Tidewater Community College

Abstract Biomass sorghum [ Sorghum bicolor (L.) Moench] is an alternative biofuel feedstock to maize [ Zea mays (L.)]. One advantage of biomass sorghum over maize is relatively lower nitrogen (N) requirement and its ability to tolerate late planting or fit into double cropping systems. To evaluate the most economic rate of N (MERN) for biomass sorghum in the Mid‐Atlantic region, a 3‐year (2017, 2018, and 2019) field trial was conducted at two locations (Holland and Hare) in Southeastern Virginia. Treatments were two planting dates (early [EP] versus late [LP]) and seven N rates (0, 56, 112, 168, 224, 280, and 336 kg ha −1 ) conducted in six environments. Data on dry matter yield of biomass sorghum, plant height, stem diameter, susceptibility to foliar diseases, and stalk lodging were recorded. Between the two planting dates, EP had 52%, 19%, and 16% higher dry matter, plant height, and stem diameter, respectively, compared to LP. However, LP exhibited lower disease pressure in four of 6 site‐years and lodging severity likely in response to weather conditions. Nitrogen rates between 112 and 224 kg ha −1 significantly improved dry matter, plant height, and stem diameter, with lower disease and lodging severity. At the MERN of 123 kg ha −1 , dry matter yield (20.66 Mg ha −1 ), plant height (454 cm), disease (61%), and lodging severity (34%) were least as compared to other treatments. Application of N at 123 kg ha −1 can provide the highest economic return while reducing the potential for N loss in the Mid‐Atlantic region.

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.

This paper discusses the development and implementation of two new topics, graphical visualization and robotics, in 'introduction to engineering' course. Sample of problems and hands-on lab activities illustrate new approaches to introduce students to graphical techniques and robotics through excel software and scope of laboratory experiences, respectively. The course trains students how to use excel tool to graph and interpret the data through visualization and introduce them to simple computer programming for path planning and navigation of robots. The initial observations and results are in favor of promoting visualizations and concepts of robotics.

Recent large language models (LLMs) such as GPT, Bard and Claude have achieved impressive progress in comprehending and generating human language. We explore the potential for LLMs to automate markup of semi-structured texts into HTML, DITA, and other related languages. Automated markup could make it possible to move documents into XML at much lower cost. Currently, there is no standard benchmark for evaluating automatic markup systems. Establishing a benchmark for the understanding and generation of structured documents and markup languages can drive innovation, standardize evaluations, identify algorithm strengths and weaknesses, clarify the State of the Art and foster interdisciplinary collaborations. This paper introduces an early version of a benchmark for this purpose.

This survey of parents of children ages 2–18 (n = 240) investigated children’s frequency of access to public library materials prior to (T1), during (T2), and predicted after (T3) the COVID-19 pandemic. Frequency of access to public library materials was compared by demographic and other factors. Repeated measures ANOVA showed a significant difference T1-T2 and T2-T3. Significant interactions were found for school environment and frequency of access over time, with online and hybrid students showing a significant decrease in access from T1 to T2 and an expected significant increase from T2 to T3. Results suggest that children’s reading behaviors were impacted, thus academic performance may have been impeded.

Abstract Tobacco thrips (Frankliniella fusca Hinds) can reduce peanut (Arachis hypogaea L.) yield if not controlled in many instances in North Carolina and Virginia. Systemic insecticides are often applied in the seed furrow at planting to suppress tobacco thrips and protect peanut yield. Foliar sprays of acephate [N‐(methoxy[methylsulfanyl]phosphoryl)acetamide] are often made regardless of the insecticide applied at planting. Imidacloprid [(NE)‐N‐[1‐([6‐chloropyridin‐3‐yl]methyl)imidazolidin‐2‐ylidene)nitramide] plus fluopyram [N‐(2‐[3‐chloro‐5‐(trifluoromethyl)pyridin‐2‐yl]ethyl)‐2‐(trifluoromethyl)benzamide])] is registered for use in peanut but information in the peer‐reviewed literature is limited regarding its effectiveness in suppressing tobacco thrips and whether or not a foliar application of acephate is needed when this product is used. Peanut injury caused by tobacco thrips was lower when phorate [diethoxy‐(ethylsulfanylmethylsulfanyl)‐sulfanylidene‐λ5‐phosphane] or imidacloprid were applied alone compared with imidacloprid plus fluopyram in 16 trials conducted in North Carolina and Virginia from 2014 to 2020. However, all insecticides applied at planting protected peanut from injury caused by tobacco thrips well enough to prevent yield loss compared with nontreated peanut. Although acephate decreased visible injury caused by tobacco thrips, yield was not affected by acephate regardless of the systemic insecticide applied at planting. The results from these trials indicate that imidacloprid plus fluopyram offers adequate suppression of tobacco thrips to protect yield. Although acephate suppressed tobacco thrips, yield was not affected by acephate.

The scope of policy issues surrounding emergency management remains inundated with several social and political challenges. There are specific events that cause added strain which make certain time periods for examining levels of resiliency relevant. The Deepwater Horizon oil spill represents a large-scale, technological disaster. Not only was there a loss of human life, but several community impacts also exist with the oil having spewed out into the water. Still, what impacts-ie, attitudes, beliefs, and behaviors-are associated with varying population groups that may be more vulnerable than others? Examining the connection between social capital and political trust is, thus, paramount as prior studies have documented that if resiliency networks are disrupted, there will be a lasting impact upon the community. This research extends the limited understanding of case-based perceptions of social and political vulnerability regarding disaster responsibility.

Community college presidents are currently retiring at rapid rates, and this turnover in leadership is expected to continue. As these important post-secondary institutions face the loss of senior-level leaders, women in mid-career positions are preparing to fill the gap in the leadership pipeline. Although previous studies have examined the leadership challenges faced by women leaders, the focus has been on presidents and vice presidents. This study fills a gap in the literature by examining the leadership experiences of women in mid-level positions - specifically deans and directors - and to identify their unique employment mobility challenges. The purpose of this qualitative, phenomenological study was to examine women leaders in mid-level positions and their perceptions of the personal, professional, and organizational challenges unique to them as leaders in urban community colleges. This study provides community college leaders and practitioners a better understanding of the importance of more effective navigational tools, better leadership training and development, and inclusive and intentional leadership funnels and hiring practices for women mid-level leaders. Ten participants from three community colleges discussed perceived challenges through in-depth, semi-structured interviews, and they described how they have attempted to navigate these challenges. Five major themes were identified: (a) leadership progression, (b) work-life balance, (c) mentorship and professional development, (d) communication, and (e) institutional climate and organizational structure. Recommendations for leaders include: providing opportunities and support for mentorship for women in mid-career positions; creating and funding professional development opportunities; and assisting women leaders with doctoral degree completion.

The current study aimed to assess perinatal depression (PD) screening and treatment practices of obstetrical health care providers. Retrospective record reviews (n = 557) evaluated the PD screening, referral, and treatment practices at an Obstetrician/Gynecology practice. This study assessed the frequency of screening for PD, rates of elevated Edinburgh Postnatal Depression Scale (EPDS) scores, treatment recommendations, demographic correlates, and predictors of elevated EPDS scores. PD screening completion rates were: 60.1% (intake), 35% (glucola test), and 85.5% (6-week follow-up). Rates of clinically elevated EPDS scores were: 18.21% (intake), 17.43% (glucola test), and 13.00% (6-week follow-up). Correlates of clinically elevated EPDS scores at intake and 6-week follow-up were history of depression, history of anxiety, and young age. History of depression and anxiety were associated with an increased likelihood of having a clinically significant EPDS score at intake. Intake EPDS score and history of depression were associated with an increased likelihood of having a clinically significant EPDS score at 6-week follow-up. Obstetric/gynecology providers should screen for perinatal depression at every obstetrical appointment. It is important to thoroughly assess history of depression and anxiety. Education and training for health care providers and perinatal women may improve the mental health experience of perinatal women. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Thomas Stout is an associate professor of Electromechanical Controls Technology at Tidewater Community College in Chesapeake, Virginia. He has worked in industrial maintenance, mechatronics and safety. He earned his BS degree from Old Dominion University in 2004 and his MS in Electronics Engineering from Norfolk State University in 2007. He served 20 years in the United States Navy working on aircraft and surface ships.