Effect of aggressive pH media on peat treated by cement and sodium silicate grout

The effects of aggressive peat nature (pH) on the strength of peat treated by cement and cement-sodium silicate grout were investigated by evaluating the changes in unconfined compressive strength,moisture content,and scanning electron microscopy (SEM) of samples with time in different pH media.The results indicate that peats treated by cement-silicate have higher strength than peats treated by cement,due to an increase in pH value of the media.Furthermore,cement and cement-silicate are highly effective in reducing the moisture content and void ratio of the treated peats.The microstructures of treated peats support the laboratory test results.

A new approach for detecting sudden hypotension in hemodialysis by using dual-channel optical system

Dialysis hypotension is one of the most prevalence symptoms of dialysis and occurs in 40%of treatment sessions.Detection and prediction of hypotension is important for the well-being of the patient and for optimizing treatment.The aim of this study was to construct optical system to monitor blood pressure(BP)continuously and without cuff in hemodialysis based on pulse transit time(PTT)method.To measure the BP changes,dual channel optical system were developed.In this study,individuals were classified into two groups of normal and hemodialysis.In both groups,BP and consequently PTT were earned three times in different positions.After the initial calibration,the regression equation was drawn for each subject.In normal group,each subject was placed in the supine position and BP was measured both by designed system and sphyg-momanometer cuff.During BP measurements,in addition to BP,blood pressure decline was also monitored by optical system.For hemodialysis group,the same measurement setup was adopted.In both groups,the error between cuff method and PTT was calculated.Correlation coefficients for BPcuf vs BP pTT were calculated and Bland-Altman plot was performed for the normal and hemodialysis groups.In this study 16 subjects participated.The results for normal group showed that maximum difference between cuff method and the present method was 14 mmHg and for.dialysis group was 16 mmHg.Bland-A ltman plot in normal group revealed limits of agreement from-13.98 to 13.18 mmHg.Considering hemodialysis group,limits of agreement were from-15.94 to 13.88 mmHg.The correlation coefficient was 0.74 for normal group and was 0.72 for hemodialysis group.The proposed system can monitor BP continuously and diagnose sudden hypotension.So it can be recommended as a useful method to indicate hypotension and can be used for dialysis unit.

A Novel Modified Fuzzy-predictive Control of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

A wind energy conversion system(WECS)based on a permanent magnet synchronous generator(PMSG)is an effective solution for renewable energy generation in modern power systems.The main advantages of PMSG include high performance at high and low speeds,minimal control effort owing to lower rotor inertia,self-excitation,high reliability,and simplicity of structure compared with induction generators.However,the intermittent nature of wind energy implies that maximum efficiency is not obtained from this system.Accordingly,maximum power point tracking(MPPT)in wind turbine systems has been proposed to address this problem.Traditional MPPT strategies suffer from severe output power fluctuations,low efficiency,and significant ripples in turbine rotation speed.This paper presents a novel MPPT control strategy based on fuzzy logic control(FLC)and model predictive control(MPC)to extract the maximum power from a PMSG-WECS and control the machine-side and grid-side converters.The simulation results obtained from Matlab/Simulink confirm the superiority of the control model in eliminating the output power fluctuations of the wind generators and accurately tracking the maximum power point.A comparative study between conventional MPPT and control methods is also conducted.

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine （SVM） classifier. The method extracts electrocardiogram＇s spectral and three timing inter- val features. Non-parametric power spectral density （PSD） estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using parti- cle swarm optimization （PSO）. These parameters are： Gaus- sian radial basis function （GRBF） kernel parameter o- and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid par- ticle swarm optimization-support vector machine （SVMPSO） classification method offers significantly improved perfor- mance over the SVM which has constant and manually ex- tracted parameter.

ZnO Nanoparticles as Ethanol Gas Sensors and the Effective Parameters on Their Performance

ZnO nanoparticles are synthesized and applied as ethanol gas sensors. In some cases, the sensitivity and response time of these particles are shown to be higher than that has been reported in the literature. It has been investigated that the most possible reason for this higher gas sensing performance can be attributed to the quantity of the activity coefficient of its initial components. However, other effects such as pH and thermal decomposition are of importance as well. Specific ion interaction （SIT） model is applied to derive the mean activity coefficient values of the additives used in synthesis of ZnO nanoparticles.