Islamic Azad University North Tehran Branch

Green synthesis of nanoparticles by biological systems especially plant extracts has become an emerging field in nanotechnology. In this study, zinc oxide nanoparticles were synthesized using Laurus nobilis L. leaves aqueous extract and two different zinc salts (zinc acetate and zinc nitrate) as precursors. The synthesized nanoparticles were characterized by Ultraviolet–Visible spectroscopy (UV–Vis), Fourier Transform Infrared Spectroscopy (FT-IR), X-Ray Diffraction analysis (XRD), Energy-Dispersive X-ray analysis (EDX) and Scanning Electron Microscopy (SEM). UV–Vis spectra showed typical absorption peaks in around 350 nm due to their large excitation binding energy at room temperature. Chemical bond formations of zinc oxide were confirmed by FT-IR analyses. XRD results revealed the formation of hexagonal wurtzite structure, and SEM analyses showed spherical shape with the average size (21.49, 25.26) nm for the synthesized nanoparticles by zinc acetate and zinc nitrate respectively. EDX analyses confirmed high purity for the synthesized nanoparticles.

Hydrogen production <italic>via</italic> solar water splitting can be enhanced by combining semiconductors with various 2-dimensional materials.

Free radicals are a group of damaging molecules produced during the normal metabolism of cells in the human body. Exposure to ultraviolet radiation, cigarette smoking, and other environmental pollutants enhances free radicals in the human body. The destructive effects of free radicals may also cause harm to membranes, enzymes, and DNA, leading to several human diseases such as cancer, atherosclerosis, malaria, coronavirus disease (COVID-19), rheumatoid arthritis, and neurodegenerative illnesses. This process occurs when there is an imbalance between free radicals and antioxidant defenses. Since antioxidants scavenge free radicals and repair damaged cells, increasing the consumption of fruits and vegetables containing high antioxidant values is recommended to slow down oxidative stress in the body. Additionally, natural products demonstrated a wide range of biological impacts such as anti-inflammatory, anti-aging, anti-atherosclerosis, and anti-cancer properties. Hence, in this review article, our goal is to explore the role of natural therapeutic antioxidant effects to reduce oxidative stress in the diseases.

The Internet of Things (IoT), as a pervasive paradigm, is becoming an integral part of the tech industry and academic research in recent years. It forms a ubiquitous heterogeneous network connecting humans and things. The basic premise is acquiring data from the environment with sensors and remote intelligent management via actuators. For IoT service providers, time and place are functional parameters. Whereas most IoT scenarios are in indoor spaces and GPS cannot fully cover them, applying an indoor positioning system (IPS) is necessary. Besides, indoor enabling technologies can leverage the capability of IoT in context-aware services. In this article, we aim to provide a panoramic view of IPSs and localization services with the centrality of IoT. First, we explain the main concepts and review the latest positioning methods, techniques, and technologies with IoT remarks. Then, we discuss technical implementation challenges and open issues with feasible solutions. Finally, we mentioned location-based services (LBSs), real IoT applications, and active vendors in the realm of positioning services. This article provides a real insight into LBSs in IoT for future research.

Following cancer, cells in a particular tissue can no longer respond to the factors involved in controlling cell survival, differentiation, proliferation, and death. In recent years, it has been indicated that alterations in the gut microbiota components, intestinal epithelium, and host immune system are associated with cancer incidence. Also, it has been demonstrated that the short-chain fatty acids (SCFAs) generated by gut microbiota are vitally crucial in cell homeostasis as they contribute to the modulation of histone deacetylases (HDACs), resulting effected cell attachment, immune cell immigration, cytokine production, chemotaxis, and the programmed cell death. Therefore, the manipulation of SCFA levels in the intestinal tract by alterations in the microbiota structure can be potentially taken into consideration for cancer treatment/prevention. In the current study, we will explain the most recent findings on the detrimental or protective roles of SFCA (particularly butyrate, propionate, and acetate) in several cancers, including bladder, colon, breast, stomach, liver, lung, pancreas, and prostate cancers.

Synthesis of chitosan–ZnO nanoparticles (CS–ZnONPs) composite beads was performed by a polymer-based method. The resulting bionanocomposite was characterized by scanning electron microscopy (SEM), X-ray powder diffraction (XRD) spectroscopy and infrared spectroscopy (FT-IR). Adsorption applications for removal of pesticide pollutants were conducted. The optimum conditions, including adsorbent dose, agitating time, initial concentration of pesticide and pH on the adsorption of pesticide by chitosan loaded with zinc oxide nanoparticles beads were investigated. Results showed that 0.5 g of the bionanocomposite, in room temperature and pH 7, could remove 99% of the pesticide from permethrin solution (25 ml, 0.1 mg L−1), using UV spectrophotometer at 272 nm. Then, the application of the adsorbent for pesticide removal was studied in the on-line column. The column was regenerated with NaOH solution (0.1 M) completely, and then reused for adsorption application. The CS–ZnONPs composite beads appear to be the new promising material in water treatment application with 56% regeneration after 3 cycles.

Abstract Multiple regression is a family of statistics used to investigate the relationship between a set of predictors and a criterion (dependent) variable. This procedure is applicable in a variety of research contexts and data structures. Consequently, and similar to quantitative traditions in sister‐disciplines such as education and psychology (see Skidmore &amp; Thompson, 2010), second language researchers have turned increasingly to multiple regression. The present study employs research synthetic techniques to describe and evaluate the use of this procedure in the field. Five hundred and forty‐one regression analyses ( K = 171) were coded for different models, variables, procedures, reporting practices, and overall variance explained ( R 2 ). Summary results reveal a number of inconsistencies (e.g., model types) as well as a lack of transparency (e.g., missing/unreported reliability estimates; see Larson–Hall &amp; Plonsky, 2015). The distribution of R 2 values (median = .32) is described to facilitate utilization and interpretation of regressions models. We also provide specific, empirically grounded recommendations for future research.

This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.

Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on "braintumor" website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.

Recently, a novel coronavirus initially designated 2019-nCoV but now termed SARS-CoV-2 has emerged and raised global concerns due to its virulence. SARS-CoV-2 is the etiological agent of “coronavirus disease 2019”, abbreviated to COVID-19, which despite only being identified at the very end of 2019, has now been classified as a pandemic by the World Health Organization (WHO). At this time, no specific prophylactic or postexposure therapy for COVID-19 are currently available. Viral entry is the first step in the SARS-CoV-2 lifecycle and is mediated by the trimeric spike protein. Being the first stage in infection, entry of SARS-CoV-2 into host cells is an extremely attractive therapeutic intervention point. Within this review, we highlight therapeutic intervention strategies for anti-SARS-CoV, MERS-CoV, and other coronaviruses and speculate upon future directions for SARS-CoV-2 entry inhibitor designs.

Abstract The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly.

This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

Abstract In order to improve the drawbacks related to hydrogels, nanocomposite hydrogels were developed by incorporating different types of nanoparticles or nanostructures in the hydrogel network. This review categorizes nanocomposite hydrogels based on the type of their nanoparticle into four groups of carbon-, polymeric-, inorganic- and metallic-based nanocomposite hydrogels. Each type has specific properties that make them appropriate for a special purpose. This is mainly attributed to the improvement of interactions between nanoparticles and polymeric chains and to the enhancement of desirable properties for target applications. The focus of this paper is on biomedical applications of nanocomposite hydrogels and the most recent approaches made to fulfill their current limitations.

This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

This paper presents the effect of robotics assisted language learning (RALL) on the vocabulary learning and retention of Iranian English as foreign language (EFL) junior high school students in Tehran, Iran. After taking a vocabulary pre-test, 46 beginner level female students at the age of 12, studying in their first year of junior-high participated in two groups of RALL (30 students) and non-RALL (16 students) in this study. The textbook used was the English book (Prospect-1) devised by the Iranian Ministry of Education for 7th graders, and the vocabulary taught and tested (pre-test and post-test) were taken from this book. Moreover, the treatment given by a teacher accompanied by a humanoid robot assistant in the RALL group took about five weeks in which half of the book was covered, and the non-RALL group was taught in a traditional method. Finally, the teacher administered the post-test and delayed post-test whose results of repeated measures ANOVA and Two Ways ANOVA indicated that there was a significant difference regarding participants' vocabulary gain and retention in RALL group comparing to non-RALL group. In addition, the teacher reported the students' positive reaction to RALL in learning vocabulary. Overall, the results revealed that RALL has been very influential in creating an efficient and pleasurable English learning environment. This study has some implications for technology-based education, language teaching, and social robotics fields.

The convection heat transfer in a countercurrent double-tube heat exchanger with various fins in a turbulent flow is investigated. The suitable heating or cooling process of fluids is the effective use of the double-pipe heat exchanger. We use water-aluminum oxide nanofluid and water-titanium dioxide at four concentrations (0.4%, 2%, 4%, 6%) as the cold fluid in the inner tube and water as the hot fluid in the annular space. The single-phase model for nanofluid modeling and the standard k-ε model with scalable wall function for simulating the turbulent flow is utilized. To better examine this novel geometry, its performance is compared with simple and rectangular-finned geometries. The results show that the water aluminum oxide nanofluid has a better convection heat transfer coefficient than water titanium dioxide and pure water. Raising the nanofluid concentration from 0.4% to 6% increases the convection heat transfer coefficient by 12%. Heat exchangers with a rectangular and curved fin have 81% and 85% better efficiency than the heat exchanger without a fin. The novel geometry causes a smaller pressure drop despite its higher convection heat transfer coefficient. Also, it is shown that with raising the Reynolds number and nanofluid concentration, the pressure drop increases.

According to recent reports, millions of people across the globe are suffering from arsenic (As) toxicity. Arsenic is present in different oxidative states in the environment and enters in the food chain through soil and water. In the agricultural field, irrigation with arsenic contaminated water, that is, having a higher level of arsenic contamination on the top soil, which may affects the quality of crop production. The major crop like rice (Oryza sativa L.) requires a considerable amount of water to complete its lifecycle. Rice plants potentially accumulate arsenic, particularly inorganic arsenic (iAs) from the field, in different body parts including grains. Different transporters have been reported in assisting the accumulation of arsenic in plant cells; for example, arsenate (AsV) is absorbed with the help of phosphate transporters, and arsenite (AsIII) through nodulin 26-like intrinsic protein (NIP) by the silicon transport pathway and plasma membrane intrinsic protein aquaporins. Researchers and practitioners are trying their level best to mitigate the problem of As contamination in rice. However, the solution strategies vary considerably with various factors, such as cultural practices, soil, water, and environmental/economic conditions, etc. The contemporary work on rice to explain arsenic uptake, transport, and metabolism processes at rhizosphere, may help to formulate better plans. Common agronomical practices like rain water harvesting for crop irrigation, use of natural components that help in arsenic methylation, and biotechnological approaches may explore how to reduce arsenic uptake by food crops. This review will encompass the research advances and practical agronomic strategies on arsenic contamination in rice crop.

Research Article| December 01, 1997 The Triassic and associated rocks of the Nakhlak and Aghdarband areas in central and northeastern Iran as remnants of the southern Turanian active continental margin Mehdi Alavi; Mehdi Alavi 1Geological Survey of Iran, P.O. Box 13185-1494, Tehran, Iran Search for other works by this author on: GSW Google Scholar Hamid Vaziri; Hamid Vaziri 2Department of Geology, Azad University, North Tehran Campus, Tehran, Iran Search for other works by this author on: GSW Google Scholar Kazem Seyed-Emami; Kazem Seyed-Emami 3Faculty of Mining Engineering, Tehran University, Tehran, Iran Search for other works by this author on: GSW Google Scholar Yaghoub Lasemi Yaghoub Lasemi 4Department of Geology, Teachers Training University, Tehran, Iran Search for other works by this author on: GSW Google Scholar GSA Bulletin (1997) 109 (12): 1563–1575. https://doi.org/10.1130/0016-7606(1997)109<1563:TTAARO>2.3.CO;2 Article history first online: 01 Jun 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Mehdi Alavi, Hamid Vaziri, Kazem Seyed-Emami, Yaghoub Lasemi; The Triassic and associated rocks of the Nakhlak and Aghdarband areas in central and northeastern Iran as remnants of the southern Turanian active continental margin. GSA Bulletin 1997;; 109 (12): 1563–1575. doi: https://doi.org/10.1130/0016-7606(1997)109<1563:TTAARO>2.3.CO;2 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGSA Bulletin Search Advanced Search Abstract The Triassic succession of the Nakhlak area in central Iran consists of: (1) the Alam Formation, which is a sequence of shallowing- and coarsening-upward, marine turbidites deposited on the forearc side of an accretionary prism, (2) the Baqoroq Formation, a sequence of coarse to fine, polymictic, fluvial conglomerates, and (3) the Ashin Formation, which comprises alternating, distal marine shales and sandstones that have turbiditic characteristics. These rocks are not lithologically similar to time-equivalent lithostratigraphic units of the Late Permian–Triassic rocks of the Aghdarband area of northeastern Iran (which are interpreted to be forearc deposits), but they may have formed in close association with them in a single tectonic and sedimentary framework. Accepting the 135° counterclockwise rotation of the central-east Iranian microcontinent with respect to the Turan plate since Triassic time, and assuming that the Triassic rocks of the Nakhlak and the Late Permian to Triassic rocks of the Aghdarband formed in a single tectonosedimentary framework on the northern side of the paleo-Tethyan oceanic realm, we present here a sequential development. In this scheme, rocks of the Nakhlak and Aghdarband areas are considered to be deposits of a forearc, basin-ridge-slope environment.The separation of the Nakhlak succession from the rest of the Turan plate and its transportation to central Iran might have occurred as (1) a lithospheric segment of the Turan plate, first detached from Turan and then attached to the Iranian plate, and finally rotated with it in a counterclockwise direction to its present site; or (2) as a thin thrust slice first obducted over the Iranian continental shelf and then displaced to central Iran by its counterclockwise rotation. This content is PDF only. Please click on the PDF icon to access. First Page Preview Close Modal You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Abstract. Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.