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High-School-Age Youth Serving as “Emotion Coaches” for Young Children in the Early Grades: A Feasibility Study of a Cross-Age Approach to Social and Emotional Learning
No full textWhile the benefits of cross-age programming within youth have been understood for decades, such programs are rarely employed with curricula specific to social and emotional learning (SEL) or with a wide age range between the two groups of youth. This article describes the findings of a pilot implementation and feasibility study of the Emotion Coaches program, which sought to explore whether the conceptual and pragmatic challenges involved in bringing early childhood students (i.e., preschool to second grade) together with adolescent teachers delivering an SEL curriculum could be overcome. Findings from the pilot evaluation suggested that middle-school-age students are likely too young to beneficially assume the role of an Emotion Coach. In contrast, through 22 interviews with youth, experts in cross-age programming, and school leadership, the feasibility study exploring high school students in this role suggested that the incorporation of long-term training, additional supports such as mental health services, incentives for the teens such as pay or credit for civic engagement requirements, and continued partnerships with universities and extension schools could help insure sustainability of the program as well as positive outcomes for both the younger and older youth involved
Detection and classification of invasive Callery pear from multisource satellite imageries in Google Earth Engine
No full textAssessing the spatial distribution of invasive species is a critical component for establishing baselines to examine the rate of spread, documenting key areas where negative impacts on ecosystems can be mitigated, and developing sustainable management strategies. Callery pear (Pyrus calleryana Decne.; PC) is a rapidly spreading invasive woody plant species in the eastern United States (U.S.). The incursion of these wild-type trees into urban, peri-urban, and rural landscapes, which have escaped from transplanted clonal cultivars, poses complex management challenges for land managers and hampers ecosystem function by competing with native plant species and altering the forest environment. We integrated winter season multispectral (Sentinel-2) and radar (Sentinel-1 and L-band) imageries with static terrain imagery to map PC distribution in four southeastern U.S. states. From these imageries, we derived spectral, textural, and elevational indices and used them with field-collected PC locations for training random forest (RF) and support vector machine (SVM) classifiers in Google Earth Engine. We also created four scenarios to sequentially add and determine the usefulness of input data in classifying PC. The scenario comprising all the imageries plus their derived indices was most accurate for RF (Accuracy [Low CI, Upper CI] = 92.6% [90.62, 94.34]) and SVM (89.6% [87.45, 92.00]), with the terrain and L-band radar indices identified as the most important inputs for discriminating PC from other classes. Accuracy increased by 10.5% for RF and 5.2% for SVM for the best scenario compared to using bands and derived indices from Sentinel-2 imagery alone. Our final classification model identified a relatively greater PC spread in the northeastern part of our study area, eastern Tennessee. Combining L-band radar in classification scenarios enhanced PC classification. Our approach demonstrates the utility of several remote sensing images in mapping and monitoring the distribution of invasive plant species on a large scale
False Information Attack Detection in a Connected Vehicle Environment With Quantum Inspired Long Short-Term Memory
No full textWireless communication Systems enabling Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) data exchange, supported by technologies such as Cellular-V2X (C-V2X), has introduced significant cybersecurity challenges, particularly the threat of false information attacks that can compromise traffic safety and efficiency. In this thesis, the author focuses on identifying false information cyber-attack on V2I, in which vehicles are sending Basic Safety Messages (BSMs) to a Roadside Unit (RSU) and RSUs are collecting, processing and communicating data back to Connected Vehicles (CVs) to support different CV applications. Despite advances in anomaly detection using Long Short-Term Memory (LSTM) networks, these models often struggle with computational efficiency and performance under real-world constraints. Addressing this issue, this study investigates the potential of Quantum Long Short-Term Memory (QLSTM) as a more efficient and effective alternative for detecting false information attacks in CV environments. The objective of the study is to evaluate and compare the performance of classical LSTM and QLSTM models in terms of accuracy, classification confidence, and computational efficiency. Unlike previous studies that primarily rely on extensive data training, the author focusses on the models\u27 robustness and sensitivity across varying classification thresholds to assess their adaptability in real-time cyberattack detection. Key findings of the study indicate that QLSTM consistently outperforms LSTM across different classification thresholds.
At the classification threshold of 0.3, QLSTM achieved 96.74% accuracy, 98.67% recall, 95.02% precision and an AUROC of 97.84%, respectively, compared to LSTM’s 93.00% accuracy, 98.36% recall, 90.22% precision and 93.16% AUROC, respectively. At the classification threshold of 0.5, QLSTM further improved its accuracy to 97.8 4%, 98.67% recall, 96.45% precision, and 97.84% AUROC, outperforming LSTM, which reached 95.40%, 98.36% recall, 93.19% precision, and 95.16% AUROC, respectively. Additionally, QLSTM demonstrated a 99% reduction in trainable parameter count and memory usage, requiring only 195 parameters (0.72 KB) compared to LSTM’s 19,777 parameters (77.21 KB). These results highlight QLSTM’s ability to achieve a high detection accuracy while significantly reducing computational cost.
The findings from the study have important implications for the future of CV cybersecurity, demonstrating that quantum-inspired models offer a promising avenue for enhancing real-time anomaly detection. By leveraging quantum principles, QLSTM can provide a more scalable and efficient solution for securing next-generation vehicular network against cyber threats
LightLink: Visible Light Communication for Batteryless Devices With Variable Computational Load
No full textVisible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables
BIM-based Automated System for Carbon Emissions and Construction Costs Estimation (BAS-CC) in Modular Design and Production
Get PDFModular construction adopts a manufacturing-like construction process, enhancing the management of embodied carbon emissions in production and construction phases. However, estimating these emissions presents challenges due to the involvement of diverse stakeholders across various lifecycle stages. Additionally, cost considerations often influence design choices and material selections in modular constructions, neglecting the impact of embodied carbon emissions. To address these challenges, we present a proof-of-concept for an automated Building Information Modeling (BIM)-based system to estimate both carbon emissions and construction costs. This system automates the calculation of embodied carbon and cost implications of different modular designs and materials, enabling informed decision-making. By integrating Social Carbon Costs and carbon taxes, it assigns a monetary value to carbon emissions, aligning sustainability with economic feasibility. This developed system promotes sustainable construction practices within the modular construction industry by providing a data-driven framework, ultimately empowering stakeholders to make more environmentally responsible and economically sound decisions
Connecting with Millennials: A Guide for Donor Engagement
Get PDFMillennials are emerging as a vital donor demographic, yet many resource-limited nonprofits, including Clemson Community Care (CCC), struggle to effectively engage this generation. This applied thesis project investigates how CCC can build meaningful connections with millennial donors by exploring nonprofit awareness, donor motivations, communication expectations, content preferences, and common barriers to giving. Using qualitative data gathered from two focus groups of millennial participants, this study reveals that millennials represent a diverse and distinct audience with unique expectations for nonprofit communication. Grounded in public relations theories of stewardship and organization-public relationships (OPR), the research highlights the importance of flexibility and autonomy in building trust and long-term engagement with millennial donors. Participants indicated a desire for personal and community-oriented connections, user-friendly digital donation systems, and control over how and when they receive communications. The findings informed the development of a millennial donor engagement guide tailored specifically for CCC, offering actionable strategies that align with the organization’s limited resources. By implementing the recommendations from this guide, CCC can enhance millennial donor retention, foster stronger community ties, and ensure the sustainability of its donor outreach efforts. This study contributes to the broader conversation on nonprofit communication and engagement strategies, offering practical insights for organizations aiming to cultivate long-term relationships with a new generation of donors
Berm-Leveling Machine for Peach Orchards: Incorporation of Tree-Sensing Feature and Profitability Study
No full textRoot collar excavation extends the productive life of peach trees in Armillaria root rot-infested soil, but the process requires trees to be planted on berms. Berm-planted peach orchards restrict the mobility of workers and machinery and can lead to increased erosion in furrows on either side of the berms. The current inter-tree berm-leveling machine depends on the driver\u27s command to dodge the peach trees. The objectives of this thesis were to (1) integrate the feeler, infrared, and LiDAR-based tree sensing systems in the berm-leveling machine to make the soil excavating head\u27s movement independent, (2) measure the impact of finger weeder-based mechanism in collapsing the standing berm mound around peach tree trunks, and (3) project the impact of owning and operating a berm-leveling machine on the Net Present Value (NPV) of ARR-infested peach orchards.
The feeler, Single-Infrared Sensing (S-IRS), and Dual-Infrared Sensing (D-IRS)-based tree detection systems were installed into the berm-leveling machine and individually tested over experimental berms with stakes placed for peach trees. The sensing approaches were analyzed for berm-leveling precision, with the uniformity of the remaining berm mound’s length serving as a key indicator. The results of Fligner-Killeen and Levene’s variance tests suggest a practically equal degree of berm-leveling precision. The LiDAR sensor did not work continuously during field trials, inhibiting its data collection. The finger weeder mechanism did not create an immediate visible impact on the standing berm-mound volume during the field tests, and its quantitative assessment yielded inconclusive results.
The berm-leveling machine\u27s fixed and variable costs were estimated and used to estimate the impact of owning and operating it in ARR-infested peach orchards. The average NPV of hypothetical treatment orchards, where inter-tree berms were leveled using the berm-leveling machine, and conventional orchards, where berms were left in place, were simulated using Monte Carlo Simulation (MCS). The results indicated that adoption of the berm-leveling machine is less profitable, with the viability gap shrinking upon lower initial investment cost of the machine, lower peach yield, lower peach price, higher discount rate, early ARR outbreak, or higher ARR-caused tree mortality.
Achieving concurrent leveling of the standing berm mounds left around the tree trunks can further enhance the value proposition of the berm-leveling machine and improve its profitability potential. To this end, evaluating stacked multiple finger weeders or larger tip finger weeder attachments offers one route of design improvement