Reactivity of the Methylene Group and cleavage of S-alkyl groups in 1-alkylmercapto-3(4H)-isoquinolone derivatives

Treatment of 2a-thiohomophthalimide 1a and 4-phenylthiohomophthalimide 1b with alkyl halides gave the products 2a – e . By further alkylation of 2a, d the corresponding ethers 3a – h were obtained. When 2a, c were treated with aryl diazonium chloride, the red azo compounds 4a – f were separated. Also 2a – c were found to react with aliphatic and aromatic aldehydes to give the bis-compounds 8a – c and 9a – f respectively. The S-alkyl group present in the aryl azo derivatives 4 was found to be cleaved by treating them independently with phenyl hydrazine, hydroxylamine and primary aromatic amines to give compounds 10, 11 and 12 respectively.     

On-line adaptive optimal load flow

This paper presents a new on-line approach for solving the optimal power flow problem without sacrificing the accuracy and constraints of the system components. A procedure which merges non-linear programming (NLP) and linear programming (LP) techniques is utilized to make use of the merits of both algorithms. The NLP and LP form a closed loop with a decision maker (DM) which directs the solution algorithm either to NLP or LP according to the analysis of the permissible errors during the calculation process. The input to the closed loop is fed either from the telemetered data or the state estimator.

To adapt the LP algorithm, the objective function and the system constraints are linearized, using Taylor expansion series, around the exact solution obtained as a result of employing the NLP technique. A new linearization algorithm is presented.

The proposed approach has been applied to a 19-bus system and the results are judged satisfactory. Its use as an on-line computational tool for power system operation and control seems very promising.     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.     

Reactions with 2-Oxo-4-thioxo-1,2,3,4-tetrahydrobenzo[f]isoquinoline and 1,2,3,4-tetrahydro-benzo[f]-isoquinoline-2,4-dione

Alkylation of 2-oxo-4-thioxo-1,2,3,4-tetrahydro-benzo[f]isoquinoline 2 gave at first the S-alkyl derivatives, then the S- and O-dialkyl derivatives were formed. Compound 2 and its alkyl derivatives coupled with aryldiazonium salts. The S-methyl derivatives of 2 , on acid hydrolysis, gave 1,2,3,4-tetrahydro-benzo[f]isoquinoline-2,4-dione 10 , which coupled with diazonium salts. 10 gave a monochloro and a monobromo derivative. 10 reacted also with p-nitrosodimethylaniline and the product was hydrolysed to the triketo compound 14 , which condensed with o-phenylenediamine to give the quinoxaline derivative 15 .