Characterization,in vitro bioactivity and biological studies of sol-gel-derived TiO2 substituted 58S bioactive glass

Bioactive glasses (BGs) have been used for bone formation and bone repair processes in recent years. This study investigated the titanium substitution effect on 58S BGs (Ti-BGs) 60SiO₂-(36 − X)CaO-4P₂O₅-XTiO₂ (X = 0, 3, and 5 mol.%) prepared by the sol-gel technique, and the main goal was to find the optimum amount of titanium in Ti-BGs. Synthesized BGs, which were investigated after immersion in simulated body fluid (SBF), were tested by X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy. Moreover alkaline phosphate (ALP) activity, 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and antibacterial studies were employed to investigate the biological properties of Ti-BGs. According to the FTIR and XRD test results, hydroxyapatite (HA) formation on Ti-BGs surfaces was confirmed. Meanwhile, the presence of 5 mol.% compared to 3 mol.% increased the HA grain distribution and their size on the Ti-BGs surface. Additionally, MTT and ALP results confirmed that the optimal amount of titanium substitution in BG was 5 mol.%. Since 5 mol.% Ti incorporated BG (BG-5) had the highest biocompatibility level, antibacterial properties, maximum cell proliferation, and ALP activity among the synthesized Ti-BGs, it is presented as the best candidate for further in vivo investigations.     

Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

Using correlation coefficients for locating partial discharge in power transformer

Most serious failure of power transformers is due to the insulation breakdown. Partial discharge (PD) that damages insulation by gradual erosion is major source of insulation failure. The effective ability of the wavelet packets analysis as a tool for disk-to-disk partial discharge faults detection and localization in transformer windings is shown in this paper. Techniques for locating a PD source are of the major importance in both the maintenance and repair of a transformer. One of the most well-known methods of PD localization in transformers is based on winding modeling and current of neutral point analysis. Since the impedance between PD location and neutral point of winding depends on the PD location in respect to neutral point, the frequency spectrum of neutral point current varies when the PD location changes. In the other word, the current components of neutral points vary according to the place where PD occurs. So in this paper, detailed model of transformer winding is modeled and the neutral point current is studied for locating PD. The used method is validated by the simulated model of transformer windings. This model produces a very acceptable current when compared to the experimental data. In this paper for locating partial discharge (PD) in transformer windings, a simulated model is developed for the transformer winding and the PD phenomenon mechanism. The impulse current test and wavelet packets transformation are used to locate PD. It is shown that the neutral current measurement of the transformer winding has useful information about PD location.     

New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II

In some statistical process control (SPC) applications, quality of a process or product is characterized by contingency table. Contingency tables describe the relation between two or more categorical quality characteristics. In this paper, two new control charts based on the WALD and Stuart score test statistics are designed for monitoring of contingency table‐based processes in Phase‐II. The performances of the proposed control charts are compared with the generalized linear test (GLT) control chart proposed in the literature. The results show the better performance of the proposed control charts under small and moderate shifts. Moreover, new schemes are proposed to diagnose which cell corresponding to different levels of categorical variables is responsible for out‐of‐control signal. In addition, we propose EWMA–WALD and EWMA–Stuart score test control charts to improve the performance of Shewhart‐based control charts in detecting small and moderate shifts in contingency table parameters. Meanwhile, we compare the performances of two proposed EWMA‐based control charts with the ones of three existing control charts called EWMA–GLT, EWMA–GLRT and an EWMA‐type control chart for multivariate binomial/multinomial processes along with the ones of the corresponding Shewhart‐based control charts. A numerical example is given to show the efficiency of the proposed methods. Finally, the effect of parameter estimation in Phase I based on m historical contingency table on the performance of the Shewhart‐based control charts is studied. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental investigation of the flow-induced motion of a square-section cylinder

The flow-induced motion(FIM)of an elastically mounted square-section cylinder is experimentally investigated over a wide range of Reynolds numbers(1.5×104＜Re＜7....     

Introducing a proper hydrogen liquefaction concept for using wasted heat of thermal power plants-case study: Parand gas power plant

A hydrogen liquefaction concept with an innovative configuration and a capacity of 4 kg·s^-1 (345.6 t·d^-1) is developed. The concept involves an ammonia absorption refrigeration system for the pre-cooling of hydrogen and MR streams from 25 ℃ to -30 ℃. The ammonia absorption refrigeration system is fed by exhaust gases of the Parand gas power plant that are normally dissipated to the environment with a temperature of 546 ℃. The simulation is performed by Aspen HYSYS V9.0, using two separate equations of state for simulating hydrogen and MR streams to gain more accurate results especially for ortho-para conversion. Results show that conversion enthalpy estimated by Aspen HYSYS, fits very well to the experimental data. Determining the important independent variables and composition of MRs are done using trial and error procedure, a functional and straightforward method for complicated systems. The minimum temperature limit in the cooling section is lowered, and an ortho-para converter is implemented in this section. The proposed concept performs well from energy aspects and leads to COP and SEC equal to 0.271 and 4.54 kW·h·kg^-1, respectively. The main advantage of this study is in the low SEC, eliminating the losses of the distribution network, and improving the ability of the hydrogen liquefaction for energy storage in off-peak times.     

A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches

Computational Economics - This paper has scrutinized the process of testing market efficiency, data generation process and the feasibility of market prediction with a detailed, coherent and...     

Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157:H7 food safety process risk model

Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.     

Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems. In these instances, classic methods cannot be used for monitoring the profiles. For example, when the response variable is binary, logistic regression methods should be used rather than ordinary least square or other classic regression methods. There are some methods for monitoring logistic profiles in the literature, but the basic assumption of these methods is the independency of the consecutive observations, while this assumption is violated in some instances for example when the successive samples are taken in short intervals. This paper considers the effect of autocorrelation presence between the observations in different levels of the independent variable in a logistic regression profile on the monitoring procedure (T ² control chart) and proposes two remedies to account for the autocorrelation within logistic profiles. In one of the remedies, upper control limit of the traditional T ² control chart is modified. In the second one, we use a generalized linear mixed model (GLMM) to estimate the regression parameters and then use the T ² control chart for monitoring autocorrelated logistic regression profiles. Simulation studies show the better performance of T ² control chart when the regression parameters are estimated by the GLMM method under both step shifts and drifts.