A general, facile, and efficient method is presented for the synthesis of 3-substituted 1,2,4-oxadiazoles from amidoximes and triethyl orthoformate. The procedure employs an iron(III) chloride/L-proline catalytic system and the 3-substituted 1,2,4-oxadiazole products are obtained in moderate to good yields.
Unusual products were obtained from cycloadditions reactions of two series of styrylcyclohexene dienes with dienophiles maleic anhydride and N‐phenylmaleimide. Experimental studies revealed that various dienes 3 underwent an initial in‐situ dehydration/double bond rearrangement prior to cycloaddition with maleic anhydride to give products 5 in 78–88 % yields. Alternatively, the cycloadducts of the reactions of 4 with N‐phenylmaleimide were hydoxylated stereoselectively in the same pot to give products 7, along with the usual adducts 6 in 83–89 % yields. Further, the reactions of the diene 4 with the dienophile dimethyl acetylenedicarboxylate were also studied, leading to the formation of the respective adducts 10 in 72–81 % yields. The stereochemistry of the new products was assigned based on their spectroscopic data and, in some cases, with X‐ray crystallography experiments.
Three different reactions were explored leading to the synthesis of various benzofurans. All reactions took place under AcOH catalysis in a one-pot manner. As a result, benzoquinone derivatives underwent heteroannulation with either itself or cyclohexanones to produce furanylidene-benzofuran or benzofuran structures, respectively.
Operational risk is an important risk component for financial institutions, especially in E-banking. Large amount of capital that are assigned to decrease this risk are evidence to this subject. One of the most important factors for modeling operational risk to estimate capital charge is loss data collections of banks. But sometimes for reasons like decreasing the costs, banks save only the losses above some determined thresholds at their database. For achieving accurate capital charge, this threshold should be considered in determining capital charge. This paper focuses on modeling truncated loss data above some given threshold. We discuss several statistical methods for modeling truncated data, and suggest the best one for modeling truncated loss data. We have tested our suggested model for some operational loss data samples. Our results indicate that our approach can be useful for increasing accuracy of estimating operational risk capital charge in E- banking
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