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Level of automation (LoA) is increasingly recognized as an important principle in improving manufacturing strategies. However, many automation decisions are made without formally assessing LoA and can be made based on a host of organizational factors, like varied mental models used by managers in decision-making. In this study, respondents (N = 186) were asked to watch five different assembly tasks being completed in an automotive manufacturing environment, and then identify “how automated” or “how manual” they perceived the task to be. Responses were given using a visual analogue scale (VAS) and sliding scale, where possible responses ranged from 0 (totally manual) to 100 (totally automated). The activity explored how and when individuals recognized the automated technologies being employed in each task. The tasks of the videos varied primarily by whether the human played active or passive role in the process. Focus group comments collected as a part of the study show how rating patterns revealed functional systems-level thinking and a focus on cognitive automation in manufacturing. While the video ratings generally followed the LoA framework discussed, slight departures in the rating of each video were found. 

Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process. 

Abstract A bright (m_F150W,AB= 24 mag),z= 1.95 supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called “Requiem,” and therefore the new SN is named “Encore.” This makes the MACS J0138.0−2155 cluster the first known system to produce more than one multiply imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a ∼10 yr window to be ≈3%. These observations have the potential to yield a Hubble constant (H₀) measurement with ∼10% precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of ∼2030, providing an unprecedented baseline for time-delay cosmography. 

Requirements play a pivotal role within the engineering design process as they provide parameters and guidelines, as well as defining the success of a project. While there is significant research that explores how to elicit requirements, there is little experimental exploration of how engineers prioritize requirements based on the stakeholder sources for the requirements. Non- technical factors may affect the prioritization of the requirements. A user study was conducted with sixty- six third year mechanical engineering undergraduate students participating in the experiment. Each student was provided two design problems with a requirements document for each. For one of the requirement documents, participants were also provided information on the stakeholder owner of the requirement (sources). Although all of the requirement sources affected the weight given to requirements – typically in the positive direction – it is found that the source had a statistically significant influence on perceived criticality for only 25% of the requirement cases. To conclude, the sources did prove to affect the prioritization of the requirements. Future work will explore how the sources influence requirements based on different typologies, such constraints versus criteria, or requirements with and without numerical targets. 

Abstract Supernova (SN) SN H0pe is a gravitationally lensed, triply imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply imaged SNe provide a rare opportunity to constrain the Hubble constant (H₀), by measuring the relative time delay between the images and modeling the foreground mass distribution. SN H0pe is located atz= 1.783 and is the first SN Ia with sufficient light-curve sampling and long enough time delays for anH₀inference. Here we present photometric time-delay measurements and SN properties of SN H0pe. Using JWST/NIRCam photometry, we measure time delays of Δt_ab= $-{116.6}_{-9.3}^{+10.8}$observer-frame days and Δt_cb= $-{48.6}_{-4.0}^{+3.6}$observer-frame days relative to the last image to arrive (image 2b; all uncertainties are 1σ), which corresponds to a ∼5.6% uncertainty contribution forH₀assuming 70 km s⁻¹Mpc⁻¹. We also constrain the absolute magnification of each image toμ_a= ${4.3}_{-1.8}^{+1.6}$,μ_b= ${7.6}_{-2.6}^{+3.6}$,μ_c= ${6.4}_{-1.5}^{+1.6}$by comparing the observed peak near-IR magnitude of SN H0pe to the nonlensed population of SNe Ia. 

A controlled vocabulary list that was originally developed for the automotive assembly environment was modified for home appliance assembly in this study. After surveying over 700 assembly tasks with the original vocabulary, additions were made to the vocabulary list as necessary. The vocabulary allowed for the transformation of work instructions in approximately 90% of cases, with the most discrepancies occurring during the inspection phase of the transfer line. The modified vocabulary list was then tested for coder reliability to ensure broad usability and was found to have Cohen’s kappa values of 0.671 < κ < 0.848 between coders and kappa values of 0.731 < κ < 0.875 within coders over time. Using this analysis, it was demonstrated that this original automotive vocabulary could be applied to the non-automotive context with a high degree of reliability and consistency.