Sr2+–Mg2+-Doped SiAlON Ceramics

In the present work, multi-cation-doped (Sr²⁺–Mg²⁺) SiAlON ceramics were investigated. MgO and SrO were used in 100:0 and 50:50 molar ratios. The mixture was sintered at 1800° and 1830°C for 1 h in a gas pressure-sintering furnace. The results showed that sintered samples were composed of mainly α- and β-SiAlON phases and small amounts of some Sr-containing phases and SiAlON polytypes. According to Rietveld analysis of X-ray diffraction patterns, Mg is incorporated into the α-SiAlON structure. However, the incorporation of Sr is limited.     

Novel Heterobimetallic Radiotheranostic: Preparation,Activity, and Biodistribution

A novel Ru^II(arene) theranostic complex is presented. It is based on a 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid macrocycle bearing a triarylphosphine and can be tracked in vivo by using the γ emission of ¹⁵³Sm atoms. Notably, the heteroditopic ligand can be selectively metalated with ruthenium at the phosphorus atom despite the presence of other functionalities that are prone to metal coordination. Subsequent labeling with radionuclides such as ¹⁵³Sm can then be performed easily. The resulting heterobimetallic complex exhibits favorable solubility and stability properties in biologically relevant media. It also shows in vitro cytotoxicity in line with that expected for this type of metallodrug, and is nontoxic to the organism as a whole. As a proof of concept, initial studies in healthy mice were performed to obtain information about the uptake, biodistribution, and excretion of the radiolabeled complex.     

Micro‐ and nanoscale structures of mesiodens dentin: Combined study of FTIR and SAXS/WAXS techniques

A mesiodens is the most common type of supernumerary tooth present in conjunction to normal dentition. A mesiodens may commonly occur in the central region of the upper or lower jaw. A mesiodens is different from normal teeth in terms of structure and shape. The aim of this study is to evaluate the micro‐ and nanoscale structural properties of mesiodens dentin by combined small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) and Fourier transform infrared (FTIR) spectroscopy. Five freshly extracted, noncarious mesiodens and five normal dentin disks prepared from human incisor teeth were compared. Using FTIR, the phosphate‐to‐amide I, carbonate‐to‐phosphate, and carbonate‐to‐amide I band area ratios and the crystallinity index were quantified. SAXS/WAXS were used to study the nanostructure of mesiodens. An increase in the mineral content in the mesiodens dentin with respect to the normal group was found. Crystallinity was also significantly increased and the protein content decreased in the mesiodens dentin compared with that of normal dentin. SAXS/WAXS results revealed that mesiodens dentin has a more calcified tissue. Further, SAXS analysis revealed a nonuniform distribution of dentin fibrils in mesiodens. Microsc. Res. Tech., 78:52–58, 2015. © 2014 Wiley Periodicals, Inc.     

A parametric study on isobaric adsorption process in a closed adsorbent bed

A numerical study on heat and mass transfer in an annular adsorbent bed filled with adsorbent granules for an isobaric adsorption process is performed. In order to reduce the number of independent parameters that influences heat and mass transfer in the bed, the governing equations and related initial and boundary conditions for the problem are non-dimensionalized and this yields two dimensionless parameters as G and Γ. The G dimensionless parameter is the ratio of heat of adsorption to sensible heat stored by adsorbent particle and Γ parameter compares mass diffusion within the adsorbent particle and heat diffusion in the radial direction of the adsorbent bed. The obtained results show that the total dimensionless time for an adsorption process can be reduced by increasing of Γ value. The total dimensionless time is independent from G for low values of Γ (i.e. Γ = 10^− 5). The results also show that the instantaneous equilibrium model can provide accurate results only for an adsorbent bed with a low value of Γ (i.e. Γ = 10^− 5). The present study is performed for Γ values from 10^− 5 to 1 and G value from 1 to 100.     

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

Two approaches for solving the buffer allocation problem in unreliable production lines

This paper presents an integrated approach to solve the buffer allocation problem in unreliable production lines so as to maximize the throughput rate of the line with minimum total buffer size. The proposed integrated approach has two control loops; the inner loop and the outer loop. While the inner loop control includes an adaptive tabu search algorithm proposed by Demir et al. [8], binary search and tabu search are proposed for the outer loop. These nested loops aim at minimizing the total buffer size to achieve the desired throughput level. To improve the efficiency of the proposed tabu search, alternative neighborhood generation mechanisms are developed. The performances of the proposed algorithms are evaluated by extensive computational experimentation, and the results are reported.     

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.     

Statistical thermal stability of PVC

Experimental design was used to optimize the processing parameters for the decomposition of poly(vinyl chloride). Factorial design and face centered composite design (FCC) were applied to determine the optimum conditions. A total of 10 g PVC powder was mixed with different amounts of zinc stearate (ZnSt₂) and natural zeolite and tested for thermal stability. Factorial fitted model was explained by first order pattern due to the significant main effect regression constants, and FCC model was described by second order model owing to higher order polynomial coefficients. FCC design was superior to factorial design as FCC considers not only its pure quadratic effects contribution but also its higher overall desirability for thermal stability of PVC. For factorial design the optimum conditions were determined as 163.06 mg for ZnSt₂, 399.99 mg for zeolite, and 140°C for temperature with desirability of 0.933. However, 400 mg for ZnSt₂, 333.24 mg for zeolite, and 140°C for temperature with desirability of 0.956 were obtained as the optimum conditions by FCC design. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010