Working in teams provides several advantages to dynamic, data-driven domains, but can also add a layer of complexity to operations. There have been several reviews on teams and team performance analysis; however, there has been limited work in the last five years that has examined micro- and macro-level factors that affect overall team performance. Previous research has proposed a framework within healthcare characterizing team characteristics into three categories: individual contributions, team processes, and organizational structures. However, it is still unclear how new emerging topics in the team literature fit within this framework. Here we provide more specific definitions of the three categories proposed and conduct a review that builds on this framework by adding topics identified from the current literature. To this end, we carried out a systematic search of the human factors literature to examine the research on team performance across various domains from the past five years centered. We then propose ideas for future research on team performance.
Teamwork and collaboration form the cornerstones of organizational performance and success. It is important to understand how the attention allocation of team members is linked to performance. One approach to studying attention allocation in a team context is to compare the scanpath similarity of two people working in teams and to explore the link between scanpath similarity and team performance. In this study, participants were recruited to work in pairs on an unmanned aerial vehicle (UAV) task that included low and high workload conditions. An eye tracker was used to collect the eye movements of both participants in each team. The scanpaths of two teammates were compared in low and high workload conditions using MultiMatch, an established scanpath comparison algorithm. The obtained scanpath similarity values were correlated with performance measures of response time and accuracy. Several MultiMatch measures showed significant strong correlations across multiple dimensions, providing insight into team behavior and attention allocation. The results suggested that the more similar each team member’s scanpath is, the better their performance. Additional research and consideration of experimental variables will be necessary to further understand how best to use MultiMatch for scanpath similarity assessment in complex domains.
<p>In the era of advancing neurotechnology, the emergence of brain-brain interfaces (BBIs) has opened up new frontiers in human communication and connectivity. BBIs are direct communication pathways between the brain of one subject and the brain of another subject that allow the users to extract and exchange information. Compared to traditional biomedical devices, brain-brain interfaces were originally more invasive between only two people; however, emerging research paves the way for new non-invasive interfaces between two or more brains. As this technology continues to grow with no current regulatory framework and cognitive connections between individuals become a tangible reality, a crucial question arises: What are the ethical implications of this remarkable technology? In this paper, we embark on a journey of ethical reflections, delving into the intricate considerations and moral dilemmas surrounding BBIs. We examine the fundamental values at stake, such as autonomy, privacy, and the potential for misuse, while drawing insights from established ethical frameworks. Analyzing the risks of this technology presents similar results, where we observe risks of safety from invasive neurosurgery, in addition to privacy-related risks upon the misuse of such an information network. Through a comprehensive analysis, we seek to shed light on the complex interplay between cognitive connections and ethical responsibilities, paving the way for informed decision-making and responsible development of this groundbreaking field. Since the maliciousness of this interface highly depends on its uses, we conclude that its uses should be currently restricted to the medical field, where it is needed the most. We also provide additional recommendations and future work aiming to pave the way to referenceable standards and frameworks that prevent the exploitation of BBI users and protect their privacy.</p>
This research examines the impact of social equity on energy consumption. We constructed a digital twin for residential energy consumption by enriching the synthetic population with real-world surveys and feeding them with other environmental and appliance data to the energy modeling framework. We analyzed household hourly energy consumption data from Albemarle County and Charlottesville City in Virginia, USA, for the year 2019. We used clustering analysis to identify patterns in social equity and energy consumption. The results demonstrated the impact of different residential attributes on energy poverty. Statistical analyses, including ANOVA and Chi-Squared tests, were conducted to test for significant differences between racial groups in quantitative and categorical variables. The study found that race is significant in determining the location and quality of housing. People of color often live in areas with higher pollution and less access to green spaces. Additionally, income levels and the age of the house are influential factors in determining energy efficiency. Future work should focus on collecting and analyzing data at the country level and using qualitative data collection methods to gain a more comprehensive understanding of social equity issues concerning energy consumption. Overall, this study provides valuable insights into the relationship between different residential attributes and energy consumption, which can inform policy development to promote more equitable and sustainable communities.
<p>In the era of advancing neurotechnology, the emergence of brain-brain interfaces (BBIs) has opened up new frontiers in human communication and connectivity. BBIs are direct communication pathways between the brain of one subject and the brain of another subject that allow the users to extract and exchange information. Compared to traditional biomedical devices, brain-brain interfaces were originally more invasive between only two people; however, emerging research paves the way for new non-invasive interfaces between two or more brains. As this technology continues to grow with no current regulatory framework and cognitive connections between individuals become a tangible reality, a crucial question arises: What are the ethical implications of this remarkable technology? In this paper, we embark on a journey of ethical reflections, delving into the intricate considerations and moral dilemmas surrounding BBIs. We examine the fundamental values at stake, such as autonomy, privacy, and the potential for misuse, while drawing insights from established ethical frameworks. Analyzing the risks of this technology presents similar results, where we observe risks of safety from invasive neurosurgery, in addition to privacy-related risks upon the misuse of such an information network. Through a comprehensive analysis, we seek to shed light on the complex interplay between cognitive connections and ethical responsibilities, paving the way for informed decision-making and responsible development of this groundbreaking field. Since the maliciousness of this interface highly depends on its uses, we conclude that its uses should be currently restricted to the medical field, where it is needed the most. We also provide additional recommendations and future work aiming to pave the way to referenceable standards and frameworks that prevent the exploitation of BBI users and protect their privacy.</p>
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