FINITE ELEMENT MODELING FOR ANALYSIS VIBRATION OF LARGE TURBO MACHINERY

Recently, finite elements method (FEM) has been used most popular for analysis of stress, vibration, heat flow and many other phenomena. With the increase in computing power, FEM is wider used for the static and dynamic analysis of rotor bearing system. In this paper, the lateral vibration of large turbo machinery is studied. The FEM model is created and the eigenvalues and eigenvectors are calculated and analyzed to find natural frequencies, critical speeds, mode shapes and unbalance responses. Then critical and mode shapes are determined. Finally, responses of unbalance force are analyzed and compared in case of isotropic bearings and anisotropic bearings

Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

AbstractIn signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time

Entanglement of a Scattered Single Photon and an Exciton

A single photon which is initially uncorrelated with an exciton will evolve to be entangled with the exciton on their continuous kinetic variables in the process of resonant scattering. We find the relations between the entanglement and their physical control parameters, which indicates that high entanglement can be reached by changing specific parameters of exciton

Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach

This paper proposes an integrated (hybrid) Artificial Neural Network-Bayesian (ANN-B) modelling approach to improve the accuracy of predicting seasonal water allocation prices in Australia’s Murry Irrigation Area, which is part of one of the world’s largest interconnected water markets. Three models (basic, intermediate and full), accommodating different levels of data availability, were considered. Data were analyzed using both ANN and hybrid ANN-B approaches. Using the ANN-B modelling approach, which can simulate complex and non-linear processes, water allocation prices were predicted with a high degree of accuracy (RBASIC = 0.93, RINTER. = 0.96 and RFULL = 0.99); this was a higher level of accuracy than realized using ANN. This approach can potentially be integrated with online data systems to predict water allocation prices, enable better water allocation trade decisions, and improve the productivity and profitability of irrigated agriculture

Dynamic association between technological advancement, green finance, energy efficiency and sustainable development: evidence from Vietnam

GHG emissions growth of Vietnam is highest all over the globe and the carbon intensity of this economy is considered second highest in Asian economies. As the energy intensity level is rising constantly, thereby, the predictions indicate that Vietnam will become highest GHG emission in the region. In order to address the issue, the current article aims to investigate the impact of green finance, technology advancement, energy efficiency, industrialization, and population growth on sustainable development in Vietnamese context. The time chosen for the study is 1991 to 2020. The study has used the Dynamic Auto-regressive Distributed Lags (DARDL) and the Bayesian Auto-regressive Distributed Lags (BARDL) model for data analysis. Findings exposed that green finance, technology advancement, REO, REC, industrialization, and population growth all are positively connected with sustainable development. The study guides the regulators in establishing regulations related to sustainable development through the adoption of green finance, energy, and technolog

Effects of Electroporation on Biological Membranes Exposed to High Potentials

This study first considers that voltages of cellular organelle membranes could significanly surpass these of plasma membranes under the influence of ultrashort and high-intensity electric pulse. This is due to the voltages induced on the membrane. Using an approximate theory coordinated with the Kotnik's analytical method, considering the electroporation, we focus on the reactions of cell membranes placed in a trapezoidal pulse. Then, we discuss conductive power dissipations of normal cell and cancer cell generated by a sinusoidal exposure which include dielectric relaxation effects. In comparison with  the complex numerical calculations of Joshi \textit{et al}, our results are in very good agreement