1,461 research outputs found
Amaresh: test #1.5 on HCDEV of cropped subject select fields
No full textAmaresh: test #1.5 on HCDEV of cropped subject select field
Amaresh Bagchi: Public Finance Economist Par Excellence.
Get PDFA tribute to Amaresh Bagchi, a discussion of his academic career, his many interests in public finance and federalism, and an outline of his important contributions in policy formulation by a friend and colleague of many years.
Formative Assessment for the Development of an Undergraduate Research Experience for College Students from Farmworker Families, North Carolina, 2020
No full textBACKGROUND: College students from families with migrant and seasonal farm work and agricultural processing experience face many barriers to educational attainment in the United States: sporadic schooling experiences, cultural and communication barriers, low pay, discrimination, and health issues from farm work. Retaining students from families with agricultural experience in higher education and research is critical for addressing educational and health inequities. In an effort to develop experiences that could serve as a pipeline for undergraduate students from farmworker and agricultural backgrounds into research careers, we conducted interviews to inform program development by exploring the research experiences of university students and recent graduates.
METHODS: Ten college-age students or recent graduates from four North Carolina universities from families with migrant or seasonal farmworker experience or agricultural processing experience were interviewed by phone between March 25, 2020, and June 17, 2020. We used a qualitative approach with inductive and deductive thematic coding of interview transcripts.
RESULTS: Three themes were identified that should be taken into consideration in the development of programs to promote research experience. The themes were: (1) Consideration of students’ lived experiences, which described the importance of a program recognizing the context of students’ experiences often as first-generation students in primarily White Institutions; (2) The importance of providing mentorship and resources, which participants highlighted the value of networks of resources and experience in navigating college; and, (3) Include strong marketing and outreach efforts, which highlighted potential barriers to hearing about opportunities.
DISCUSSION: Our findings show that research programs for undergraduate students from MSFW families are of interest to students. Such programs should consider the context of students’ experiences as (often) first-generation students in (often) primarily White institutions, include advice to successfully navigate college, and have strong marketing and outreach efforts to reach potential participants
The Uncertainty of ALB Measurements Within the Water Column
No full textAirborne LIDAR bathymetry (ALB) is a laser remote sensing technology for mapping the coastal zone. The mapping product from the ALB system is a point cloud of laser measurements representing the seafloor. The location and depth of each laser measurement is given with respect to a geographic frame of reference. In an effort to understand the value of lidar-derived data for a number of hydrographic applications, this study’s focus is on the uncertainties associated with ALB measurements. Most critical are the horizontal and vertical uncertainties of the laser pulse due to scattering through the water column. To address this issue, a lidar simulator was constructed for conducting a quantitative evaluation on the contributions from hardware and environmental factors. In this presentation, the lidar simulator system will be reviewed and future steps for the study outlined.
Presenter Bio
Amaresh M. V. Kumar is an Electric Engineering Ph.D. student at the Center for Coastal and Ocean Mapping (CCOM) at the University of New Hampshire (UNH). Amaresh has received two Master’s degrees, the first from Sathya Sai University, India in nuclear and particle physics, and the second from UNH in physics. He has also worked in many other fields of physics. Prior research has includes the development of a variable angle spectroscopic ellipsometer for the measurement of optical properties of thin films and the development of simulations for a scanning sky monitor module, x-ray detector module and CZTI imaging detector module for an ASTROSAT mission. He has also been part of a theoretical study on ring resonators in a mesoscopic regime: Effect of magnetic flux and of electron momentum on the transmission amplitude in the Aharonov-Bohm ring (2005)
Towards a rational system of centre-state revenue transfers in India: An exploration.
Get PDFIntergovernmental fiscal relations
Deep Neural Network & Dynamic Functional Connectivity Analysis of Functional MRI Data
No full textDEEP NEURAL NETWORK & DYNAMIC FUNCTIONAL CONNECTIVITY ANALYSIS OF FUNCTIONAL MRI DATA
Amaresh Kumar Mishra
University of Houston-Clear Lake, 2022
Thesis Chair: Unal ‘Zak’ Sakoglu, PhD
This thesis work presents a dynamic functional connectivity (DFC)-based classification analysis of an already collected and completely de-identified functional magnetic resonance imaging (fMRI) dataset from two groups, veterans with Gulf War Illness (GWI), vs matched controls. Neuroimaging or brain imaging is the use of various techniques to either directly or indirectly image the structure, function, or pharmacology of the nervous system. fMRI is a neuroimaging technique which is used to measure brain activity by detecting changes associated with blood oxygenation level dependence (BOLD), which is an indirect measure of neural activity, and it helps obtain three spatial dimensional (3D) brain activation maps associated with certain stimulus and/or a task, depending on the experiments performed during the fMRI scan.
Whole-brain resting-state fMRI (rsfMRI) data which were scanned from 23 GWI veterans (mean age 49.4) and 30 normal control (NC) veterans (mean age 49.8) were used for analyses. A computational method using DFC features, deep learning, and machine learning techniques were used to correctly classify GWI vs NC. Results show that, support vector machine (SVM) -based machine learning technique, combined with simple t-test method for feature extraction (using the DFC), performed better than convolutional neural network (CNN) deep learning method, in terms of classification accuracy (upwards of 98% accuracy for the former vs. upwards of 60% accuracy for the latter)
A Design Practice on Communicating Emotions Through Visual, Tactile and Auditory Simulations
No full textEmotional expression is an important human behavior, which enriches communication. Sensory organs play crucial role in emotional perception. Today, communication is mostly done via digital mediators, which dominantly address vision excluding other senses; therefore, communication becomes less affective.
Wearable technology can appeal to sensory organs from very close distance due to its intimate interaction with human body. Hence, this technology can be used in order to make distant communication more affective by enabling multi-sensory interaction. This paper presents a user-centered design practice on wearable products that simulate sensorial feedbacks (tactile, visual and auditory) to express basic emotions. Three prototypes that transmit emotional messages were designed, built and tested to observe user behavior. This paper discusses how user experience obtained through the user test can be taken further to design new communication products, which can find solutions for different user needs
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