The cumulative index to Nursing and Allied Health Literature (CINAHL) is a useful research tool for accessing articles of interest to nurses and health care professionals. More than 2,800 journals are indexed by CINAHL and can be searched easily using assigned subject headings. Detailed instructions about conducting, combining, and saving searches in CINAHL are provided in this article. Establishing an account at EBSCO further allows a nurse to save references and searches and to receive e-mail alerts when new articles on a topic of interest are published.
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This bibliometric analysis aimed to define important topics and developments across wide awake local anaesthesia no tourniquet (WALANT) hand surgery, an innovative ambulatory technique that gained popularity during the COVID-19 pandemic. Articles were searched and screened using the Web of Science core collection database. VOSviewer 1.
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The immunogenicity of rabies vaccines is commonly measured by serological testing, which includes measuring rabies virus-neutralising antibody titre levels in the serum. Apart from humoral immunity, cellular immunity measurements are also helpful in assessing the immunogenicity and efficacy of rabies vaccinations. Recently, there has been an increased emphasis on cellular immunity measurements against rabies in humans and animals.
View Article and Find Full Text PDFEnhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.
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Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
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