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Objective: Women who are deaf experience higher rates of reproductive healthcare barriers and adverse birth outcomes compared with their peers who can hear. This study explores the pregnancy experiences of women who are deaf to better understand their barriers to and facilitators of optimal pregnancy‐related health care. Design: Qualitative study using thematic analysis. Setting: Semi‐structured, individual, remote or in‐person interviews conducted in the USA. Sample Forty‐five women who are deaf and communicate using American Sign Language (ASL) and gave birth in the USA within the past 5 years participated in the interviews. Methods: Semi‐structured interviews explored how mothers who are deaf experienced pregnancy and birth, including access to perinatal information and resources, relationships with healthcare providers, communication access and their involvement with the healthcare system throughout pregnancy. A thematic analysis was conducted. Main outcome measures: Barriers and facilitators related to a positive experience of perinatal care access among women who are deaf. Results: Three major themes emerged: (1) communication accessibility; (2) communication satisfaction; and (3) healthcare provider and team support. Common barriers included choosing healthcare providers, inconsistent communication access and difficulty accessing health information. However, when women who are deaf were able to use ASL interpreters, they had more positive pregnancy and birth experiences. Self‐advocacy served as a common facilitator for more positive pregnancy and healthcare experiences. Conclusions: Healthcare providers need to be more aware of the communication and support needs of their patients who are deaf, especially how to communicate effectively. Increased cultural awareness and consistent provision of on‐site interpreters can improve pregnancy and birth experiences for women who are deaf. Linked article: This article is commented on by Moreland et al., pp.522–523 in this issue. To view this mini commentary visit https://doi.org/10.1111/1471-0528.17362 This paper has a Video Abstract presented by Jenna Stewardson, Deaf Health Laboratory Research Program Coordinator at the NTID Research Center on Culture and Language available at: https://vimeo.com/761853406. [ABSTRACT FROM AUTHOR]

AI for AEC The article surveys current methods of data integration, artificial intelligence (AI), optimization, and control and their (potential) applications in architecture, engineering and construction. The survey includes symbolic AI‐methods as well as subsymbolic AI methods, i. e., machine learning. The article presents these methods in the context of applications that provide insight into current research projects at the Cluster of Excellence "Integrative Computational Design and Construction for Architecture" (IntCDC) at the University of Stuttgart: (1) Data integration to link data silos in design and construction processes, (2) knowledge graphs to represent knowledge in multidisciplinary design processes, (3) automated planning for scheduling and distribution of construction tasks, (4) supervised learning to estimate the results of expensive building simulations such as operational energy or of the behavior of natural materials such as wood, (5) unsupervised learning to visualize optimization results, (6) reinforcement learning for building with fibers and bamboo, and (8) control for construction robotics. The article concludes that integrative computational design and construction requires the cooperation of humans, material, and machines, and that AI – instead of merely automating design and construction processes – can moderate this cooperation. [ABSTRACT FROM AUTHOR]

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Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. In this paper, we present a novel method for XML duplicate detection, called XMLDup. XMLDup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation, a novel pruning strategy, capable of significant gains over the unoptimized version of the algorithm, is presented. Through experiments, we show that our algorithm is able to achieve high precision and recall scores in several data sets. XMLDup is also able to outperform another state-of-the-art duplicate detection solution, both in terms of efficiency and of effectiveness. [ABSTRACT FROM AUTHOR]

Duplicate detection determines different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. We scale-up duplicate detection in graph data (ddg) to large amounts of data and pairwise comparisons, using the support of a relational database management system. To this end, we first present a framework that generalizes the ddg process. We then present algorithms to scale ddg in space (amount of data processed with bounded main memory) and in time. Finally, we extend our framework to allow batched and parallel ddg, thus further improving efficiency. Experiments on data of up to two orders of magnitude larger than data considered so far in ddg show that our methods achieve the goal of scaling ddg to large volumes of data. [ABSTRACT FROM AUTHOR]

Objective: Women who are deaf experience higher rates of reproductive healthcare barriers and adverse birth outcomes compared with their peers who can hear. This study explores the pregnancy experiences of women who are deaf to better understand their barriers to and facilitators of optimal pregnancy-related health care., Design: Qualitative study using thematic analysis., Setting: Semi-structured, individual, remote or in-person interviews conducted in the USA., Sample: Forty-five women who are deaf and communicate using American Sign Language (ASL) and gave birth in the USA within the past 5 years participated in the interviews., Methods: Semi-structured interviews explored how mothers who are deaf experienced pregnancy and birth, including access to perinatal information and resources, relationships with healthcare providers, communication access and their involvement with the healthcare system throughout pregnancy. A thematic analysis was conducted., Main Outcome Measures: Barriers and facilitators related to a positive experience of perinatal care access among women who are deaf., Results: Three major themes emerged: (1) communication accessibility; (2) communication satisfaction; and (3) healthcare provider and team support. Common barriers included choosing healthcare providers, inconsistent communication access and difficulty accessing health information. However, when women who are deaf were able to use ASL interpreters, they had more positive pregnancy and birth experiences. Self-advocacy served as a common facilitator for more positive pregnancy and healthcare experiences., Conclusions: Healthcare providers need to be more aware of the communication and support needs of their patients who are deaf, especially how to communicate effectively. Increased cultural awareness and consistent provision of on-site interpreters can improve pregnancy and birth experiences for women who are deaf., (© 2022 John Wiley & Sons Ltd.)