Background Patient activation influences patients’ ability to meaningfully engage in critical heart failure self-care. The purpose of this study was to identify whether patient activation is associated with patient-reported health outcomes in an urban and racially diverse inpatient sample of patients with heart failure. Methods We prospectively recruited patients with heart failure hospitalized at an urban academic medical center from October 2016 to May 2017 and measured patient activation, physical and affective symptoms, physical function, self-care, perceived control, and self-efficacy. Differences in patient-reported health outcomes between low and high activation groups were compared with the use of linear regression models adjusting for age, sex, education, left ventricular ejection fraction, and New York Heart Association functional classification. Results A total of 96 patients completed the study (mean age 57 ± 12.4 y); 39% identified as black and 35% as Latino, 35% were female, and 50% reported not having enough income to make ends meet. Based on the 4 levels of activation defined by the Patient Activation Measure–13, 22% of patients reported being “disengaged and overwhelmed,” 14% were “becoming aware, but still struggling,” 39% were “taking action,” and 26% were “maintaining behaviors and pushing further.” Higher patient activation was associated with better applied cognitive abilities, self-care behaviors, perceived control, and self-efficacy. Conclusion Patient activation can be easily measured in hospitalized patients with heart failure and is associated with clinically meaningful patient-reported health outcomes.
An all digital low-power CMOS edge detection image sensors array is presented. Each pixel contains a voltagecontrolled ring oscillator to achieve low power and cost efficient digital only edge detection. While conventional edge detection methods require high computing power as well as large chip area to process intensity maps, this work implements all-digital parallel processing algorithm that detects differences between neighboring pixel pairs on-chip, hence reducing the aforementioned power and cost overheads. In particular, a simple columnshared frequency comparator enables low power operation by eliminating arithmetic computations with large memory requirement. Such simple edge detection algorithm allows the processor area to be less than 16% of the entire image sensor, therefore maximizing the proportion of active optical area. The prototype image sensor presented in this work is fabricated using a fourmetal 180 nm CMOS image sensor process and contains 105×92 pixels. An individual pixel size is 8×8µm 2 with fill-factor of 11.69%, while the total chip area is 1×1.3 mm 2 . The image sensor exhibits a frame rate of 30 frame/sec and a power consumption of 8 mW which is 27.7 nW/pixel/frame at VDD of 1.6 V.Sobel (0.08) Prewitt (0.08) Our Original 1549-7747 (c)
In this article, we present an accurate and easy to use augmented reality (AR) application for mobile devices. In addition, we show how to better organize and track artifacts using augmented reality for museum employees using both the mobile device and a 3D graphic model of the museum in a PC server. The AR mobile application can connect to the server, which maintains the status of artifacts including its 3D location and respective room location. The system relies on 3D measurements of the rooms in the museum as well as coordinates of the artifacts and reference markers in the respective rooms. The measured coordinates of the artifacts through the AR mobile application are stored in the server and displayed at the corresponding location of the 3D rendered representation of the room. The mobile application allows museum managers to add, remove, or modify artifacts' locations simply by touching the desired location on the touch screen showing live video with AR overlay. Therefore, the accuracy of the touch screen-based artifact positioning is very important. The accuracy of the proposed technique is validated by evaluating angular error measurements with respect to horizontal and vertical field of views that are 60[Formula: see text] and 47[Formula: see text], respectively. The worst-case angular errors in our test environment exhibited 0.60[Formula: see text] for horizontal and 0.29[Formula: see text] for vertical, which is calculated to be well within the error due to touch screen sensing accuracy.
Additive manufacturing (AM), also known as 3D printing technology, is applied to fabricate complex fin structures for heat transfer enhancement at inner surface of tubes, which conventional manufacturing technology cannot make. This work considered rectangular fins, scale fins, and delta fins with staggered alignment at the inner wall of heat transfer tubes for heat transfer enhancement of internal flows. Designed fin structures are trial-printed using plastic material to exam the printability. Laminar flow convective heat transfer has been numerically studied, and heat transfer performance of the tubes with 3D-printed interrupted fins has been compared to that with conventional straight continued fins. The benefit from heat transfer enhancement and the loss due to increased pumping pressure is evaluated using the total entropy generation rate in the control volume of heat transfer tube. As the conclusion of the study, better heat transfer tubes with 3D-printed internal fins are recommended.
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