Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

Post-synthetic modification of nanoporous Cu3(BTC)2 metal-organic framework via immobilization of a molybdenum complex for selective epoxidation

Abstract: In the present work, nanoporous Cu3(BTC)2 (abbreviated as CuBTC and BTC = benzene-1,3,5-tricarboxylate) metal-organic framework (MOF) was modified in a two-step post-synthetic reaction by covalent attachment of aminopyridine groups followed by the reaction with bis(acetylacetonato) dioxomolybdenum(VI). The prepared molybdenum containing MOF was utilized as a heterogeneous catalyst in the epoxidation of olefins and allylic alcohols. Characterization of the prepared catalyst was performed using Fourier transform infrared, atomic absorption spectroscopies, CHN elemental analysis, powder X-ray diffraction, scanning electron microscopy, thermogravimetric analysis and nitrogen adsorption/desorption techniques. The resulting catalyst showed high selectivity and catalytic activity toward the epoxidation reaction. Moreover, the catalyst demonstrated size-selective properties suggested the reaction occurred inside the pores.

Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale Optimization and Water Cycle Algorithms

Abstract: This paper proposes a new mathematical model for multi-product economic order quantity model with imperfect supply batches. The supply batch is inspected upon arrival using “all or None” policy and if found defective, the whole batch will be rejected. In this paper, the goal is to determine optimal order quantity and backordering size for each product. To develop a realistic mathematical model of the problem, three robust possibilistic programming (RPP) approaches are developed to deal with uncertainty in main parameters of the model. Due to the complexity of the proposed RPP models, two novel meta-heuristic algorithms named water cycle and whale optimization algorithms are proposed to solve the RPP models. Various test problems are solved to evaluate the performance of the two novel meta-heuristic algorithms using different measures. Also, single-factor ANOVA and Tukey’s HSD test are utilized to compare the effectiveness of the two meta-heuristic algorithms. Applicability and efficiency of the RPP models are compared to the Basic Possibilistic Chance Constrained Programming (BPCCP) model within different realizations. The simulation results revealed that the RPP models perform significantly better than the BPCCP model. At the end, sensitivity analyses are carried out to determine the effect of any change in the main parameters of the mathematical model on the objective function value to determine the most critical parameters.

Reflections on Mixing Methods in Applied Linguistics Research

Abstract: This commentary advocates the use of mixed methods research—that is the integration of qualitative and quantitative methods in a single study—in applied linguistics. Based on preliminary findings from a research project in progress, some reflections on the current practice of mixing methods as a new trend in applied linguistics are put forward. Furthermore, theoretical and practical issues concerning mixed methods research in applied linguistics are briefly discussed.