Effects of Eriobotrya japonica (Lindl.) flower extracts on mercuric chloride-induced hepatotoxicity in rats

Mercury(II) is an important factor in hepatotoxicity that can enter the body through marine diets and amalgams.In the present study,the protective effect of the Eriobotrya japonica flower extract(EJFE) on HgCl 2-induced hepatotoxicity was investigated.Five mg/kg of mercuric chloride in drinking water was given to rats either with saline or EJFE(100 and 200 mg/kg as intraperitoneal(IP)) for 30 d.The mercury levels in different groups of liver tissues of the rats were measured with flameless atomic absorption spectroscopy(F-AAS).Also,mercury accumulation in the liver of the rats was modeled by using a parallel chemical kinetic model.The results showed that HgCl 2-induced oxidative damage led to a significant decrease in glutathione(GSH) and the total antioxidant capacity(TAC) levels,and to a significant increase in lipid peroxidation level.Accumulated mercury was 14.47% more in the livers of the stress groups than in those of the control groups(P<0.001),whereas the amount of Hg was adjusted to 13.49% and 13.93% in groups treated with 100 and 200 mg/kg of EJFE respectively,as compared with stress groups(P<0.001).HPLC analysis of EJFE revealed that hesperetin and gallic acid are the major antioxidants in EJFE.Results demonstrate that flowers of the Eriobotrya japonica cause a significant protection against HgCl 2 induced hepatotoxicity in all diagnostic parameters by strengthening the antioxidant defense mechanisms and they may have a therapeutic function in free radical mediated diseases.     

Effects of cold rolling on the microstructure and mechanical properties of Fe-Ni-Mn-Mo-Ti-Cr maraging steels

Effects of cold rolling on the microstructure and mechanical properties of Fe-Ni-Mn-Mo-Ti-Cr maraging steels were studied. To investigate the microstructure and mechanical properties, optical microscopy, scanning electron microscopy, X-ray diffraction, tensile test, and hardness test were used. The results show that the solution-annealing treatment in the cold-rolled steel redounds to the formation of submicrocrystalline Fe₂(Mo, Ti) Laves phase particles, which are stable at high temperatures. These secondary Laves phase particles prevent from recrystallization at high temperatures and correspond to semi-brittle fracture in the subsequent aging treatment.     

Towards skin polarization characterization using polarimetric technique

Measurement of optical properties of skin is an expanding and growing field of research. Recent studies have shown that the biological tissue, especially skin, changes the polarization state of the incident light. Using this property will enable the study of abnormalities and diseases that alter not only the light intensity but also its polarization state. In this paper we report an experimental study for measuring changes of polarization state of the light scattered from a phantom similar to a sample model of scattering skin. Using the notation of Stokes vector for the polarized light and Mueller matrix for the sample with its polarization properties, we have shown that some elements of the matrix were particularly sensitive to the changes of the polarization-altering physical properties of the scatterers within the phantom.     

Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function.     

Singular spectrum analysis: methodology and application to economics data

This paper describes the methodology of singular spectrum analysis (SSA) and demonstrate that it is a powerful method of time series analysis and forecasting, particulary for economic time series. The authors consider the application of SSA to the analysis and forecasting of the Iranian national accounts data as provided by the Central Bank of the Islamic Republic of Iran. This research was in part supported by a grant (No. 88/121230) from Institute for Trade Studies and Research (ITSR), Tehran, Iran.     

Fabrication and densification enhancement of SiC-particulate-reinforced copper matrix composites prepared via the sinter-forging process

The fabrication of copper (Cu) and copper matrix silicon carbide (Cu/SiC_p) particulate composites via the sinter-forging process was investigated. Sintering and sinter-forging processes were performed under an inert Ar atmosphere. The influence of sinter-forging time, temperature, and compressive stress on the relative density and hardness of the prepared samples was systematically investigated and subsequently compared with that of the samples prepared by the conventional sintering process. The relative density and hardness of the composites were enhanced when they were prepared by the sinter-forging process. The relative density values of all Cu/SiC_p composite samples were observed to decrease with the increase in SiC content.     

A self-similar local neuro-fuzzy model for short-term demand forecasting

This paper proposes a selfsimilar local neurofuzzy （SSLNF） model with mutual informati onbased input selection algorithm for the shortterm electricity demand forecasting. The proposed self similar model is composed of a number of local models, each being a local linear neurofuzzy （LLNF） model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree （NLOLIMOT） learning algorithm which partitions the input space into axisorthogonal subdomains and then fits an LLNF model and its associated validity function on each subdomain. Furthermore, the proposed approach allows different input spaces for rule premises （validity functions） and consequents （local models）. This appealing property is employed to assign the candidate input variables （i.e., previous load and temperature） which influence shortterm electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from shortterm load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.     

Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. Copyright © 2012 John Wiley & Sons, Ltd.     

Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.