Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

Factors influencing innovation decision making in Portuguese firms

There is an undeniable positive effect of innovation for both firms and the economy, with particular regards to the financial performance of firms. However, there is an important role of the decision making process for the allocation of resources to finance the innovation process. The aim of this paper is to understand what factors explain the decision making process in innovation activities of Portuguese firms. This is an empirical study, based on the modern theoretical approaches, which has relied on five key aspects for innovation: barriers, sources, cooperation, funding; and the decision making process. Primary data was collected through surveys to firms that have applied for innovation programmes within the Portuguese innovation agency. Univariate and multivariate statistical techniques were used. Our results suggest that the factors that mostly influence the Portuguese firms’ innovation decision-making processes are economical and financial (namely those related to profit increase and labour costs reduction).

Adipocyte Secreted Factors Enhance Aggressiveness of Prostate Carcinoma Cells

Obesity has been associated with increased incidence and risk of mortality of prostate cancer. One of the proposed mechanisms underlying this risk association is the change in adipokines expression that could promote the development and progression of the prostate tumor cells. The main goal of this study was to evaluate the effect of preadipocyte and adipocyte secretome in the proliferation, migration and invasion of androgen independent prostate carcinoma cells (RM1) and to assess cell proliferation in the presence of the adiposity signals leptin and insulin. RM1 cells were co-cultured in with preadipocytes, adipocytes or cultured in their respective conditioned medium. Cell proliferation was assessed by flow cytometry and XTT viability test. Cell migration was evaluated using a wound healing injury assay of RM1 cells cultured with conditioned media. Cellular invasion of RM1 cells co-cultured with adipocytes and preadipocytes was assessed using matrigel membranes. Preadipocyte conditioned medium was associated with a small increase in RM1 proliferation, while adipocytes conditioned media significantly increased RM1 cell proliferation (p<0.01). Adipocytes also significantly increased the RM1 cells proliferation in co-culture (p <0.01). Cell migration was higher in RM1 cells cultured with preadipocyte and adipocyte conditioned medium. RM1 cell invasion was significantly increased after co-culture with preadipocytes and adipocytes (p <0.05). Insulin also increased significantly the cell proliferation in contrast to leptin, which showed no effect. In conclusion, prostate carcinoma cells seem to be influenced by factors secreted by adipocytes that are able to increase their ability to proliferate, migrate and invade.

Factors of repatriates’ turnover: literature review

One of the important challenges for multinational organisations is to increase the ability to retain their repatriates. The literature review shows high turnover rates amongst repatriates in the first two years following return from the international assignment. This paper discusses organisation’s (in)ability to retain its repatriates. A diversity of predictable factors of repatriates’ turnover is identified, including factors related to the job, the organisation, qualification, career development, personality, social context and the market. The paper analysis organisational support practices designed to increase the power of retention and decrease the turnover. We concluded that research about retention versus turnover of repatriates is scarce, mainly regarding the role a formal repatriation and retention programme in facilitating the retention of these employees with acquired international knowledge and skills, that is, the role that organisational factors play in voluntary turnover decisions. Further research is suggested at the end of the article.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.