1,720,970 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
ALL Operation Outbreak simulations
No full text<p>See the file format.txt for a full description of the format of all the included files.</p>
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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Predictive and Causal Models of the Impact of Pandemics on Cardiovascular Disease Patient Biomarkers
No full textPandemic-induced disruptions to routine healthcare and lifestyle changes in cardiovascular diseases (CVDs) patients triggered changes in critical CVD biomarkers (measurable parameters of the body that can indicate health or illness). Prior work has overlooked models for predicting these biomarker trajectories or modeling causality during pandemics. Utilizing a first-of-a-kind Electronic Health Record (EHR) dataset of over 400,000 patients treated at the UMass Memorial hospital before and during the Covid pandemic, this doctoral dissertation created ML predictive and causal models for evaluating the COVID-19 pandemic’s impact on CVD patient biomarkers. To create these models, this dissertation explored three methodological approaches. The first explored traditional ML models on EHR data attributes, to predict the impact of the COVID-19 pandemic on CVD patient biomarker (BP, LDL cholesterol, HbA1c and BMI) trajectories and ML causal analysis using the Debiased ML for Difference-in-Differences approach. To interpret model results, SHAP values were calculated on selected ML models. Study results revealed CATBoost and XGBoost showed the best performance for predicting LDL cholesterol and HbA1c, with scores of 0.13 and 0.10. Random Forest performed best for BMI and blood pressure, with values of 0.192 and 0.071. Key features influencing biomarker changes included age, socioeconomic status, and race/ethnicity, underscoring the impact of social determinants of health. causal analysis revealed a significant rise in BMI and systolic BP (p < 0.05) among CVD patients during the COVID-19 pandemic, while HbA1c and LDL cholesterol improved, indicating varied pandemic effects on different biomarkers. However, as evidenced by the Low values, traditional ML models were limited in effectively capturing pandemic impact. This was due to the pervasiveness of categorical input variables and high variability in numerical biomarker levels. To address limitations of the first work including low predictive capacity of traditional ML models (low values, high MSE values), we explored the Genetic Algorithm Neural Architecture Search (GANAS) for automated, large CVD DL model design, and to enhance the low predictive capacity of traditional ML models. NAS is an automated approach for DL model design and determining optimal hyperparameter values, obviating the need for expertise. For the first time, we applied GA, an optimization technique inspired by the principles of natural selection and evolution, to optimize NAS and generate a CVD biomarker DL model. GANAS outperformed existing automated DL model design approaches such as ENAS, DARTS, and traditional ML models in predicting both HbA1c and LDL cholesterol levels. For HbA1c, it achieved 0.9739 accuracy, 0.9485 precision, 0.9739 recall, and a 0.9610 F1 score. For LDL cholesterol, it maintained strong results with 0.9117 accuracy, 0.9143 precision, 0.9100 recall, and a 0.9120 F1 score. SHAP analysis identified socioeconomic status—especially middle-class—and the pre/post-pandemic period as key predictors. The final study aimed at providing more robustness in design by addressing the lack of uncertainty estimation, exploiting temporal data relationships and capturing biomarker interdependency by via multi-target predictions. To do this, we created BMT-CB, a Bayesian multi-target Transformer model (BMT) that combines Bayesian variational inference (BVI) and multi-target prediction with ClinicalBERT (CB), a pretrained BERT-based transformer architecture to jointly predict multiple CVD biomarkers at the first pandemic-onset clinical visit. The model leverages BVI to estimate uncertainties, embeddings to capture temporal and patient characteristics and a DeepMTR model to capture biomarker interdependencies. BMT-CB outperformed other models by having the lowest mean value with an MAE of 0.00887, RMSE of 0.0135 and MSE of 0.00027. Uncertainty estimation revealed the model was able to effectively capture uncertainty and patient interdependency during prediction This research will advance predictive and causal modeling of pandemic impacts on CVD patients and facilitate tools for early detection of health deterioration, uncovering latent trends, and forecasting pandemic-induced health risks. Such tools could be utilized by healthcare professionals, epidemiologists, and public health policymakers for more informed decision-making and targeted interventions during pandemics
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