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I have a folder or articles in my EBSCOhost account. On the left of each article, there are words like "export", "cite", and "notes". What do they do?
Please see the answers to your questions below about features in EBSCO's MyEBSCO folder. On the left of each article, there are words like "export", "cite", and "notes".
- These features allow you to manage the contents of the folder. For example, if you want to export the citation to a citation management (https://guides.lib.lsu.edu/c.php?g=279388&p=1862193) tool like Endnote or Zotero, you can do that. Or if you want to make notes about the citation and come back to them at a later date, the note for that citation will still be there. If you are interested in learning more about citation management and how it can help you organize your research, refer to our Citation LibGuide (https://guides.lib.lsu.edu/citation) as well as our workshop (https://www.youtube.com/watch?v=wRcFIlrWpDg&list=PL5ZtZe36ynPlutYgX4_x0…) about Zotero where you will learn how to set up Zotero to save and store sources and generate citations. To view upcoming workshops, please use our events calendar (https://lsu.libcal.com/calendar/eventsandprogramming) . What do these features actually do? How long does content remain in my folder?
- As long as you are logged into the MyEBSCOhost platform, the citations will stay there until you remove them. Here are some FAQs (https://connect.ebsco.com/s/article/EBSCOhost-Folder-Frequently-Asked-Q….) regarding how long content is kept in the folder. How do I delete an article from my folder if it is not relevant?
- To remove result(s) from your personalized folder, select the result by clicking on the appropriate check box, then click the Delete Items link. You may find this page (https://connect.ebsco.com/s/article/How-to-Use-the-My-EBSCOhost-Folder?…) useful in learning how to use the folder as well. Feel free to reach out to your subject librarian (https://www.lib.lsu.edu/staff?field_staff_type=2&search_api_staff=&fiel…) for more assistance. eResources Staff Answered by: Electronic Resources

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2065217
Technological catch-up: A new measure and patent-based evidence from China's manufacturing industries
The technological catch-up literature has not yet systematically assessed how different industries from latecomer economies have progressed in catching up. A significant challenge is the lack of reliable measures of innovation capability across countries, industries, and time. In this study, we develop a new measure of innovation capability called quality-weighted revealed innovation advantage (QRIA), which captures innovation capability more comprehensively and reliably than extant measures by simultaneously addressing issues of patent quantity distortion and patent quality heterogeneity. We apply QRIA to evaluate how globally competitive Chinese manufacturing industries (N = 22) have become in terms of innovation capability by using data from all invention patents granted by the US Patent and Trademark Office (USPTO) between 1983 and 2017. Using China as the empirical context, our study provides the first worldwide comparative evidence of technological catch-up across countries and industries over time. Our analyses reveal that while Chinese manufacturing industries have seen significant growth in patents, there are notable differences and time-varying changes in their innovation capabilities compared to their global counterparts. Only two industries have narrowed their gaps with global leaders: (1) computer, electronic, and optical products manufacturing and (2) electrical equipment manufacturing. The other industries have either fallen further behind global leaders or remained close to the average innovation capability.
The early bird catches the worm, but falls exhausted from the branch at dusk: developing the FIPS – Facets of (Mal)adaptive precrastination scale
The tendency to postpone tasks until the very last minute, whether in an academic setting, at school, or at the workplace, is a many common phenomenon experienced by individuals. This behavior, known as procrastination, has been extensively researched. While a significant number of people prefer to tackle tasks immediately rather than delaying them, there is limited research on the opposite behavior, often referred to as “precrastination.” This study aims at addressing this lack of research by developing the first measurement tool to make the construct of precrastination researchable. The questionnaire is named Facets of (Mal)adaptive Precrastination Scale (FIPS). To accomplish this, a sample of N = 214 German young to middle-aged adults was selected for the initial development of the instrument. Subsequently, another sample of N = 354 individuals was used for confirmatory factor analyses. The FIPS was validated by testing relationships with previously researched and well-known constructs, such as various personality traits. The outcome of this study is a three-dimensional questionnaire measuring functional precrastination, as well as precrastination driven by fear and by compulsion. By capturing both the behavior and its underlying motivations, the FIPS provides a reliable and efficient self-report instrument that enables research beyond laboratory paradigms and facilitates studies with larger and more diverse samples.
Catch before they fall: a pose-guided attention framework for indoor safety
Falls can pose a serious health threat, especially for older people, often leading to fractures, head injuries, or long-term disability. This highlights the need for reliable and non-invasive detection systems. Existing solutions often suffer from limitations such as user discomfort with wearables, constrained coverage of fixed sensors, or environmental challenges in vision-based methods. This paper proposes a dual-stream attention-guided robust indoor fall detection framework to solve the problem. The approach combines a pose-guided stream with a video stream, in which the former captures high-level skeletal features through MediaPipe and simultaneously processes spatiotemporal dynamics from raw RGB frames using a 3D Convolutional Neural Network (CNN) with a temporal attention module. In order to enhance classification precision, features from each stream are adaptively fused in a block based on attention mechanisms, which improves the model’s interpretation of posture and movement semantics. Testing on the KFall dataset reveals that the proposed method achieves 98.71% accuracy, surpassing pre-existing benchmarks. Triggering a visual alert by displaying a red rectangle on the screen upon the occurrence of this event is a subsequent outcome of this work. An ablation study highlights the effectiveness of each component. Finally, the proposed work advances fall detection by combining pose estimation and attention-based deep learning to deliver an accurate, interpretable, and deployable solution.