stress and anxiety prevalence among the general population during the COVID-19 pandemic were searched in the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases, without a lower time limit and until May 2020 In order to perform a meta-analysis of the collected studies, the random effects model was used, and the heterogeneity of studies was investigated using the I2 index Moreover data analysis was conducted using the Comprehensive Meta-Analysis (CMA) software The prevalence of stress in 5 studies with a total sample size of 9074 is obtained as 296% (95% confidence limit: 243–354), the prevalence of anxiety in 17 studies with a sample size of 63,439 as 319% (95% confidence interval: 275–367), and the prevalence of depression in 14 studies with a sample size of 44,531 people as 337% (95% confidence interval: 275–406) COVID-19 not only causes physical health concerns but also results in a number of psychological disorders The spread of the new coronavirus can impact the mental health of people in different communities Thus, it is essential to preserve the mental health of individuals and to develop psychological interventions that can improve the mental health of vulnerable groups during the COVID-19 pandemic

A new study on the mathematical modelling of human liver with Caputo–Fabrizio fractional derivative

Abstract: In this research, we aim to propose a new fractional model for human liver involving Caputo–Fabrizio derivative with the exponential kernel. Concerning the new model, the existence of a unique solution is explored by using the Picard–Lindelof approach and the fixed-point theory. In addition, the mathematical model is implemented by the homotopy analysis transform method whose convergence is also investigated. Eventually, numerical experiments are carried out to better illustrate the results. Comparative results with the real clinical data indicate the superiority of the new fractional model over the pre-existent integer-order model with ordinary time-derivatives.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

A comprehensive review of COVID-19 characteristics

Abstract: In December 2019, a novel coronavirus, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or (2019-nCoV) with unknown origin spread in Hubei province of China. The epidemic disease caused by SARS-CoV-2 called coronavirus disease-19 (COVID-19). The presence of COVID-19 was manifested by several symptoms, ranging from asymptomatic/mild symptoms to severe illness and death. The viral infection expanded internationally and WHO announced a Public Health Emergency of International Concern. To quickly diagnose and control such a highly infectious disease, suspicious individuals were isolated and diagnostic/treatment procedures were developed through patients’ epidemiological and clinical data. Early in the COVID-19 outbreak, WHO invited hundreds of researchers from around the world to develop a rapid quality diagnosis, treatment and vaccines, but so far no specific antiviral treatment or vaccine has been approved by the FDA. At present, COVID-19 is managed by available antiviral drugs to improve the symptoms, and in severe cases, supportive care including oxygen and mechanical ventilation is used for infected patients. However, due to the worldwide spread of the virus, COVID-19 has become a serious concern in the medical community. According to the current data of WHO, the number of infected and dead cases has increased to 8,708,008 and 461,715, respectively (Dec 2019 –June 2020). Given the high mortality rate and economic damage to various communities to date, great efforts must be made to produce successful drugs and vaccines against 2019-nCoV infection. For this reason, first of all, the characteristics of the virus, its pathogenicity, and its infectious pathways must be well known. Thus, the main purpose of this review is to provide an overview of this epidemic disease based on the current evidence.

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies.

Abstract: Purpose: Coronavirus disease 2019 (COVID-19) is an emerging disease that was first reported in Wuhan city, the capital of Hubei province in China, and has subsequently spread worldwide. Risk factor...

Progress in Conductive Polyaniline-Based Nanocomposites for Biomedical Applications: A Review.

Abstract: Inherently conducting polymers (ICPs) are a specific category of synthetic polymers with distinctive electro-optic properties, which involve conjugated chains with alternating single and double bonds. Polyaniline (PANI), as one of the most well-known ICPs, has outstanding potential applications in biomedicine because of its high electrical conductivity and biocompatibility caused by its hydrophilic nature, low-toxicity, good environmental stability, and nanostructured morphology. Some of the limitations in the use of PANI, such as its low processability and degradability, can be overcome by the preparation of its blends and nanocomposites with various (bio)polymers and nanomaterials, respectively. This review describes the state-of-the-art of biological activities and applications of conductive PANI-based nanocomposites in the biomedical fields, such as antimicrobial therapy, drug delivery, biosensors, nerve regeneration, and tissue engineering. The latest progresses in the biomedical applications of PANI-based nanocomposites are reviewed to provide a background for future research.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles

Abstract: In a near future, electric vehicles (EVs) will constitute considerable part of transportation systems due to their important aspects such as being environment friendly. To manage high number of EVs, developing hydrogen storage-based intelligent parking lots (IPLs) can help power system operators to overcome caused problems by high penetration of EVs. In this work, a new method is applied to get optimal management of IPLs in an uncertain environment and provide optimal bidding curves to take part in power market. The main purpose of this work is to get optimal bidding curves with considering power price uncertainty and optimal operation of IPLs. To model uncertainty of power price in the power market and develop optimal bidding curve, the opportunity, deterministic and robustness functions of the information gap decision theory (IGDT) technique has been developed. Obtained results has been presented in three strategies namely risk-taker, risk-neutral, and risk-averse corresponding to opportunity, deterministic, and robustness functions of the IGDT technique. In order to demonstrate the effects of demand response program (DRP), each strategy is optimized with and without DRP cases. The mixed-integer non-linear programming model is used to formulate the proposed problem which is solved using the GAMS optimization software under DICOPT solver.

A review on the cleavage priming of the spike protein on coronavirus by angiotensin-converting enzyme-2 and furin.

Abstract: The widespread antigenic changes lead to the emergence of a new type of coronavirus (CoV) called as severe acute respiratory syndrome (SARS)-CoV-2 that is immunologically different from the previous circulating species. Angiotensin-converting enzyme-2 (ACE-2) is one of the most important receptors on the cell membrane of the host cells (HCs) which its interaction with spike protein (SP) with a furin-cleavage site results in the SARS-CoV-2 invasion. Hence, in this review, we presented an overview on the interaction of ACE-2 and furin with SP. As several kinds of CoVs, from various genera, have at their S1/S2 binding site a preserved site, we further surveyed the role of furin cleavage site (FCS) on the life cycle of the CoV. Furthermore, we discussed that the small molecular inhibitors can limit the interaction of ACE-2 and furin with SP and can be used as potential therapeutic platforms to combat the spreading CoV epidemic. Finally, some ongoing challenges and future prospects for the development of potential drugs to promote targeting specific activities of the CoV were reviewed. In conclusion, this review may pave the way for providing useful information about different compounds involved in improving the effectiveness of CoV vaccine or drugs with minimum toxicity against human health.Communicated by Ramaswamy H. Sarma.

Resource Management Approaches in Fog Computing: a Comprehensive Review

Abstract: In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

Potential effects of curcumin in the treatment of COVID-19 infection.

Abstract: Coronavirus disease 2019 (COVID-19) outbreak is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with considerable mortality worldwide. The main clinical manifestation of COVID-19 is the presence of respiratory symptoms, but some patients develop severe cardiovascular and renal complications. There is an urgency to understand the mechanism by which this virus causes complications so as to develop treatment options. Curcumin, a natural polyphenolic compound, could be a potential treatment option for patients with coronavirus disease. In this study, we review some of the potential effects of curcumin such as inhibiting the entry of virus to the cell, inhibiting encapsulation of the virus and viral protease, as well as modulating various cellular signaling pathways. This review provides a basis for further research and development of clinical applications of curcumin for the treatment of newly emerged SARS-CoV-2.

When pandemics impact economies and climate change: Exploring the impacts of COVID-19 on oil and electricity demand in China

Abstract: Despite all the scientific and technological developments in the past one hundred years, biologic issues such as pandemics are a constant threat to society. While one of the aspects of a pandemic is the loss of human life, the outbreak has multi-dimensional impacts across regional and global societies. In this paper, a comparative regressive and neural network model is developed to analyze the impacts of COVID-19 (coronavirus) on the electricity and petroleum demand in China. The environmental analysis shows that the epidemic severeness significantly affects the electricity and the petroleum demand, both directly and indirectly. The outputs of the model stated that the elasticity of petroleum and electricity demand toward the population of the infected people is -0.1% and -0.65%, respectively. The mentioned results show that pandemic status has a significant impact on energy demand, and also its impacts can be tracked into every corner of human society.

A mathematical model for COVID-19 transmission by using the Caputo fractional derivative

Abstract: We present a mathematical model for the transmission of COVID-19 by the Caputo fractional-order derivative. We calculate the equilibrium points and the reproduction number for the model and obtain the region of the feasibility of system. By fixed point theory, we prove the existence of a unique solution. Using the generalized Adams-Bashforth-Moulton method, we solve the system and obtain the approximate solutions. We present a numerical simulation for the transmission of COVID-19 in the world, and in this simulation, the reproduction number is obtained as R 0 = 1 : 610007996 , which shows that the epidemic continues.

Conjugate natural convection flow of Ag–MgO/water hybrid nanofluid in a square cavity

Abstract: The conjugate natural convection of a new type of hybrid nanofluid (Ag–MgO/water hybrid nanofluid) inside a square cavity is addressed. A thick layer of conductive solid is considered over the hot wall. The governing partial differential equations (PDEs) representing the physical model of the natural convection of the hybrid nanofluid along with the boundary conditions are reported. The thermophysical properties of the nanofluid are directly calculated using experimental data. The governing PDEs are transformed into a dimensionless form and solved by the finite element method. The effect of the variation of key parameters, such as the volume fraction of nanoparticles, Rayleigh number, and the ratio between the thermal conductivity of the wall and the thermal conductivity of the hybrid nanofluid (Rk), is studied. Furthermore, the effects of the key parameters are investigated on the temperature distribution, local Nusselt number, and average Nusselt number. The results of this study show that the heat transfer rate increases by adding hybrid nanoparticles for a conduction-dominant regime (low Rayleigh number). The heat transfer rate is an increasing function of both the Rayleigh number and the thermal conductivity ratio (Rk). In the case of a convective-dominant flow (high Rayleigh number flow) and an excellent thermally conductive wall, the local Nusselt number at the surface of the conjugate wall decreases substantially by moving from the bottom of the cavity toward the top.

Biosorption, an efficient method for removing heavy metals from industrial effluents: A Review

Abstract: Common methods for removing heavy metals have numerous drawbacks, including low efficiency and high costs. In the biosorption of heavy metals, ions biosorbed on surfaces and active sites of biosorbents. In this paper, the relationship between the factors (pH, temperature, biosorbent dosage, retention time and Functional groups) and removal efficiency has been investigated. The purpose of this work is to introduce optimal conditions for biosorption reaction. Also, by introducing various types of biosorbents, expressed the advantages and method of preparation for each one. In various papers, not all biosorption isotherm models have been mentioned, there are described various types of biosorption isotherm, kinetics and thermodynamics models and important process data is set up for quick access to the tables.

Analysis of the model of HIV-1 infection of CD4+$CD4^{+}$ T-cell with a new approach of fractional derivative

Abstract: By using the fractional Caputo–Fabrizio derivative, we investigate a new version for the mathematical model of HIV. In this way, we review the existence and uniqueness of the solution for the model by using fixed point theory. We solve the equation by a combination of the Laplace transform and homotopy analysis method. Finally, we provide some numerical analytics and comparisons of the results.

COVID-19 Pandemic and Comparative Health Policy Learning in Iran.

Abstract: BACKGROUND: On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus disease (COVID-19) a global pandemic. Starting in December 2019 from China, the first cases were officially announced on February 19 in Qom city, Iran. As of April 3, 2020, 206 countries have reported a total of 932166 cases with 46764 deaths. Along with China, USA, Italy, Spain, and Germany, Iran has been suffering the hardest burden of COVID-19 outbreak. Worse still, countries like Iran are struggling with the double burden of political sanctions to provide lifesaving medical equipment and medicines to combat the emergency. METHODS: Using systematic document content analysis and through the lenses of health policy triangle, this article aims to compare the policies and strategies that Iran is adopting, with the experience and recommendations of China and WHO to combat COVID-19. RESULTS: Iran has formulated contextual-based policies to combat COVID-19 outbreak before and after virus entrance. Insufficient whole-government, whole-society approach in managing the outbreak, inadequate lifesaving and protective equipment, and delayed decisive governance are the biggest challenges in policy making to combat COVID-19. COVID-19 policies are a public health concern and require professional advocacy attempts through appropriate inter-sectoral collaboration and whole-government coalitions. CONCLUSION: COVID-19 is an unfolding outbreak; hence, policy learning is crucial to formulate appropriate policies and implement them accordingly. Iran has made many efforts to defeat the outbreak, but more coherent, timely and efficient action is required, now, more than ever, to save lives and slow the spread of this pandemic.

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

Abstract: This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n -order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

A Comprehensive Survey of “Metamaterial Transmission-Line Based Antennas: Design, Challenges, and Applications”

Abstract: In this review paper, a comprehensive study on the concept, theory, and applications of composite right/left-handed transmission lines (CRLH-TLs) by considering their use in antenna system designs have been provided. It is shown that CRLH-TLs with negative permittivity (e <; 0) and negative permeability (μ <; 0) have unique properties that do not occur naturally. Therefore, they are referred to as artificial structures called “metamaterials”. These artificial structures include series left-handed (LH) capacitances (C L ), shunt LH inductances (L L ), series right-handed (RH) inductances (LR), and shunt RH capacitances (CR) that are realized by slots or interdigital capacitors, stubs or via-holes, unwanted current flowing on the surface, and gap distance between the surface and ground-plane, respectively. In the most cases, it is also shown that structures based on CRLH metamaterial-TLs are superior than their conventional alternatives, since they have smaller dimensions, lower-profile, wider bandwidth, better radiation patterns, higher gain and efficiency, which make them easier and more cost-effective to manufacture and mass produce. Hence, a broad range of metamaterial-based design possibilities are introduced to highlight the improvement of the performance parameters that are rare and not often discussed in available literature. Therefore, this survey provides a wide overview of key early-stage concepts of metematerial-based designs as a thorough reference for specialist antennas and microwave circuits designers. To analyze the critical features of metamaterial theory and concept, several examples are used. Comparisons on the basis of physical size, bandwidth, materials, gain, efficiency, and radiation patterns are made for all the examples that are based on CRLH metamaterialTLs. As revealed in all the metematerial design examples, foot-print area decrement is an important issue of study that have a strong impact for the enlargement of the next generation wireless communication systems.