Arkansas Electric Cooperative Corporation

About: Arkansas Electric Cooperative Corporation is a based out in . It is known for research contribution in the topics: Microstructure & Superalloy. The organization has 1365 authors who have published 1171 publications receiving 7766 citations.

Preclinical development of molecular-targeted agents for cancer

Abstract: Molecular-targeted agents are increasingly used for the treatment of cancer. However, the attrition rate for drugs that enter early clinical trials is higher than for other branches of internal medicine, suggesting that preclinical development has not been successful in identifying agents that can modify the outcome of human cancer. New preclinical strategies including genetically engineered mouse models and small-interfering RNAs are being used to evaluate novel agents, and have aided in the development of compounds, such as inhibitors of phosphatidylinositol 3-kinase or poly(ADP-ribose) polymerase. In addition, these techniques have helped in the identification of promising combinations of targeted drugs. In this Review, we describe methods for the preclinical evaluation of novel agents, their limitations, and strategies for improvement.

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery:

Abstract: Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery Some popular classifica

Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

Abstract: Lack of typical fault samples remains a huge challenge for intelligent fault diagnosis of gearbox. In this paper, a novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples. Firstly, new-type deep multi-wavelet auto-encoder is designed for learning important features of the collected vibration signals of gearbox. Secondly, high-quality auxiliary samples are selected based on similarity measure to well pre-train a source model sharing similar characteristics with the target domain. Thirdly, parameter knowledge acquired from the source model is transferred to target model using very few target training samples. Transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach even if the working conditions have significant changes.