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The fourth industrial revolution tempts manufacturing companies to transform into smart factories, which in turn affects the human work. We examine this complexity from the perspective of human factors and ergonomics (HF/E) by analysing the organisational capabilities needed when seeking and selecting new technology solutions, and designing and implementing them into local application. This qualitative study focuses on manufacturing companies that have pioneered adopting high technologies in their processes. Empirical material was collected from 15 Finnish manufacturing companies of different sizes. Particular attention is paid to how HF/E are present when companies (1) sense new opportunities for technological development of their production, (2) seise these opportunities through design actions, and (3) shift the solutions into use. Analyses revealed that companies, regardless of their size and the technology being implemented, do not fully understand the fundamentals and value of HF/E, resulting in technology and production-oriented solutions in which human skills and capabilities are often neglected. A novel road map is proposed for integrating HF/E expertise and knowledge when seeking, selecting, and utilising new production technologies. This study increases understanding of the role of HF/E when adopting new technologies in manufacturing. The road map improves possibilities for fact-based decision-making. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

Robotaxi services, or rideshare operated by autonomous vehicles, present an opportunity for independent and convenient transportation for people with disabilities. The proliferation of robotaxis in California has been met with mixed reactions from the disability community. To better understand perceptions of and expectations for robotaxis, this report uses semi-structured interviews with representatives from disability advocacy organizations. For many people with disabilities, especially for people with intellectual, developmental, and/or physical disabilities, robotaxis are inaccessible. Given the intricacies of accommodating a wide audience, not all interviewees were confident that robotaxi design and programming will be inclusive. Some interviewees trusted that autonomous vehicle companies will independently pursue accessibility features in their robotaxis. Other interviewees regarded statewide accessibility standards as essential to ensure accessibility. Overall, interviewees shared that people with disabilities want to be included in the process of designing, testing, and regulating robotaxis. Implications for accessible robotaxi governance in California is presented, with an emphasis on wheelchair access, considering impact on public transportation service, and centering people with disabilities. Interviewees also discussed ideal accommodations that would allow a broad audience to request a ride, board the robotaxi, communicate with the operation system, and exit the vehicle. These findings may be useful to AV companies as they consider how robotaxis can accommodate people with a variety of disabilities.

INTRODUCTION: Effective communication in the operating room (OR) is crucial. Addressing a colleague by their name is respectful, humanising, entrusting and associated with improved clinical outcomes. We aimed to enhance team communication in the perioperative environment by offering personalised surgical caps labelled with name and provider role to all OR team members at a large academic medical centre. MATERIALS AND METHODS: This was a quasi-experimental, uncontrolled, before-and-after quality improvement study. A survey regarding perceptions of team communication, knowledge of names and roles, communication barriers, and culture was administered before and after cap delivery. Survey results were measured on a 5-point Likert Scale; descriptive statistics and mean scores were compared. All cause National Surgical Quality Improvement Project (NSQIP) morbidity and mortality outcomes for surgical specialties were examined. RESULTS: 1420 caps were delivered across the institution. Mean survey scores increased for knowing the names and roles of providers around the OR, feeling that people know my name and feeling comfortable communicating without barriers across disciplines. The mean score for team communication around the OR is excellent was unchanged. The highest score both before and after was knowing the name of an interdisciplinary team member is important for patient care. A total of 383 and 212 providers participated in the study before and after cap delivery, respectively. Participants agreed or strongly agreed that labelled surgical caps made it easier to talk to colleagues (64.9%) while improving communication (66.0%), team culture (60.5%) and patient care (56.8%). No significant differences were noted in NSQIP outcomes. CONCLUSIONS: Personalised labelled surgical caps are a simple, inexpensive tool that demonstrates promise in improving perioperative team communication. Creating highly reliable surgical teams with optimal communication channels requires a multifaceted approach with engaged leadership, empowered front-line providers and an institutional commitment to continuous process improvement.

Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries' profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems. [ABSTRACT FROM AUTHOR]

Picker to parts order picking (OP) systems are one of the most labour-intensive warehouse operations, accounting for 55% of warehouse expenses in the e-commerce industry. For this reason, the e-commerce industry has invested in fully automated and robotised warehouses. However, these warehouse solutions are very expensive. Furthermore, with the advent of Industry 5.0 with a human-centric focus, the OP system's automation should be designed and implemented in ways to improve the working conditions of the order pickers rather than replacing them completely. A pick and transport robot (PTR) is a solution for OP systems which is compatible with Industry 5.0. An example of such a solution is Grab™ by SOLWR. Previous studies have barely investigated the human factor effects of using PTRs in OP systems while optimising the performance of the systems. This research develops a mathematical model to optimise the performance of a picker to parts OP system using PTRs in terms of productivity, quality, and the well-being of the order pickers. The developed model is tested by using data from a case company. The results of this study support managerial decisions by achieving a better knowledge of how to set up such PTR solutions. [ABSTRACT FROM AUTHOR]

Robotisation is increasing in warehouse operations, but human employment continues to be relevant. Traditionally manual activities, such as order picking, are being re-designed into collaborative human–robot tasks. This trend exemplifies the transition towards a human-centric Industry 5.0, focusing on synergy instead of seeking human replacement. However, human workers are increasingly hard to recruit and retain. We contribute to the underrepresented literature on human factors within the domain of operations and production management research and investigate the deployment of robotic technologies alongside human workers in a sustainable way. With a unique real-effort experiment, we investigate how the manipulation of picker's experienced levels of autonomy affects their job satisfaction and core self-evaluations, two key behavioural outcomes that determine employee turnover intentions. We establish that the introduction of human–robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (i) gaining control (the human leading the robot) and (ii) ceding control (the human following the robot). This positive effect is larger when the human is following the robot. We additionally find that following the robot positively affects pickers' self-esteem and that self-efficacy related to human–robot interaction benefits from the introduction of collaborative robotics, regardless of the setup dynamics. [ABSTRACT FROM AUTHOR]

Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]

Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers' work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition's relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations. [ABSTRACT FROM AUTHOR]

Social media platforms are frequently used to share information and opinions around vaccinations. The more often a message is reshared, the wider the reach of the message and potential influence it may have on shaping people’s opinions to get vaccinated or not. We used a negative binomial regression to investigate whether a message’s linguistic characteristics (degree of concreteness, emotional arousal, and sentiment) and user characteristics (political ideology and number of followers) may influence users’ decisions to reshare tweets related to the COVID-19 vaccine. We analyzed US English-language tweets related to the COVID-19 vaccine between May 2020 and October 2021 (N = 236,054). Tweets with positive and high-arousal words were more often retweeted than negative, low-arousal tweets. Tweets with abstract words were more often retweeted than tweets with concrete words. In addition, while Liberal users were more likely to have tweets with a positive sentiment reshared, Conservative users were more likely to have tweets with a negative sentiment reshared. Our results can inform public health messaging on how to best phrase vaccine information to impact engagement and information resharing, and potentially persuade a wider set of people to get vaccinated.

Designing user-adaptive search systems necessitates modeling the user's knowledge state during information seeking. Gaze data offers insights into cognitive processes during task-based reading. Despite its potential, cognitive perspectives have been insufficiently explored in the representation of the user's knowledge state when designing search systems. We reanalyzed an eye-tracking dataset and constructed mixed-effects user models to identify which measurements of gaze activities (i.e., gaze metrics captured by eye trackers) are reflective of the user. Our study's findings indicate that there are statistically significant correlations between gaze metrics that measure the variability of saccadic eye movement and search performance. The accuracy of answers has been significantly influenced by the interaction between the control of saccade trajectories, measured by the standard deviation of absolute saccadic directions and the difficulty of the search task. We discuss the implications of these findings for the design of search systems adaptable to the user's state of knowledge.

Despite computational algorithms outperforming humans in certain tasks, algorithmic advice is less used than human advice (algorithm aversion). Thus, algorithmic advice should be designed to avoid algorithm aversion. However, few studies have discussed the use of advice with an interval (e.g., 60.0 ± 2.0 %), a common format in algorithmic advice. This study confirmed in two behavioral experiments (N = 200) that differences in advice sources lead to differences in advice use, mainly by influencing the step at which the judge decides whether to ignore the advice. Therefore, we proposed to individualize the presentation of advice so that the advice would be such that decreases the rate advice being ignored. Our individualization of the advice presentation focused on the distance between the advice and the initial judgment, a significant factor in advice utilization. Another behavioral experiment (N = 100) confirmed that our proposed advice design overcomes differences among advisors.

Team coordination is essential for effective performance during critical, stressful events. To better understand processes and states involved at multiple levels of team coordination, we assessed the correspondence between low- and high-level coordination in teams participating in simulation-based medical team training. We computed a measure of low-level team coordination with Multidimensional Recurrence Quantification Analysis, applied to arm movement, heart rate, and skin conductance data. High-level team coordination was captured by annotating video recordings for explicit and implicit, information and action coordination. Three linear mixed-effects model were run, each predicting a type of low-level coordination, based on high-level coordination annotations, accounting for multiple observations per team. Our findings showed that, compared to periods without annotated coordination, explicit- and implicit- information coordination corresponded to significantly different low-level team coordination across each of the studied modalities. Further research is required to assess additional factors related to the temporal variability observed in low-level coordination.

What makes Lassie a smart dog? People have strong intui-tions about dogs' intelligence, yet the content and organiza-tion of these intuitions remain unknown. Two studies ex-amined the structure of laypeople's concepts of dog intelli-gence, creating a conceptual map of what people represent as a “smart” or “dumb” dog. Study 1 elicited open-ended ideas about dog intelligence. We turned consistent themes into items in a 50-item survey. Study 2 asked participants to picture either a smart or dumb dog and rate that dog on the items derived from Study 1. Participants strongly agreed in their ratings of smart and dumb dogs, and we discovered a coherent dimensional structure underlying people's intui-tions. They represent smart dogs as socially skilled with a good temperament, and dumb dogs as bad at physical rea-soning and avoiding threats. These representations align well with findings from canine research and with dog train-ers' practical knowledge.

Prior literature shows that some mechanisms, e.g., commitment, could give rise to cooperation. However, participants' diverse propensities to cooperate may limit such mechanisms' effectiveness. Thus, we bring individual differences in their propensities to cooperate into the reasoning of long-term social dynamics of cooperation through an agent-based modeling approach. Our results suggest that commitment may still guarantee cooperation when individuals have different propensities to cooperate but has weaker effects, and the setups of commitment are also important. Our study highlights the importance of integrating individual preferences in analyzing collective dynamics of a population consisting of individuals of heterogeneous characteristics, thus offering implications to facilitate cooperation in rich real-world scenarios.

Transcription with Incremental Presentation of the Stimulus (TIPS) is a novel approach relying on micro-behaviours proposed by Colarusso and colleagues (2023) to study users' cognition with data visualizations. The study in this paper has two primary objectives: (a) investigate whether TIPS can measure an individual's competence with data visualizations; and (b) explore the potential enhancement of TIPS measures by normalizing them with the individual's performance on tests of visuo-spatial abilities and memory capacity. We test 30 participants with different expertise and cognitive skills. Results reveal that TIPS provides some promise for individual competence assessment, but only when normalized with the individual's performance on a test of rigid transformation of mental images. Other tests measuring visuospatial abilities or memory capacity did not produce effective normalizations.

We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multi-dimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI).

Intelligent systems that record, analyze, and respond to events have become major parts of our lives. They are available as Decision Support (DS) for many tasks and can enhance the information on which decision-makers can base their decisions. Decision makers need to evaluate the available information, and they also have to decide whether to seek information from additional information sources. The information is often costly, and its costs and benefits must be weighted. Also, integrating information from multiple sources can complicate the decision task. Here, we study the combined decision process that chooses information sources and integrates them, if chosen, in a classification decision.In an online experiment with 75 engineering students, we manipulated the redundancy level of information received from DS with already existing information. Participants' task in two between-subjects conditions was to classify binary events with the option to access up to two DS systems. In one of the conditions, the two DSs provided non-redundant information, and in the second condition, one of them provided fully redundant information, and the other provided non-redundant information. We found that the decision to access information was not affected by whether some information was redundant (strongly correlated with already available information). Participants used the information to improve classification performance, and the improvement was significantly higher when they used non-redundant information. However, the benefits gained were smaller than predicted from a normative model. Moreover, the use of information from multiple non-correlated sources can increase mental workload, as was evident in our results, possibly because of conflicting information from different sources.

The photo-taking-impairment effect refers to the detrimental impact photo taking has on one's memory for the photographed object. We explored the role of two mechanisms that have been said to underlie the effect, namely offloading and attentional disengagement. In this online study 107 undergraduate students were shown 3x5 paintings and were instructed to simply observe them, take a picture that would be available for later usage, or take a picture and delete it. Afterwards, they were presented with a visual details multiple-choice test. It was expected that if attentional disengagement was the mechanism underlying the photo-taking-impairment effect, then the effect would not be present in the current study. This expectation was due to the non-distracting nature of the photo taking task that was used in the study. The results were in line with the expectations and did not indicate the presence of the photo-taking-impairment effect. Consequentially, it supported the hypothesis that attentional disengagement, rather than offloading, is the mechanism underlying this effect.

Do decision-makers' strategies of integrating different risks depend on framing? In the present study, participants were either instructed that they were choosing between two solutions to a complex problem or between two generic options. The former was framed as an industrial scenario that required choices between modifying and replacing a module (Adapt or Exchange). The risk was higher for Adapt to harm the product and for Exchange to harm the plant. Participants were either told that the consequences of both risks were equally severe (content-same group), or that harming the plant was worse (content-different group). A third group received a generic task framing (no-content group). We expected framing to affect risk integration, inducing different choices and strategies in the content-same than the no-content group. The data refuted this hypothesis, but decisions clearly diverged from the content-different group. These findings question whether ecological validity can be enhanced merely by framing.

We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric for AI models focusing on story understanding. CogME is a framework grounded in human thinking strategies and story elements that involve story understanding. With a specific breakdown of the questions, this approach provides a nuanced assessment revealing not only AI models' particular strengths and weaknesses but also the characteristics of the benchmark dataset. Our case study with the DramaQA dataset demonstrates a refined analysis of the model and the benchmark dataset. It is imperative that metrics align closely with human cognitive processes by comprehending the tasks' nature. This approach provides insights beyond traditional overall scores and paves the way for more sophisticated AI development targeting higher cognitive functions.

This study examined how variations in signature complexity affected the ability of forensic document examiners (FDEs) and laypeople to determine whether signatures are authentic or simulated (forged), as well as whether they are disguised. Forty‐five FDEs from nine countries evaluated nine different signature comparisons in this online study. Receiver Operating Characteristic (ROC) analyses revealed that FDEs performed in excess of chance levels, but performance varied as a function of signature complexity: Sensitivity (the true‐positive rate) did not differ much between complexity levels (i.e., 65% vs. 79% vs. 79% for low vs medium vs high complexity), but specificity (the true‐negative rate) was the highest (95%) for the medium complexity signatures and lowest (73%) for low complexity signatures. The specificity of high‐complexity signatures (83%) was between these values. The sensitivity for disguised comparisons was only 11% and did not vary across complexity levels. One hundred‐one novices also completed the study. A comparison of the area under the ROC curve (AUCs) revealed that FDEs outperformed novices in medium and high‐complexity signatures but not low‐complexity signatures. Novices also struggled to detect disguised signatures. While these findings elucidate the role of signature complexity in lay and expert evaluations, the error rates observed here may differ from those in forensic practice due to differences in the experimental stimuli and circumstances under which they were evaluated. This investigation of the role of signature complexity in the evaluation process was not intended to estimate error rates in forensic practice. [ABSTRACT FROM AUTHOR]

Truck collisions with vulnerable road users (VRUs) are infrequent, but often severe or fatal. While research has investigated factors contributing to safety and collisions, a synthesis of human-related contributors remains missing. This scoping review follows PRISMA guidelines to synthesise studies on the effects of human factors on truck-VRU safety. With a focus on human factors related to pedestrians, cyclists, and truck drivers, five scientific databases were searched and 3,414 records were screened. Twenty-four articles met the inclusion criteria. Most were published after 2015, indicating a limited but growing focus on human factors affecting truck-VRU safety. Half of the studies analysed police collision reports, while others used qualitative data collection methods, like questionnaires and focus groups, or human subject experiments. Compared to passenger-vehicle literature, notably fewer behavioural studies were identified, highlighting a need for further behavioural human factors studies of truck-VRU interactions. Sociodemographic and vision-related factors were most frequently investigated and found to significantly affect collision occurrence and severity. Driving and cycling experience and training, and road user distractions were examined less, albeit being important. This review bridges a literature gap by focusing on human characteristics, states, decisions, and errors affecting truck-VRU safety, offering insights for road-user-centred mitigation strategies. [ABSTRACT FROM AUTHOR]

Background: Ventricular assist devices (VADs) are effective therapy for patients with end‐stage heart failure. Current VAD controllers offer improved interactivity, yet limitations of the visual, tactile, and auditory interface persist that impact patient experience and quality of life (QoL). This study explores how VAD controllers can be redesigned using a human‐centered design approach to enhance the emotional and functional experience of the device for patients. Methods: VAD patients (n = 21), caregivers (n = 4) and healthcare practitioners (n = 24) were interviewed to uncover design opportunities. From this, a series of realistic scenarios to design for emerged. A "design by analogy" method took inspiration from existing consumer products to ideate new functionality for the VAD wearable system, creating concepts for a controller interface and paired wearable device. An additional 15 patients and 2 caregivers were engaged to explore current VAD controller experiences and evaluate the future‐state concepts. Results: This research validated the need for increased automation and emergency functionality in VAD controllers, including remote monitoring of data, accurate communication of battery status, and automated medical alerts for critical device alarms. "Manage my health," "Feeling normal," "Social belonging," "Feeling safe," and "Sense of control" emerged as key patient concerns to be met by future VAD controller designs. Conclusions: The study demonstrated an innovative and relevant approach to improve usability of future VAD peripherals. By considering both emotional and functional perspectives in the design of lifesaving medical devices such as VADs, device manufacturers can uncover new opportunities to improve patient QoL through improved user experiences. [ABSTRACT FROM AUTHOR]

Brugada syndrome is an inherited cardiovascular disorder, which can affect the rhythm of the heart. Dental treatment can often present as an obstacle for patients with Brugada syndrome as the use of local anaesthetic can have adverse effects on the electrical conduction system of the heart. Aim: The aim of this series is to discuss the aetiopathogenesis, presentation and management of Brugada syndrome and its relevance to dentistry and the dental team. Methods: The authors present a series of three Brugada syndrome patients who underwent dental intervention in a hospital setting. Results and Conclusion: The series highlighted the importance of multidisciplinary care for patients with Brugada syndrome. Liaising with other teams, such as cariologists and GMPs, allows a patient centred approach and better understanding of patient management. [ABSTRACT FROM AUTHOR]

The topic of human-centric design of smart production and logistics processes is increasingly debated in both academic and industrial communities. However, there is still little existing research on the integration of this concept with companies' daily operations. The research presented in this paper aims to expand this area of research by proposing a framework designed for the logistics sector that includes human factors among the criteria for choosing the best technology to adopt based on the company's requirements. The framework, which is, therefore, a decision support tool for technology adoption, is subsequently validated through a multiple case study. The results of the validation highlight the framework's potential as a tool to better understand the human factors involved in tasks and technologies to be adopted in order to avoid possible critical issues related to technological changes (e.g. acceptability, need for additional investment in training, identification of required skills). [ABSTRACT FROM AUTHOR]

AbstractIn recent years, there has been a wave of artificial intelligence (AI) technologies that offer to solve problems from shopping habits to mortgage approvals to critical systems operations. The rapidity of the development of these systems has led to both excitement and apprehension about the roles these systems should play in our modern societies. This paper focuses on the critical infrastructure industry, in general, and nuclear power generation, in particular, and seeks to scrutinize how we can leverage these novel technologies in human-centered ways to maintain or enhance the established high levels of reliability and resilience in these industries. First, we discuss the broader aspects of cognitive systems and activities that are critical to understanding the human-AI space. Then we explore different approaches to explainability in AI and the notions of trust. We then move on to discuss several human factors concepts and methods and how they can support the design of human-AI teams. We then explore recent research related to nuclear power that has been undertaken and evaluate the current industry and regulatory landscapes. Finally, we discuss identified research gaps and recommendations for solving these for the critical infrastructure space. [ABSTRACT FROM AUTHOR]

Compared to traditional lower-limb prostheses (LLPs), intelligent LLPs are more versatile devices with emerging technologies, such as microcontrollers and user-controlled interfaces (UCIs). As emerging technologies allow a higher level of automation and more involvement from wearers in the LLP setting adjustments, the previous framework established to study human factors elements that affect wearer-LLP interaction may not be sufficient to understand the new elements (e.g., transparency) and dynamics in this interaction. In addition, the increased complexity of interaction amplifies the limitations of the traditional evaluation approaches of wearer-LLP interaction. Therefore, to ensure wearer acceptance and adoption, from a human factors perspective, we propose a new framework to introduce elements and usability requirements for the wearer-LLP interaction. This paper organizes human factors elements that appear with the development of intelligent LLP technologies into three aspects: wearer, device, and task by using a classic model of the human-machine systems. By adopting Nielsen's five usability requirements, we introduce learnability, efficiency, memorability, use error, and satisfaction into the evaluation of wearer-LLP interaction. We identify two types of wearer-LLP interaction. The first type, direct interaction, occurs when the wearer continuously interacts with the intelligent LLP (primarily when the LLP is in action); the second type, indirect interaction, occurs when the wearer initiates communication with the LLP usually through a UCI to address the current or foreseeable challenges. For each type of interaction, we highlight new elements, such as device transparency and prior knowledge of the wearer with the UCI. In addition, we redefine the usability goals of two types of wearer-LLP interaction with Nelson's five usability requirements and review methods to evaluate the interaction. Researchers and designers for intelligent LLPs should consider the new device elements that may additionally influence wearers' acceptance and the need to interpret findings within the constraints of the specific wearer and task characteristics. The proposed framework can also be used to organize literature and identify gaps for future directions. By adopting the holistic usability requirements, findings across empirical studies can be more comparable. At the end of this paper, we discuss research trends and future directions in the human factors design of intelligent LLPs. [ABSTRACT FROM AUTHOR]

Introduction: In human-agent interaction, trust is often measured using human-trust constructs such as competence, benevolence, and integrity, however, it is unclear whether technology-trust constructs such as functionality, helpfulness, and reliability are more suitable. There is also evidence that perception of "humanness" measured through anthropomorphism varies based on the characteristics of the agent, but dimensions of anthropomorphism are not highlighted in empirical studies. Methods: In order to study how different embodiments and qualities of speech of agents influence type of trust and dimensions of anthropomorphism in perception of the agent, we conducted an experiment using two agent "bodies", a speaker and robot, employing four levels of "humanness of voice", and measured perception of the agent using human-trust, technology-trust, and Godspeed series questionnaires. Results: We found that the agents elicit both human and technology conceptions of trust with no significant difference, that differences in body and voice of an agent have no significant impact on trust, even though body and voice are both independently significant in anthropomorphism perception. Discussion: Interestingly, the results indicate that voice may be a stronger characteristic in influencing the perception of agents (not relating to trust) than physical appearance or body. We discuss the implications of our findings for research on human-agent interaction and highlight future research areas. [ABSTRACT FROM AUTHOR]

Introduction: Operating room (OR) efficiency is a key factor in determining surgical healthcare costs. To enable targeted changes for improving OR efficiency, a comprehensive quantification of the underlying sources of variability contributing to OR efficiency is needed. Previous literature has focused on select stages of the OR process or on aggregate process times influencing efficiency. This study proposes to analyze the OR process in more fine-grained stages to better localize and quantify the impact of important factors. Methods: Data spanning from 2019-2023 were obtained from a surgery center at a large academic hospital. Linear mixed models were developed to quantify the sources of variability in the OR process. The primary factors analyzed in this study included the primary surgeon, responsible anesthesia provider, primary circulating nurse, and procedure type. The OR process was segmented into eight stages that quantify eight process times, e.g., procedure duration and procedure start time delay. Model selection was performed to identify the key factors in each stage and to quantify variability. Results: Procedure type accounted for the most variability in three process times and for 44.2% and 45.5% of variability, respectively, in procedure duration and OR time (defined as the total time the patient spent in the OR). Primary surgeon, however, accounted for the most variability in five of the eight process times and accounted for as much as 21.1% of variability. The primary circulating nurse was also found to be significant for all eight process times. Discussion: The key findings of this study include the following. (1) It is crucial to segment the OR process into smaller, more homogeneous stages to more accurately assess the underlying sources of variability. (2) Variability in the aggregate quantity of OR time appears to mostly reflect the variability in procedure duration, which is a subinterval of OR time. (3) Primary surgeon has a larger effect on OR efficiency than previously reported in the literature and is an important factor throughout the entire OR process. (4) Primary circulating nurse is significant for all stages of the OR process, albeit their effect is small. [ABSTRACT FROM AUTHOR]

Introduction: The integration of artificial intelligence (AI) in intensive care units (ICUs) presents both opportunities and challenges for critical care nurses. This study delves into the human factor, exploring how nurses with leadership roles perceive the impact of AI on their professional practice. Objective: To investigate how nurses perceive the impact of AI on their professional identity, ethical considerations surrounding its use, and the shared meanings they attribute to trust, collaboration, and communication when working with AI systems. Methods: An interpretive phenomenological analysis was used to capture the lived experiences of critical care nurses leading with AI. Ten nurses with leadership roles in various ICU specializations were interviewed through purposive sampling. Semi-structured interviews explored nurses’ experiences with AI, challenges, and opportunities. Thematic analysis identified recurring themes related to the human factor in leading with AI. Findings: Thematic analysis revealed two key themes which are leading with AI: making sense of challenges and opportunities and the human factor in leading with AI. The two main themes have six subthemes which revealed that AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training. New challenges emerged, including adapting to new workflows and managing potential bias. Clear communication and collaboration were crucial for successful AI integration. Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis. Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions. Conclusion: While AI presents opportunities for automation and data analysis, successful integration hinges on addressing concerns about overreliance, workflow adaptation, and potential bias. Building trust and fostering collaboration are fundamentals for AI integration. Transparency in AI systems allows nurses to confidently delegate tasks, while collaboration empowers them to focus on human-centered care with AI support. Ultimately, dealing with the ethical concerns of AI in ICU care requires prioritizing patient autonomy and ensuring accountability in AI-driven decisions. [ABSTRACT FROM AUTHOR]

Human decision-making (HDM) is a complex process. Various human factors play a significant role in this process. Human factors directly or indirectly affect the entire process of decision-making (DM). In this study, an attempt has been made to integrate some of the human factors like past experiences (pe), emotion (ef), times factors (tf), and uncertain (un) with the reinforcement learning (RL) method to develop model for HDM. For this Iowa gambling Task (IGT) has been used as a data collection tool, data of 57 subjects were collected. It is a well-known experience-based task that helps to identify the DM behaviour of participants. An AHP method has been also used to decide the criteria weight to different human factors in the HDM model. Four learning models are developed that are the combination of different utility functions, learning rules, and choice rules. The AHP method decides the preference of various factors incorporated in the developed models. From the results, it is observed that the model based on prospect utility, decay RL, and trial dependency (PU-DRI-TDC Model) performs better when the emotion factor was given the highest preference than others. In addition to this, the IGT learning of participants was also analysed. [ABSTRACT FROM AUTHOR]

Security operation centers (SOCs) are increasingly established to meet the growing threat against cyber security. The operators of SOCs respond to complex incidents under time constraints. Within critical infrastructure, the consequences of human error or low performance in SOCs may be detrimental. In other domains, situation awareness (SA) has proven useful to understand and measure how operators use information and decide the correct actions. Until now, SA research in SOCs has been restricted by a lack of in-depth studies of SA mechanisms. Therefore, this study is the first to conduct a goal-directed task analysis in a SOC for critical infrastructure. The study was conducted through a targeted series of unstructured and semi-structured interviews with SOC operators and their leaders complemented by a review of documents, incident reports, and in situ observation of work within the SOC and real incidents. Among the presented findings is a goal hierarchy alongside a complete overview of the decisions the operators make during escalated incidents. How the operators gain and use SA in these decisions is presented as a complete set of SA requirements. The findings are accompanied by an analysis of contextual differences in how the operators prioritize goals and use information in network incidents and security incidents. This enables a discussion of what SA processes might be automated and which would benefit from different SA models. The study provides a unique insight into the SA of SOC operators and is thus a steppingstone for bridging the knowledge gap of Cyber SA. [ABSTRACT FROM AUTHOR]

Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator's current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator's stress. [ABSTRACT FROM AUTHOR]

Background: The utilization of robot-assisted approaches to surgery has increased significantly over the last two decades. This has introduced novel complexities into the operating room environment, requiring management of new challenges and workflow adaptation. This study aimed to analyze challenges in the surgical setup for complex upper gastrointestinal robot-assisted surgery (UGI-RAS) and identify opportunities for solutions. Methods: Direct observations of surgical setup processes for UGI-RAS were performed by a trained Human Factors researcher at a non-profit academic medical center in Southern California. Setup tasks were subdivided into five phases: (1) before wheels-in; (2) patient transfer and anesthesia induction; (3) patient preparation; (4) surgery preparation; and (5) robot docking. Start/end times for each phase/task were documented along with workflow disruption (FD) narratives and timestamps. Setup tasks and FDs were analyzed using descriptive statistics. Results: Twenty UGI-RAS setup procedures were observed between May-November 2023: sleeve gastrectomy +/- hiatal hernia repair (n = 9, 45.00%); para-esophageal hernia repair +/- fundoplication (n = 8, 40.00%); revision to Roux-en-Y gastric bypass (n = 2, 10.00%); and gastric band removal (n = 1, 5.00%). Frequent FDs included planning breakdowns (n = 20, 29.85%), equipment/supply management (n = 17, 25.37%), patient care coordination (n = 8, 11.94%), and equipment challenges (n = 8, 11.94%). Eleven of 20 observations were first-start cases, of which 10 experienced delayed starts. Discussion: Interventions aimed at improving workflows during UGI-RAS setup include performing pre-operative team huddles and conducting trainings aimed at team coordination and equipment challenges. These solutions could result in improved teamwork, efficiency, and communication while reducing case start delays and turnover time. [ABSTRACT FROM AUTHOR]

• Mobile phone use while driving remains a road safety concern for road transport authorities. • The driving context plays a role in the decision to engage in mobile phone use while driving. • Even after the intention to use the mobile phone while driving is formed, the moment where engagement occurs will be determined by the demands of the driving context. Mobile phone use is one of the most frequent causes of distraction among drivers. While there have been a significant number of studies that have examined individuals' intentions to use a mobile phone while driving, the influence of individuals' in-situ judgement of driving conditions has received considerably less attention. The aim of this investigation was to provide a systematic understanding of how factors associated with the driving context and environment influence a driver's decision to engage in mobile phone use while driving. Following a systematic classification scheme, 41 research articles from the years 2011 to 2020 were reviewed and synthesised to identify the contextual determinants of mobile phone distraction. Overall, the findings provided support for the role that contextual features play in influencing individuals' mobile phone use engagement. This finding was particularly the case in instances where mobile phone tasks required relatively high cognitive and physical demands on an individual, such as texting and/or reading mails. The findings also indicated that as contextual complexity increases, mobile phone use decreases as well. A deeper understanding of the relationship between contextual factors and phone use while driving may aid in the design of more efficient driver support systems and the development of distraction-sensitive road design guides. This understanding can also assist in the identification of mobile phone use hotspots and the improvement of law enforcement and educational strategies to prevent the behaviour. [ABSTRACT FROM AUTHOR]

If appropriate colour coding is applied, gymnasts’ visual perception during landing may improve, potentially leading to reduced injuries and enhanced performance. An experiment and a survey were conducted with professional gymnasts at the University of Oklahoma. For the experiment, gymnasts’ visual perceptions were assessed for different manoeuvres using distinctive colours: floral white, lime yellow, and deep blue. Subsequently, we conducted a survey to investigate their preferred colour(s), reasons behind their choices, and any previous injury experiences related to poor visibility. Significant differences between floral white and lime yellow for more complex manoeuvres (e.g., double layout flip with single twist, p = .01, and double layout flip with double twist, p = .03). Survey results indicate that floral white colour was not preferred since the gymnasts reported difficulty distinguishing the floor from the ceiling. Lime yellow was preferred due to its high contrast, and deep blue was similarly favoured because gymnasts were accustomed to it. Additionally, the gymnasts reported other potential colour(s) and/or patterns that could enhance visual perception. The study provides insights into the potential benefits of colour-coded landing areas and establishes a foundation for further research on utilizing other hue variations, lightness, chroma intensity, patterns, and related factors. [ABSTRACT FROM AUTHOR]

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Background: To enhance patient safety in healthcare, it is crucial to address the underreporting of issues in Critical Incident Reporting Systems (CIRSs). This study aims to evaluate the effectiveness of generative Artificial Intelligence and Natural Language Processing (AI/NLP) in reviewing CIRS cases by comparing its performance with human reviewers and categorising these cases into relevant topics. Methods: A case–control feasibility study was conducted using CIRS cases from the German CIRS-Anaesthesiology subsystem. Each case was reviewed by a human expert and by an AI/NLP model (ChatGPT-3.5). Two CIRS experts blindly assessed these reviews, rating them on linguistic quality, recognisable expertise, logical derivability, and overall quality using six-point Likert scales. Results: On average, the CIRS experts correctly classified 80% of human CIRS reviews as created by a human and misclassified 45.8% of AI reviews as written by a human. Ratings on a scale of 1 (very good) to 6 (failed) revealed a comparable performance between human- and AI-generated reviews across the dimensions of linguistic expression (p = 0.39), recognisable expertise (p = 0.89), logical derivability (p = 0.84), and overall quality (p = 0.87). The AI model was able to categorise the cases into relevant topics independently. Conclusions: This feasibility study demonstrates the potential of generative AI/NLP in analysing and categorising cases from the CIRS. This could have implications for improving incident reporting in healthcare. Therefore, additional research is required to verify and expand upon these discoveries. [ABSTRACT FROM AUTHOR]

Background: Generalized myasthenia gravis (gMG) is a chronic, autoimmune neuromuscular disease, often accompanied by high treatment burden. The objective of this usability study was to validate that the zilucoplan prefilled syringe (PFS) can be used safely and effectively. Methods: The study recruited 75 participants representing five distinct user groups (injection-naïve gMG patients, injection-experienced gMG patients, injection-naïve caregivers, injection-experienced caregivers, and healthcare professionals), who each simulated an injection without being directed to use the instructions for use (IFU). Participants were then asked to repeat the process as directed by the IFU. Participants were assessed and use errors identified using performance-based criteria. Results: 73/75 (97.3%) participants were able to safely administer the dose of medication during the first simulated use, with or without the IFU. When utilizing the IFU, all 75 (100.0%) participants were able to administer the medication. The rate of use errors was low, with pass rates for observation-based tasks ranging from 85.3% to 100.0%, and in general, after participants were instructed to refer to the IFU, the rate of use errors was reduced. Conclusions: This human factors study demonstrated that the zilucoplan PFS is safe and effective for intended users, including patients with gMG and their caregivers. [ABSTRACT FROM AUTHOR]

Introduction: In operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents. Methods: Thus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques. Results and discussion: The findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p < 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p < 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability. [ABSTRACT FROM AUTHOR]

Background Objectives Subjects Methods Results Conclusions and Clinical Importance Minimizing harm from antimicrobials requires use of the narrowest spectrum drug, at an effective dose for the minimum effective duration. Many prescribers are not currently following these guidelines. To address suboptimal antimicrobial use, the underlying reasons must be understood.To identify factors influencing choices of antimicrobial drug, dose, and duration for companion animals.Twenty‐two veterinarians treating companion animals in Australia. Diversity of participants was deliberately sought.Semistructured interviews were conducted online. Two case studies were discussed, and then a range of broader questions was posed. Transcripts were analyzed thematically, using an inductive approach.Few participants chose guideline‐concordant management for the case studies. Prescribing choices were influenced by a complex array of factors associated with the clinical case, pet owner, drug, veterinarian, veterinary colleagues, and external factors. Key factors driving broad‐spectrum antimicrobial use included a sense of safety, habit, ease of administering the drug (especially in cats), pharmaceutical marketing, and the self‐perpetuating dispensary cycle. Many participants were concerned about antimicrobial resistance, but insufficiently informed about how to minimize this risk. Several participants believed that longer duration of treatment and ensuring patients finish a predetermined course would decrease the risk of antimicrobial resistance and improve clinical outcomes.Veterinarians are engaged with the concept of antimicrobial stewardship, but face numerous practical barriers and require more information. In particular, improved education is needed on enhancing patient safety by minimizing both spectrum of activity and duration of treatment, and dispelling myths about “finishing the course.” [ABSTRACT FROM AUTHOR]

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, cloud computing, sensors, and communications with the work from many varied disciplines (e.g., civil engineering, architecture, etc.), mixed with aspects of environmental and business sustainability, makes the management of these projects really challenging. Reports forecast a scarcity of qualified candidates, given this complexity and the growth of activity in SC projects. The European project SMACITE has addressed the requirements of the qualification of an ICT workforce with an analysis of multiples sources of information from the labor market, feedback from involved stakeholders, and the literature. The goal was the development of two occupational ICT profiles as a reference for training and for the availability of candidates for job vacancies. The result is two ICT role profiles for engineers and technicians, mapped with the European skills frameworks ESCO and EN16234. The profiles determined the whole set of requirements, including not only the technical areas and soft skills, but also additional technical areas and sustainability and managerial skills and the analysis of different sources of information. Our work has also determined which existing ESCO occupations are similar to the two reference profiles, so they are better adapted to SC projects. The training activities of SMACITE have also suggested the amount of training expected for a varied sample of candidates who want to be qualified for SC projects. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

AbstractDrones are likely to enter social spaces in the foreseeable future. Novel Human-Drone Interactions (HDI) will foster beyond typical drone-operator interaction, posing new human factors challenges. However, the specific focus areas for HDI research remain unclear. This study conducts 11 expert interviews to identify potential use cases and human factors challenges for HDI in public spaces. Initial drone use cases include emergency response and delivery scenarios, where the general public may interact as recipients and bystanders, each posing unique challenges. Uncertainty, stemming from a lack of awareness, emerges as a significant human factors concern, impacting perceived risk. Addressing this uncertainty, especially in recipients, may involve refining drone behaviour, physical appearance, and interface design. The challenges identified in this study lay the groundwork for future HDI research in public spaces. [ABSTRACT FROM AUTHOR]

Effective training programs are essential for safely integrating exoskeletons (EXOs) in industrial workplaces. Since the effects of wearable systems depend highly upon their proper use, lack of training of end-users may cause adverse effects on users. We reviewed articles that incorporated training and familiarization protocols to train novices on proper operation/use of EXOs. Findings showed variation in training methods that were implemented to train study participants in EXO evaluation studies. Studies also indicate that multiple (up to four) sessions may be needed for novice EXO wearers to match movement patterns of experts, and training can offer benefits in enhancing motor learning in novices. Biomechanical assessments and ergonomic evaluations can be helpful in developing EXO-specific training protocols by determining training parameters (duration/number of sessions and task difficulty). Future directions include development of personalized training approaches by assessing user behavior/performance through integration of emerging sensing technologies. Application of simulators and use of data-driven approaches for customizing training protocols to individuals, tasks, and EXO design are provided along with a comprehensive training framework. Discussed elements in this article can be helpful to exoskeleton researchers in familiarizing novice users to EXOs prior to evaluation, and to practitioners in developing protocols for training workforce. [ABSTRACT FROM AUTHOR]

Decision‐making in emergency situations, such as those faced by fire, police, and health service personnel, presents unique challenges due to the high‐stakes and time‐pressured environment. Here we aim to better understand what emergency responders regard as constituting 'good' and 'bad' decisions in emergency situations. We administered a modified Delphi study, eliciting opinions from decision‐makers across all these sectors towards consensus around the key elements of good and bad decision‐making. Participants were first asked to define what makes a 'good' and a 'bad' decision, and subsequently to identify the top five most important elements of each. While consensus was not found, important insights were identified that can assist improve the standard of decision making at all levels of emergency response and management. We observed (i) a lack of a common understanding between participants of what a decision is, and how a decision differs from pre‐decision and postdecision components; (ii) responses varied according to whether a free text description or the identification of separate elements was requested; (iii) respondents valued 'goodness' across different and at times unrelated components a scaled measure of decision quality as opposed to a binary evaluation of 'rightness'; and (iv) pre‐ and postdecision elements are considered more important than the decision itself when determining the quality of a decision. To address the issues highlighted by the study we recommend improvements in training, and improvements to organization doctrine related to decision making, risk tolerance, assessment of decision quality and development of intent‐based or principle‐based operational guidance. Ultimately, incident controllers from all services must remain cognizant that they will face considerable scrutiny if they cannot explain how they arrived at the decision they made. [ABSTRACT FROM AUTHOR]