Cultural Events with TRiO/SSS
The Academic Resource Center hosts two cultural events yearly for eligible TRiO/SSS students. Watch your UMassD email for upcoming trips, workshops, and on-campus events.
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Events
Jan
6
Jan
6
10:00AM
DFO PhD Dissertation Defense presented by Cole Carrano
Department of Fisheries Oceanography "Modeling Index Selectivity for Fishery Stock Assessments" By: Cole Carrano Advisor Steven X. Cadrin (University of Massachusetts Dartmouth) Committee Members Pingguo He (University of Massachusetts Dartmouth), Gavin Fay (University of Massachusetts Dartmouth), Lisa Kerr (University of Maine) Monday January 6th, 2025 10:00 AM SMAST East 101-103 836 S. Rodney French Blvd, New Bedford and via Zoom Abstract: Abundance indices are crucial components of fishery stock assessments because they provide a time series of relative abundance for estimating absolute stock size, derived from the response of relative indices to the absolute magnitude of fishery removals. Selectivity is the relative vulnerability to a fishery or fishery-independent survey for each species or demographic group within a species (e.g., size or age class). In an age-based assessment model, selectivity parameters are needed to relate observed stock indices to model estimates of abundance at age. Thus, selectivity estimates must be carefully modeled to ensure an accurate depiction of the stock's age structure. The objectives of this research are to improve the accuracy and utilization of indices in fisheries stock assessment models by understanding the effect of alternative approaches to estimating index selectivity. Chapter One provides a general introduction to the topic and a review of the relevant literature. Chapter Two involves splitting a fishery-independent survey into two series to account for vessel and methodological changes by estimating distinct catchability and selectivity parameters for each series. Results indicated improvement in model performance for stocks with sufficient contrast in the new index, and no improvement for stocks with limited years of data or contrast in the recent indices. Chapter Three develops fleet-structured assessment models to improve selectivity estimates for fishery and the fishery-dependent indices. Splitting catch into fleets improves selectivity estimates for respective CPUE indices, but robust catch-at-age data is desirable for fleets that make up a large portion of the total catch. Chapter Four involves simulation cross-testing as a method to evaluate performance of assessments that assume a single index series that is calibrated for changes in survey technology vs. assuming separate indices in stock assessment models. Results from this chapter suggest that the consequences of assuming a split when there truly wasn't one were not severe, but that assuming there wasn't a split when there truly was one can produce significant biases in model results This work examines how decisions about modeling fleet structure or changes in survey systems affect the performance of an assessment model and how sensitive models are to these decisions. This research will emphasize the importance of selectivity estimates to stock assessment and advance our understanding of how to effectively utilize abundance indices in an assessment model. ************ Join Zoom Meeting https://umassd.zoom.us/j/94890073016 Note: Meeting passcode required, email contact below to receive ************** To request the Zoom passcode or for any other questions, please email Callie Rumbut at c.rumbut@umassd.edu
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Jan
7
Jan
7
10:00AM
EAS Doctoral Proposal Defense by Gulfam Ahmed Saju
Topic: Leveraging AI and Physics-based Models for Solving MRI Inverse Problems Location: Zoom https://umassd.zoom.us/j/93562746288?pwd=K2mJgnGVyxrKODTHfdet26mPWd6H5v.1 Meeting ID: 935 6274 6288 Passcode: 860060 Abstract In the forward model of magnetic resonance imaging (MRI), the physical restrictions such as data undersampling, motion corruption, and inaccurate estimation of coil profiles cause aliasing and motion artifacts, low signal-to-noise ratio (SNR), and blurring effects in reconstructed image. State-of-the-art (SOTA) approaches to solving MRI inverse problems combine artificial intelligence (AI) and physics-based models, but they still have some limitations. The limitations include: (1) Aliasing and motion artifacts are intertwined and they are difficult to be separated and suppressed; (2) Pre-trained priors are ineffective for joint estimation of coil sensitivity and the reconstructed image; (3) Out-of-distribution problem arises in training MRI data; (4) Planning has not been explored in the context of solving the MRI inverse model; (5) There is a lack of a general prior to address multiple degradation factors in the forward model. This doctoral proposal presents six key contributions. First, an untrained neural networks (UNN) model has been proposed for Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI reconstruction to suppress blurring and aliasing, which incorporates physical priors. Building on this UNN, a new attention-based architecture comprising of spatial and channel attention for UNN has been developed to accelerate MRI reconstruction. Second, a novel synthetic blade augmentation technique is applied for the first time in a deep unrolled network for enhanced PROPELLER MRI reconstruction, which also introduces a novel synthetic blade generation process. Third, an ensemble-based approach is proposed to address multiple types of motion artifacts in MRI by employing ensemble of three distinct Cycle Consistent Generative Adversarial Networks (CycleGANs). Fourth, two novel priors are introduced to improve the joint sensitivity encoding (JSENSE) approach by incorporating accurate coil profile estimation within an iterative optimization framework. Building on the limitations of the two priors, a general unified prior, which is based on ensemble framework is proposed for joint sensitivity encoding to address multiple degradation factors. Fifth, AI based planning, traditionally not considered in this field, has been preliminarily studied and will be further explored in the dissertation. Finally, instead of using a specialized prior for MRI reconstruction, a general prior will be investigated to solve multiple degradation factors in the inverse model. The proposed methods will be validated through comparisons with SOTA approaches and qualitative assessments by MRI physicists. It is anticipated that these methods will advance MRI inverse problem solving and enhance MRI applications in clinical settings. Advisor: Dr. Yuchou Chang, Department of Computer and Information Science Committee Members: Dr. Haiping Xu, Department of Computer and Information Science Dr. Long Jiao, Department of Computer and Information Science Dr. Donghui Yan, Department of Mathematics For further information please contact Dr Yuchou Chang at ychang1@umassd.edu
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Jan
9
Jan
9
1:00PM
SMAST DFO MS Thesis Defense presented by Fiona Edwards
Department of Fisheries Oceanography "Portfolio Theory: an Important Tool For Ecosystem-Based Fisheries Management" By: Fiona Edwards Advisor: Steven X. Cadrin (University of Massachusetts Dartmouth) Committee Members Gavin Fay (University of Massachusetts Dartmouth), Lauran Brewster (University of Massachusetts Dartmouth), Jason Link (National Marine Fisheries Service) Thursday January 9th, 2025 1pm SMAST East 101-103 836 S. Rodney French Blvd, New Bedford and via Zoom Abstract: Traditional single-species fisheries management does not account for multi-species interactions and has not always performed well for avoiding overfishing or rebuilding many fisheries. Considering these interactions has become increasingly important for effectively managing fisheries because of climate change and divergent stock trends. Ecosystem-based fishery management (EBFM) is a more holistic approach to fisheries management which has gained traction over the last several decades. EBFM considers the biological, physical, and social-economic components which may influence fisheries. Implementing EBFM requires new tactics that can be informed by interdisciplinary research. One way risk associated with achieving a target reward has been analyzed in the finance field analysis is through portfolio optimization whereby the financial risk of a portfolio is minimized for given levels of return based on portfolio covariance. A set of fishery stocks landing values can be analyzed similarly to a set of financial assets in an investment portfolio. In this study, a candidate fisheries portfolio is analyzed for New England demersal species caught in the same fisheries. The sensitivity of this portfolio to data decisions such as species composition and time series length is investigated by developing efficient frontiers for different sets of fishery stocks and different time periods. Efficient frontiers were developed using portfolio optimization techniques from the finance field and adding harvest constraints to account for limits on harvesting in fisheries. Sensitivity analyses showed that risk estimates were sensitive to both species exclusion and time series selection. Examination of the changes in the frontiers to different periods of the time series characterized by regional shifts in management strategy allowed for evaluation of the degree of flexibility afforded to fishers during these times. Efficient frontier analyses based upon historic landings data indicated that the same target revenue could have been achieved with less or similar risk had a portfolio approach to management been taken for these species. Portfolio effects as applied to fisheries management can provide additional catch stability through increased diversification of multispecies fisheries and can reduce the risk of foregone revenue, all of which make it an important tool to consider for implementing EBFM. Join Zoom Meeting https://umassd.zoom.us/j/94065204146 Note: Meeting passcode required. To request the Zoom passcode or for any other questions, please email Callie Rumbut at c.rumbut@umassd.edu
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Jan
15
Jan
15
1:00PM
EAS Doctoral Proposal Defense by Mostafa KhazaeeKuhpar
Topic: Experimental Study on Fluid-Structure Interactions of Highly Flexible High Aspect Ratio Wings Location: SENG 110 (Materials Science Lab) Abstract: High aspect-ratio wings have garnered substantial interest due to their aerodynamic benefits, particularly their ability to reduce undesirable tip vortex effects, thereby achieving a superior lift-to-drag ratio compared to lower aspect-ratio wings. However, this structural configuration inherently results in increased flexibility, which can lead to aeroelastic instabilities such as flutter, divergence, and control reversal. Flutter instability, in particular, poses a critical design challenge due to its potential to cause catastrophic failure. While extensive research has addressed both linear and nonlinear dynamics related to the onset of flutter in high aspect-ratio wings, few studies have systematically investigated the post-flutter behavior. Understanding this post-flutter response is essential for predicting and managing complex flutter phenomena, thereby enhancing design safety and resilience. The objective of this study is to fill the gap by conducting a thorough experimental study of the interaction between fluid and structure in highly flexible wings during the post-critical phase. When studying the performance of high aspect-ratio wings, it's crucial to recognize the Fluid-Structure Interaction (FSI) at play. This involves a full coupling between the fluid dynamics and structural mechanics. Therefore, a comprehensive understanding demands a simultaneous exploration of both the structural and flow aspects, allowing for a full understanding of the interaction dynamics. The majority of studies focusing on flow visualization around airfoils have been conducted on either stationary airfoils or those with limited degrees of freedom. While the study of flow around rigid airfoils has contributed to our fundamental understanding, the dynamics of flow around rigid wings differ significantly from the complex, three-dimensional dynamics seen in flexible wings. Recent efforts have offered insights into how wing flexibility influences surrounding flow; however, the complexity of 3-D flow physics and its interaction with flexible wing structures remains unexplored, and this gap is further compounded by a shortage of integrated studies that concurrently examine both structural and fluid dynamics. Additionally, the lack of comprehensive experimental data limits the ability of numerical models to accurately capture the three-dimensional flow behavior around flexible wings. This research presents a detailed experimental investigation of the flow-induced vibration characteristics of a highly flexible wing, focusing on parameters such as vibration amplitude, dominant frequencies, mode shapes, and mean deflection, with special attention to the post-flutter phase. A modal analysis-based method, along with digital image correlation (DIC) technique, was employed to measure the wings structural response. Concurrently, flow behavior around the wing was analyzed quantitatively using time-resolved volumetric particle tracking velocimetry (TR-PTV) and two-dimensional particle image velocimetry (TR-2D-PIV) techniques. The study examines a wide range of angles of attack and flow velocities to provide a comprehensive view of fluid-structure interactions in high aspect-ratio wings under varied operational conditions. Our preliminary results show that changes in the angle of attack significantly affect the onset of limit cycle oscillations, as well as the dominant oscillation frequencies and mode shapes. At higher flow velocities and angles of attack, a significant increase in tip deflection was observed, while minimal deflection occurred at lower or zero angles of attack. By employing the Q-criterion, we identified and visualized the coherent structure of vortices, uncovering the substantial influence of angle of attack and flow velocity on their behavior. At lower angles of attack, the leading edge and trailing edge vortices were almost vertical, with minimal interaction with the tip vortex. As the angle of attack increased, these vortices tilted to follow the wing's curvature and became larger and stronger, interacting more with the tip vortex. Our results show that at low-amplitude oscillations, the vortices dissipated quickly, whereas at high-amplitude oscillations, they were able to sustain their coherence for a longer duration, influencing the downstream flow pattern. ADVISOR(S): Dr. Banafsheh Seyed-aghazadeh, Department of Mechanical Engineering (b.aghazadeh@umassd.edu) COMMITTEE MEMBERS: Dr. Mehdi Raessi, Department of Mechanical Engineering and Dr. Hangjian Ling, Department of Mechanical Engineering and Dr. Geoffrey Cowles, SMAST Department of Fisheries Oceanography All EAS Students are ENCOURAGED to attend.
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Jan
17
Jan
17
1:00PM
CIS Thesis Defense by Melanie Thibodeau
Title: Optimizing Datasets for Lyme Disease Detection Advisor: Iren Valova PhD, Associate Dean - College of Engineering - Professor, Computer & Information Science - University of Massachusetts Dartmouth Committee: Gokhan Kul PhD, Computer & Information Science - University of Massachusetts Dartmouth Firas Khatib PhD, Computer & Information Science - University of Massachusetts Dartmouth Date: Jan 17, 2025 Time: 1pm Location: Zoom https://umassd.zoom.us/j/98403102776?pwd=VKmd3RikQZbqdTkhOaIhoJdyXQE91k.1 Abstract: This thesis focuses on optimizing image datasets through augmentation methods for the detection of Lyme disease. Lyme disease often is accompanied by an erythema migrans rash, but other sorts of rashes could look similar to it. Using a public crowdsourced dataset, the object is to improve the accuracy of YoloV7 through image enhancements and augmentations. The study utilizes a combination of data preprocessing techniques, including CLAHE, photometric deformation, elastic deformation, and mixup to improve image quality and address dataset imbalances. YoloV7, an object detection model was trained on the enhanced dataset to accurately differentiate Lyme-related rashes from other dermatological conditions. The results favored the CLAHE result over the others. This work contributes to the development of more reliable, automated diagnostic tools for individual user. For further information contact Dr. Iren Valova at ivalova@umassd.edu
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Fall 2024 Hours
ARC Central Office, LARTS 005A
Monday-Friday, 8am-4pm
Business Center, LARTS 010
Monday-Friday, 9am-5pm
STEM Learning Lab, SENG 217
Monday-Friday, 9am-5pm
Online tutoring available nights and weekends.