Synthesis and Biological Evaluation of Novel Tetracyclic Benzothiazepines

6-Arylidene-2,3-dimethyl-6,7,8,9-tetrahydro-benzocyclohepten-5-one 2a–l were obtained by the condensation of 2,3-dimethyl/3-methyl benzocyclohepten-5-one 1 with appropriate aromatic aldehydes, and upon condensation with 2-aminothiophenol in ethyl alcohol yielded 1,5-benzothiazepine derivatives 3a–l, respectively. Compounds 3d and 3h were found to possess antimicrobial activity when tested against B. Subtilis. Compounds 3i and 3j were found to possess moderate anti-inflammatory activity. Compound 3b was found to possess comparable antifungal activity when compared to clotrimazole against Trichomonas species.

Supplemental materials are available for this article. Go to the publisher's online edition of Phosphorus, Sulfur, and Silicon and the Related Elements to view the free supplemental file.     

Studies on organophosphorus compounds: reactions of benzosuberones with 2,4-bis(p-methoxyphenyl)-1,3,2,4-dithiadiphosphetane-2,4-disulfide (Lawesson's reagent)

6-Arylidene-3-methyl-6,7,8,9-tetrahydro-5H-benzo[a]cyclohepten-5-ones (2a-h), obtained by the condensation of 3-methylbenzocyclohepten-5-one 1 with appropriate aromatic aldehydes, on reaction with Lawesson's reagent in xylene yielded phosphorus containing compounds 3a-h. A number of these compounds showed promising anti-inflammatory activity.     

An innovation based random weighting estimation mechanism for denoising fiber optic gyro drift signal

In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal.