Search results for: Michelle Joseph

Authors: Gail Joseph

Abstract:

In the United States, to advance the quality and education requirements of PreK teachers, there are concerns regarding barriers for existing early childhood educators to access formal degrees and ongoing professional development. Barriers exist related to affordability and access. Affordability is a key factor that impacts teachers access to degree programs. The lack of financial resources makes it difficult for many qualified candidates to begin, and complete, degree programs. Even if funding was not an issue, accessibility remains a pressing issue in higher education. Some common barriers include geography, long work hours, lack of professional community, childcare, and clear articulation agreements. Greater flexibility is needed to allow all early childhood professionals to pursue college coursework that takes into consideration the many competing demands on their schedules. For these busy professionals, it is particularly important that professional development opportunities are available “on demand” and are seen as relevant to their work. Courses that are available during non-traditional hours make attendance more accessible, and professional development that is relevant to what they need to know and be able to do to be effective in their current positions increase access to and the impact of ongoing professional education. EarlyEdU at the University of Washington provides institutes of higher education and state professional development systems with free comprehensive, competency based college courses based on the latest science of how to optimize child learning and outcomes across developmental domains. The coursework embeds an intentional teaching framework which requires teachers to know what to do in the moment, see effective teaching in themselves and others, enact these practices in the classroom, reflect on what works and what does not, and improve with thoughtful practices. Reinforcing the Intentional Teaching Framework in EarlyEdU courses is the Coaching Companion, an open source, web-based video annotation learning tool that supports coaching in higher education by enabling students to view and refine their teaching practices. The tool is integrated throughout EarlyEdU courses. With the Coaching Companion, students see upload teaching interactions on video and then reflect on the degree to which they incorporate evidence-based practices. Coaching Companion eliminates the traditional separation of theory and practice in college-based teacher preparation. Together, the Intentional Teaching Framework and the Coaching Companion transform the course instructor into a job-embedded coach. The instructor watches student interactions with children on video using the Coaching Companion and looks specifically for interactions defined in course assignments, readings, and lectures. Based on these observations, the instructor offers feedback and proposes next steps. Developed on federal and philanthropic funds, all EarlyEdU courses and the Coaching Companion are available for free to 2= and 4-year colleges and universities with early childhood degrees, as well as to state early learning and education departments to increase access to high quality professional development. We studied the impact of the Coaching Companion in two courses and demonstrated a significant increase in the quality of teacher-child interactions as measured by the PreK CLASS quality teaching assessment. Implications are discussed related to policy and practice.

Keywords: education technology, distance education, early childhood education, professional development

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

Abstract:

Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Sophy Joseph

Abstract:

The study uses the concept of ‘Settler city’ to understand the nature of peripheralization that a neoliberal city initiates. The settler city designs powerless communities without inherent rights, title and sovereignty. Kathputli Colony, home to generations of artists/craftsmen, who have kept heritage of arts/crafts alive, has undergone eviction of its population from urban space. The proposed study, ‘Neoliberal Settler City: Socio-spatial segregation and livelihood of artists/craftsmen in Delhi’ would problematize the settler city as a colonial technology. The colonial regime has ‘erased’ the ‘unwanted’ as primitive and swept them to peripheries in the city. This study would also highlight how structural change in political economy has undermined their crafts/arts by depriving them from practicing/performing it with dignity in urban space. The interconnections between citizenship and In-Situ Private Public Partnership in Kathputli rehabilitation has become part of academic exercise. However, a comprehensive study connecting inherent characteristics of neoliberal settler city, trajectory of political economy of unorganized workers - artists/craftsmen and legal containment and exclusion leading to dispossession and marginalization of communities from the city site, is relevant to contextualize the trauma of spatial segregation. This study would deal with political, cultural, social and economic dominant behavior of the structure in the state formation, accumulation of property and design of urban space, fueled by segregation of marginalized/unorganized communities and disowning the ‘footloose proletariat’, the migrant workforce. The methodology of study involves qualitative research amongst communities and the field work-oral testimonies and personal accounts- becomes the primary material to theorize the realities. The secondary materials in the forms of archival materials about historical evolution of Delhi as a planned city from various archives, would be used. As the study also adopt ‘narrative approach’ in qualitative study, the life experiences of craftsmen/artists as performers and emotional trauma of losing their livelihood and space forms an important record to understand the instability and insecurity that marginalization and development attributes on urban poor. The study attempts to prove that though there was a change in political tradition from colonialism to constitutional democracy, new state still follows the policy of segregation and dispossession of the communities. It is this dispossession from the space, deprivation of livelihood and non-consultative process in rehabilitation that reflects the neoliberal approach of the state and also critical findings in the study. This study would entail critical spatial lens analyzing ethnographic and sociological data, representational practices and development debates to understand ‘urban otherization’ against craftsmen/artists. This seeks to develop a conceptual framework for understanding the resistance of communities against primitivity attached with them and to decolonize the city. This would help to contextualize the demand for declaring Kathputli Colony as ‘heritage artists village’. The conceptualization and contextualization would help to argue for right to city of the communities, collective rights to property, services and self-determination. The aspirations of the communities also help to draw normative orientation towards decolonization. It is important to study this site as part of the framework, ‘inclusive cities’ because cities are rarely noted as important sites of ‘community struggles’.

Keywords: neoliberal settler city, socio-spatial segregation, the livelihood of artists/craftsmen, dispossession of indigenous communities, urban planning and cultural uprooting

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Authors: P. Ramesh, Aby Joseph, Arya G. Lal, U. S. Aji

Abstract:

Integration of distributed green renewable energy sources in addition with battery energy storage is an inevitable requirement in a smart grid environment. To achieve this, an Intelligent Green Energy Management System (i-GEMS) needs to be incorporated to ensure coordinated operation between supply and load demand based on the hierarchy of Renewable Energy Sources (RES), battery energy storage and distribution grid. A bi-directional energy harvester is an integral component facilitating Intelligent Green Energy Management System (i-GEMS) and it is required to meet the technical challenges mentioned as follows: (1) capability for bi-directional mode of operation (buck/boost) (2) reduction of circuit parasitic to suppress voltage spikes (3) converter startup problem (4) high frequency magnetics (5) higher power density (6) mode transition issues during battery charging and discharging. This paper is focused to address the above mentioned issues and targeted to design, develop and implement a bi-directional energy harvester with galvanic isolation. In this work, the hardware architecture for bi-directional energy harvester rated 3.5 kW is developed with Isolated Full Bridge Boost Converter (IFBBC) as well as Dual Active Bridge (DAB) Converter configuration using modular power electronics hardware which is identical for both solar PV array and battery energy storage. In IFBBC converter, the current fed full bridge circuit is enabled and voltage fed full bridge circuit is disabled through Pulse Width Modulation (PWM) pulses for boost mode of operation and vice-versa for buck mode of operation. In DAB converter, all the switches are in active state so as to adjust the phase shift angle between primary full bridge and secondary full bridge which in turn decides the power flow directions depending on modes (boost/buck) of operation. Here, the control algorithm is developed to ensure the regulation of the common DC link voltage and maximum power extraction from the renewable energy sources depending on the selected mode (buck/boost) of operation. The circuit analysis and simulation study are conducted using PSIM 9.0 in three scenarios which are - 1.IFBBC with passive clamp, 2. IFBBC with active clamp, 3. DAB converter. In this work, a common hardware prototype for bi-directional energy harvester with 3.5 kW rating is built for IFBBC and DAB converter configurations. The power circuit is equipped with right choice of MOSFETs, gate drivers with galvanic isolation, high frequency transformer, filter capacitors, and filter boost inductor. The experiment was conducted for IFBBC converter with passive clamp under boost mode and the prototype confirmed the simulation results showing the measured efficiency as 88% at 2.5 kW output power. The digital controller hardware platform is developed using floating point microcontroller TMS320F2806x from Texas Instruments. The firmware governing the operation of the bi-directional energy harvester is written in C language and developed using code composer studio. The comprehensive analyses of the power circuit design, control strategy for battery charging/discharging under buck/boost modes and comparative performance evaluation using simulation and experimental results will be presented.

Keywords: bi-directional energy harvester, dual active bridge, isolated full bridge boost converter, intelligent green energy management system, maximum power point tracking, renewable energy sources

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

Abstract:

Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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