A Cylindrical Triboelectric Energy Harvester for Capsule Endoscopes

Capsule endoscopy is a new technology that has the potential to replace conventional endoscopy in the near future due to its non-invasive nature. A major limitation for their functionality is the limited battery life. We have investigated a triboelectric energyharvester inside a capsule endoscope that can generate power from natural contractions of gastrointestinal (GI) tract. The periodic contacts and separations of two triboelectric materials inside the capsule endoscope create an alternating current that can be used to charge the capsule endoscope battery, which is used for imaging the GI tract. This study presents an analytical closed form solution for the output power of a cylindrical triboelectric energy harvester. Energy harvester sizes have been optimized to maximize the output power

An Investigation of Social Support Features of Digital Health Applications

Using digital health applications for health behaviour intervention is becoming popular. Among all application features designed for nudging users\u27 behaviour changes, social support features have received great attention, as social support is shown to reduce patients\u27 uncertainty and improve health behaviour engagement. A variety of social support features have been implemented, but how they are related to app performance, such as app ratings, downloads and review numbers, is unknown. This paper aims to understand the relationship between social support features and these app performance indicators, and identify common ways of providing social support. Three types of social support features have been identified through a review of selected health apps: the support from social networks, from in-app communities, and from health professionals. Apps with social support features are found to have higher review numbers, although not correlate with ratings or download numbers. Potential theoretical and practical implications are discussed, together with the future research plan

Chinese Job Well-Being: Its Concept and Scale Developing

The aim of this paper is to construct a scale of job well-being based on Chinese culture. The following steps were involved for this construction. First, we reviewed the literature on context free well-being and job well-being from western perspectives (e.g., hedonism, eudemonism) as well as Chinese culture. Then we completed a qualitative study (interviews, focus group discussions) and proposed a preliminary job well-being model. A preliminary scale was developed based on the job well-being model and existing scales in related fields. Further, we conducted another quantitative study to verify and modify the qualitative findings. We finally established a 7-dimension scale for Chinese job well-being. It has satisfactory reliability and validity, suggesting its potential applicable to Chinese culture. Despite being similar to the western model in some dimensions (e.g., intrinsic satisfaction, job competence), this job well-being model has some dimensions reflecting the uniqueness of Chinese culture (e.g., harmony, recognition from others). Moreover, high positive affect and autonomy, which are important for well-being in the west, are not obvious in this model. The different findings from China suggest the significance of conducting indigenous job well-being studies

Biofilm Growths With Sucrose As Substrate

This study was conducted to: (1) Evaluate the effect of DO on cell yield in a fixed film reactor using 1,000 mg/L sucrose as a substrate; (2) evaluate the correlations of the biofilm thickness and density with DO and their resultant substrate stabilization rates; and (3) examine the response of biofilm communities as a result of DO and biofilm thickness changes. Data obtained from this study indicate that DO has only a minor effect on the cell yield. However, the thickness of aerobic biofilm is definitely related to DO, or thickness (mm) = (2.08 x DO)/(9.2 + DO). The biofilm density is also related to its thickness. At a DO of 5 mg/L or lower, the biofilm texture is firm and has a wet density of 27-48 mg/cm3. At a higher DO (5-16 mg/L), the biofilm becomes porous and filled with air pockets, with its density being reduced to 25 mg/cm3. The biological community in biofilm at a high DO environment (16 mg/L) is predominantly short rods grouped in a chain structure. At a low DO environment (0.5 mg/L), however, the prevalent forms are large rods, none of which are in chain grouping. © ASCE

Tunable ferroelectric topological defects on 2D topological surfaces: strain engineering skyrmion-like polar structures in 2D materials

Polar topological structures in ferroelectric thin films have recently drawn significant interest due to their fascinating physical behaviors and promising applications in high-density nonvolatile memories. However, most polar topological patterns are only observed in the perovskites superlattices. Here, we report the discovery of the tunable ferroelectric polar topological defective structures designed and achieved by strain engineering in two-dimensional PbX (X=S, Se, and Te) materials using multiscale computational simulations. First, the first-principles calculations demonstrate the strain-induced recoverable ferroelectric phase transition in such 2D materials. The unique polar topological vortex pattern is then induced by applied mechanical indentation, evidenced by molecular dynamics simulations based on a developed deep-learning potential. According to the strain phase diagram and applied complex strain loadings, the diverse polar topological structures, including antivortex structure and flux-closure structure, are predicted to be emergent through the finite-element simulations. We conclude that strain engineering is promising to tailor various designed reversible polar topologies in ultra-flexible 2D materials, which provide excellent opportunities for next-generation nanoelectronics and sensor devices.Comment: 36 pages, 6 figures for manuscript, 11 figures for supplementary informatio

Deep Active Learning for Computer Vision: Past and Future

As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.Comment: Accepted by APSIPA Transactions on Signal and Information Processin