Showing papers by "Islamic Azad University published in 2017"

The role of nanomaterials as effective adsorbents and their applications in wastewater treatment

Abstract: Nanomaterials have been extensively studied for heavy metal ions and dye removals from wastewater. This article reviews the role of nanomaterials as effective adsorbents for wastewater purification. In recent years, numerous novel nanomaterial adsorbents have been developed for enhancing the efficiency and adsorption capacities of removing contaminants from wastewater. The innovation, forthcoming development, and challenges of cost-effective and environmentally acceptable nanomaterials for water purification are discussed and reviewed in this article. This review concludes that nanomaterials have many unique morphological and structural properties that qualify them to be used as effective adsorbents to solve several environmental problems.

The importance of minerals in human nutrition: Bioavailability, food fortification, processing effects and nanoencapsulation

Abstract: Background As minerals have diverse functionalities and potentials in the body's metabolism and homeostasis, deficiency of these bioactive constituents can result in an abundant incidence of common disorders and disease symptoms. Maintenance knowledge of the mineral content in terms of safe food fortification and processing techniques can significantly increase their absorption and bioavailability rate. Scope and approach This overview mainly discusses current investigations about the identification of high-available sources and remarkable functions of mineral elements, quantification methods for the bioavailability assessment, and influence of different processing practices and usual fortification strategies on mineral content and quality of staple food products. Key findings and conclusions The most dominant minerals to fortify various food preparations are iron, calcium, zinc and iodine. Utilization of isotopic approaches can sensitively determine the bioavailability values of food minerals. Modern processing techniques (e.g., high pressure and sonication) compared with the conventional processes have lower negative impacts on the content of micro- and macro-minerals. Accumulation of mineral elements in the edible tissues of crops using agrobiotechnological techniques (e.g., gene overexpression and activation control) and their direct fortification into formulation of processed foods along with nanoencapsulation could enhance the concentration and bioaccessibility of these bioactive ingredients.

Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran

Abstract: Excessive use of chemical fertilizers, especially nitrogen fertilizers to increase crop and improper purification, and delivery of municipal and industrial wastewater are proposed as factors that increase the amount of nitrate in groundwater in this area. Thus, investigation of nitrate contamination as one of the most important environmental problems in groundwater is necessary. In the present study, modeling and estimation of nitrate pollution in groundwater of marginal area of Zayandeh-rood River, Isfahan, Iran, was investigated using water quality and artificial neural networks. 100 wells (77 agriculture well, 13 drinking well and 10 gardens well) in the marginal area of Zayandeh-rood River, Isfahan, Iran were selected. MATLAB software and three-layer Perceptron network were used. The back-propagation learning rule and sigmoid activation function were applied for the training process. After frequent experiments, a network with one hidden layer and 19 neurons make the least error in the process of network training, testing and validation. ANN models can be applied for the investigation of water quality parameters.

Charged-particle nuclear modification factors in PbPb and pPb collisions at √sNN=5.02 TeV

Abstract: we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).

A comprehensive review of Flywheel Energy Storage System technology

Abstract: Energy storage systems (ESSs) play a very important role in recent years. Flywheel is one of the oldest storage energy devices and it has several benefits. Flywheel Energy Storage System (FESS) can be applied from very small micro-satellites to huge power networks. A comprehensive review of FESS for hybrid vehicle, railway, wind power system, hybrid power generation system, power network, marine, space and other applications are presented in this paper. There are three main devices in FESS, including machine, bearing, and Power Electronic Interface (PEI). Furthermore, advantages and disadvantages all of them have been presented. In addition a brief review of new and conventional power electronic converters used in FESS, have been discussed. Finally, practical ways to develop this technology in the future are presented.

Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at √s=13 TeV

Abstract: Properties of the Higgs boson are measured in the H → ZZ → 4l (l = e, μ) decay channel. A data sample of proton-proton collisions at $ \sqrt{s}=13 $ TeV, collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 35.9 fb$^{−1}$ is used. The signal strength modifier μ, defined as the ratio of the observed Higgs boson rate in the H → ZZ → 4l decay channel to the standard model expectation, is measured to be μ = 1.05$_{− 0.17}^{+ 0.19}$ at m$_{H}$ = 125.09 GeV, the combined ATLAS and CMS measurement of the Higgs boson mass. The signal strength modifiers for the individual Higgs boson production modes are also measured. The cross section in the fiducial phase space defined by the requirements on lepton kinematics and event topology is measured to be 2. 92$_{− 0.44}^{+ 0.48}$ (stat)$_{− 0.24}^{+ 0.28}$ (syst)fb, which is compatible with the standard model prediction of 2.76 ± 0.14 fb. Differential cross sections are reported as a function of the transverse momentum of the Higgs boson, the number of associated jets, and the transverse momentum of the leading associated jet. The Higgs boson mass is measured to be m$_{H}$ = 125.26 ± 0.21 GeV and the width is constrained using the on-shell invariant mass distribution to be Γ$_{H}$ < 1.10 GeV, at 95% confidence level.

Nanocellulose in Sensing and Biosensing

Abstract: Because of its multifunctional character, nanocellulose (NC) is one of the most interesting nature-based nanomaterials and is attracting attention in a myriad of fields such as biomaterials, engineering, biomedicine, opto/electronic devices, nanocomposites, textiles, cosmetics and food products. Moreover, NC offers a plethora of outstanding properties, including inherent renewability, biodegradability, commercial availability, flexibility, printability, low density, high porosity, optical transparency as well as extraordinary mechanical, thermal and physicochemical properties. Consequently, NC holds unprecedented capabilities that are appealing to the scientific, technologic and industrial community. In this review, we highlight how NC is being tailored and applied in (bio)sensing technology, whose results aim at displaying analytical information related to various fields such as clinical/medical diagnostics, environmental monitoring, food safety, physical/mechanical sensing, labeling and bioimaging appli...

Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

Abstract: Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-based, fault-based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- radial based function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.

Cellulose-Lignin Biodegradable and Flexible UV Protection Film

Abstract: There is significant interest in biodegradable and transparent UV protection films from renewable resources for many different applications. Herein, the preparation and characterization of semitransparent flexible cellulose films containing low amounts of covalently bonded lignin with UV-blocking properties are described. Azide modified cellulose dissolved in dimethylacetamide/lithium chloride (DMAc/LiCl) was reacted with propargylated lignin to produce 0.5%, 1%, and 2% by weight lignin containing materials. Cellulose-lignin films were prepared by regeneration in acetone. These covalently bonded cellulose-lignin films were homogeneous, unlike the simple blends of cellulose and lignin. Prepared films showed high UV protection ability. Cellulose film containing 2% lignin showed 100% protection of UV-B (280–320 nm) and more than 90% of UV-A (320–400 nm). The UV protection of prepared films was persistent when exposed to thermal treatment at 120 °C and UV irradiation. Thermogravimetric analysis of the films s...

Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling

Abstract: This paper presents GIS-based new ensemble data mining techniques that involve an adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution, and particle swarm optimization for landslide spatial modelling This research was tested in Hanyuan County, which is a landslide-prone area in Sichuan Province, China Different continuous and categorical landslide conditioning factors according to a literature review and data availability were selected, and their maps were digitized in a GIS environment These layers are the slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index, distance to faults, distance to rivers, distance to roads, lithology, normalized difference vegetation index and land use According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the Sichuan Land Resources Bureau of China, 225 landslides were identified in the study area The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme In this research, a probability certainty factor (PCF) model was used for the evaluation of the relationship between the landslides and conditioning factors In the next step, three data mining techniques combined with the ANFIS model, including ANFIS-genetic algorithm (ANFIS-GA), ANFIS-differential evolution (ANFIS-DE), and ANFIS-particle swarm optimization (ANFIS-PSO), were used for the landslide spatial modelling and its zonation Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve The results showed that the area under the curve (AUC) of all of the models was > 075 At the same time, the highest AUC value was for the ANFIS-DE model (0844), followed by ANGIS-GA (0821), and ANFIS-PSO (0780) In general, the proposed ensemble data mining techniques can be applied for land use planning and management of landslide susceptibility and hazard in the study area and in other areas

An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain

Abstract: The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach.

Interactive effects of drought stress and chitosan application on physiological characteristics and essential oil yield of Thymus daenensis Celak

Abstract: Thymus daenensis , a perennial herb, is often grown in areas that experience drought conditions during its growing period. Application of chitosan may compensate for the negative impact of drought stress on the yield of oil and secondary metabolites in Thymus . The interactive effects of foliar application of chitosan and drought stress on dry matter, essential oil yield, and selected physiological characteristics including photosynthetic pigments, osmotic adjustment, and lipid peroxidation of Thymus were investigated in a two-year study from 2014 to 2015. Treatments consisted of 0, 200, and 400 μL L − 1 chitosan applied to plants grown under field capacity, mild drought stress (50% field capacity), and severe drought stress (25% field capacity). Dry matter yield decreased substantially as drought stress intensified. However, essential oil content increased under stress conditions, with the highest essential oil yield obtained from plants under mild drought stress. Foliar application of chitosan compensated to some extent for dry matter and oil yield reduction of plants grown under drought stress. The highest essential oil yield (1.52 g plant − 1 ) was obtained by application of 400 μL L − 1 chitosan under the mild stress condition in 2015 when plants were mature. The compensatory effect of chitosan in reducing the negative impact of stress conditions on dry matter and oil yield was due mainly to stimulation of osmotic adjustment through proline accumulation and reduction of lipid peroxidase level, which increased the integrity of cell membranes of thyme leaves.

Formulation and application of a new generation of lipid nano-carriers for the food bioactive ingredients

Abstract: Background Lipid nanoparticles are innovative delivery systems, which are similar to the prevalently used emulsions, with the differences in size and structure in which the water-insoluble core is dispersed in a combination of solid and liquid lipids stabilized by surfactants. Solid lipid nanoparticles (SLNs) plus nanostructured lipid carriers (NLCs) are novel and promising nano-vehicles, which are of great interest to be applied in the food sector owing to their exclusive properties, as investigated in this revision. Scope and approach LNs and NLCs are produced to unite the advantages of structures like liposomes and emulsions and they can be formulated to achieve desirable protection and release of food bioactive ingredients. This review highlights the pros and cons of using SLNs and NLCs in the food industry. Furthermore, the commonly applied production methods and formulations along with the recently conducted studies in the field of food science and technology are underlined. Key findings and conclusions These nano-vehicles have the potential to be employed in the food industrial applications in regard to their beneficial properties like simple production technology, low cost and scale up ability. These nanocarriers have been mostly applied in the pharmaceutical industries and are recently being utilized in the food sector, which seems to have great impacts on this industry as well as their commercialization in the near future.

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

Abstract: In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25–50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

Searches for invisible decays of the Higgs boson in pp collisions at √s = 7, 8, and 13 TeV

Abstract: Searches for invisible decays of the Higgs boson are presented. The data collected with the CMS detector at the LHC correspond to integrated luminosities of 5.1, 19.7, and 2.3 fb−1 at centre-of-mass energies of 7, 8, and 13 TeV, respectively. The search channels target Higgs boson production via gluon fusion, vector boson fusion, and in association with a vector boson. Upper limits are placed on the branching fraction of the Higgs boson decay to invisible particles, as a function of the assumed production cross sections. The combination of all channels, assuming standard model production, yields an observed (expected) upper limit on the invisible branching fraction of 0.24 (0.23) at the 95% confidence level. The results are also interpreted in the context of Higgs-portal dark matter models.

Nanomaterial-based biosensors for detection of pathogenic virus

Abstract: Viruses are real menace to human safety that cause devastating viral disease. The high prevalence of these diseases is due to improper detecting tools. Therefore, there is a remarkable demand to identify viruses in a fast, selective and accurate way. Several biosensors have been designed and commercialized for detection of pathogenic viruses. However, they present many challenges. Nanotechnology overcomes these challenges and performs direct detection of molecular targets in real time. In this overview, studies concerning nanotechnology-based biosensors for pathogenic virus detection have been summarized, paying special attention to biosensors based on graphene oxide, silica, carbon nanotubes, gold, silver, zinc oxide and magnetic nanoparticles, which could pave the way to detect viral diseases and provide healthy life for infected patients.