Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

A review of mentorship measurement tools

© 2016 Elsevier Ltd. Objectives: To review mentorship measurement tools in various fields to inform nursing educators on selection, application, and developing of mentoring instruments. Design: A literature review informed by PRISMA 2009 guidelines. Data Sources: Six databases: CINHAL, Medline, PsycINFO, Academic Search Premier, ERIC, Business premier resource. Review Methods: Search terms and strategies used: mentor* N3 (behav* or skill? or role? or activit? or function* or relation*) and (scale or tool or instrument or questionnaire or inventory). The time limiter was set from January 1985 to June 2015. Extracted data were content of instruments, samples, psychometrics, theoretical framework, and utility. An integrative review method was used. Results: Twenty-eight papers linked to 22 scales were located, seven from business and industry, 11 from education, 3 from health science, and 1 focused on research mentoring. Mentorship measurement was pioneered by business with a universally accepted theoretical framework, i.e. career function and psychosocial function, and the trend of scale development is developing: from focusing on the positive side of mentorship shifting to negative mentoring experiences and challenges. Nursing educators mainly used instruments from business to assess mentorship among nursing teachers. In education and nursing, measurement has taken to a more specialised focus: researchers in different contexts have developed scales to measure different specific aspects of mentorship. Most tools show psychometric evidence of content homogeneity and construct validity but lack more comprehensive and advanced tests. Conclusion: Mentorship is widely used and conceptualised differently in different fields and is less mature in nursing than in business. Measurement of mentorship is heading to a more specialised and comprehensive process. Business and education provided measurement tools to nursing educators to assess mentorship among staff, but a robust instrument to measure nursing students' mentorship is needed

Data Clustering And Visualization Through Matrix Factorization

Clustering is traditionally an unsupervised task which is to find natural groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information from unlabeled data. In order to present the extracted useful knowledge obtained by clustering in a meaningful way, data visualization becomes a popular and growing area of research field. Visualization can provide a qualitative overview of large and complex data sets, which help us the desired insight in truly understanding the phenomena of interest in data. The contribution of this dissertation is two-fold: Semi-Supervised Non-negative Matrix Factorization (SS-NMF) for data clustering/co-clustering and Exemplar-based data Visualization (EV) through matrix factorization. Compared to traditional data mining models, matrix-based methods are fast, easy to understand and implement, especially suitable to solve large-scale challenging problems in text mining, image grouping, medical diagnosis, and bioinformatics. In this dissertation, we present two effective matrix-based solutions in the new directions of data clustering and visualization. First, in many practical learning domains, there is a large supply of unlabeled data but limited labeled data, and in most cases it might be expensive to generate large amounts of labeled data. Traditional clustering algorithms completely ignore these valuable labeled data and thus are inapplicable to these problems. Consequently, semi-supervised clustering, which can incorporate the domain knowledge to guide a clustering algorithm, has become a topic of significant recent interest. Thus, we develop a Non-negative Matrix Factorization (NMF) based framework to incorporate prior knowledge into data clustering. Moreover, with the fast growth of Internet and computational technologies in the past decade, many data mining applications have advanced swiftly from the simple clustering of one data type to the co-clustering of multiple data types, usually involving high heterogeneity. To this end, we extend SS-NMF to perform heterogeneous data co-clustering. From a theoretical perspective, SS-NMF for data clustering/co-clustering is mathematically rigorous. The convergence and correctness of our algorithms are proved. In addition, we discuss the relationship between SS-NMF with other well-known clustering and co-clustering models. Second, most of current clustering models only provide the centroids (e.g., mathematical means of the clusters) without inferring the representative exemplars from real data, thus they are unable to better summarize or visualize the raw data. A new method, Exemplar-based Visualization (EV), is proposed to cluster and visualize an extremely large-scale data. Capitalizing on recent advances in matrix approximation and factorization, EV provides a means to visualize large scale data with high accuracy (in retaining neighbor relations), high efficiency (in computation), and high flexibility (through the use of exemplars). Empirically, we demonstrate the superior performance of our matrix-based data clustering and visualization models through extensive experiments performed on the publicly available large scale data sets

An exploration of the structure of mentors' behavior in nursing education using exploratory factor analysis and Mokken scale analysis

© 2016 Elsevier Ltd. Background: To understand nursing students' expectation from their mentors and assess mentors' performance, a scale of mentors' behavior was developed based on literature review and focus group in China. Objectives: This study aims to explore the structure of mentors' behavior. Design: A cross-sectional survey. Setting: Data were collected from nursing students in three hospitals in southwest China in 2014. Participant: A total of 669 pre-registered nursing students in their final year clinical learning participated in this study. Methods: Exploratory factor analysis and Mokken scale analysis was employed to explore the structure and hierarchical property of mentors' behavior. Results: Three dimensions (professional development, facilitating learning and psychosocial support) were identified by factor analysis and confirmed by Mokken scaling analysis. The three sub-scales showed internal consistency reliability from 87% to 91%, and moderate to strong precision in ordering students' expectation about mentors' behavior and a small Mokken scale showing hierarchy was identified. Conclusion: Some insight into the structure of mentoring in nursing education has been obtained and a scale which could be used in the study of mentoring and in the preparation of mentors has been developed

Numerical simulation on the damage of buried thermal-pipeline under seismic loading based on thermal-mechanical coupling

AbstractThe thermal-mechanical coupling effect is one of important factors in construction engineering, which will cause the buried thermal-pipeline to damage. So a three-dimensional finite element model is established based on ADINA-TMC, which considers thermal-mechanical coupling and seismic loading simultaneously. In this model, seismic loads and faults movement are defined. According to the numerical results, stresses and strains under gravity, seismic loading, and temperature load are compared, which provides theoretical method for failure analysis of buried thermal-pipeline

The structure of mentors' behaviour in clinical nursing education: Confirmatory factor analysis

Aims: To study if a three-factor structure of mentors' behaviour identified through exploratory factor analysis could be confirmed in a dataset assessing mentors' performance using structural equation modelling. Background: To measure mentor's behaviour in clinical nursing education in China, a specific instrument was developed and preliminarily validated; a three-factor structure (professional development, facilitating learning and psychosocial support) was identified in a dataset of assessment of the importance of mentors' each behaviour using exploratory factor analysis and Mokken scale analysis. Design: A cross-sectional study with online and hard copy survey was applied. Methods: Convenience sampling was conducted. Nursing students (n = 634) in Southwest China participated in the study from July to August 2014. Confirmatory factor analysis was used. Results: Mentors' behaviour can be perceived as a secondary order factor with three first order factors: professional development; facilitating learning; and psychosocial support. Conclusion: The three-factor structure of mentors' behaviour was confirmed by structural equation modelling. This structure is visible in mentors' real performance and implies that this instrument could be used to assess mentors' behaviour in addition to students' expectation from mentors

La interacción entre identidad y emoción de los jóvenes profesores de IDAI (idiomas distintos al inglés) en los inicios de su carrera profesional: La teoría sociocultural

There is conflicting evidence regarding the relationship between teachers’ identities and emotions. The inconsistency may be explained by a one-way causal relationship or two-way support. However, from a sociocultural perspective, the two might dialectically influence each other. Furthermore, most studies on teacher identities have focused solely on teaching and research identities without considering their social service roles as well. Further, little is known about LOTE (Languages Other than English) teachers in higher education, and even less is known about younger novice teachers. To fill these gaps, this study included a survey of 843 young LOTE teachers and in-depth interviews with three respondents at universities to gain a deeper understanding of their identities and emotions. As a result, the interaction between the two variables in the teaching dimension was observed to have a more robust relationship. Teachers’ identities and emotions were inextricably linked in either a positive or negative manner. Specifically, a higher sense of identity and positive emotion correlated closely, whereas a lower sense of identity and negative emotion walked together. As a result of the weak sense of research identity, this relationship was evident in teaching and social service, de-emphasizing research. The present study provides additional evidence with respect to the dialectical relationship between teacher identity and teacher emotion. FUNDING INFORMATION. The project has been primarily supported by Research Fund for Yunshan Outstanding Scholar, Guangdong University of Foreign Studies (No. 2023RC031), the MOE Project at Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Shanghai International Studies University Peak Discipline (Linguistics) Construction Project (N0. 41004525/001), and Shanghai International Studies University Mentor Academic Leadership Program (N0. 2022113041). The paper has also been supported by: An Empirical Study on the Impact of Public Sponsored Study Abroad on the Professional Development of University Teachers-Taking Guangdong as an Example (N0: GD22CJY10).La literatura existente es contradictoria sobre la relación entre las identidades y las emociones de los profesores. La inconsistencia puede explicarse mediante una relación causal unidireccional o un apoyo bidireccional. Sin embargo, desde una perspectiva sociocultural, ambas podrán influenciarse dialéctica. Además, la mayoría de los estudios sobre las identidades de los profesores ha habido centrado en las enseñanzas e investigadoras sin el servicio social. Por lo demás, se ha investigado poco sobre los profesores IDAI (idiomas distintos al inglés) y aún menos sobre los profesores jóvenes. Para colmar estas lagunas, el estudio incluyó encuestas a 843 jóvenes profesores y entrevistas con tres de los encuestados. Mostró que las dos interacciones destacaron en la enseñanza y el servicio social, mientras que se restaron importancia a la investigación debido al débil sentido de la identidad investigadora. Y las identidades y emociones se desarrollaron en un patrón inextricable en una tendencia positiva o negativa. Un mayor sentido de la identidad y la emoción positiva estuvieron estrechamente relacionados, mientras que la identidad de bajo sentido o de reconstrucción y la emoción negativa caminaron juntos. El presente estudio demuestra una evidencia adicional con respecto a la relación dialéctica entre las identidades y las emociones de los profesore