Seeking languagelessness: maker literacies mindsets to disrupt normative practices

This article challenges an over-reliance on language as the primary means to communicate knowledge by adopting a languagelessness approach to maker pedagogies and maker literacies. Having conducted makerspace and design-based research for some time, we separately and together noticed a productive relationship between wordless relational makerspace and making moments focused on craft, tools, technologies, and materials, and ways that an absence of verbal and written communication opens possibilities within learning environments. After meetings and discussions, we co-wrote the article to examine ways that language-light, even language-free pedagogical spaces allow for a different quality of design work that motivates and fosters innovation. There are three international research projects that serve as research vignettes to investigate the efficacy of languagelessness. The theory foregrounded to anchor and interpret the three vignettes draws from maker literacies research and sociomaterial orientations to knowledge development

Exploring perceptions of healthcare technologies enabled by artificial intelligence: An online, scenario-based survey

BACKGROUND: Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants\u27 level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies. We also explored socio-demographic, health-related, and psychosocial correlates of these perceptions. METHODS: We developed a measure depicting six AI-driven technologies that either diagnose, predict, or suggest treatment. We administered the measure via an online survey to adults (N = 936) in the United States using MTurk, a crowdsourcing platform. Participants indicated their level of openness to using the AI technology in the healthcare scenario. Items reflecting potential concerns and benefits associated with each technology accompanied the scenarios. Participants rated the extent that the statements of concerns and benefits influenced their perception of favorability toward the technology. Participants completed measures of socio-demographics, health variables, and psychosocial variables such as trust in the healthcare system and trust in technology. Exploratory and confirmatory factor analyses of the concern and benefit items identified two factors representing overall level of concern and perceived benefit. Descriptive analyses examined levels of openness, concern, and perceived benefit. Correlational analyses explored associations of socio-demographic, health, and psychosocial variables with openness, concern, and benefit scores while multivariable regression models examined these relationships concurrently. RESULTS: Participants were moderately open to AI-driven healthcare technologies (M = 3.1/5.0 ± 0.9), but there was variation depending on the type of application, and the statements of concerns and benefits swayed views. Trust in the healthcare system and trust in technology were the strongest, most consistent correlates of openness, concern, and perceived benefit. Most other socio-demographic, health-related, and psychosocial variables were less strongly, or not, associated, but multivariable models indicated some personality characteristics (e.g., conscientiousness and agreeableness) and socio-demographics (e.g., full-time employment, age, sex, and race) were modestly related to perceptions. CONCLUSIONS: Participants\u27 openness appears tenuous, suggesting early promotion strategies and experiences with novel AI technologies may strongly influence views, especially if implementation of AI technologies increases or undermines trust. The exploratory nature of these findings warrants additional research

Types and characteristics of urban and peri-urban green spaces having an impact on human mental health and wellbeing: a systematic review

Green spaces have been put forward as contributing to good mental health. In an urban context, space is a scarce resource while urbanisation and climate change are increasingly putting pressure on existing urban green space infrastructures and increasing morbidity caused by mental health disorders. Policy makers, designers, planners and other practitioners face the challenge of designing public open spaces as well as preserving and improving natural resources that are important for maintaining and optimizing human wellbeing. Knowing which types of blue and green spaces, with which characteristics, are most beneficial for mental health and wellbeing is critical. EKLIPSE received a request from the Ministry in charge of the Environment of France (MTES) to review: “Which types of urban and peri‐urban green and blue spaces, and which characteristics of such spaces, have a significant impact on human mental health and wellbeing?”. After a preliminary scoping, a decision was made to perform two systematic reviews (SR) assessing the specific types and characteristics of blue space (SR1) and green space (SR2) with respect to mental health and wellbeing. This report presents the systematic review for green space (SR2)

European Nature and Health Network Initiatives

Attention to the importance of nature and human health linkages has increased in the past years, both in science and in policy. While knowledge about and recognition of the importance of nature and human health linkages are increasing rapidly, challenges still remain. Among them are building bridges between relevant but often still somewhat disconnected sectors and topics. There is a need to bring together researchers in the fields of health sciences, ecology, social sciences, sustainability sciences and other interdisciplinary sciences, as well as for cooperation between governments, companies and citizens. In this chapter, we introduce European networking initiatives aimed at building such bridges

Performance of the LHCb Vertex Detector Alignment Algorithm determined with Beam Test Data

LHCb is the dedicated heavy flavour experiment at the Large Hadron Collider at CERN. The partially assembled silicon vertex locator (VELO) of the LHCb experiment has been tested in a beam test. The data from this beam test have been used to determine the performance of the VELO alignment algorithm. The relative alignment of the two silicon sensors in a module and the relative alignment of the modules has been extracted. This alignment is shown to be accurate at a level of approximately 2 micron and 0.1 mrad for translations and rotations, respectively in the plane of the sensors. A single hit precision at normal track incidence of about 10 micron is obtained for the sensors. The alignment of the system is shown to be stable at better than the 10 micron level under air to vacuum pressure changes and mechanical movements of the assembled system.Comment: accepted for publication in NIM