A Two-Stage Hall Magnetometer with Improved Characteristics Provided by an HTSC Sensor

Instruments and Experimental Techniques - It is demonstrated that the sensitivity of a standard Hall magnetometer can be increased from ~2.5 × 10–3 to ~8 × 10–7...     

Evaluation of TBM performance prediction models and sensitivity analysis of input parameters

Bulletin of Engineering Geology and the Environment - In recent years, anumber of models and formulas have been proposed to estimate the penetration rate of hard rock tunnel boring machines (TBMs)....     

Poly(vinyl alcohol) membranes in wound-dressing application: microstructure,physical properties,and drug release behavior

Poly(vinyl alcohol) (PVA) hydrogel membranes were prepared through three different preparation methods including freeze-thawing (FT), solution casting (SC) followed by thermal annealing, and phase separation (PS). The prepared hydrogels were characterized by Fourier transform-infrared spectroscopy, X-ray diffractometry, and scanning electron microscopy. Nitrofurazone (NFZ) was then loaded in the hydrogels. FT and SC methods led to obtaining dense membranes, while PS method resulted in an asymmetric one. The effects of hydrogel preparation method on water absorption, gel fraction, water vapor and oxygen permeabilities, bacterial barrier, tensile properties, and drug release profiles were investigated. The water vapor permeability of the hydrogel prepared through PS method was about 1.5 times higher than those obtained through FT and SC methods. Gel formation in PS method is probably responsible for the highest degree of crystallinity, and consequently the maximum gel fraction for the corresponded membrane. The elongation-at-break for this membrane in wet state was 41% higher than that made by FT method and 18% greater than that of SC method. Membranes prepared by all three methods showed excellent barrier property against bacterial penetration during 1 week. The results showed that PS membrane could control the release of NFZ more effectively as compared with the other two samples.     

An adaptive plan-oriented and continuous software migration to cloud in dynamic enterprises

Replacing existing software/hardware components with their equivalent cloud services is an important decision faced by IT managers in today's enterprises. A variety of possible migration targets and cloud services with too many configurations and cost models, disparate and changing strategic objectives of the enterprise management that triggers the migration process, and the complex structure of the legacy applications make software migration to the cloud a challenging issue. In contrast to the existing approaches that model the migration process as an optimization problem to find the optimal deployment of software components on cloud services without presenting a practical migration plan, in this paper, a plan-oriented migration approach is proposed by which the enterprise management is able to follow migration steps of a valid plan. All valid plans are modeled using a labeled transition system, and a recommender engine directs the management through the possible migration paths using predefined fitness functions. It was observed that, particularly in dynamic and changing conditions that a flexible migration plan is essential, the proposed plan-oriented method is very much effective in satisfying the enterprise strategic objectives. Evaluations have been performed using two quality indicators: total cost of ownership and scalability index.     

Performance evaluation of multilayer perceptron,radial basis function,fuzzy inference system,and an adaptively tuned fuzzy wavelet neural network in parameter prediction of multiphase flow measurement instrumentation

Application of predictive models in industrial multiphase flow metering has attracted an increasing attention recently. Void fraction (VF), water–liquid ratio (WLR), and flow regime are key parameters, measured by oil/water/gas multiphase flow metres (MPFM) in petroleum industry. Artificial neural networks and fuzzy inference systems (FIS) are reliable and efficient computational models, which can be simply implemented on microprocessors of MPFMs, having the advantages of trainability, adaptability, and capability to model non‐linear functions. In this paper, a wavelet‐based adaptive neuro‐FIS (WANFIS) is introduced and validated by the prediction of multiphase flow measurement critical parameters including flow regime, VF, and WLR. The performance of the proposed WANFIS model is then compared with multilayer perceptron (MLP), radial basis function (RBF) network, and an FIS trained by fuzzy c‐means and a subtractive clustering method in the prediction of flow parameters in a customized designed structure of oil/water/gas MPFM. Structural parameters of all predictive models are first optimized to yield the most efficient structure for the available dataset. Comparison is then made between the optimized predictive models, in terms of mean squared error of parameter prediction, computation time, and repeatability of the prediction process. According to the obtained results, MLP model using Levenberg–Marquardt training algorithm and WANFIS model using gradient‐based back propagation dynamical iterative learning algorithm are the most efficient models, which give the best performance compared with other used models. All predictive models can predict the flow regime with 100% accuracy, whereas the highest inaccuracy is related to the prediction of WLR. The results of this study can be used to select and develop the most appropriate predictive model applicable in predicting and identifying flow measurement parameters in industrial MPFMs.     

Optimization of Run-of-River Hydropower Plant Design under Climate Change Conditions

The assessment of climate change and its impacts on hydropower generation is a complex issue. This paper evaluates the application of representative concentration pathways (RCPs, 2.6, 4.5, and 8.5) with the change factor (CF) method and the statistical downscaling method (SDSM) to generate six climatic scenarios of monthly temperature and rainfall over the period 2020–2049 in the Karkheh basin, Iran. The identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) model was employed to simulate runoff for the purpose of designing a run-of-river hydropower plant in the Karkheh basin. The non-dominated sorting genetic algorithm (NSGA)-II was employed to maximize yearly energy generation and the plant factor, simultaneously. Results indicate the runoff scenarios associated with the SDSM lead to higher run-of-river hydropower generation in 2020–2049 compared to the CF results.     

Rapid biologically one-step synthesis of stable bioactive silver nanoparticles using Osage orange (Maclura pomifera) leaf extract and their antimicrobial activities

Synthesis of nanoparticles by using natural products as reducing and stabilizing agents have been widely used in various fields especially medicine, primarily because of its lower cost, simplicity, and less toxic byproducts. In the present work, silver nanoparticles (Ag NPs) were rapidly synthesized from silver nitrate in a green one-step synthesis by the aqueous extracts of Osage orange (Maclura pomifera) leaf as a reducing and stabilizing agent simultaneously. The effects of pH, extract quantity, and silver salt concentration were investigated to determine the optimum conditions of green synthesis of Ag NPs. The synthesized Ag NPs were characterized by different techniques including UV–Visible (UV–Vis) absorption spectroscopy, X-ray diffraction (XRD), Fourier transform Infrared (FT-IR) Spectroscopy, and Transmission Electron Microscopy (TEM). The Ag NPs showed surface plasmon resonance centered at 415?nm. The XRD pattern and TEM analysis revealed spherical, stable, and uniform Ag NPs with the average particle size of about 12?nm. The FT-IR spectroscopy showed that mainly hydroxyl functional groups, as both the reducing and stabilizing agent are responsible for silver nanoparticles synthesis. The antimicrobial activity of the synthesized Ag NPs showed a significant microbicidal effect on all clinical isolates especially, Gram-negative bacteria and fungi. These results suggest that such stable and uniform Ag NPs can be synthesized rapidly and simply for clinical as well as pharmaceutical applications.     

An atomistic investigation into water adsorption behavior on the surfaces of quartz (001) and calcite (001)

The adsorption measurement of various substances in presence of the reservoir minerals has been investigated in the last few years to describe the existing phenomena in the oil and gas energy-related sectors. However, there still needs further research to understand pure water adsorption behavior on reservoir minerals as the fundamental study. In this study, molecular dynamics simulation was carried out to investigate the adsorption behavior of water molecules on the surface of minerals. In this matter, composite integrated layers were built for quartz-water and calcite-water systems to conduct water adsorption calculations from ambient temperature to the reservoir temperature. Results illustrated less amount of water adsorption on quartz; however, the hydrophilicity of calcite surface by disturbing aqueous layers along with a distance of 1.5 Å–2.5 Å. In addition, the temperature effect was observed into lesser adsorption energy by heating up the water toward its’ boiling point. This study contributes to a wider insight into pure water adsorption features before designing complex microstructures.     

Generalized Storage Equations for Flood Routing with Nonlinear Muskingum Models

Water Resources Management - The nonlinear Muskingum model is a leading method for hydrologic routing. The efficiency of the nonlinear Muskingum model for routing of hydrograph outflow has been...     

Vision-based real estate price estimation

Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.