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To ensure the safety and dependability of rubber components, deflection analysis and prediction play a crucial role in process of design. Material property testing and finite element analysis (FEA) are combined to forecast the maximum deflection of a railway elastomeric pad. IRMRA (Indian Rubber Manufacturer's Research Association) developed the chloroprene rubber. Using the FEA method, maximum deflections of an anti-vibration mount under several compressive loads are calculated. Mooney-Rivlin nonlinear hyperelastic three parameter model with element type Plane 182 is used for and FEA. Curve fitting of the uniaxial tensile test results is used to extract three parameter Mooney-Rivlin model constants by using FEA. Then, these Mooney-Rivlin model constants are used to analyze anti-vibration mount and predict the deflections at different compressive loads. The outcomes are contrasted with the technical specifications provided by the Research Designs and Standards Organization of Indian Railway and predicted deflections are within the limits of maximum values allowed. The results are also contrasted with data from literature, and 10% variation is observed between results obtained and literature results. This methodology can be used to predict deflections of any newly developed rubber at initial stage of design. [ABSTRACT FROM AUTHOR]

This research provides a web application for stock prediction that analyze financial news sentiment and predicts market performance using machine learning techniques. Machine learning techniques used are vector based, lexicon based analysis and LSTM. For sentiment analysis and stock prediction. With 86% accuracy, the sentiment analysis algorithm categorizes news headlines as positive or negative. The stock prediction model estimates stock performance with a mean absolute inaccuracy of 3.4 percent. When both models are combined, they forecast stock performance with an accuracy of 83%. The system's potential for usage in the stock market is demonstrated by the results, which provide vital insights into machine learning algorithms for stock data prediction along with sentiment analysis utilizing headlines from financial news. [ABSTRACT FROM AUTHOR]

In recent years, reversible logic gates have garnered significant interest because of their potential to decrease energy consumption and meet the growing need for low-power computing systems. Unlike conventional logic gates, reversible logic gates ensure that no information loss happens during computation, allowing for the reversal of the entire computation process. This unique characteristic opens up new avenues for developing energy-efficient digital circuits. This review paper serves as a vital contribution to the field by addressing a noticeable gap in the existing literature regarding reversible logic gates. The study not only comprehensively analyzes the array of reversible logic gates available but also underscores their practical applications and significance. It encompasses a wide variety of reversible logic gates, including Toffoli gates, Fredkin gates, and newer innovations. It is found that Toffoli gates outperformed in terms of gate count and quantum cost reduction, making them a preferred choice for quantum circuit optimization. Additionally, Fredkin gates showed exceptional performance in specific applications, like data swapping and quantum state control. The digital circuits like adders, multiplexers, ALU etc. are successfully designed using reversible gates like HNG, DKG etc. The significant gap this study fills lies in the need for a consolidated and in-depth analysis of the state-of-the-art reversible logic gates and their real-world utility. While prior research has discussed these gates individually, this paper takes a novel approach by offering a holistic assessment of their performance, quantum cost, gate count, and practical applications, thereby presenting a comprehensive resource for researchers, engineers, and designers in the field. This innovative contribution plays a pivotal role in shaping the progress of energy-efficient and quantum computing systems as well as in optimizing VLSI chip designs for various applications, with a particular emphasis on enhancing cryptographic and data processing capabilities. The findings of this review aim to stimulate further research and development in reversible computing, contributing to the advancement of energy-efficient and information-preserving computing systems. [ABSTRACT FROM AUTHOR]

In many applications, pumps are tested against standard specifications to define the maximum allowable vibration amplitude limits of a pump. It is essential to identify the causes of vibration and methods to attenuate the same to ensure the safe and satisfactory operation of a pump. Causes of vibration can be classified mainly into mechanical and hydraulic nature. Respective unbalance masses are the two major factors which cause dynamic effects and excitation forces leading to undesirable vibrations. In this paper, the procedure of vibration magnitude measurement of a vertical turbine pump at site and the process of dynamic balancing to measure mechanical unbalance of an impeller are explained. After that, the impact of hydraulic eccentricity on the vibration displacement of a vertical turbine pump has been explained using numerical simulation procedure based on "One-way Fluid Structure Interaction (FSI)." The experimental results from a pump at site are used to compare the numerical results. After the solver validation, the one-way FSI approach is used to find the critical hydraulic eccentricity magnitude of a vertical turbine pump impeller to limit the vibration magnitudes on motor component to less than 100 μm. From the numerical simulations, it is deduced that the critical hydraulic eccentricity should be limited to 400 μm in X and Y direction. The process can be used as a guideline procedure for limiting the hydraulic unbalance in vertical turbine pumps by limiting the hydraulic eccentricity. [ABSTRACT FROM AUTHOR]