An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Review of Non-destructive Testing (NDT) Techniques and their applicability to thick walled composites

A tier 1 automotive supplier has developed a novel and unique kinetic energy recovery storage system for both retro-fitting and OEM application for public transport systems where periodic stop start behaviour is paramount. A major component of the system is a composite flywheel spinning at up to 36,000 rpm (600 Hz). Material soundness is an essential requirement of the flywheel to ensure failure does not occur. The component is particularly thick for a composite being up to 30 mm cross section in some places. The geometry, scale and material make-up pose some challenges for conventional NDT systems. Damage can arise in composite materials during material processing, fabrication of the component or in-service activities among which delamination, cracks and porosity are the most common defects. A number of non-destructive testing (NDT) techniques are effective in testing components for defects without damaging the component. NDT techniques like Ultrasonic Testing, X-Ray, Radiography, Thermography, Eddy current and Acoustic Emission are current techniques for various testing applications. Each of these techniques uses different principles to look into the material for defects. However, the geometry, physical and material properties of the component being tested are important factors in the applicability of a technique. This paper reviews these NDT techniques and compares them in terms of characteristics and applicability to composite parts

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance