Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

Design and analysis of DC gain and transconductance boosted recycling folded cascode OTA

A new technique for improving the transconductance and low frequency output impedance of recycling folded cascode (RFC) amplifiers is presented. This enhancement was achieved by using a positive feedback and upgrading the recycling structure. The new structure profits from better transconductance, slew rate, and DC gain in comparison with conventional folded cascode (FC) amplifier. Moreover, the input referred noise is reduced and the phase-margin improved. The enhanced amplifier, simulated in 0.18 μm CMOS technology, exhibits a DC gain enhancement of 16.3 dB as well as 115.5 MHz increase in gain bandwidth compared to conventional FC configuration. The amplifier consumes 360 μW @ 1.2 V which makes it suitable for low-voltage applications.     

Plant‐mediated green synthesis of silver nanoparticles using Trifolium resupinatum seed exudate and their antifungal efficacy on Neofusicoccum parvum and Rhizoctonia solani

In recent years, biosynthesis and the utilisation of silver nanoparticles (AgNPs) has become an interesting subject. In this study, the authors investigated the biosynthesis of AgNPs using Trifolium resupinatum (Persian clover) seed exudates. The characterisation of AgNPs were analysed using ultraviolet–visible spectroscopy, X‐ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infra‐red spectroscopy. Also, antifungal efficacy of biogenic AgNPs against two important plant‐pathogenic fungi (Rhizoctonia solani and Neofusicoccum Parvum) in vitro condition was evaluated. The XRD analysis showed that the AgNPs are crystalline in nature and have face‐centred cubic geometry. TEM images revealed the spherical shape of the AgNPs with an average size of 17 nm. The synthesised AgNPs were formed at room temperature and kept stable for 4 months. The maximum distributions of the synthesised AgNPs were seen to range in size from 5 to 10 nm. The highest inhibition effect was observed against R. solani at 40 ppm concentration of AgNPs (94.1%) followed by N. parvum (84%). The results showed that the antifungal activity of AgNPs was dependent on the amounts of AgNPs. In conclusion, the AgNPs obtained from T. resupinatum seed exudate exhibit good antifungal activity against the pathogenic fungi R. solani and N. Parvum.Inspec keywords: silver, nanoparticles, botany, ultraviolet spectra, visible spectra, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, nanobiotechnology, biological techniquesOther keywords: plant‐mediated green synthesis, silver nanoparticles, Trifolium resupinatum seed exudate, antifungal efficacy, Neofusicoccum parvum, Rhizoctonia solani, biosynthesis, ultraviolet–visible spectroscopy, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, plant‐pathogenic fungi, XRD analysis, TEM images, antifungal activity, temperature 293 K to 298 K, Ag     

Study on Soybean Oil Plant Wastewater Treatment Using the Electro‐Fenton Technique

Due to its low viscosity, soybean oil is commonly used as edible oil. Its production is accompanied by a large amount of wastewater containing hazardous materials like phenolic compounds. In this study, the electro‐Fenton process with response surface methodology was applied for oil removal from wastewater of soybean oil plants. Five independent variables, including reaction time, pH, current density, molar ratio of H₂O₂ to Fe²⁺, and volume fraction of H₂O₂ to wastewater, were investigated for their effects on oil removal. The significant independent variables were optimized by the Box‐Behnken design. The optimum conditions were statistically obtained, and the results were confirmed by experiment. 92.66 % oil removal were experimentally found for the optimum conditions.     

Modeling and analysis of MEMS disk resonators

Microsystem Technologies - In this work a very accurate process for modeling a microdisk resonator is presented and the dynamic behavior of the resonator is investigated. Using the minimization of...     

The effect of mass irregularities of weft yarns on fabric surface protrusion

Fabric surface unevenness and defects are usually created by yarn irregularity and defects in weaving process. These unevenness and irregularities appear on fabric and affect various fabric properties. In order to investigate weft yarn mass irregularities on fabric surface unevenness and defects, various plain fabric samples were prepared in which they differ only in weft yarn mass irregularity. One of the effective factors on fabric surface unevenness is yarn protrusion in fabric structure. Yarn protrusion in fabric structure is influenced by yarn’s physical and mechanical properties and fabric structural characteristics. In this work, relationship between mass irregularities of weft yarn and fabric surface unevenness was investigated using angular power spectrum curve, a measure of yarn protrusion in fabric structures. The results showed a high significant correlation between these two parameters.     

A comparative study on some properties of wood plastic composites using canola stalk,Paulownia, and nanoclay

In this research, the reinforcing effect of fillers including canola stalk, paulownia and nanoclay, in polypropylene (PP) has been investigated. In the sample preparation, 50 wt% of lignocellulosic materials and 0, 3, and 5 wt% of nanoclay particles were used. The results showed that while flexural and tensile properties were moderately enhanced by the addition of nanoclay in the matrix, notched Izod impact strengths decreased dramatically. However, with increase in the nanoclay content (5 wt%), the flexural and tensile properties decreased considerably. The mechanical properties of composites filled with paulownia are generally greater than canola stalk composites, due to the higher aspect ratio. The thickness swelling and water absorption of the composites significantly decreased with the increase in nanoclay loading. Except tensile modulus, the differences between the type of fibrous materials and nanoclay contents had significant influence on physicomechanical properties. Morphologies of the composites were analyzed using transmission electron microscopy (TEM) and X‐ray diffraction (XRD), and the results showed increased d‐spacing of clay layers indicating enhanced compatibility among PP, clay, and lignocellulosic material. TEM micrographs also confirmed that the composites containing 3 wt% nanoclay had uniform dispersion and distribution of clay layers in the polymer matrix. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Cloud service selection using cloud service brokers: approaches and challenges

Cloud computing users are faced with a wide variety of services to choose from. Consequently, a number of cloud service brokers (CSBs) have emerged to help users in their service selection process. This paper reviews the recent approaches that have been introduced and used for cloud service brokerage and discusses their challenges accordingly. We propose a set of attributes for a CSB to be considered effective. DifFerent CSBs' approaches are classified as either single service or multiple service models. The CSBs are then assessed, analyzed, and compared with respect to the proposed set of attributes. Based on our studies, CSBs with multiple service models that support more of the proposed effective CSB attributes have wider application in cloud computing environments.