[{"id":9375,"date":"2026-06-23T00:10:56","date_gmt":"2026-06-23T05:10:56","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?post_type=tribe_events&p=9375"},"modified":"2026-06-23T00:10:56","modified_gmt":"2026-06-23T05:10:56","slug":"natalie-anderson-doctoral-project-defense-tuesday-june-30-2026-1100-am-central-time","status":"publish","type":"tribe_events","link":"https:\/\/www.pvamu.edu\/graduateschool\/event\/natalie-anderson-doctoral-project-defense-tuesday-june-30-2026-1100-am-central-time\/","title":{"rendered":"Natalie Anderson Doctoral Project Defense, Tuesday, June 30, 2026 @ 11:00 am Central Time"},"content":{"rendered":"

COMMITTEE CHAIR<\/strong>: Dr. Chloe Gaines<\/p>\n

TITLE<\/strong>: IMPROVING HOT DEBRIEF COMPLETION RATES AFTER RAPID RESPONSE TEAM EVENTS THROUGH QR CODE ACCESS AND DIGITAL REMINDERS: A QUALITY IMPROVEMENT PROJECT<\/p>\n

ABSTRACT: <\/strong>Background: Childhood obesity is a significant public health concern associated with metabolic, cardiovascular, and psychosocial complications. Despite evidence-based recommendations, adherence to pediatric obesity management guidelines remains inconsistent in primary care settings. Purpose: The purpose of this evidence-based quality improvement project was to determine whether provider education on the 2023 American Academy of Pediatrics (AAP) Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents with Obesity, combined with implementation of an electronic health record (EHR) clinical decision support prompt, improved provider knowledge and adherence to guideline-recommended obesity management practices. Methods: A quasi-experimental pre-post design was conducted in a privately owned pediatric primary care clinic in Northwest Houston, Texas. The intervention consisted of a provider education session and implementation of an EHR prompt that activated when a patient’s body mass index (BMI) was at or above the 95th percentile. Provider knowledge was assessed using pre- and post-test surveys. Adherence was evaluated through retrospective chart reviews comparing pre-intervention and post-intervention outcomes for pediatric patients with obesity. Results: Provider knowledge improved following the educational intervention. Mean knowledge scores increased from 7.33 on the pre-test to 9.00 on the post-test, with a mean difference of 1.67 points and a large effect size (Cohen’s d = 1.29). Adherence among patients aged 10 years and older increased from 7.7% (3\/39) during the pre-intervention period to 12.5% (4\/32) during the post-intervention period. Adherence among patients younger than 10 years remained 0.0% during both periods. Although adherence increased following implementation, Fisher’s exact test indicated no statistically significant association between time period and adherence to AAP guideline recommendations (p = .701). Cohen’s h was 0.119, indicating a small effect size. Conclusion: Provider education combined with EHR clinical decision support improved provider knowledge and was associated with a very small increase in adherence to evidence-based pediatric obesity management guidelines. Although statistically significant improvements in adherence were not observed during the three-month implementation period, the intervention demonstrated feasibility within a pediatric primary care setting and showed potential to support guideline-concordant obesity management. Continued implementation and ongoing monitoring may further improve adherence and support sustainable practice change.<\/p>\n

Keywords: <\/strong>Childhood obesity management, provider education, electronic health record prompt, guideline adherence<\/p>\n

\u00a0<\/strong>Location Online:<\/strong><\/p>\n

Zoom Link<\/strong>: https:\/\/pvpanther.zoom.us\/j\/93479828577?pwd=inkUMW0Ajr8tuCmjFE4fxzWVVlU2T6.1<\/p>\n

Meeting ID<\/strong>: 934 7982 8577<\/p>\n

Passcode<\/strong>: 955076<\/p>\n

\u00a0<\/u><\/strong><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":432,"featured_media":7549,"template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":""},"tags":[],"tribe_events_cat":[1296],"class_list":["post-9375","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-main-events","cat_main-events"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/9375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"version-history":[{"count":0,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/9375\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media\/7549"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=9375"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tags?post=9375"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events_cat?post=9375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":9374,"date":"2026-06-22T20:39:31","date_gmt":"2026-06-23T01:39:31","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?post_type=tribe_events&p=9374"},"modified":"2026-06-22T20:39:39","modified_gmt":"2026-06-23T01:39:39","slug":"parthiv-debnath-masters-thesis-defense-tuesday-july-7-2026-1100-am-central-time","status":"publish","type":"tribe_events","link":"https:\/\/www.pvamu.edu\/graduateschool\/event\/parthiv-debnath-masters-thesis-defense-tuesday-july-7-2026-1100-am-central-time\/","title":{"rendered":"Parthiv Debnath Master\u2019s Thesis Defense, Tuesday, July 7, 2026 @ 11:00 am Central Time"},"content":{"rendered":"

COMMITTEE CHAIR<\/strong>: Dr. Lin Gong<\/p>\n

TITLE<\/strong>: DISTRIBUTED COORDINATION AND OPTIMIZATION OF UTILITY-SCALE BATTERY ENERGY STORAGE SYSTEMS VIA MULTI-AGENT REINFORCEMENT LEARNING<\/p>\n

ABSTRACT: <\/strong>The increasing integration of renewable energies into modern power systems has created significant operational challenges, including transmission congestion, renewable energy curtailment, and increased real-time supply-demand variability, which makes effective coordination and optimization of multiple distributed Battery Energy Storage Systems (BESS) across power systems a critical problem, a useful solution, yet an unresolved challenge. Existing centralized optimization methods require perfect future information and are incompatible with the confidentiality constraints of deregulated electricity markets, while rule-based approaches fail to capture the multi-objective complexity of real-world power grid operations. This thesis proposes a Multi-Agent Proximal Policy Optimization (MAPPO) framework with Centralized Training and Decentralized Execution (CTDE) to coordinate multiple utility-scale BESS agents placed at strategic locations across large-scale power systems. The proposed framework is trained using an enhanced eighteen-dimensional observation space and an eight-component reward function that simultaneously captures price arbitrage, congestion relief, curtailment mitigation, and battery degradation objectives, while a Gurobi-based Mixed-Integer Quadratic Program (MIQP) solver with perfect foresight about the future electricity market and operating information serves as a benchmark to establish a theoretical performance upper bound. During training, the CTDE approach employs a centralized critic that observes the complete global state of all agents, enabling accurate advantage estimation and stable multi-agent learning, while each actor operates fully independently at deployment using only local information with no inter-agent communication. The synthetic Texas 2000-bus grid is studied as a representative of the Independent System Operator (ISO)-level power systems to evaluate the effectiveness and efficiency of the proposed methods. Experimental results show that the proposed MAPPO framework achieves 80.5% of the Gurobi-based optimal benchmark performance with a Sharpe ratio of 5.53 compared to 2.19 for the best rule-based baseline, with the remaining 19.4% optimality gap representing the fundamental value attributable to the perfect foresight of future LMP trajectories and system-wide operating states that the conventional optimization solver exploits but no real-time policy can really achieve. The numerical studies demonstrate that the proposed MAPPO framework can achieve high-quality and computationally efficient BESS coordination while maintaining full operational feasibility, which outperforms the conventional optimization approach and the evaluated rule-based baseline methods.<\/p>\n

Keywords: <\/strong>Power System Operations, Battery Energy Storage Systems, Multi-Agent Reinforcement Learning, Distributed Optimization<\/p>\n

Room Location: <\/strong>Electrical Engineering Conference Room 315D<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":432,"featured_media":7636,"template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":""},"tags":[],"tribe_events_cat":[1296],"class_list":["post-9374","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-main-events","cat_main-events"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/9374","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"version-history":[{"count":0,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/9374\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media\/7636"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=9374"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tags?post=9374"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events_cat?post=9374"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":9371,"date":"2026-06-22T11:12:36","date_gmt":"2026-06-22T16:12:36","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?post_type=tribe_events&p=9371"},"modified":"2026-06-22T11:12:36","modified_gmt":"2026-06-22T16:12:36","slug":"nana-rasmata-masters-thesis-defense-monday-july-6-2026-200-pm-central-time","status":"publish","type":"tribe_events","link":"https:\/\/www.pvamu.edu\/graduateschool\/event\/nana-rasmata-masters-thesis-defense-monday-july-6-2026-200-pm-central-time\/","title":{"rendered":"Nana Rasmata Master\u2019s Thesis Defense, Monday, July 6, 2026 @ 2:00 pm Central Time"},"content":{"rendered":"

COMMITTEE CHAIR<\/strong>: Dr. Cajetan Akujuobi<\/p>\n

TITLE<\/strong>: DESIGN AND HARDWARE VALIDATION OF A 5G NR OFDM TRANSMITTER ON THE INTEL DE10-NANO FPGA<\/p>\n

ABSTRACT: <\/strong>Fifth generation (5G) wireless networks have made Orthogonal Frequency Division Multiplexing (OFDM) the backbone of modern physical-layer communication, enabling high spectral efficiency, resilience to multipath fading, and support for everything from smartphones to industrial sensors. Field Programmable Gate Arrays (FPGAs) are a natural choice for prototyping these systems: they are reconfigurable, support massive parallelism, and behave predictably in real time. Even so, moving from a clean floating-point Simulink simulation to a working, fixed-point FPGA implementation is far harder than it looks, and that gap is poorly documented in the existing literature. This thesis walks through the full journey. Starting from a behavioral MATLAB\/Simulink model of a 5G NR OFDM transmitter, the work covers fixed-point optimization, VHDL code generation using MATLAB HDL Coder, synthesis and compilation in Intel Quartus Prime, deployment onto the Intel DE10-Nano development board, and real-time hardware verification using Intel SignalTap Logic Analyzer. The transmitter implements 16-point Quadrature Amplitude Modulation (16-QAM) symbol mapping via a 2-D Lookup Table (LUT), a FIFO-based streaming buffer, and a 64-point radix-2\u00b2 Streaming Delay-Feedback IFFT core with approximately 118 cycles of pipeline latency, all wrapped in an Advanced eXtensible Interface 4-Stream (AXI4-Stream)-compatible handshake interface. Using 4-bit symbol inputs and 8-bit data paths throughout, the design achieved zero DSP block utilization and consumed approximately 3% of all available Cyclone V resources: 1,095 of 41,910 Adaptive Logic Modules (ALMs), 2,892 registers, and 78,144 block memory bits. Timing analysis confirmed a positive setup slack of 9.429 ns at 50 MHz across worst-case temperature corners. SignalTap captured live waveforms from 12 internal pipeline nodes, including FIFO occupancy, QAM-mapped symbol data, IFFT input and output, and twiddle factor phase sequencing, and all 12 verification criteria passed. BER simulation in MATLAB confirmed error-free operation at SNR values above 18 dB for 16-QAM over an Additive White Gaussian Noise (AWGN) channel. This thesis makes five original contributions, each grounded in implementation methodology and hardware evidence rather than algorithmic novelty: a hardware-verified VHDL implementation of a 5G NR-inspired OFDM transmitter baseband subsystem on the DE10-Nano; a fixed-point optimization strategy that eliminates DSP block usage entirely while preserving correct BER performance; to the best of the author’s knowledge, one of the few documented applications of SignalTap to verify an HDL Coder-generated 5G NR Physical Layer (PHY) design; a practical debugging methodology for the HDL Coder-to-Quartus Prime workflow; and concrete evidence that 5G NR physical layer prototyping is achievable on low-cost, accessible hardware using freely available tools.<\/p>\n

Keywords: <\/strong>5G NR, OFDM, FPGA, DE10-Nano, Cyclone V, HDL Coder, Simulink, Fixed-Point, SignalTap, 16-QAM, IFFT, Hardware Verification<\/p>\n

Room Location: <\/strong>Electrical Engineering Conference Room 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