1,720,967 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A Regression-Based Method for Monthly Electric Load Forecasting in South Korea
No full textIn this study, we propose a regression-based method for forecasting monthly electricity consumption in South Korea. The regression model incorporates key external variables such as weather conditions, calendar data, and industrial activity to capture the major factors influencing electricity demand. These predictor variables were identified through comprehensive data analysis. Comparative experiments were conducted with various existing methods, including univariate time series models and machine learning techniques like Holt–Winters, LightGBM, and Long Short-Term Memory (LSTM). Additionally, ensemble methods combining two or more of these existing methods were tested. In the empirical analysis, the proposed model was used to forecast monthly electricity demand for a 24-month period (2022–2023), achieving a mean absolute percentage error (MAPE) of approximately 2%. The results demonstrated that the proposed method consistently outperforms all benchmarks tested in this study
Real-time order promising methods considering scheduling of production lines under make-to-order environments
No full textA Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model
No full textTourism is a core sector of Singapore’s economy, contributing significantly to Gross Domestic Product (GDP) and employment. Accurate tourism demand forecasting is essential for strategic planning, resource allocation, and economic stability, particularly in the post-COVID-19 era. This study develops a SARIMAX-based forecasting model to predict monthly visitor arrivals to Singapore, integrating web search data from Google Trends and external factors. To enhance model accuracy, a systematic selection process was applied to identify the effective subset of external variables. Results of the empirical experiments demonstrate that the proposed SARIMAX model outperforms traditional univariate models, including SARIMA, Holt–Winters, and Prophet, as well as machine learning-based approaches such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs). When forecasting the 24-month period of 2023 and 2024, the proposed model achieves the lowest Mean Absolute Percentage Error (MAPE) of 7.32%
Regression-Based Methods for Daily Peak Load Forecasting in South Korea
No full textThis study examines the daily peak load forecasting problem in South Korea. This problem has become increasingly important due to the continually changing energy environment. As such, it has been studied by many researchers over the decades. South Korea is geographically located such that it experiences four distinct seasons. Seasonal changes are among the main factors affecting electricity demand. In addition, much of the electricity consumption in a strong manufacturing country like South Korea is driven by industry rather than by residential customers. In order to forecast daily peak loads of South Korea, in this study we proposed multiple linear regression-based methods where several season-specific regression models (i.e., summer, winter, and all-season models) were included. The most appropriate model among the three models was selected considering the characteristics of the electricity demand, and was then applied to daily forecasting. The performance of the proposed methods were evaluated through computational experiments. Forecasts obtained by the proposed methods were compared with those obtained by existing forecasting methods, including a machine learning method. The results showed that the proposed methods had mean absolute percentage errors around 1.95% and outperformed all benchmarks
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