Scholarly Works, Industrial and Systems Engineering

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Comparative Risk of Developing Interstitial Cystitis with Childhood Gastrointestinal, Urological, Autoimmune, or Psychiatric Disorders
Alipour-Vaezi, Mohammad; McNamara, Robert S.; Rukstalis, Margaret R.; Gentry, Emily C.; Rukstalis, Daniel B.; Penzien, Donald B.; Tsui, Kwok-Leung; Zhong, Huaiyang (Wiley, 2025-09-01)
Aims: Interstitial cystitis (IC) is a chronic urological condition associated with significant discomfort, posing diagnostic and therapeutic challenges. Although its etiology remains unclear, early-life conditions such as gastrointestinal (GI) disorders, urological anomalies (UA), psychiatric disorders (PD), and autoimmune diseases (AD) have been hypothesized as potential risk factors for developing IC in adulthood. This study aims to investigate these associations by conducting a retrospective cohort analysis utilizing data from the TriNetX US Collaborative Network, encompassing over 118 million patient records. Methods: The study and control groups were established across four categories of childhood disorders, with IC incidence monitored over a 14-year period. Statistical methodologies, including propensity score matching and Kaplan-Meier survival analysis, were employed to compare outcomes between cohorts. Results: Findings indicate that childhood GI and UA conditions significantly elevate the risk of IC in adulthood, with irritable bowel syndrome (IBS) and urinary tract infections (UTIs) exhibiting risk ratios of 2.9 and 3.2, respectively. Gender disparities were also noted, with females exhibiting higher incidences of diseases included, particularly UA and AD during adolescence. Additionally, individuals with these early-life conditions demonstrated a higher prevalence of comorbidities, underscoring the complex interplay of health factors contributing to IC pathogenesis. Conclusions: These findings suggest that childhood GI and UA conditions may serve as predictive markers for IC, emphasizing the need for targeted early interventions and preventative care strategies. By identifying at-risk populations, this study provides valuable insights into early detection and management approaches, potentially mitigating the long-term burden of IC on affected individuals. Trial Registration: This paper includes an observational retrospective study. No clinical trial has been conducted.
Bounding-focused discretization methods for the global optimization of nonconvex semi-infinite programs
Turan, Evren M.; Jaschke, Johannes; Kannan, Rohit (Springer, 2025-01-01)
We use sensitivity analysis to design bounding-focused discretization (cutting-surface) methods for the global optimization of nonconvex semi-infinite programs (SIPs). We begin by formulating the optimal bounding-focused discretization of SIPs as a max-min problem and propose variants that are more computationally tractable. We then use parametric sensitivity theory to design an effective heuristic approach for solving these max-min problems. We also show how our new iterative discretization methods may be modified to ensure that the solutions of their discretizations converge to an optimal solution of the SIP. We then formulate optimal bounding-focused generalized discretization of SIPs as max-min problems and design heuristic algorithms for their solution. Numerical experiments on standard nonconvex SIP test instances from the literature demonstrate that our new bounding-focused discretization methods can significantly reduce the number of iterations for convergence relative to a state-of-the-art feasibility-focused discretization method.
Strong Partitioning and a Machine Learning Approximation for Accelerating the Global Optimization of Nonconvex Quadratically Constrained Quadratic Programs
Kannan, Rohit; Nagarajan, Harsha; Deka, Deepjyoti (Institute for Operations Research and the Management Sciences (INFORMS), 2025-09-19)
We learn optimal instance-specific heuristics for the global minimization of nonconvex quadratically constrained quadratic programs (QCQPs). Specifically, we consider partitioning-based convex mixed-integer programming relaxations for nonconvex QCQPs and propose the novel problem of strong partitioning to optimally partition variable domains without sacrificing global optimality. Because solving this max-min strong partitioning problem exactly can be very challenging, we design a local optimization method that leverages generalized gradients of the value function of its inner-minimization problem. However, even solving the strong partitioning problem to local optimality can be time consuming. To address this, we propose a simple and practical machine learning (ML) approximation for homogeneous families of QCQPs. Motivated by practical applications, we conduct a detailed computational study using the open-source global solver Alpine to evaluate the effectiveness of our ML approximation in accelerating the repeated solution of homogeneous QCQPs with fixed structure. Our study considers randomly generated QCQP families, including instances of the pooling problem, that are benchmarked using state-of-the-art global optimization software. Numerical experiments demonstrate that our ML approximation of strong partitioning reduces Alpine’s solution time by a factor of 2–4.5 on average, with maximum reduction factors ranging from 10 to 200 across these QCQP families. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0424 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0424 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
Cetinkaya, Ibrahim; Büyüktahtakın, İ. Esra; Shojaee, Parshin; Reddy, Chandan K. (2025-10-14)
Our study contributes to the scheduling and combinatorial optimization literature with new heuristics discovered by leveraging the power of Large Language Models (LLMs). We focus on the single-machine total tardiness (SMTT) problem, which aims to minimize total tardiness by sequencing n jobs on a single processor without preemption, given processing times and due dates. We develop and benchmark two novel LLM-discovered heuristics, the EDD Challenger (EDDC) and MDD Challenger (MDDC), inspired by the well-known Earliest Due Date (EDD) and Modified Due Date (MDD) rules. In contrast to prior studies that employed simpler rule-based heuristics, we evaluate our LLM-discovered algorithms using rigorous criteria, including optimality gaps and solution time derived from a mixed-integer programming (MIP) formulation of SMTT. We compare their performance against state-of-the-art heuristics and exact methods across various job sizes (20, 100, 200, and 500 jobs). For instances with more than 100 jobs, exact methods such as MIP and dynamic programming become computationally intractable. Up to 500 jobs, EDDC improves upon the classic EDD rule and another widely used algorithm in the literature. MDDC consistently outperforms traditional heuristics and remains competitive with exact approaches, particularly on larger and more complex instances. This study shows that human–LLM collaboration can produce scalable, high-performing heuristics for NP-hard constrained combinatorial optimization, even under limited resources when effectively configured.
Nested benders decomposition for a deterministic biomass feedstock logistics problem
Singh, Sanchit; Sarin, Subhash C.; Sangha, Sandeep Singh (Springer, 2025-01-01)
In this paper, we address a biomass feedstock logistics problem to supply biomass from production fields to satellite storage locations (SSLs) and from there to bioenergy plants (BePs) and then to a biorefinery. It entails a new problem feature of routing load-out equipment sets among the SSLs to perform loading/unloading of biomass and/or its pre-processing operations. The ownership of the loading equipment is a very capital-intensive link of the ethanol production supply chain, which when loaded onto trucks and routed along the logistics chain significantly brings down the ethanol production costs. This will make ethanol a cost-competitive alternative to fossil fuels, lead to sustainable use of fossil fuels and add to the overall relevance of the bioenergy sector. In this regard, the objective of our problem is to minimize the total cost incurred due to the ownership of equipment sets, fixed setups, and land rental cost, as well as the cost of transporting biomass from the fields to the BePs and biocrude oil from the BePs to the refinery. A mixed-integer mathematical model of the problem is presented, and a nested Benders decomposition-based solution approach is developed which involves decomposing this large problem into three stages. Stage 1 deals with the selection of fields, BePs, and SSLs, and assignment of fields to the SSLs. The remaining model consists of multiple Capacitated Vehicle Routing Problems (CVRPs) that are separable over individual BePs. For each BeP, the CVRP is further decomposed into Stage 2 and Stage 3 sub-problems where the Stage 2 problem is an allocation problem that assigns SSLs to tours associated to each BeP, and the Stage 3 problem is a variant of Traveling Salesman Problem (TSP) that determines the sequence in which equipment is routed over the predesignated set of SSLs for each tour. These sub-problems are integer programs rather than linear programs. First novelty of our proposed approach is to effectively handle the integrality of variables arising due to the consideration of the routing of load-out equipment. Second is solution methodology and in the use of proposed multi-cut version of optimality cuts that capture the solution value at an integer solution for the sub-problems. These cuts aid in faster convergence and are shown to be stronger than those proposed in the literature. The applicability of the proposed methodology is demonstrated by applying it to a real-life problem that utilizes available GIS data for the catchment area of regions around Gretna and Bedford in Virginia. We then solved a set of varying problem size instances using the state-of-the-art CPLEX (R) Branch-and-Bound and Benders Strategy methods. The CPLEX (R) algorithms struggled to solve instances even 10 times smaller than the real-life problem size instances; with MIP optimality gaps ranging from 5.85% to 82.79% in the allowed time limit of 10,000 s. On the other hand, our proposed nested Benders decomposition algorithm was able to achieve faster convergence and provided optimal solutions for all the considered problem instances with an average CPU run-time of around 3,700 s. This validates the efficacy and superiority of our solution approach. Lastly, we summarize our work and point out some interesting potential future research opportunities.
Navigating the Golden Triangle: The Need to Jointly Consider Modularization and Interface Choices When Making Performance, Cost, and Schedule Tradeoffs for Complex System Development
Topcu, Taylan G.; Szajnfarber, Zoe (Wiley, 2025-05-01)
Decomposition is a critical enabler of complex system development, as it enables both task specialization and efficiency through parallel work. The process of decomposing involves partitioning system parameters into tightly coupled modules and managing any cross-module coupling by designing passive interfaces or through active coordination. A rich literature has developed algorithms and tools to support this process. However, we contend that this view has placed too much emphasis on module selection, and not enough on the interaction with interface design. This perspective has significant implications for lifecycle costs and development time. To that end, this study explores how earlier consideration of interface design can create more valuable options to better navigate performance, cost, and schedule tradeoffs. Specifically, through an abstract simulation experiment, we demonstrate that (1) a sequential approach that first selects modules and then designs interfaces to support those modules, yields lower performance than an integrated approach that considers modules and supporting interfaces simultaneously; and (2) this result is even stronger when schedule and cost are considered as part of the evaluation. In other words, an integrated approach provides more options for project managers seeking to navigate the performance-cost-schedule tradeoff known as the golden triangle. These results emphasize the need for a decomposition aid that adopts a holistic view of the optimization problem, accounting for interface creation, intra-organization collaboration, and valuing nonperformance measures of effectiveness.
Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning
Liu, Yang; Yue, Xubo; Zhang, Junru; Zhai, Zhenghao; Moammeri, Ali; Edgar, Kevin J.; Berahas, Albert S.; Al Kontar, Raed; Johnson, Blake N. (American Chemical Society, 2024-12-11)
While some materials can be discovered and engineered using standalone self-driving workflows, coordinating multiple stakeholders and workflows toward a common goal could advance autonomous experimentation (AE) for accelerated materials discovery (AMD). Here, we describe a scalable AMD paradigm based on AE and "collaborative learning". Collaborative learning using a novel consensus Bayesian optimization (BO) model enabled the rapid discovery of mechanically optimized composite polysaccharide hydrogels. The collaborative workflow outperformed a non-collaborating AMD workflow scaled by independent learning based on the trend of mechanical property evolution over eight experimental iterations, corresponding to a budget limit. After five iterations, four collaborating clients obtained notable material performance (i.e., composition discovery). Collaborative learning by consensus BO can enable scaling and performance optimization for a range of self-driving materials research workflows driven by optimally cooperating humans and machines that share a material design objective.
Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning
Ramalho, André; Assad, Anis; Bevans, Benjamin; Deschamps, Fernando; Santos, Telmo G.; Oliveira, J. P.; Rao, Prahalada (Elsevier, 2025-08-17)
This work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. Humping and humping-induced porosity are leading stochastic causes of poor WA-DED part quality that occur despite extensive optimization of processing conditions. It is therefore essential to understand, detect and control the causal meltpool phenomena linked to these instabilities. Accordingly, we used a high-speed camera to capture the meltpool dynamics of multi-layer depositions of ER90SG steel parts and meltpool flow behavior related to process instabilities were demarcated and quantified. Next, physically intuitive meltpool morphology signatures were extracted from the imaging data. These signatures were used in a machine learning model trained to autonomously detect process instabilities. This novel processaware machine learning approach classified onset of instabilities with ~85 % accuracy (F1-score), outperforming black-box deep learning models (F1-score <66%). These results pave the way for a physically intuitive processaware machine learning strategy for monitoring and control of the WA-DED process.
Classifying Diverse Manual Material Handling Tasks Using Vision Transformers and Recurrent Neural Networks
Rajabi, Mohammad Sadra; Ojelade, Aanuoluwapo; Kim, Sunwook; Nussbaum, Maury A. (SAGE Publications, 2025-09)
Frequent or prolonged manual material handling (MMH) is a major risk factor for work-related musculoskeletal disorders, which cause considerable health and economic burdens. Assessing physical exposures is essential for identifying high-risk tasks and implementing targeted ergonomic interventions. However, variability in MMH task performance across individuals and work settings complicates physical exposure assessments. Further, conventional tools often suffer from limitations such as bias, discomfort, behavioral interference, and high costs. Noncontact (ambient) methods and automated data collection and analysis present promising alternatives for assessing physical exposure. We investigated the use of vision transformers and recurrent neural networks for non-contact MMH task classification using RGB video for eight simulated MMH tasks. Spatial features were extracted using the Contrastive Language-Image Pre-training vision transformer, then classified by a Bidirectional Long Short-Term Memory model to capture temporal dependencies between video frames. Our model achieved a mean accuracy of 88% in classifying MMH tasks, demonstrating comparable performance to methods using depth cameras or wearable sensors, while potentially offering better scalability and feasibility for real environments. Future work includes improving temporal modeling, integrating task-adapted feature extraction, and validating across more diverse workers and occupational environments.
Optimizing passive exoskeleton torque for dynamic overhead work: Phase-specific analysis on muscle activity and perceived exertion
Casu, Giulia; Barr, Alan; Kim, Sunwook; Nussbaum, Maury A.; Rempel, David; Pau, Massimiliano; Harris-Adamson, Carisa (Elsevier, 2025-09)
Purpose: This study investigated how different levels of torque provided by a passive arm-support exoskeleton (ASE) influence upper extremity muscle activity, perceived exertion, and fatigue during arm ascent and descent phases of a Dynamic Overhead (DO) task. Methods: The DO task involved 20 cycles of simulated drilling and was completed by 20 individuals by using a light-duty drill in four conditions: without supporting torque (no ASE) and with three increasing levels of ASE torque (i.e., 50, 75, and 100% of the torque required to support the arm in 90° of flexion). Surface electromyography was measured bilaterally over six shoulder muscles. Moreover, participants indicated torque preference, ratings of perceived exertion (RPE), and fatigue in the shoulder. Results: Increasing torque levels caused significant reductions in shoulder agonist muscle activity (up to 47%) and significant decreases in RPE and fatigue during the ascent phase. In contrast, higher levels of torque increased muscular activity for some antagonist muscles during the descent phase. Conclusions: While torque levels of 75% and 100% received the most positive ratings, we suggest that 75% torque could be an effective supporting condition, by reducing shoulder muscle flexor activity during arm ascent and minimizing antagonist muscle activity during arm descent.
Evaluating Back-Support Exoskeletons in Simulated Construction-Relevant Tasks: Effects on Task Completion Time and Aspects of Usability
Ojelade, Aanuoluwapo; Kim, Sunwook; Morris, Wallace; Barr, Alan; Harris-Adamson, Carisa; Nussbaum, Maury A. (2025-09)
Back-support exoskeletons (BSEs) are a promising intervention in reducing physical demands during diverse occupational tasks. However, limited information is available about the effectiveness of different BSE designs during construction work and if those effects are consistent between novices and experienced workers. In our study, we aimed to identify the benefits and potential unintended consequences of BSEs during construction work, considering worker experience levels. Forty participants (20 novices and 20 experienced, balanced in both groups by biological sex) completed lab-based simulations of several construction-relevant tasks. These tasks were performed under a control condition (no BSE) and with three BSEs, each of which was tested in two support settings (on and off). Task performance was measured using completion time, and perceptions of diverse aspects of usability were obtained. Generally, BSE use increased task completion time, perceived discomfort, and perceived interference of BSEs during simulated tasks, while its effects on perceived physical effort were mixed. Rigid BSEs particularly increased perceived movement restrictions, while exosuits did not. In a few cases, the effects of BSEs on completion time and BSE usability differed between novice and experienced groups. Nonetheless, we suggest that future work could generalize results from novice participants to experienced participants. Overall, our results suggested that the effects of BSEs on completion time and perceptions of usability were distinct and task-specific, with no single BSE design emerging as being clearly superior across the simulated tasks.
MIND: A Multimodal AI Framework for Detecting and Forecasting Motor RRBs among Children with ASD
Shen, Mengqi; Cantin-Garside, Kristine D.; Kim, Sunwook; Nussbaum, Maury A. (2023-08-01)
Motor restricted and repetitive behaviors (RRBs), including self-injurious behavior (SIB) and stereotypical motor movements (SMM), hinder social interactions and adversely impact the physical and psychological well-being of individuals with autism spectrum disorder (ASD) and their families. Although behavioral interventions can effectively address RRBs, their accurate detection and forecasting were previously considered unattainable due to their impulsive nature and individualized behavior types, triggers, and patterns. Monitoring these behaviors may be possible via wearable sensors, but is challenged by expected inconsistencies in how sensors are worn, especially given the often low compliance observed among children with ASD. In this study, we introduced a novel AI framework for detecting and forecasting motor RRBs – Multimodal, Interpretation, Numeration, and Deep neural decoding (MIND). We observed what we term ”transition behaviors,” in which participants exhibited subtle changes in their behavior patterns or facial expressions immediately preceding the onset of motor RRBs. Identifying these transition behaviors provided evidence that motor RRBs can, in fact, be forecasted. Through a series of assumptions, the multimodal interpretation within MIND connects wearable sensor functionality to existing behavioral and psychological evidence about motor RRBs. Additionally, novel signal processing guidelines categorize modality into motion and biological modalities. These guidelines process the signal based on their generalized functionality, ensuring robustness to inconsistent data and minimizing the impact of sensor specifications (i.e., range and units of measurement, sensor resolution, sensor orientation). Analyses of modalities supported the noted assumptions. The multimodal optimization under MIND framework suggested the effective use of a single wearable device integrating several sensors (or modalities). Crucially, all children in the study were willing to wear the sensor at the optimized location, highlighting its practicality. MIND achieved 100% accuracy in detecting motor RRBs in new subjects with unfamiliar behavior types and 92.2% accuracy in forecasting (2 sec. in advance) motor RRBs. Cross-validation using various sampling methods showcased that MIND has the potential to generalize to a broader sample of children with ASD. MIND provides an advancement in the automated detection and forecasting of motor RRBs.
Algorithms in Art and Code: How Teaching Embodied Artmaking Procedures Can Stimulate Analytical Thinking in Art Crafting and Computer Programming
Bruen, Jacqueline; Jeon, Myounghoon (ACM, 2025-06-23)
People have pointed to a connection between the creative arts and computing. In the present longitudinal pilot study we taught six programmers and six non-programmers how to read and write written crochet patterns with or without the accompanying crochet gestures. Half of programmers (three participants) and nonprogrammers (three participants) were taught with the gestures, while the other halves were not. Over two weeks we individually taught participants crochet during three separate 30 minute sessions. In a fourth session, we tested participants on crochet and elementary programming and algorithms. Test results showed that programmers and non-programmers performed better on average on both tests when they learned with gestures. We interviewed all participants afterwards; programmers provided examples of how crochet demonstrated elementary programming ideas, while nonprogrammers described what they thought about programming. Our empirical study provides evidence of embodied cognition and offers contributions towards developing novel teaching methods in computer science.
AI-Supported Dance Performances Provoke Audiences to Seek Creative Merit and Meaning in AI's Artistic Decisions
Bruen, Jacqueline; Jeon, Myounghoon (ACM, 2025-06-23)
With the development of tools using generative artificial intelligence (GenAI) to create art, stakeholders cannot come to an agreement on the value of these works. In this study we uncovered the mixed opinions surrounding art made by AI. We developed two versions of a dance performance augmented by technology either with or without GenAI. For each version we informed audiences of how the performance was developed either before or after they had taken a survey on their perceptions of the performance. There were thirty-nine participants (13 males, 26 female) recruited and divided between the four performances. After the survey, we conducted focus groups with a subset of audience members. Results demonstrated that individuals were more inclined to attribute artistic merit to works made by GenAI when they were unaware its use. Our work contributes to the understanding of the design and reception of AI-made art.
Investigating the Effects of Simulated Eye Contact in Video Call Interviews
Jelson, Andrew; Tausif, Md Tahsin; Lim, Sol; Khanna, Soumya; Lee, Sang Won (ACM, 2025-04-26)
Some people suggest that deliberately watching the camera during video calls can simulate eye contact and help build trust. In this study, we investigated the effects of simulated eye contact in video calls and job interviews through an experimental study and a survey. Study 1 involved participants in a mock interview as an interviewer, where a confederate interviewee simulated eye contact half the time. The gaze patterns of the participants were tracked to understand the effects. In Study 2, we conducted an online survey to confirm the findings of Study 1 on a larger scale by asking those with experience interviewing to evaluate interviewees based on interview videos, half of which simulated eye contact. The results of both studies indicate that simulated eye contact had little impact on their evaluation compared to common belief. We discuss how the results motivate future work and how computational approaches to correcting eye gaze can be deceptive.
"Look at My Planet!": How Handheld Virtual Reality Shapes Informal Learning Experiences
Moon, Hayoun; Bautista Isaza, Carlos Augusto; Gallagher, Matthew; McDaniel, Clara; Vernier, Atlas; Ican, Leah; Springer, Karina; Cohn, Madelyn; Bennett, Sylvia; Nair, Priyanka; Ricard, Alayna; Pochiraju, Nayha; Enriquez, Daniel; Lee, Sang Won; Ogle, J. Todd; Newbill, Phyllis; Jeon, Myounghoon (ACM, 2025-04-26)
Handheld virtual reality offers a promising tool for fostering engagement in informal learning environments, providing safe, shared, and inclusive experiences. This study investigated the potential of a handheld VR-based educational program, Solar System Explorer, in a science museum setting. Fifty-three participants, aged 5 to 13, engaged in six interactive scenes using handheld tablets, involving room-scale exploration of virtual environments in small groups guided by a docent. Findings showed that dynamic, room-scale content encouraged active physical movement, while visually rich, interactive scenes fostered knowledge sharing and elicited positive emotional responses. Social engagement was strongest during creative activities, such as planet building, which facilitated interactions even among unfamiliar peers. These insights inform design guidelines for developing fun, active, and collaborative VR learning environments, contributing to scalable and inclusive handheld VR applications for informal education.
A child-robot theater afterschool program can promote children’s conceptualization of social robots’ mental capacities and engagement in learning
Dong, Jiayuan; Yu, Shuqi; Choi, Koeun; Jeon, Myounghoon (Frontiers, 2025-03-14)
Research on integrating emerging technologies, such as robots, into K-12 education has been growing because of their benefits in creating engaging learning environments and preparing children for appropriate human-robot interactions in the future. However, most studies have focused on the impact of robots in formal educational settings, leaving their effectiveness in informal settings, such as afterschool programs, unclear. The present study developed a 9-week afterschool program in an elementary school to promote STEAM (STEM + Art) education for elementary school students. The program incorporated four modules (Acting, Dancing, Music & Sounds, and Drawing), each with specific learning objectives and concluding with a theater play at the end. This program facilitated hands-on activities with social robots to create engaging learning experiences for children. A total of 38 students, aged 6–10 years, participated in the afterschool program. Among these students, 21 took part in research activities, which included answering questions about their perceptions of robots compared to other entities (i.e., babies and beetles), learning interest and curiosity, and their opinions about robots. In addition, four teachers and staff participated in interviews, sharing their reflections on children’s learning experiences with robots and their perceptions of the program. Our results showed that 1) children perceived robots as having limited affective and social capabilities but gained a more realistic understanding of their physiological senses and agentic capabilities; 2) children were enthusiastic about interacting with robots and learning about robot-related technologies, and 3) teachers recognized the importance of embodied learning and the benefits of using robots in the afterschool program; however, they also expressed concerns that robots could be potential distractions and negatively impact students’ interpersonal relationships with peers in educational settings. These findings suggest how robots can shape children’s perceptions of robots and their learning experiences in informal education, providing design guidelines for future educational programs that incorporate social robots for young learners.
Happiness improves perceptions and game performance in an escape room, whereas anger motivates compliance with instructions from a robot agent
Dong, Jiayuan; Jeon, Myounghoon (Academic Press - Elsevier, 2025-08)
Emotions have been discovered to have critical impacts on human-robot interaction (HRI), but research has focused more on robots’ emotion expressions than user emotions. The present study investigated the impact of users’ emotions (happiness and anger) on their perceptions and trust toward robots, perceived workload, and task performance in an escape room with a robot agent. Forty-six college students participated in our study. The results suggested that happy participants rated the robot agent as significantly more likable, safer, and more comfortable than angry participants. Angry participants complied significantly more with the robot agent's instructions than happy participants, but fewer succeeded. Among the participants who failed to escape the room, angry participants showed significantly higher cognitive trust in the robot than happy participants. The results underscored the importance of user emotions in shaping user perceptions and trust in robots, providing valuable theoretical and practical implications for emotions in HRI.
Investigating drivers' responses to cyber-attacks while conducting non-driving related tasks in highly automated vehicles
Ban, Gayoung; Jeon, Myounghoon (Academic Press - Elsevier, 2025-08)
As automated vehicles (AVs) advance, understanding human factors in cybersecurity incidents is essential to ensuring driver safety and system resilience. While prior research has explored driver responses to cyber-attacks in partially automated (Level 2–3) vehicles, less is known about how drivers in highly automated vehicles respond. In Level 4 automation, drivers are not required to monitor the roadway continuously but may still need to intervene in unforeseen cyber-attack, making re-engagement dynamics fundamentally different from lower levels of automation. This study examines the impact of non-driving-related task (NDRT) engagement and cyber-attack criticality on situation awareness, visual attention, response time, and workload in Level 4 AVs. To this end, forty-five participants drove in a driving simulator with two types of cyber-attack criticality (non-safety-related, and safety-related as within-subjects) and three non-driving related tasks (NDRTs) engagement levels (no, single and dual as between-subjects). Results indicate that drivers engaged in any level of NDRT (Single or Dual) had significantly reduced situation awareness of road conditions and delayed response time and gaze reallocation to critical information after a cyber-attack, particularly in Dual NDRT conditions. Additionally, safety-related cyber-attacks induced greater cognitive workload, suggesting that drivers exert more mental effort when responding to high-risk threats. These findings highlight the unique re-engagement challenges in Level 4 AVs, where drivers must transition from passive engagement in NDRTs to active situation awareness during cybersecurity incidents. The results emphasize the need for human-centered AV cybersecurity systems that optimize alert delivery, minimize cognitive overload, and facilitate rapid driver response to emerging threats in highly automated driving environments.
Mapping the complex causal mechanisms of drinking and driving behaviors among adolescents and young adults
Hosseinichimeh, Niyousha; MacDonald, Rod; Li, Kaigang; Fell, James C.; Haynie, Denise L.; Simons-Morton, Bruce; Banz, Barbara C.; Camenga, Deepa R.; Iannotti, Ronald J.; Curry, Leslie A.; Dziura, James; Mayes, Linda C.; Andersen, David F.; Vaca, Federico E. (Pergamon-Elsevier, 2022-03)
Background: The proportion of motor vehicle crash fatalities involving alcohol-impaired drivers declined substantially between 1982 and 1997, but progress stopped after 1997. The systemic complexity of alcohol-impaired driving contributes to the persistence of this problem. This study aims to identify and map key feedback mechanisms that affect alcohol-impaired driving among adolescents and young adults in the U.S. Methods: We apply the system dynamics approach to the problem of alcohol-impaired driving and bring a feedback perspective for understanding drivers and inhibitors of the problem. The causal loop diagram (i.e., map of dynamic hypotheses about the structure of the system producing observed behaviors over time) developed in this study is based on the output of two group model building sessions conducted with multidisciplinary subject-matter experts bolstered with extensive literature review. Results: The causal loop diagram depicts diverse influences on youth impaired driving including parents, peers, policies, law enforcement, and the alcohol industry. Embedded in these feedback loops are the physical flow of youth between the categories of abstainers, drinkers who do not drive after drinking, and drinkers who drive after drinking. We identify key inertial factors, discuss how delay and feedback processes affect observed behaviors over time, and suggest strategies to reduce youth impaired driving. Conclusion: This review presents the first causal loop diagram of alcohol-impaired driving among adolescents and it is a vital first step toward quantitative simulation modeling of the problem. Through continued research, this model could provide a powerful tool for understanding the systemic complexity of impaired driving among adolescents, and identifying effective prevention practices and policies to reduce youth impaired driving.

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