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An enhanced zero-voltage transition boosting converter (EZVTBC) is introduced here which belongs to higher-order family. It exhibits lower source current and load voltage ripples and also it maintains better voltage gain with respect to traditional step-up converter. The zero-voltage transition is attained with an aid of a LCS resonant cell integrating L r  C r resonance tank network along with an extra switch. LCS resonant cell is the modified version of conventional ZVT switch cell and the salient feature of this cell is to eliminate peak current stress and conduction losses of main switch as this remains a predominant problem in hard-switched boost converter and it also improves efficiency. Initially, time domain expressions of EZVTBC are derived using Kirchhoff's laws for different operational stages to predict the resonant transition phenomenon. The simulation is progressed in PSIM software in order to verify its soft-switching performance on a 12-24 V, 30 W converter and also dynamic performance of the converter has been studied with line and load variations. It is found that for rated load conditions, efficiency of the soft-switched converter is improved 5 to 10% approximately and resulted in 97%. Moreover the peak current stress and conduction losses were eliminated.

Cardiovascular diseases are among the foremost common serious diseases’ poignant human health. Disorder is also prevented or relieved by early diagnosis, and this might scale back mortality rates. Distinctive risk factors mistreatment machine learning models may be a promising approach. The model that comes with totally different strategies to realize effective prediction of heart disease. For this planned model to be successful, economical information collection, information Pre-processing and information Transformation methods to form correct info for the coaching model. The model has a combined dataset. Appropriate options are hand-picked by using the Relief and LASSO techniques. New hybrid classifiers like Random Forest based Machine Learning are developed by group action the normal classifiers with fabric and boosting methods, that are employed in the coaching process. Some machine learning algorithms to calculate the accuracy, sensitivity, error rate, precision. The results are shown singly to supply comparisons. Supported the result analysis will conclude that our planned model created the very best accuracy whereas mistreatment RFBM and Relief feature choice method.

Abstract. Design aspects of a linear, stabilized voltage to current transducer for driving YIG tuned microwave devices are analysed. Typical circuits with OP AMP interfacial input and bipolarlVMOS current amplifier outputs are sketched. Attention i s drawn to such circuit refinements as (i) augmenting differentiator for miniinising sweep delay; (ii) linearizer to compensate for nonlinearity due to core saturation; and (iii) low noise, low drift and minimal voltage pushing options.