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Abstract: This research uses the analytical hierarchy process (AHP) to analyze the key factors in evaluating and screening managers of the intellectual property rights speciality in Taiwan, related to industry. Industry can utilize the key factors of human resource management to provide self-growth and obtain competitive advantage opportunities for expanding the global market share. This research summarizes the evaluation and screening criteria of managers of the intellectual property rights speciality via a questionnaire of 29 managerial or technical staff from the IP rights-related industries, technology or management experts from the governmental departments related to IP rights industries, and scholars with backgrounds in IP rights related industries who had actually participated in IP rights managers speciality decisions or related tasks and who had employed AHP screening criteria. The findings include three key factors in evaluating and screening the managers’ speciality: evaluating IP rights competency, locating the core patent group competency, and calculating the chance of patents to be granted competency. This research can provide a reference for Taiwan’s related industry, when evaluating and screening managers of the IP rights speciality. [Copyright &y& Elsevier]

• Statistical and machine learning methods for imputing missing electric power data are studied. • Multiple missing feature values of each electric power data are considered. • The performance of different methods over the summer and non-summer season data is compared. • The imputation performances during the peak, off-peak, and semi-peak times are examined. Demand for electricity is gradually increasing in many countries. Efforts in related studies have been made for the application of data mining techniques over related electric power data for the development of more effective energy management systems. However, one major challenge is how to compensate for parts of the collected dataset, such as power consumption, voltage, or electric current that may be missing for a specific period of time. In the literature, several methods have been employed for imputation of the missing data, especially single feature value imputation. However, the performance of the different types of imputation methods, i.e. statistical and machine learning methods, for multiple missing features of electric power data has not been fully explored. Moreover, variations in their imputation performance during the summer/non-summer seasons and in the peak/off-peak/semi-peak times have not been investigated. In this paper, the performance of five well-known imputation methods for processing electric power data, two statistical methods, autoregressive integrated moving average (ARIMA) and linear interpolation (LI) models, and three machine learning methods, k-nearest neighbor (K-NN), multilayer perceptron (MLP), and support vector regression (SVR) is compared. The experimental results, based on electric power data for a two-year period in Taiwan, show that the machine learning methods generally perform better than the statistical ones, with K-NN and SVR performing the best. In particular, all of the imputation methods produced higher error rates during the summer season than the non-summer seasons. Moreover, the machine learning methods (especially K-NN) are better choices for the imputation of missing data during peak times, whereas the statistical methods (especially LI) are better for off-peak and semi-peak times. [ABSTRACT FROM AUTHOR]