The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting‐state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) and the B‐SNIP biomarker‐based (Biotype) approach. Statistical group differences and cross‐validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM‐IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM‐IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM‐IV and biology‐based categories while also emphasizing the importance of future work in this direction, including employing further data types.
This content will become publicly available on June 28, 2024
A Schema for robotics operations in construction
This study gathered data into a construction robot schema (CRS) with an initial data structure that can be used to collect and exchange various construction robots’ information based on the data requirements of construction planners for robotics operations. To develop the CRS, the study conducted a systematic literature review using the Web of Science database to filter and identify relevant papers which were published from 2018 to 2022. Based on 279 eligible papers, the study identified significant information which involved data requirements of the construction domain on robotics using Nvivo software. To structure the information, the study summarized the information into parameters then categorized, defined, matched data types, and exemplified for these parameters. All the parameters were grouped into four categories, including ontological properties, operational requirements, activity, and safety. As a result, CRS supports data structure including 4 categories and 35 parameters with corresponding definitions, data types, examples, and references.
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- Award ID(s):
- 1928626
- NSF-PAR ID:
- 10466065
- Editor(s):
- Turkan, Y. and
- Date Published:
- Journal Name:
- ASCE International Conference on Computing in Civil Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We novelly applied established ecology methods to quantify and compare language diversity within a corpus of short written student texts. Constructed responses (CRs) are a common form of assessment but are difficult to evaluate using traditional methods of lexical diversity due to text length restrictions. Herein, we examined the utility of ecological diversity measures and ordination techniques to quantify differences in short texts by applying these methods in parallel to traditional text analysis methods to a corpus of previously studied college student CRs. The CRs were collected at two time points (Timing), from three types of higher-ed institutions (Type), and across three levels of student understanding (Thinking). Using previous work, we were able to predict that we would observe the most difference based on Thinking, then Timing and did not expect differences based on Type allowing us to test the utility of these methods for categorical examination of the corpus. We found that the ecological diversity metrics that compare CRs to each other (Whittaker’s beta, species turnover, and Bray–Curtis Dissimilarity) were informative and correlated well with our predicted differences among categories and other text analysis methods. Other ecological measures, including Shannon’s and Simpson’s diversity, measure the diversity of language within a single CR. Additionally, ordination provided meaningful visual representations of the corpus by reducing complex word frequency matrices to two-dimensional graphs. Using the ordination graphs, we were able to observe patterns in the CR corpus that further supported our predictions for the data set. This work establishes novel approaches to measuring language diversity within short texts that can be used to examine differences in student language and possible associations with categorical data.more » « less
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null (Ed.)There is a significant amount of synergy between virtual reality (VR) and the field of robotics. However, it has only been in approximately the past five years that commercial immersive VR devices have been available to developers. This new availability has led to a rapid increase in research using VR devices in the field of robotics, especially in the development of VR interfaces for operating robots. In this paper, we present a systematic review on VR interfaces for robot operation that utilize commercially available immersive VR devices. A total of 41 papers published between 2016–2020 were collected for review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Papers are discussed and categorized into five categories: (1) Visualization, which focuses on displaying data or information to operators; (2) Robot Control and Planning, which focuses on connecting human input or movement to robot movement; (3) Interaction, which focuses on the development of new interaction techniques and/or identifying best interaction practices; (4) Usability, which focuses on user experiences of VR interfaces; and (5) Infrastructure, which focuses on system architectures or software to support connecting VR and robots for interface development. Additionally, we provide future directions to continue development in VR interfaces for operating robots.more » « less
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