BackgroundIn the last few decades, mobile technologies have been widely adopted in the field of health care services to improve the accessibility to and the quality of health services received. Mobile health (mHealth) has emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported.ObjectiveThe purpose of this study was to analyze the current state of research, including publication outputs, in the field of mHealth to uncover in-depth collaboration characteristics and topic burst of international mHealth research.MethodsThe authors collected literature that has been published in the last 20 years and indexed by Thomson Reuters Web of Science Core Collection (WoSCC). Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author, institution, and country collaboration level. The temporal visualization map of burst terms was drawn, and the co-occurrence matrix of these burst terms was analyzed by hierarchical cluster analysis and social network analysis.ResultsA total of 2704 bibliographic records on mHealth were collected. The earliest paper centered on mHealth was published in 1997, with the number of papers rising continuously since then. A total of 21.28% (2318/10,895) of authors publishing mHealth research were first author, whereas only 1.29% (141/10,895) of authors had published one paper. The total degree of author collaboration was 4.42 (11,958/2704) and there are 266 core authors who have collectively published 53.07% (1435/2704) of the total number of publications, which means that the core group of authors has fundamentally been formed based on the Law of Price. The University of Michigan published the highest number of mHealth-related publications, but less collaboration among institutions exits. The United States is the most productive country in the field and plays a leading role in collaborative research on mHealth. There are 5543 different identified keywords in the cleaned records. The temporal bar graph clearly presents overall topic evolutionary process over time. There are 12 important research directions identified, which are in the imbalanced development. Moreover, the density of the network was 0.007, a relatively low level. These 12 topics can be categorized into 4 areas: (1) patient engagement and patient intervention, (2) health monitoring and self-care, (3) mobile device and mobile computing, and (4) security and privacy.ConclusionsThe collaboration of core authors on mHealth research is not tight and stable. Furthermore, collaboration between institutions mainly occurs in the United States, although country collaboration is seen as relatively scarce. The focus of research topics on mHealth is decentralized. Our study might provide a potential guide for future research in mHealth.

Purpose The purpose of this paper is to analyze the research status and outputs of information behavior in China in order to reveal its in-depth research pattern and trends. Design/methodology/approach The author collected literature during the past 29 years from China Academic Journal Network Publishing Database. Bibliometric analysis, including publication growth analysis, core authors and collaborative degree analysis, core journals analysis, and institutions distribution, was performed. The temporal visualization map of burst term was drawn, and the co-occurrence matrix of these keywords was analyzed by the hierarchical cluster analysis, strategic diagram, and social network analysis. Findings The earliest article on information behavior in China was published in 1987. And the number of articles has risen continually since then, which follows the logical growth law of literature. The collaborative degree of authors is on the rise in general. The distribution of these articles obeys the Bradford’s Law of Scattering. School of Information Management of Wuhan University remarkably ranks the top in most publications. In all, ten important research directions were identified, which are in the imbalanced development. And a newly appearing topic with great potential for further development, namely information seeking and information security, is identified. Originality/value This study provides important insights into the research status and trends on information behavior in China, which might provide a potential guide for the future research.

This paper details the design of a 64 × 32 bit 4-read 2-write register file in TSMC 65 nm LP process. The register file avoids cell banking with pseudo-differential sensing scheme. Moreover, this approach enables a fully shareable and completely symmetry cell layout which shows competitive area results. Non-full-swing technique is proposed to avoid over design and improve energy efficiency. As for the timing control module, clocked pull-down circuit cuts off a possible short-current path at high clock frequency. A prototype is implemented in TSMC 65 nm LP technology. The measured results demonstrate operation of 0.77 GHz, consuming 7.08 mW at 1.2 V, and occupying 0.018 mm 2 .

Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage methods for reinforced concrete structures are based on the segmentation of two-dimensional planes without consideration to the actual size of concrete damage. Furthermore, on-site structural monitoring requires the installation of a large number of contact-based sensing devices, resulting in the potentially excessive consumption of time and financial resources. Therefore, a new vision-based damage assessment method for reinforced concrete structures using a novel intelligent inspection robot with Internet of things–enabled data communication system is proposed in this article. In the first part of this article, the data acquisition system of the inspection robot and the algorithm for three-dimensional structural reconstruction using a stereo camera is discussed. The discussion is followed by a description of the method for crack quantification based on a new proposed deep-learning technique. Finally, to accomplish damage localization, the quantified concrete damage with actual size information is projected onto a three-dimensional surface point cloud reconstruction of the inspected structure. To verify the proposed method, a reinforced concrete column that has undergone cyclic loading failure is used as an inspection subject. The validation experiment demonstrated the ability of the proposed system to segment, localize, and quantify the damage in three-dimensional space with high accuracy.

We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.

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This article presents a vision‐aided framework to achieve three‐dimensional (3D) concrete damage quantification and finite element (FE) model geometric updating for reinforced concrete structures. The framework can process images and point clouds to extract damage information and update it in an FE model. First, a mask region convolutional neural network was used to realize highly precise damage detection and segmentation based on images. Second, a 3D point cloud was adopted in conjunction with the processed images for 3D damage qualification. The model‐updating method enables an FE model to delete concrete elements to update the variations in volume caused by structural damage. This framework supports interaction with mainstream FE software for further analysis. To demonstrate the efficiency of the proposed framework, it was used in an experiment on a reinforced‐concrete shear wall.