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It’s critical to understand how to use artificial intelligence (AI) to foster innovation in the modern world as AI becomes more integrated into creative and problem-solving tasks. Using the sustainable washing machine as a primary example, this study designed and developed AI design assistant AIDA as a web-based chatbot to facilitate design ideation, leveraging large language models. AIDA prompts design tasks and assesses user-generated ideas for validity, novelty, and feasibility using RoBERTa-based models. As in the initial phase of an ongoing project, we conducted a human-subject experiment to validate a baseline version of AIDA and examined user performance and perceptions. The participants demonstrated smooth interaction with AIDA and consistent performance. They reported mostly positive perceived usefulness, enjoyment, and trust. Moreover, females and participants equal to or over 25 showed a comparable level of trust for general automated systems and AIDA, whereas male and under 25 participants were more skeptical about AIDA. This research offers a framework for technical development, tailored interactions, and real-time feedback, as well as insights into the use of AI chatbots to mediate engineering design. By analyzing user behavior and survey responses, we identified future directions in designing AI systems in engineering education and early-stage design. 

Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states. 

In eukaryotes, the origin recognition complex (ORC) is required for the initiation of DNA replication. The smallest subunit of ORC, Orc6, is essential for prereplication complex (pre-RC) assembly and cell viability in yeast and for cytokinesis in metazoans. However, unlike other ORC components, the role of human Orc6 in replication remains to be resolved. Here, we identify an unexpected role for hOrc6, which is to promote S-phase progression after pre-RC assembly and DNA damage response. Orc6 localizes at the replication fork and is an accessory factor of the mismatch repair (MMR) complex. In response to oxidative damage during S phase, often repaired by MMR, Orc6 facilitates MMR complex assembly and activity, without which the checkpoint signaling is abrogated. Mechanistically, Orc6 directly binds to MutSα and enhances the chromatin-association of MutLα, thus enabling efficient MMR. Based on this, we conclude that hOrc6 plays a fundamental role in genome surveillance during S phase. 

Twenty-four centrifuge model tests have been conducted at nine different geotechnical centrifuge facilities around the world as part of the international LEAP effort (liquefaction experiments and analysis projects). All of the centrifuge models represent a 4 m deep 5 degree sloping submerged sand deposit. The mean effective PGA of the input motion for all of the experiments was approximately 0.15 g and the mean relative density was approximately 65%, but the effective PGA’s varied between about 0.07 g and 0.3 g, and the relative densities varied between about 40% and 75%. The test matrix was designed to enable experimental quantification of not only the median response but also the trend and sensitivity of the model response to density and shaking intensity. Quantification of the sensitivity of the response to initial conditions is a prerequisite for objective evaluation of the quality of the model test data. In other words, a discrepancy between two experiments should be evaluated after accounting for the uncertainty in the initial conditions and the sensitivity of the response to initial conditions. For the first time, a sufficient number of experiments has been performed on a similar problem to provide meaningful quantitative evaluation of the trend between PGA, density, and displacement. The sensitivity is quantified by the gradient of the trend and the uncertainty of the trend is quantified from the residuals between the fitting data and the trend. 

Twenty-four centrifuge model tests of liquefaction and lateral spreading, performed as part of a round robin test program, are shared and compared in this archive. Please see the general report section of the published project for an overview comparison and background of all of the experiments. One document in the report (with “ReadMe” in the file name) describes the organization of the data archive. The comparisons presented in the general report section will serve as an index to help the users find individual experiments of interest. This data from 24 model tests is published as nine separate experiments in this archive (one experiment per centrifuge facility). Each experiment includes two or three model tests and each model test includes between one and three destructive shaking events. All of the tests modeled a 4 m thick deposit of Ottawa F-65 sand with a 5-degree surface slope in a rigid box. The tests covered a range of ground motion intensities and a range of relative densities to define the median response and the sensitivity of the response to relative density and shaking intensity. The nine centrifuge facilities involved in this test program included Cambridge University (UK), Ehime University (Japan), IFSTTAR (France), NCU (Taiwan), KAIST (Korea), Kyoto University (Japan), RPI (USA), UC Davis (USA), and Zhejiang University (China).