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Increased level of serum cholesterol (hyperlipidemia) is the most significant risk factor for the development of atherosclerosis. Cholesterol levels are affected by factors such as rate of endogenous cholesterol synthesis, biliary cholesterol excretion and dietary cholesterol absorption. Acyl CoA: Cholesterol O-acyl transferases (ACAT) are a small family of enzymes that catalyze cholesterol esterification and cholesterol absorption in intestinal mucosal cells and maintain the cholesterol homeostasis in the blood. Inhibition of the ACAT enzymes is one of the attractive targets to treat hyperlipidemia. Literature survey shows that structurally diverse compounds possess ACAT inhibitory properties. In this review, a comprehensive presentation of the literature on diverse ACAT inhibitors has been given.

System and subsystem maintenance is a significant task for every dynamic system. A plethora of approaches, both quantitative and qualitative, have been proposed to ensure the system safety and to minimize the system downtime. The rapid progress of computing technologies and different machine learning approaches makes it possible to integrate complex machine learning techniques with maintenance strategies to predict system maintenance in advance. The present work analyzes different methods of integrating an Artificial Neural Network (ANN) and ANN with Principle Component Analysis (PCA) to model and predict compressor decay state coefficient and turbine decay state coefficient of a Gas Turbine (GT) mounted on a frigate characterized by a Combined Diesel-Electric and Gas (CODLAG) propulsion plant used in naval vessels. The input parameters are GT parameters and the outputs are GT compressor and turbine decay state coefficients. Due to the presence of a large number of inputs, more hidden layers are required, and as a result a deep neural network is found appropriate. The simulation results confirm that most of the proposed models accomplish the prediction of the decay state coefficients of the gas turbine of the naval propulsion. The results show that a consistently declining hidden layers size which is proportional to the input and to the output outperforms the other neural network architectures. In addition, the results of ANN outperforms hybrid PCA-ANN in most cases. The ANN architecture design might be relevant to other predictive maintenance systems.

<p>This paper presents an approach to minimize the harmonics contained in the input current of single phase Modified Half Bridge Resonant inverter fitted induction heating equipment. A switch like IGBT, GTO, and MOSFET are used for this purpose. It analyzes the harmonics or noise content in the sinusoidal input current of this inverter. Fourier Transform has been used to distinguish between the fundamental and the harmonics, as it is a better investigative tool for an unknown signal in the frequency domain. An exhaustive method for the selection of different power semiconductor switches for Modified Half Bridge Resonant inverter fed induction heater is presented. Heating coil of the induction heater is made of litz wire which reduces the skin effect and proximity effect at high operating frequency. With the calculated optimum values of input current of the system at a particular operating frequency, Modified Half Bridge Resonant inverter topology has been simulated using P-SIM software. To obtain the Input current waveforms through it, further analysis has been employed. From this analysis selection of suitable power semiconductor switches like IGBT, GTO and MOSFET are made. Waveforms have been shown to justify the feasibility for real implementation of single phase Modified Half Bridge Resonant inverter fed induction heater in industrial application.</p>

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