International research partnerships: establishing partner selection criteria in the university-university context

The main objective of the study was to identify and evaluate criteria for international partner selection in university-university context. This study attempted at promoting better understanding of how universities should proceed in selecting partners for producing joint research publications. Thus, the aim of the study was to gain an understanding of how research collaborations can be developed and how partners can be selected. The choice of a right partner has been identified as a precondition for partnership success. In international research collaborations partnering scientists with different skills and backgrounds bring together complementary knowledge into research projects, which in most cases results in a higher quality output. Therefore, prior to selecting a partner, the set of criteria should be established. This research examined twelve Russian universities with the status of national research university as potential partners for Lappeenranta University of Technology, and selected the most appropriate universities based on established set of criteria. Potential partners’ evaluation was done using secondary sources by tracking partners’ academic success during the period 2005 – 2010. Based on established criteria, the study calculated the partnership index for each university. The results of the research reveal that among twelve examined universities there are four potential partners who have been rather active in publishing scientific articles during 2005 – 2010.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

Validation and Selection of Transportation Cost Drivers with Advanced Methods

In today's logistics environment, there is a tremendous need for accurate cost information and cost allocation. Companies searching for the proper solution often come across with activity-based costing (ABC) or one of its variations which utilizes cost drivers to allocate the costs of activities to cost objects. In order to allocate the costs accurately and reliably, the selection of appropriate cost drivers is essential in order to get the benefits of the costing system. The purpose of this study is to validate the transportation cost drivers of a Finnish wholesaler company and ultimately select the best possible driver alternatives for the company. The use of cost driver combinations as an alternative is also studied. The study is conducted as a part of case company's applied ABC-project using the statistical research as the main research method supported by a theoretical, literature based method. The main research tools featured in the study include simple and multiple regression analyses, which together with the literature and observations based practicality analysis forms the basis for the advanced methods. The results suggest that the most appropriate cost driver alternatives are the delivery drops and internal delivery weight. The possibility of using cost driver combinations is not suggested as their use doesn't provide substantially better results while increasing the measurement costs, complexity and load of use at the same time. The use of internal freight cost drivers is also questionable as the results indicate weakening trend in the cost allocation capabilities towards the end of the period. Therefore more research towards internal freight cost drivers should be conducted before taking them in use.