Microwave absorption properties of double‐layer nanocomposites based on polypyrrole/natural rubber

Thin flexible double‐layer microwave absorbers have been fabricated based on polypyrrole (PP)/natural rubber (NR) nanocomposites and their reflection loss characteristics were studied in the range of 8–18 GHz. The PP‐NR matrix was prepared from PP and NR in the ratio of 15:85. The polymers used in this work not only serve as the matrix but also improve the microwave absorption properties. The first layer or impedance matching layer which is comprised of graphite, Fe₃O₄, and TiO₂ nanoparticles in PP‐NR transmits the electromagnetic (EM) wave without reflection. The second layer which is made up of PP‐NR filled with Fe₃O₄ disperses the EM wave energy. The design of a double‐layer nanocomposite is a method to match the wave impedance, enhance wave absorption ability, and broaden the absorption frequencies. In order to achieve high absorption properties, the EM parameters such as permittivity, permeability, and thickness were controlled precisely according to quarter‐wave plate. The morphology, absorption properties, scattering parameters, thermal and wetting characteristics of double‐layer nanocomposites were investigated. The minimum reflection loss (R_L) was ?32 dB at 12.1 GHz and the absorbing bandwidth in which the R_L < ?10 dB was 9 GHz for optimum specimen with 2 mm thickness. For this specimen, the contact angle was equal to 118.7° with water as the liquid. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46565.     

Modification of Magnetite Cellulose Nanoparticles via Click Reaction for use in Controlled Drug Delivery

Superparamagnetic Fe₃O₄ nanoparticles (MNPs) were functionalized by modified cellulose. The modified cellulose was synthesized through bromoacetylation of cellulose (BACell) followed by the substitution of sodium azide to form BACell-N₃. The remaining methylene bromide groups on BACell-N₃ was further reacted with the MNPs to form Fe₃O₄/Cell-N₃. Then propargyl alcohol (PA) was immobilized on the azide-terminated Fe₃O₄ nanoparticles through copper (I)-catalyzed azide-alkyne cycloaddition (click reaction) to form Fe₃O₄/Cell/TAA nanoparticles. Doxorubicin (DOX) was loaded on prepared nanoparticles and release profiles of the DOX as a model drug from the Fe₃O₄/Cell/TAA nanoparticles and its loading capacity were determined by UV–Vis absorption at λ_max 483?nm.     

Porous Porphyrin‐Based Organosilica Nanoparticles for NIR Two‐Photon Photodynamic Therapy and Gene Delivery in Zebrafish

Periodic mesoporous organosilica nanoparticles emerge as promising vectors for nanomedicine applications. Their properties are very different from those of well‐known mesoporous silica nanoparticles as there is no silica source for their synthesis. So far, they have only been synthesized from small bis‐silylated organic precursors. However, no studies employing large stimuli‐responsive precursors have been reported on such hybrid systems yet. Here, the synthesis of porphyrin‐based organosilica nanoparticles from a large octasilylated metalated porphyrin precursor is described for applications in near‐infrared two‐photon‐triggered spatiotemporal theranostics. The nanoparticles display unique interconnected large cavities of 10–80 nm. The framework of the nanoparticles is constituted with J‐aggregates of porphyrins, which endows them with two‐photon sensitivity. The nanoparticle efficiency for intracellular tracking is first demonstrated by the in vitro near‐infrared imaging of breast cancer cells. After functionalization of the nanoparticles with aminopropyltriethoxysilane, two‐photon‐excited photodynamic therapy in zebrafish is successfully achieved. Two‐photon photochemical internalization in cancer cells of the nanoparticles loaded with siRNA is also performed for the first time. Furthermore, siRNA targeting green fluorescent protein complexed with the nanoparticles is delivered in vivo in zebrafish embryos, which demonstrates the versatility of the nanovectors for biomedical applications.     

Cooperative spectrum sensing in the presence of primary user emulation attack in cognitive radio network: multi-level hypotheses test approach

The inherent nature of cognitive radio (CR) networks has brought new threats to wireless communications. Primary user emulation attack (PUEA) has been widely studied as a serious threat to cooperative spectrum sensing (CSS) in CR networks. In PUEA, a malicious user can obstruct CR users from accessing idle frequency bands by imitating licensed primary user (PU) signal characteristics. The present study introduces a new CSS scheme in the presence of a malicious PUEA based on multi-level hypothesis testing (MLHT). In the proposed method, generalizing from binary hypothesis testing to MLHT, we partition the decision space to four decision options and apply minimum Bayes cost criteria to determine the channel status. We also discuss practical limitation issues that need to be considered when applying the MLHT approach. Simulation results are provided to indicate the performance improvement of the proposed MLHT method against PUEA, compared with the conventional method.     

Toward a hierarchical and architecture‐based virtual machine allocation in cloud data centers

Number of cloud data centers which consists of hundreds of hosts has increased tremendously around the world due to increase in demands for cloud services. It is expected energy consumption of data centers will reach 139.8 billion Kwh by 2020. Many algorithms are proposed to reduce energy consumption as well as service level agreement violationby minimizing the number of active hosts. Current proposed algorithms do not consider data center architecture, the physical position of hosts, and energy consumption of numerous switches that are in data centers. In this paper, a novel hierarchical cloud resource management is proposed that not only minimizes the number of hosts but also aggregates virtual machines on a limited subset of data center racks and modules to minimize energy consumption. Experimental results with Cloudsim show that our proposed algorithm reduces energy consumption up to 26% and service level agreement violation up to 96%.     

Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting

High accurate wind speed forecasting plays an important role in ensuring the sustainability of wind power utilization. Although deep neural networks (DNNs) have been recently applied to wind time-series datasets, their maximum performance largely leans on their designed architecture. By the current state-of-the-art DNNs, their architectures are mainly configured in manual way, which is a time-consuming task. Thus, it is difficult and frustrating for regular users who do not have comprehensive experience in DNNs to design their optimal architectures to forecast problems of interest. This paper proposes a novel framework to optimize the hyperparameters and architecture of DNNs used for wind speed forecasting. Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters. For designing the enhanced version of GOA, the chaotic theory and levy flight strategies are applied to make an efficient balance between the exploitation and exploration phases of the GOA. Moreover, the mutual information (MI) feature selection algorithm is utilized to select more correlated and effective historical wind speed time series features. The proposed model’s performance is comprehensively evaluated on two datasets gathered from the wind stations located in the United States (US) for two forecasting horizons of the next 30-min and 1-h ahead. The experimental results reveal that the proposed model achieves the best forecasting performance compared to seven prominent classical and state-of-the-art forecasting algorithms.

     

Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

     

A Methodology for the Breakdown of NRW into Real and Administrative Losses

The estimation of Non Revenue Water (NRW) is simple and easy for water suppliers who keep records of the system input volume and the billed authorized consumption. However, the breakdown of NRW into its two main components real and administrative which refers to the unbilled authorized consumption plus apparent losses is not an easy or straight forward task. Methods reported in the literature for the breakdown of NRW into its components are top down approach and bottom up approach. Both approaches suffer from certain limitations and shortcomings that limits their use and reduce our confidence in the results obtained by them. This paper presents a methodology that can be used to draw a line between the real and the administrative losses with an acceptable level of accuracy. This methodology is based on the fact that the administrative losses are delivered to the demand site and consequently reach the wastewater collection system whereas the real losses are lost from the system and consequently do not reach the wastewater collection system. The methodology applies water balance from the water treatment plant outlet till the inlet of the wastewater treatment plant (WWTP). The mass balance approach of the Water Evaluation And Planning (WEAP) system was implemented for this purpose. In this methodology, the breakdown of NRW into its two main components is adjusted iteratively so that the difference between WEAP calculated and measured inflow to the WWTP is minimal. The presented methodology was applied to Amman and Zarqa cities in Jordan which return their wastewater to As Samra WWTP. The results showed that this methodology is capable of dividing NRW water into its two main components with an acceptable level of accuracy.     

Multi Objective Simulation-Optimization Approach in Pollution Spill Response Management Model in Reservoirs

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.