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Studies show that young autistic adults are under- or unemployed, with almost half never holding a paying job in their 20’s. Unemployment within this population leads to decreased personal growth and increased dependence on caregivers. Research suggests that the interview process is one of the largest barriers to employment for this population. Autistic individuals often struggle with emotion regulation, which can be exacerbated by the interview process. To address this, we propose the use of a stress detection model in conjunction with a virtual reality interview simulator. This combination will allow for the interview to adapt to the state of the participant to improve the skills and engagement of the user and positively influence their comfort level. Data regarding negative affective responses to categories of questions can also be used to inform employers on better interviewing techniques. A model was designed using data obtained from neurotypical participants completing a modified Computerized Paced Serial Addition Task (PASAT-C) and evaluated on a dataset obtained from Autistic participants who took part in a simulated interview. Agreement between the model and ground truth was compared based on Pearson correlation coefficients. It was found that was r(289) = 0.28, which was statistically significant (p < .001; CI: 0.17 to 0.38). Our preliminary results provide evidence for the validity of observer-based labeling of data captured using a wrist-worn physiological sensor. 

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. 

A Large Ion Collider Experiment (ALICE) has been conceived and constructed as a heavy-ion experiment at the LHC. During LHC Runs 1 and 2, it has produced a wide range of physics results using all collision systems available at the LHC. In order to best exploit new physics opportunities opening up with the upgraded LHC and new detector technologies, the experiment has undergone a major upgrade during the LHC Long Shutdown 2 (2019–2022). This comprises the move to continuous readout, the complete overhaul of core detectors, as well as a new online event processing farm with a redesigned online-offline software framework. These improvements will allow to record Pb-Pb collisions at rates up to 50 kHz, while ensuring sensitivity for signals without a triggerable signature.

 

Recent measurements of charm-baryon production in hadronic collisions have questioned the universality of charm-quark fragmentation across different collision systems. In this work the fragmentation of charm quarks into charm baryons is probed, by presenting the first measurement of the longitudinal jet momentum fraction carried by${\mathrm{\Lambda }}_c^{+}$baryons,$z_{\parallel }^{\mathrm{ch}}$, in hadronic collisions. The results are obtained in proton-proton ($pp$) collisions at$\sqrt{s}=13\mathrm{TeV}$at the LHC, with${\mathrm{\Lambda }}_c^{+}$baryons and charged (track-based) jets reconstructed in the transverse momentum intervals of$3\le p_T^{{\mathrm{\Lambda }}_c^{+}}<15\mathrm{GeV}/c$and$7\le p_T^{\text{jet ch}}<15\mathrm{GeV}/c$, respectively. The$z_{\parallel }^{\mathrm{ch}}$distribution is compared to a measurement of${\mathrm{D}}^0$-tagged charged jets in$pp$collisions as well as to 8 simulations. The data hints that the fragmentation of charm quarks into charm baryons is softer with respect to charm mesons, in the measured kinematic interval, as predicted by hadronization models which include color correlations beyond leading-color in the string formation.

© 2024 CERN, for the ALICE Collaboration2024CERN 

The first measurement of the cross section for incoherent photonuclear production of$J/\psi$vector mesons as a function of the Mandelstam$|t|$variable is presented. The measurement was carried out with the ALICE detector at midrapidity,$|y|<0.8$, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of$\sqrt{s_{\mathrm{NN}}}=5.02\mathrm{TeV}$. This rapidity interval corresponds to a Bjorken-$x$range$(0.3–1.4)\times {10}^{-3}$. Cross sections are given in five$|t|$intervals in the range$0.04<|t|<1{\mathrm{GeV}}^2$and compared to the predictions by different models. Models that ignore quantum fluctuations of the gluon density in the colliding hadron predict a$|t|$dependence of the cross section much steeper than in data. The inclusion of such fluctuations in the same models provides a better description of the data.

© 2024 CERN, for the ALICE Collaboration2024CERN 

Luminosity determination within the ALICE experiment is based on the measurement, in van der Meer scans, of the cross sections for visible processes involving one or more detectors (visible cross sections). In 2015 and 2018, the Large Hadron Collider provided Pb–Pb collisions at a centre-of-mass energy per nucleon pair of √s_NN= 5.02 TeV. Two visible cross sections, associated with particle detection in the Zero Degree Calorimeter (ZDC) and in the V0 detector, were measured in a van der Meer scan.This article describes the experimental set-up and the analysis procedure, and presents the measurement results. The analysis involves a comprehensive study of beam-related effects and an improved fitting procedure, compared to previous ALICE studies, for the extraction of the visible cross section. The resulting uncertainty of both the ZDC-based and the V0-based luminosity measurement for the full sample is 2.5%. The inelastic cross section for hadronic interactions in Pb–Pb collisions at √s_NN= 5.02 TeV, obtained by efficiency correction of the V0-based visible cross section, was measured to be 7.67 ± 0.25 b, in agreement with predictions using the Glauber model.