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0818181712
No full texthttps://digitalscholarship.tnstate.edu/m-chamberlain-landscapes/1000/thumbnail.jp
Impacts of altitude on plant green leaf, fresh litter, and soil stoichiometry in subtropical forests
Get PDFBackgroundEcological stoichiometric characteristics of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) serve as crucial indicators of nutrient cycling and limitation in terrestrial ecosystems. However, our current understanding of stoichiometric characteristics in subtropical forests and their response to different climate conditions is still limited.MethodsWe selected six altitudes ranging from 700 m to 1,200 m to simulate different climate conditions of an evergreen broadleaf forest in Wuyi Mountain, Fujian Province, China. We investigated C, N, P, and K stoichiometry and homeostasis in the green leaves, newly senesced leaf litter (fresh litter), and soil of this forest.ResultsLeaf P and K levels showed a decline with increasing altitude. Notably, the stoichiometric ratios in different components exhibited a bimodal distribution along the altitudinal gradient. Additionally, a decline trend of N resorption efficiencies was observed as altitude increased. Moreover, weak homeostasis was observed in P and K in green leaves. These findings highlighted the significant impact of altitude on the stoichiometry in evergreen broadleaf forest. This study also contributed to our understanding of the nutrient cycling mechanism and plant growth strategies of evergreen forests under different climate conditions
Tigritude: A Journal of Student Writing 2024 - 2025
No full texthttps://digitalscholarship.tnstate.edu/tigritude/1000/thumbnail.jp
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No full texthttps://digitalscholarship.tnstate.edu/m-chamberlain-stones/1026/thumbnail.jp
0914181035c
No full texthttps://digitalscholarship.tnstate.edu/m-chamberlain-landscapes/1002/thumbnail.jp
Microgrid Control Using Reinforcement Learning Operating in Grid Tied and Islanded Modes
No full textThe growing demand for Distributed Energy Resources (DERs) has caused increased work in the development of Microgrids. It has been shown to have many economic benefits for utilities and consumers, reduce the amount of greenhouse gas emissions, allow user monitoring and controlling with local utilities, reduce strain during peak load events, and work in standalone operations. Unfortunately, Microgrids can pose threats to utilities and consumers by voltage instabilities, injection of harmful harmonics that can reduce Power Quality (PQ), and dependency on acceptable environmental conditions. These challenges can be resolved by implementing controllers that can monitor and control for voltage stability, improve PQ, and optimize the generation of power from the DERs. This work developed a Reinforcement Learning (RL) Smart Controller to monitor and control an 8 MVA Microgrid system consisting of a Photovoltaic (PV) System, Battery Energy Storage System (BESS), Wind Energy Conversion System, and Synchronous Generator. The RL agent was trained using the Proximal-Policy Optimization algorithm to output controls for Maximum Power-Point Tracking (MPPT) of the PV system, charging/discharging of the BESS, and adaptively tune the Proportional-Integral (PI) controllers for each DER. To prove the effectiveness of the proposed controller, it was compared to standard controller implementations using non-adaptive PI controllers and Perturb and Observe MPPT algorithm for different fault and load scenarios using MATLAB Simulink. The proposed controller performed better than the standard controller in terms of voltage stability, and PQ. It also showed improvement of the Total Harmonic Distortion up to 2% and increase efficiency by 7%
Disease Detection in Plants Using UAS and Deep Neural Networks
No full textDetecting diseases in plants poses a significant challenge, addressed in this study through the integration of Unmanned Aerial Systems (UAS) and Deep Neural Networks (DNNs). The primary obstacle lies in identifying objects, such as leaves, in computer vision, exacerbated by the scarcity of large labeled datasets necessary for effective neural network training. To tackle this, the research employs a UAS equipped with a 3D image capture system, utilizing Jetson Nano and a ZED camera for streamlined data capture, storage, and subsequent analysis. The study\u27s key objectives include developing a functional 3D image capture system and formulating a unique approach for detecting diseased leaves within the dataset. Utilizing images from the ZED camera, the study utilizes a pre-trained EfficientDet model from TensorFlow, initially trained on nine classes of leaves, to identify plant health. Despite limited labeled data, the model undergoes training using the available collected data and corresponding labeled instances. The proposed approach demonstrates commendable performance in identifying diseased leaves and distinguishing leaf types based on color variations resulting from physiological changes and disease conditions. The results affirm the feasibility and effectiveness of the developed system and detection methodology, contributing significantly to advancing leaf and disease detection through UAS and deep neural networks. This research represents progress in overcoming challenges associated with data scarcity in training these networks for leaf detection, highlighting the potential of 3D image capture systems and pre-trained models in this domain