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No full texthttps://digitalcommons.pvamu.edu/woodwind-ensembles/1001/thumbnail.jp
Deep Learning For Resource Constraint Devices
Get PDFThe amount of Internet-of-things (IoT) devices is rapidly expanding. This also triggered the necessity of smart IoT devices which are capable of conducting any task by itself. Deep learning techniques are also booming due to the increased computing power and refined algorithms. The advantage of deep learning is that it can be tuned into any application without the manual feature extraction process. Now, the combination of deep learning with smart IoT devices/edge devices can result in any application that can be used in machine vision, vision inspection, autonomous vehicle, and many more. These applications can be automated which requires human operation. Now, combining deep learning and edge device together and running the application can be a difficult task. The main reason is that deep learning requires large computation power and edge devices does not have that capability. This study focused on this problem. Ie used techniques to encrypt and compress data which is essential for the edge devices. In addition, we developed novel methods to protect user privacy for data collection and learning on edge devices. Also, we conducted a study to evaluate different edge devices for different application purposes with certain compression technique of the models. Lastly, we conducted a real life experiment of collecting data, creating different models and evaluating it on different edge devices.
index terms - IoT, computer vision, deep learning, machine learning, quantization, autoencoder, mobilenet v1, mobilenet v2, inception v3, face mask detectio
Physicochemical Properties Of Poly (Ε-Caprolactone) And Magnesium Oxide Incorporated Poly (Ε-Caprolactone) Nanofibers
Get PDFPolymer nanofibers are used to develop materials that possess customized characteristics for diverse applications. The applications of nanofibers are influenced by their significant surface-to-volume ratio, the porosity of the nanofiber lattice, and distinctive physicochemical characteristics. The molecular orientation of electrospun nanofibers is a crucial and intricate feature that has a direct impact on the structures and properties of the nanofiber mat. The utilization of Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), and X-Ray Diffractometry (XRD), facilitated the determination of the morphology, chemical structure, and thermal properties of nanofibers. The SEM analysis revealed that the nanofibers exhibited a random and interconnected orientation. The findings indicate that the level of crystallinity exhibited by the magnesium oxide-incorporated PCL (ε-caprolactone) nanofibers, surpassed that of the PCL nanofibers. Increased crystallinity indicates chain mobility changes, leading to improved mechanical characteristics. Further evaluation was conducted on the DSC findings. The study delved into the kinetics of non-isothermal crystallization of PCL and MgO-PCL nanofibers with varying cooling rates.
The study used DSC-3 apparatus produced by Mettler Toledo to acquire crystallization information and investigate the kinetics behavior of the two types of nanofibers under different cooling rates ranging from 0.5-5 K/min. Several mathematical models, including Jeziorny, Ozawa, and Mo\u27s models, were utilized to determine the parameters of non-isothermal crystallization kinetics. Mo\u27s approach generates consistent ratios of Avrami exponent to Ozawa exponent (α) that are approximately 1.4 for PCL, MgO-PCL nanofibers, and bulk-PCL. The similarity of α values indicates that the structures of crystallization formed at different levels of relative crystallinity were analogous. The investigation with the Friedman method exhibited an increase in relative crystallinity was associated with a decrease in temperature and a rise in activation energy. According to the Kissinger and Friedman methodologies, it was observed that the activation energy of bulk-PCL was comparatively lower than that of PCL and MgO-PCL nanofibers. The observed phenomenon can be attributed to the nanoconfinement effect, which is characterized by geometric constraints imposed on PCL nanofibers
Dr. J. Cornelius Briefing on L\u27il David/Honor, Honor and Go Down, Moses
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Vedrai carino from Don Giovanni
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Impact Of A Training Intensive On Advanced Practice Registered Nurses’ Intent And Self-Efficacy In Implementing Research Findings
No full textImplementation of research findings in real-life settings should be the end goal of any research project. Despite the importance of research implementation, there exists a lag between the research\u27s completion and its findings\u27 implementation. Sometimes up to a 14-year time-lapse exists between the completion of research and the implementation of its findings. It is estimated that less than 50% of clinical research findings are utilized in clinical settings. Shortening the time lag between research and implementation requires involving research stakeholders such as advanced practice registered nurses (APRNs). This project evaluated the impact of a three-day training intensive on APRNs\u27 intent to use and self-efficacy in using research findings after participating in the intensive. The project had a cross-sectional non-experimental design. Eleven APRNs participated in a three-day training intensive in October 2021, and seven APRNs participated in May 2022. The training intensive was based on comparative effectiveness research findings and the research implementation process. The APRNs completed assessment survey questionnaires after each day of training and completed a program evaluation after the three-day intensive. The program evaluation questionnaire evaluated participants\u27 self-efficacy in implementing research findings and intent to use research findings in their
clinical practice. The assessment and post-attendance questionnaires were analyzed using descriptive statistics and the Kirkpatrick Model. The project\u27s objectives were met, as all participants responded positively to the survey question about their intent to use comparative effectiveness research findings in their practice setting. They also rated their self-efficacy in implementing research findings as fairly- confident.
Keywords: research implementation, comparative effectiveness research, training intensive, intent to use, self-efficac