Urea enzymatic hydrolysis reaction: optimization by response surface methodology based on potentiometric measurements

The enzymatic hydrolysis reaction of urea by urease is optimized in this work by the chemometric response surface methodology (RSM), based on an initial rate potentiometric measurement using an NH(4)(+) ion-selective electrode (ISE). In this investigation, the ranges of critical variables determined by a preliminary "one at a time" (OVAT) procedure were used as input for the subsequent RSM chemometric analysis. The RSM quadratic response was found to be quite appropriate for modeling and optimization of the hydrolysis reaction as illustrated by the relatively high value of the determination coefficient (R(2)=90.1%), along with the satisfactory results obtained by the analysis of variance (ANOVA). All the evaluated analytical characteristics of the optimized method such as: the linear calibration curve, the upper and lower detection limits, the within-day precisions at low and at high levels, the assay recovery in pool serum media, along with the activation kinetic parameters, were also reported. Further, in order to check the quality of the optimization and the validity of the model, the assay of urea, both in aqueous laboratory and human serum samples, were performed. It has to be noted that the kinetic initial rate measurement method used in this work, permitted to overcome the general problem of NH(4)(+) ISE low selectivity against Na(+) and K(+) interfering ions in real samples.     

Isolation of beta-glucan from the cell wall of Saccharomyces cerevisiae

Beta-glucan, one of the major cell wall components of Saccharomyces cerevisiae (S. cerevisiae), has been found to enhance immune functions. At present study, we developed an optimal procedure to extract and purify beta-glucan. At first, yeast cells were grown in sabouraud dextrose agar and then cultured in yeast extract-peptone-glucose (YPG) broth. After incubation, cells were harvested, washed and disrupted by means of sonication method. The obtained cell walls were used to prepare alkali-soluble beta-glucan (glucan-S1). In this regard, 2% sodium hydroxide (NaOH) and 3% acetic acid were used in alkaline-acid extraction, respectively. This preparation contained 2.4% protein. In the next step, DEAE sephacel chromatography was used to remove remaining proteins (glucan-S2). Subsequently this preparation was applied into concanavalin-A sepharose column to remove manann. Finally, beta-glucan free of mannoprotein complexes was prepared (glucan-S3).     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

On a Class of Recursively Enumerable Sets

We define a class of so-called ∑(n)-sets as a natural closure of recursively enumerable sets W_n under the relation “∈” and study its properties.     

The simple chaotic model of passive dynamic walking

Recent findings on the dynamical analysis of human locomotion characteristics such as stride length signal have shown that this process is intrinsically a chaotic behavior. The passive walking has been defined as walking down a shallow slope without using any muscular contraction as an active controller. Based on this definition, some knee-less models have been proposed to present the simplest possible models of human gait. To maintain stability, these simple passive models are compelled to show a wide range of different dynamics from order to chaos. Unfortunately, based on simplifications, for many years the cyclic period-one behavior of these models has been considered as the only stable response. This assumption is not in line with the findings about the nature of walking. Thus, this paper proposes a novel model to demonstrate that the knee-less passive dynamic models also have the ability to model the chaotic behavior of human locomotion with some modifications. The presented novel model can show chaotic behavior as a stable and acceptable answer using a chaotic function in heel-strike condition. The represented chaotic model is also able to simulate different types of motor deficits such as Parkinson’s disease only by manipulating the value of chaotic parameter. Our model has extensively examined in complexity and chaotic behavior using different analytical methods such as fractal dimension, bifurcation and largest Lyapunov exponent, and it was compared with conventional passive models and the stride signal of healthy subjects and Parkinson patients.