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Practical Real-time Permanent Magnet Energy Spectrometer using a Diode Array for Therapeutic Electron Beam Tuning and Quality Assurance
No full textPurpose: Develop a practical, real-time permanent magnet electron energy spectrometer and evaluate it for beam tuning and quality assurance (QA) of therapeutic electron beams.
Methods: Aim 1: A 0.55 T permanent magnet was coupled to Sun Nuclear SRS MapCHECK® PC boards, providing two interlaced diode arrays. Detector response, Rmeas(zdiode) versus diode position zdiode, was extracted from raw diode array readings, correcting for diode sensitivity and other factors. Using Monte Carlo (MC) computed, monoenergetic detector response functions, a curve-fitting algorithm extracted the energy spectrum, from Rmeas(zdiode). The energy spectra for 7-20 MeV beams were measured for six matched Elekta accelerators (Linacs). Aim 2: ‘Synthetic’ percent dose versus depth (PDD) curves were produced by convolving the energy spectrum with monochromatic PDDs, precomputed in water using EGSnrc MC. Synthetic PDDs were compared to PDDs measured in water for 7-20 MeV beams. Aim 3: Spectrometer utility as a QA device was assessed by comparing it to daily and monthly clinical QA measurements. Aim 4: Spectrometer utility as a beam tuning tool was assessed using its energy spectrum, synthetic PDD, and metrics.
Results: Aim 1: A practical, real-time lightweight (11.4 kg), and compact electron energy spectrometer was developed. The energy spectrum for 7-20 MeV beams from six matched Linacs were measured and compared. The energy spectrum for cumulative (15 s) and real-time (0.8 s) modes agreed. Aim 2: Synthetic PDDs showed accuracy ≤1 mm and precision ≤0.5 mm. Spectrometer real-time operation (≥1 Hz) showed clinically insignificant differences from cumulative operation. Aim 3: The spectrometer performed similar (within 1%) to daily and monthly clinical QA measurements. Aim 4: Synthetic PDDs showed shifts equaling those of PDDs measured in water before and after beam tuning, and they showed the ability to return a PDD back into clinical tolerance through beam tuning. Using and synthetic PDDs, FWHM and R80-20were reduced from clinical beam tune values, while maintaining clinical R50 values for 7-20 MeV beams.
Conclusions: A practical, real-time permanent magnet electron energy spectrometer was developed that showed the ability to tune and perform QA measurements for therapeutic 7-20 MeV electron beams
Forecasting coastal hypoxia using a blend of mechanistic and artificial intelligence models
No full textDaily fluctuations in coastal hypoxia significantly impact marine ecosystems, requiring forecasts that balance efficiency and accuracy. Statistical models are computationally efficient but often fall short in prediction performance, while mechanistic models are accurate yet resource-intensive. Here, we present a lightweight artificial intelligence (AI) model for daily hypoxia forecasting on the Louisiana–Texas shelf, trained and validated using a 14-year mechanistic ROMS hindcast. The AI model integrates observed riverine nutrient loads and 2-day hydrodynamic forecasts and achieves strong predictive performance: median (± 1 s.d.) accuracy of 0.85 ± 0.07 and F1 score of 0.72 ± 0.18 against the hindcast test set, and 0.67 ± 0.10 accuracy with 0.62 ± 0.14 F1 score against shelf-wide cruise observations. The model remains robust when applied to independent hydrodynamic forecasts (accuracy = 0.71 ± 0.09; F1 score = 0.64 ± 0.17). Beyond forecasting, the AI model enables rapid scenario testing for coastal management. Nutrient reduction assessments suggest that reductions exceeding 90% may be required to meet Gulf Hypoxia Task Force goals. Ablation experiments identify water column stratification as the dominant predictor of daily hypoxia events. This study demonstrates the potential of AI to enhance real-time water quality forecasting, support management decision-making, and inform adaptive cruise planning in dynamic coastal systems
THE EASTERN OYSTER BEHAVIORAL RESPONSE TO LOW SALINITY AND HYPOXIA IN CONTROLLED AND NATURAL SETTINGS
Get PDFOyster aquaculture is expanding in the northern Gulf of Mexico, but climate change may challenge the sustainability of this industry. Projected increases in the frequency and intensity of low salinity and low dissolved oxygen (DO) periods could impact the behavior, metabolism, growth, and survival of oysters. Characterizing how oyster behavior links to environmental stressors could help better identify thresholds in their tolerance and inform predictive tools for aquaculture professionals. In this research, the effect of separate and concomitant exposures to low salinity and DO on the behavioral response of oysters was examined in the lab. The impact of other environmental variables on oysters’ behavioral response to low salinity and DO was also characterized in situ. In the lab, a fully crossed experiment with two levels of salinity (20 or 3) and DO (~7.0 and \u3c 2.0 mg L-1) was conducted on adult oysters equipped with high-frequency valve opening recorders. The results were analyzed through 3 stages: Before (5 days of holding oysters under controlled conditions), Transition (3 days of rapidly adjusting water parameters to selected treatments), and After (5 days of holding oysters under selected treatments). Oysters spent more time fully open under low DO in isolation, but less time under concurrent low salinity and DO. Low salinity and DO showed a synergistic effect on oyster survival, but low salinity was more hazardous than low DO. In the field, oysters were deployed in a southern Louisiana estuary during summer 2023 and winter 2024 (3 months each). Generalized linear mixed models were used to test if temperature or chlorophyll-a influenced behavior during periods of low salinity (\u3c 5), low DO (\u3c 2 mg L-1), suboptimal salinity (5–10), and suboptimal DO (2–4 mg L-1). For each behavioral indicator the effect of temperature and chlorophyll-a on behavior was highly variable based on interacting environmental conditions. As environmental conditions continue to shift under climate change, understanding how oysters behaviorally respond to stressful conditions and their interactions can provide valuable insight into eastern oysters’ resilience in the future and aid in restoration and aquaculture enterprises
STATE-OF-CHARGE ESTIMATION USING DEEP LEARNING FOR ELECTRIC VEHICLES
Get PDFThis thesis investigates the application of deep learning models for State of Charge (SOC) estimation in Battery Management Systems (BMS) for electric vehicles (EVs), focusing on optimizing EV range, lifespan, and performance while addressing challenges like range anxiety. The study explores three deep learning architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—each designed to capture complex temporal dependencies in battery data. The LSTM model is trained on EV battery data, including voltage, current, temperature, and SOC, providing a strong baseline for SOC estimation. The BiLSTM model enhances accuracy by processing data in both forward and backward directions, thus capturing contextual relationships within the data more effectively. The GRU model offers a simpler design that maintains reasonable accuracy, focusing on computational efficiency for real-time BMS applications. A hybrid deep learning model is also introduced, combining BiLSTM layers with fully connected layers (FCLs) to leverage both bidirectional processing and complex pattern recognition. This hybrid approach effectively integrates past and future time steps, achieving superior SOC prediction accuracy across various test scenarios, especially under complex driving cycles and varied environmental conditions. The models were evaluated on a Tesla battery dataset, with performance comparisons demonstrating the hybrid model’s effectiveness in adapting to dynamic conditions. By advancing robust SOC estimation methods, this research supports the development of efficient BMS solutions that contribute to the adoption of EVs and the advancement of sustainable transportation
Creatures of Fashion: Animals, Global Markets, and the Transformation of Patagonia by John Soluri (review)
No full textPrediction of Wear in Start–Stop Systems Using Continuum Damage Mechanics
No full textA vehicle start–stop system automatically shuts down and restarts the internal combustion engine to reduce the time the engine spends idling, thereby reducing fuel consumption and emissions. For the start–stop system to work, the engine must be at a certain temperature and conditions. If the engine is too hot, the system may not activate. This study explores the tribological characteristics of the start–stop system by applying principles of Continuum Damage Mechanics (CDM) to predict both the lifespan and wear volume subsequent to the start–stop cycles. A series of pin-on-disk tests were conducted to evaluate the efficacy of the modeling and predictions. The results from these tests were compared to the CDM predictions, demonstrating satisfactory accuracy. Additionally, a Finite Element Method (FEM) analysis was employed to model temperature variations during the start–stop cycles. Findings indicate that an increase in consecutive start–stop cycles impedes the system’s ability to sufficiently cool, thereby increasing wear. Conversely, extending the duration of the stop phase reduces wear and enhances the system’s lifespan
Exploring the effects of Connected and Autonomous Vehicles on traffic safety and operation at full-cloverleaf interchanges
No full textThe effects of Connected and Autonomous Vehicles (CAVs) on interchanges such as full-cloverleaf interchanges have not yet been examined in prior studies. This study aims to explore the impacts of CAVs on traffic operation and safety at full-cloverleaf interchanges. It also assesses the influence of various Market Penetration Rates (MPRs) of CAVs on travel time, queues lengths, and conflict points considering various weaving lengths using VISSIM. The model calibration and validation were conducted considering different measures of effectiveness (throughput, speed, and travel time). The finding showed that higher MPRs of CAVs led to consistent travel time reductions across on-ramps, off-ramps, and weaving segments. Specifically, moving from the base scenario to 100% MPRs, travel times decreased by 46%, 41%, and 32% at the three interchanges under investigation, respectively. Additionally, safety evaluations utilizing the Surrogate Safety Assessment Model demonstrated considerable declines in the numbers of vehicle conflicts with increases in MPRs, averaging reductions of 20%, 32%, and 26% at the three interchanges from the base scenario to full MPRs, respectively. Also, it was found that increasing the weaving lengths enhanced traffic operation and safety. Overall, the findings highlight the promising benefits of integrating CAVs into the transportation system at full-cloverleaf interchanges
The Full Range Leadership Model (FRLM): Department Head Perceptions of Leadership Style and Development
Get PDFThe purpose of this study is to discover the perceived leadership styles of department heads as categorized by the Full Range Leadership Model (FRLM), developed by Avolio and Bass (1991). The FRLM categorizes leadership style along a continuum, from laissez-faire leadership, to transactional leadership, and ultimately transformational leadership. Even though department heads occupy a pivotal leadership role, many do not have formal leadership training, and instead rise to power through disciplinary expertise. This study will collect data on the experiences contributing to the development of leadership style in department heads. Understanding the leadership styles and experiences of department heads will help to improve the training and selection of candidates who possess the characteristics of effective leadership. Effective department head leadership is essential in creating positive outcomes for departments, stakeholders, and university communities, in addition to advancing knowledge and innovation in society.
This study focuses on three research questions: 1) What is the frequency and variation of perceived leadership style by department heads? 2) What experiences prepare department heads for various levels of leadership? 3) What leadership experiences do department heads perceive as most important? These questions were addressed using an embedded, mixed methods survey, utilizing both quantitative and qualitative elements framed by the Multifactor Leadership Questionnaire (MLQ), designed to categorize leadership style based on the Full-Range Leadership Model (FRLM).
Results indicate that most department heads perceive themselves as transformational leaders, although only a small percentage may be correct in their assessment. Additionally, only a small percentage of respondents received formal leadership training. Leadership experiences that contribute to the department head experience include university and external leadership opportunities, networking, mentoring, formal leadership training, and on-the-job training. Areas cited for improvement include business operations, conflict management, and general leadership skills