In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media—and not in the visible or manifest public sphere (official campaign platform, mass media)—where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election campaign, the latter potentially forming an enormous online support army of Abe's agenda.
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.
The physical load ensuing from the repositioning and moving of patients puts health care workers at risk of musculoskeletal complaints. Technical equipment developed to aid with patient handling should reduce physical strain and workload; however, the efficacy of these aids in preventing musculoskeletal disorders and complaints is still unclear. A systematic review of controlled intervention studies was conducted to examine if the risk of musculoskeletal complaints and disorders is reduced by technical patient handling equipment. MEDLINE®/PubMed®, EMBASE®, Allied and Complementary Medicine Database (AMED), and Cumulative Index of Nursing and Allied Health Literature (CINAHL®) were searched using terms for nursing, caregiving, technical aids, musculoskeletal injuries, and complaints. Randomized controlled trials and controlled before-after studies of interventions including technical patient handling equipment were included. The titles and abstracts of 9554 publications and 97 full-texts were screened by two reviewers. The qualitative synthesis included one randomized controlled trial (RCT) and ten controlled before-after studies. A meta-analysis of four studies resulted in a pooled risk ratio for musculoskeletal injury claims (post-intervention) of 0.78 (95% confidence interval 0.68–0.90). Overall, the methodological quality of the studies was poor and the results often based on administrative injury claim data, introducing potential selection bias. Interventions with technical patient handling aids appear to prevent musculoskeletal complaints, but the certainty of the evidence according to GRADE approach ranged from low to very low.
Analysis 1.3. Comparison 1 Psychological interventions (including health education) vs usual care, Outcome 3 Proportion returning to work medium term (6 months-1 year) by CHD severity.
Background The consent management is an essential component for supporting the implementation of consents and withdrawals and thus, the realisation of patient’s rights. In MIRACUM, one of the four consortia of the Medical Informatics Initiative (MII), ten university hospitals intend to integrate the generic Informed Consent Service® (gICS) in their Data Integration Center (DIC). To provide a tool that supports the local workflows of the MIRACUM sites, the gICS should be improved. Methods We used three standardised questionnaires with 46 questions to elicit requirements from the ten sites. Each site answered the questions from the current and the desired future perspective. This made it possible to understand the individual processes at each site and it was possible to identify features and improvements that were generally necessary. Results The results of the survey were classified according to their impact on the gICS. Feature requests of new functionalities, improvements of already implemented functionalities and conceptual support for implementing processes were identified. This is the basis for an improved gICS release to support the ten sites’ individual consent management processes. Conclusions A release plan for the feature requests and improvements was coordinated with all sites. All sites have confirmed that the implementation of these features and enhancements will support their software-based consent management processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.