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Featured Application: This framework can be applied to evaluate plant density and health in different types of green stormwater infrastructures, agriculture land, and forests. Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R2 value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery. [ABSTRACT FROM AUTHOR]

LIDAR sensor is capable of recording traffic data including the number of passing vehicles, pedestrians, and bicyclists, the speed of vehicles, and the number of conflicts among different road users. As part of intelligent mobility and in order to collect real-time traffic data and investigate the safety of different road users, particularly motorized vehicles and pedestrians in different approaches of the intersection, a LIDAR sensor was installed at Cold Spring Ln - Hillen Rd intersection in Baltimore city. The intersection was chosen based on substantial vehicle traffic, proximity to Morgan State University, and the history of recent crashes. One of the efficient capabilities of the installed LIDAR sensor is recording the "Post Encroachment Threshold (PET)" between two vehicles, vehicle-pedestrian, and vehicle-bicyclists. The paper aims to concentrate on vehiclevehicle (including car-car, car-bus, car-truck, and bus-truck) conflicts. The frequency and severity of conflicts were analyzed and the results highlighted that 857 conflicts were recorded during a 5-month time interval. The frequency and severity of conflicts were investigated by three methods including leading-following vehicles, right-turn and left-turn movements, and in different phases of traffic signal. The critical zones were recognized, and K-means clustering was performed on severity of conflicts subcategories while selecting the optimal number of clustering. The significant error of each subcategory was specified, and the error values were compared with Bonferroni test error values. The results demonstrated that the error values from k-means clustering are consistent with the error values from the Bonferroni test. Furthermore, the statistical analysis showed that: 1) In terms of conflict frequency and severity, the eastern and middle zones are crucial 2) all errors are consistent with 95% confidence interval, and 3) the severity of conflicts by different movements has a significant correlation with speed at conflict point, hourly time interval, weather, and the entrance vehicle volume to each zone. [ABSTRACT FROM AUTHOR]

Much is told regarding the need for greater diversity in the biomedical research workforce in terms of race, ethnicity, and socioeconomic status. However, there are few evidence-based models that are tested and can have significant effects in this regard. Thus, there is a need for development and evaluation of innovative models that may help train a more diverse biomedical research workforce. In this study, we provided the rationale, conceptual model, and preliminary evaluation of a program called "A Student-Centered Entrepreneurship Development (ASCEND)". This training program was designed, implemented, and evaluated between 2017 and 2020 at Morgan State University, Baltimore, Maryland, United States. The program's conceptual model is based on four stages: Attraction and Inspiration, Ideation and Innovation, Research Implementation, and Career Growth. Results of the comparative survey between 50 students who participated in ASCEND and 86 non-member controls showed an increase in science identity, academic self-concept, science self-efficacy, and peer support. The only domain that did not show a larger increase in participants in our program compared to controls was social self-concept. In addition, a total of 59 students submitted 48 research concepts, and 16 undergraduate student projects were funded. Of participants in the Health Research Concepts Competition, 39 students graduated, and 13 were pursuing graduate programs in STEM fields at the time of evaluation. The number of research projects and trainees who started a graduate degree were also reported. The ASCEND training model fosters an entrepreneurial mindset among undergraduate students. Such a program might be effective in diversifying the biomedical research workforce. While this preliminary evaluation indicates the efficacy of the ASCEND model, there is a need for further long-term and multi-center evaluations with the trainees' research productivity and receipt of independent funding as outcomes. [ABSTRACT FROM AUTHOR]

For many years, Dr. Johnson served on the Management Board for the Consortium for Risk Evaluation with Stakeholder Participation that provides expert advice and technical assistance to the Department of Energy (DOE) to deal with their nuclear weapons legacy wastes. Since retiring from Howard University, Dr. Johnson has continued his commitment to environmental education by being a Visiting Professor at Morgan State University, and Chair of the Maryland Association of Community Colleges that oversees 16 community colleges. Dr. Johnson reiterated the importance of putting the environmental risk questions, and the need for a STEM education, within the context of their environment. James H. Johnson Jr. - Listen and find commonalities. [Extracted from the article]

In this article, we explore the experiences and self-reflections of faculty as CANVAS Ambassadors at Morgan State University, a Historically Black University in Baltimore, Maryland, during the COVID-19 global pandemic. The university transitioned to remote/online instruction using the CANVAS™ learning management system in March of 2020. The findings include several themes and lessons: the positive impact on colleagueship when faculty train faculty, the ability to close technology gaps, and the opportunity to broaden overall faculty teaching and learning development at the university. The paper suggests an example of transferable learning experience to other universities and suggests future research plans in preparation for unpredictable teaching and learning in education. [ABSTRACT FROM AUTHOR]