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A community of practice (COP) can offer learning and support as a group of people who come together to share concerns, best practices, or new knowledge about some shared interest or passion. However, creating or joining a COP may present challenges, especially for those whose networks are relatively undeveloped. In this article, we define a COP and share how vicarious learning and crowdsourcing, as pragmatic, relational, and information-gathering processes, offer important benefits to teaching and learning COPs. After discussing how vicarious learning and crowdsourcing can be extended within a COP, we offer specific theory-to-practice learning ideas and suggestions. We end the article with brief insights for other management educators about our own COP experiences. 

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase and a simple neural network for the deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to improve tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and costs. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, a speed increase by 37.56%, and identity switches decreased by 46.81%. 

“Was making full professor a Pyrrhic victory?” is the question which guides this reflective personal narrative on attaining the rank of Full Professor, the first and only Black woman full professor in a STEM discipline at Florida A&M University (FAMU), a large Historically Black College and University (HBCU). In assessing the costs, the author expresses concrete experience of institutional trauma, academic betrayal, burnout, and structural violence along with values, motivations involved in persisting on her journey. The author is also the principal investigator of an NSF ADVANCE award (EES-1824267) and is directly involved with leading institutional transformation efforts. 

Abstract This paper explores the critical role of male faculty members at a Minority Serving Institution (MSI) in promoting cultural humility and using a hermeneutical lens to achieve gender equity. Cultural humility is the awareness that no gender/discipline/race/religion is the norm and that everyone belongs at this MSI. Hermeneutics is a philosophical interpretation process that may help to understand the social identity of Black women in STEM/SBS. Examining Black women in STEM/SBS at a large MSI extends the research of intersectionality by focusing on a context where Black women students are the majority, and Black women STEM/SBS faculty are the minority. The authors provide self-reflections practicing cultural humility using a hermeneutical lens to avoid contributing to the trauma Black women in STEM/SBS face at this MSI. 

Food insecurity impacts the lives of 7.6 million U.S. adults aged 60 and older and is linked to numerous life challenges. This study examined the nature of food insecurity among community-based participants ≥65 years in a north Florida county and conceptualized food insecurity as encompassing the lack of food and individual adaptability. Thus, food insecurity was measured using three dependent variables: (1) worrying that food would run out, (2) cutting meal size or skipping meals, and (3) food not lasting. Logistic regression revealed that older participants, those with better-perceived health status, and those who were confident that they could find solutions to their problems had lower odds of reporting food insecurity. However, respondents who lived in low-income, low-access zip codes and those who received food assistance were more likely to report food insecurity. To improve outcomes and reduce healthcare disparities, solutions to food insecurity must vary in focus and approach. 

Minimal leisure research focuses exclusively on Black women, with even fewer studies examining outdoor recreation experiences among active participants. The purpose of this study was to explore the outdoor recreation experiences of Black women in their own words. Convenience sampling was used to recruit Black women employed at a university located in the U.S. Southeast who have frequented beach areas along the Florida Panhandle. Questions asked related to recreation experi- ences, including the motivation and barriers to visiting beach areas, participation in leisure activities, and history of their first beach visit. Motivation for visiting beaches included social or solo traditions, informal self-care retreats to disengage and reflect through writing and meditation, and access to local amenities. Barriers to visiting coastal beaches ranged from work and personal life commitments, lack of discretionary funds, and safety concerns from the fear of being a Black woman traveling alone or through segregated areas. In addition, this study addressed stereo- types of leisure experiences among Black women that included hair care and rec- reating in spaces typically occupied by non-Black beachgoers. The importance of Black women’s leisure surfaced as a theme as respondents connected beach recrea- tion to overall health, well-being, and the idea of pleasure being intertwined with work rather than being separate or in conflict with their leisure time. Using a Black feminist standpoint, the stories Black women tell about themselves in leisure are a rich resource to provide insight into this topic. 

AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps.