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Health Care Provider’s Perceptions of the Transition Between Pediatric to Adolescent and Adulthood: A Qualitative Inquiry
Get PDFThis study examines the current practices provided from pediatric to adult health care for children with special health care needs at FQHCs using the Six Core Elements of Health Care. Methods: A phenomenological approach was used to recruit and interview ten (10) health leaders from FQHCs. The participants were recruited through this letter sent to the Mid-Atlantic Association of Community Health Center. The researchers-initiated contact with the key informant to introduce them to the study. After consent was obtained, demographic information collected, and interviews were scheduled. During the semi-structured interview session, the key informant was asked questions related to their knowledge about the transition from pediatric to adult health care. Each interview lasted approximately 20 minutes, and analysis using ATLAS.ti version 8. Results: Ten (n=10) leaders participated in the study, of which three (n=3) were males and seven (n=7) females. Three main themes identified were Education and Training, Barriers to transitional practices, and Organizational Infrastructure hurdles. Discussion and Conclusion: Our current study finds that healthcare transition practices in FQHCs are not aligned with the six core elements of healthcare transition. Structured health care transition is likely when leadership and resources are accessible to achieve support for the required responsibilities
Srgan Images And Object Detection On The Xview Dataset
Get PDFObject Detection is a very popular and essential task in computer vision. This research explored super-resolution images generated via a super-resolution generative Adversarial Network (SRGAN) for object detection with various models. Specifically, the perceptual loss calculation uses an SRGAN, which is modeled after the Keras VGG19 network for deep feature extraction. This generated adversarial network is typically used to increase the quality of low-resolution imagery. Therefore, in the use case of satellite imagery, it can increase the pixel count per object instance in the image, resulting in better detection results. Based on previous models that were trained on original View data, it was observed that the maximum mean average precision (mAP) scores were 46.6 % in classes with adequate representation. Even lower mAP scores were observed in small objects and classes with few instances throughout the data set. The performance of newer YOLO models has increased due to advancements in anchor calculations, which use unsupervised learning techniques such as K-means. The results showed that newer improved models using the SRGAN data set improved on previous versions in the following way: increased Intersection Over Union (IOU), recall, and precision score. However, for more significant improvements, data pre-processing techniques should take priority as model architecture and optimizers aid in the process; the root issue is the challenges presented by this unique form of data. The continued use of model fine-tuning and overcoming obstacles associated with satellite data, such as high instance count per image, low pixel representation of small objects, and monochromatic photos/ backgrounds, should make the goal of accurate and fast object detection a reality. The advancements made in each You Only Look Once (YOLO) model paired with the fine tuning of hyperparameters and super-resolution images is the start of conquering the View Data set.
Index terms: Convolutional Neural Networks (CNN), Enhanced Super Resolution Generative Adversarial Network (ESRGAN), Generative Adversarial Network (GAN), Hyperparameters, Intersection Over Union (IOU), K-Means, Mean Squared Error (MSE), Recurrent Convolutional Neural Networks (RCNN), Rectified Linear Unit (ReLU), Regions of Interest (ROI), Super Resolution Generative Adversarial Network (SRGAN), Single Stage Detector (SSD), Unmanned Aerial Vehicles (UAVs), You-Only-Look-Once (YOLO
(R1951) Numerical Solution for a Class of Nonlinear Emden-Fowler Equations by Exponential Collocation Method
Get PDFIn this research, exponential approximation is used to solve a class of nonlinear Emden-Fowler equations. This method is based on the matrix forms of exponential functions and their derivatives using collocation points. To demonstrate the usefulness of the method, we apply it to some different problems. The numerical approximate solutions are compared with available (existing) exact (analytical) solutions to show the accuracy of the proposed method. The method has been checked with several examples to show its validity and reliability. The reported examples illustrate that the method is reasonably efficient and accurate
(R1986) Neutrosophic Soft Contra e-Continuous Maps, Contra e-Irresolute Maps and Application using Distance Measure
Get PDFWe introduce and investigate neutrosophic soft contra e-continuous maps and contra e-irresolute maps in neutrosophic soft topological spaces with examples. Also, neutrosophic soft contra econtinuous maps are compared with neutrosophic soft continuous maps, δ-continuous maps, δ- semi continuous maps, δ-pre continuous maps and e∗ continuous maps in neutrosophic soft topological spaces. We derive some useful results and properties related to them. An application in decision making problem using distance measure is given. An example of a candidate selection from a company interview is formulated as neutrosophic soft model problem and the hamming distance measure is applied to calculate the distance between the interview candidates and the ideal candidate. The candidate having less distance with the ideal solution is the desirable candidate
Overture to The Barber of Seville
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