Catalytic oxidative desulfurization of dibenzothiophene utilizing molybdenum and vanadium oxides supported on MCM-41

As a series of bimetallic nanocatalysts, molybdenum/vanadium oxides supported on the silica (MoO₃/V₂O₅/MCM-41) were prepared by the impregnation. Their catalytic activity in the oxidation of dibenzothiophene was investigated using H₂O₂ as an oxidant. Textures and surface properties of the prepared catalysts were characterized using FT-IR, XRD, FESEM-EDX and N₂ adsorption/desorption techniques. The effects of main process variables including H₂O₂/DBT molar ratio, catalyst dosage, reaction temperature and reaction time were analyzed by employing the response surface methodology (RSM). The effect of the MoO₃/V₂O₅ loading on the catalytic performance of the catalysts was also investigated. The results indicated that the catalytic activity of the catalyst was increased by enhancing the MoO₃/V₂O₅ content. Thus, the catalyst with high MoO₃/V₂O₅ loading (20%MoO₃/20%V₂O₅/60%MCM-41) indicated the highest catalytic activity and could convert 99.06% of dibenzothiophene under the optimum conditions. Mass and FT-IR analysis demonstrated that the major product of dibenzothiophene oxidation was its corresponding sulfone. The catalyst could be recycled five times without any considerable reduction in its catalytic activity. The kinetics of the reaction fitted the pseudo-first-order equation pretty well. Eventually, a reaction mechanism for the catalytic oxidation of DBT in the presence of MoO₃/V₂O₅/MCM-41 was proposed.     

A fuzzy reinforcement learning algorithm for inventory control in supply chains

In the real world, applications with very large state and action spaces and unknown state transition probability, classical reinforcement learning algorithms usually show poor performance. One way to address the performance problem is to approximate the policy or value function. Fuzzy rule-based systems are amongst the well-known function approximators. This paper presents a Flexible Fuzzy Reinforcement Learning algorithm, in which value function is approximated by a fuzzy rule-based system. The proposed algorithm has a separate module for tuning the structure of fuzzy rules. Moreover, the parameters of the system are tuned during the learning phase. Next, the proposed algorithm is applied to the problem of inventory control in supply chains. In this problem, a fuzzy agent (supplier) should determine the amount of orders for each retailer based on their utility for supplier, by considering its limited supply capacity. Finally, a simulation is performed to show the capability of the proposed algorithm.     

Highly efficient synthesis of coumarin derivatives in the presence of H14[NaP5W30O110] as a green and reusable catalyst

A variety of heteropolyanions including: Keggin, Dawson, Preyssler, mixed addenda and sandwich types, catalyzed the formation of 4-methylnaphtho-(1,2-b)-pyran-2-one (coumarin) from the condensation of α-naphthol and ethylacetoacetate in a solvent free system and under heating conditions. Our data vividly indicate that sodium30–tungsto pentaphosphate, [NaP₅W₃₀O₁₁₀]¹⁴⁻, which so-called Preyssler’s anion, with high hydrolytic (pH 0–12) and thermal stability is the catalyst of choice. This catalyst catalyzed the synthesis of other coumarin derivatives in high yields and good selectivity.     

A Proteomic Analysis of Human Follicular Fluid: Comparison between Younger and Older Women with Normal FSH Levels

The follicular fluid (FF) is produced during folliculogenesis and contains a variety of proteins that play important roles in follicle development and oocyte maturation. Age-related infertility is usually considered as a problem that can be solved by assisted reproduction technology. Therefore, the identification of novel biomarkers that are linked to reproductive aging is the subject of this study. FF was obtained from healthy younger (20–32 years old) and older (38–42 years old) women undergoing intracytoplasmic sperm injection (ICSI) due to male factor infertility. The FF was analyzed by two-dimensional gel electrophoresis (2-DE). The power of two-dimensional gel electrophoresis and the identification of proteins were exploited using matrix-assisted laser desorption-ionization time-of-flight/time-of-flight (MALDI-TOF-TOF) mass spectrometry. Twenty three protein spots showed reproducible and significant changes in the aged compared to the young group. Of these, 19 protein spots could be identified using MALDI-TOF-TOF-MS. As a result of MASCOT search, five unique downregulated proteins were identified in the older group. These were identified as serotransferrin, hemopexin precursor, complement C3, C4 and kininogen. A number of protein markers were found that may help develop diagnostic methods of infertility.     

Model Misspecification of Generalized Gamma Distribution for Accelerated Lifetime-Censored Data

Abstract

The performance of reliability inference strongly depends on the modeling of the product’s lifetime distribution. Many products have complex lifetime distributions whose optimal settings are not easily found. Practitioners prefer to use simpler lifetime distribution to facilitate the data modeling process while knowing the true distribution. Therefore, the effects of model mis-specification on the product’s lifetime prediction is an interesting research area. This article presents some results on the behavior of the relative bias (RB) and relative variability (RV) of pth quantile of the accelerated lifetime (ALT) experiment when the generalized Gamma (GG₃) distribution is incorrectly specified as Lognormal or Weibull distribution. Both complete and censored ALT models are analyzed. At first, the analytical expressions for the expected log-likelihood function of the misspecified model with respect to the true model is derived. Consequently, the best parameter for the incorrect model is obtained directly via a numerical optimization to achieve a higher accuracy model than the wrong one for the end-goal task. The results demonstrate that the tail quantiles are significantly overestimated (underestimated) when data are wrongly fitted by Lognormal (Weibull) distribution. Moreover, the variability of the tail quantiles is significantly enlarged when the model is incorrectly specified as Lognormal or Weibull distribution. Precisely, the effect on the tail quantiles is more significant when the sample size and censoring ratio are not large enough. Supplementary materials for this article are available online.