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Background. An assumption of Digital Image Correlation (DIC) is that the displacement field within each subset is relatively smooth, captured with reasonable accuracy by, for example, linear or quadratic shape functions. Although this assumption works well for many materials, it becomes problematic for heterogeneous materials, such as fiber networks, wherein the length scale of heterogeneity matches the size of a subset. Objective. Here we applied DIC to fibrous networks made of collagen, for which displacements at the scale of a subset are highly heterogeneous, but errors caused by the heterogeneity are difficult to quantify. We developed a method to quantify such errors. Methods. We began by generating a synthetic three-dimensional fiber network with structure matching that of gels made of fibrous collagen. We then formulated an algorithm to mimic the way in which a confocal microscope images the fibers at its focal plane, thereby generating synthetic images similar to those obtained in experiments. Displacement boundary conditions were applied to the synthetic fiber networks, and the resulting displacement fields were computed using a finite element solver. DIC was applied to the synthetic images, and displacements were compared to the data from the finite element method, enabling rigorous quantification of error. Results. Point-wise errors in the DIC-measured displacements were substantial, often exceeding 40%, but over regions far larger than the length scales of heterogeneity or the DIC subset size, errors were modest, e.g., ≤15%. Conclusions. Although DIC can accurately measure displacements of fiber networks at length scales larger than the subset window, quantification of mechanical behavior at the scale of material heterogeneity will require new methods to complement or replace the use of DIC. 

Employment of autistic individuals is strikingly low in relation to the skill level and capabilities of this population. Roughly 65% of autistic adults are either unemployed or underemployed relative to their abilities but there is increasing recognition that this number could be greatly improved through empowering autistic individuals while simultaneously providing a boost to the economy. Much of this disparity can be attributed in part to the lack of awareness and understanding among employers regarding behavior of autistic individuals during the hiring process. Most notably, the job interview—where strong eye contact is traditionally expected but can be extremely uncomfortable for autistic individuals—presents an unreasonable initial barrier to employment for many. The current work presents a data visualization dashboard that is populated with quantitative data (including eye tracking data) captured during simulated job interviews using a novel interview simulator called Career Interview Readiness in Virtual Reality (CIRVR). We conducted a brief series of case studies wherein autistic individuals who took part in a CIRVR interview and other key stakeholders provided lived experiences and qualitative insights into the most effective design and application of such data visualization dashboard. We conclude with a discussion of the role of information related to visual attention in job interviews with an emphasis on the importance of descriptive rather than prescriptive interpretation. 

The employment settings for autistic individuals in the USA is grim. As more children are diagnosed with ASD, the number of adolescent and young adult with ASD will increase as well over the next decade. Based on reports, one of the main challenges in securing and retaining employment for individual with ASD is difficulty in communicating and working with others in workplace settings. Most vocational trainings focused on technical skills development and very few addresses teamwork skills development. In this study, we present the design of a collaborative virtual environment (CVE) that support autistic individual to develop their teamwork skills by working together with a partner in a shared virtual space. This paper described the CVE architecture, teamwork-based tasks design and quantitative measures to evaluate teamwork skills. A system validation was also carried out to validate the system design. The results showed that our CVE was able to support multiple users in the same shared environment, the tasks were tolerable by users, and all the quantitative measures are recorded accordingly.