TQM consultant selection in SMEs with TOPSIS under fuzzy environment

Although much has been written about TQM in SMEs, little attention has been paid to the role of external consultant in the successful implementation of a TQM program in this kind of firms. Sometimes there are some candidates and companies have to select the best one. In general, many factors affect this problem which adheres to uncertain and imprecise data, and usually several people from different functional areas of the company are involved in this process. This study aims to improve the quality of decision in this area. In this paper a systematic decision process for selecting external consultant is proposed. The proposed method is based on TOPSIS method in fuzzy environment. Decision criteria are obtained from the nominal group technique (NGT). Additionally, a real case study is presented to illustrate the application of the proposed method.     

Electron back-scattered diffraction and nanoindentation analysis of nanostructured Al tubes processed by multipass tubular-channel angular pressing

Microstructural evolution and mechanical properties of nanostructured 1060 aluminum alloy tubes processed by tubular-channel angular pressing (TCAP) process were investigated using electron back-scattered diffraction (EBSD), transmission electron microscopy (TEM) and nanoindentation analyzes. EBSD scans revealed a homogeneous ultrafine grained microstructure after the third passes of the TCAP process. Apart from that the mean grain sizes of the TCAP processed tubes were refined to 566 nm, 500 nm and 480 nm respectively after the first, second and third passes. The results showed that after the three TCAP passes, the grain boundaries with a high angle comprised 78% of all the boundaries. This is in comparison to the first pass processed sample that includes approximately 20% HAGBs. The TEM inspection afforded an appreciation of the role of very low-angle misorientation boundaries in the process of refining microstructure. Nanoindentation results showed that hardness was the smallest form of an unprocessed sample while the largest form of the processed sample after the three passes of TCAP indicated the highest resistant of the material. In addition, the module of elasticity of the TCAP processed samples was greater from that of the unprocessed sample.     

Mixed matrix membranes on the basis of Matrimid and palladium-zeolitic imidazolate framework for hydrogen separation

Iranian Polymer Journal - Gas separation membranes with enhanced performance were developed by the introduction of nanosized palladium particles. In this study, gas separation performance of...     

Radiosynthesis and biodistribution of [^18F]-tetracosactide using a semi-automated [^18F]SFB production module

In order to prepare a specific melanocortin type 2 receptor (MC2R) ligand, b1-24-corticotrophin was pre-pared in one-step reaction with [18F] SFB and b-1-24-corticotrophin pharmaceutical solution (1 mg/mL, pH=6.5). [18F]SFB was prepared in a semi-automated module in two steps with an overall radiochemical yield of 47% to EOB (not-decay corrected) in 90 min. The 18F-labeled intermediates and 18F-labeled peptide was checked by RTLC and HPLC. The results show that the radiochemical purity is >95% and the yield to EOB (not-decay corrected) is 29% for final 18F-labeled peptide at optimized conditions. Preliminary in vivo studies in normal mice were performed to deter-mine biodistribution of the 18F-labeled peptide for 150 min. The results show that the major tracer uptake is consistent with the natural distribution of MC2R receptors in mammals. Testes/blood and testes/muscle ratios for 18F-labeled peptide at 150 min were 184 and 1.56, respectively, and adipocyte/blood and adipocyte/muscle ratios at 120 min were 221 and 142, respectively. The data support the specific receptor binding of the radiolabeled peptide as reported for MC2R receptor accumulation in adipocytes and testes and demonstrates the retention of biological activity of the pep-tide. This tracer can be used in detection of MC2R distribution in malignancies and sex organ diseases.     

A new optimization method based on cellular automata for VVER-1000 nuclear reactor loading pattern

This paper presents a new and innovative optimization technique, which uses cellular automata for solving multi-objective optimization problems. Due to its ability in simulating the local information while taking neighboring effects into account, the cellular automata technique is a powerful tool for optimization. The fuel-loading pattern in nuclear reactor cores is a major optimization problem. Due to the immensity of the search space in fuel management optimization problems, finding the optimum solution requires a huge amount of calculations in the classical method. The cellular automata models, based on local information, can reduce the computations significantly. In this study, reducing the power peaking factor, while increasing the initial excess reactivity inside the reactor core of VVER-1000, which are two apparently contradictory objectives, are considered as the objective functions. The result is an optimum configuration, which is in agreement with the pattern proposed by the designer. In order to gain confidence in the reliability of this method, the aforementioned problem was also solved using neural network and simulated annealing, and the results and procedures were compared.     

Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

     

Fabrication of superparamagnetic adsorbent based on layered double hydroxide as effective nanoadsorbent for removal of Sb (III) from water samples

In this study, the superparamagnetic adsorbent as Fe@Mg‐Al LDH was synthesised by different methods with two steps for the removal of heavy metal ions from water samples. An easy, practical, economical, and replicable method was introduced to remove water contaminants, including heavy ions from aquatic environments. Moreover, the structure of superparamagnetic adsorbent was investigated by various methods including Fourier transform infrared spectroscopy, field emission scanning electron microscopy, energy‐dispersive X‐ray spectroscopy, and vibrating sample magnetometer. For better separation, ethylenediaminetetraacetic acid ligand was used, forming a complex with antimony ions to create suitable conditions for the removal of these ions. Cadmium and antimony ions were studied by floatation in aqueous environments with this superparamagnetic adsorbent owing to effective factors such as pH, amount of superparamagnetic adsorbent, contact time, sample temperature, volume, and ligand concentration. The model of Freundlich, Langmuir, and Temkin isotherms was studied to qualitatively evaluate the adsorption of antimony ions by the superparamagnetic adsorbent. The value of loaded antimony metal ions with Fe@Mg‐Al LDH was resulted at 160.15 mg/g. The standard deviation value in this procedure was found at 7.92%. The desorption volume of antimony metal ions by the adsorbent was found to be 25 ml. The thermodynamic parameters as well as the effect of interfering ions were investigated by graphite furnace atomic absorption spectrometry.