Efficient tool path computation using multi-core GPUs

Tool path generation is one of the most complex problems in Computer Aided Manufacturing. Although some efficient strategies have been developed, most of them are only useful for standard machining. However, the algorithms used for tool path computation demand a higher computation performance, which makes the implementation on many existing systems very slow or even impractical. Hardware acceleration is an incremental solution that can be cleanly added to these systems while keeping everything else intact. It is completely transparent to the user. The cost is much lower and the development time is much shorter than replacing the computers by faster ones. This paper presents an optimisation that uses a specific graphic hardware approach using the power of multi-core Graphic Processing Units (GPUs) in order to improve the tool path computation. This improvement is applied on a highly accurate and robust tool path generation algorithm. The paper presents, as a case of study, a fully implemented algorithm used for turning lathe machining of shoe lasts. A comparative study will show the gain achieved in terms of total computing time. The execution time is almost two orders of magnitude faster than modern PCs.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

The differentiated effects of CSR actions in the service industry

Purpose – The purpose of this study is to attempt to explain why the impact of Corporate Social Responsibility (CSR) initiatives may be different and/or more important in service firms compared to manufacturing firms. CSR is becoming a common strategy, hence its extensive research. Central to it is the analysis of the effect of CSR on a firm’s performance, whose outcome depends on firm-specific and industry-related factors. Design/methodology/approach – The event study methodology is applied to all the 248 companies that have ever traded on the Spanish Stock Market between 1990 and 2007. A regression analysis examines potential different effects of CSR on service and goods firms. Findings – The results show that CSR activities have a positive impact on firm performance that is higher for service firms than for manufacturing firms. Actions related to the environment, responsible labor relationships and good corporate governance are especially important in the service context. Research limitations/implications – This research is focused on shareholders’ performance, but it does not consider other stakeholders, such as real consumer behavior or employees’ commitment and productivity. Practical implications – Service firms are likely to gain from focusing on some CSR activities (environment, employees and good corporate governance) and should use their responsible behavior as a valuable tool for public relations and differentiation in the market. Originality/value – This article is the first attempt to empirically test and explain why the relationship between CSR and firm performance may be different (more positive) for service vs manufacturing firms.