Fang located at Civil Engineering Dept., The University of Texas at Arlington, P.O.BOX 19308, Rm 431, Nedderman Hall, 416 Yates St., Arlington, TX 76019-0308, conducts research in the field of surface water and groundwater including floodplain studies, hydrologic/hydraulic modeling, water treatment, hydrodynamic simulation, storm water management modeling, and water quality assessment for a number of watersheds and areas in Texas, Florida, Connecticut, California, and Louisiana. He is going to perform research projects under a Research Program entitled “Review and Research Development for Stormwater Modeling/Monitoring/Management and Watershed Characterization for the Dallas-Fort Worth International Airport (DFW)” to specifically (1) study first flush stormwater system (FFSW), (2) develop a nowcasting toolkit to assist decision makers at Airport for determining icing phenomena on the airport runways, (3) monitor stormwater quantitatively and qualitatively at selected downstream sections with implemented detention ponds, infiltration trenches, and bioswales, (4) perform downstream assessments for the selected sites to ensure that the downstream areas do not get worse when adding detention pond, (5) develop a holistic scheme to evaluate and test some erosion control measures along with conceptual designs for the restoration, (6) conduct in-situ infiltration measurements for infiltration trenches, bioswales, and other innovative approaches, and (7) develop an advanced flood warning system for Airport. Dr. Nick Z. Fang will utilize the technical expertise of UTA to meet the objectives identified in the Research Program.
There is a critical need to understand the regulation of cell wall metabolism in Mycobacterium tuberculosis because it contributes to antibiotic tolerance, which exacerbates tuberculosis outcomes.
The objective of this proposal is to build a molecular model for how environmental information flows through phosphorylation of three cell wall regulators to dynamically control cell wall metabolism in mycobacteria. The central hypothesis of this proposal is that the central regulators of the cell wall during growth also regulate it in stress. This hypothesis is based on our data that the phosphatase PstP controls growth as well as stress responses, and that the phosphorylated regulators CwlM and DivIVA are required for growth and antibiotic survival. The rationale for this research is that a molecular understanding of cell wall regulation will pave the way for better TB drugs.
Aim 1: Determine how the phosphatase PstP orchestrates cell wall metabolism. Our working hypothesis is that phosphorylation of PstP regulates its activity against cell wall factors and helps coordinate the transition from growth to stasis. We will: a) determine how PstP phosphorylation is affected by stresses in Mtb; b) identify the key substrates of PstP and determine the activity of different PstP phospho-isoforms on each substrate; and c) determine how PstP contributes to antibiotic tolerance in Mtb.
Aim 2: Determine how CwlM regulates multiple peptidoglycan enzymes. Our working hypothesis is that CwlM is regulated by phosphorylation and recycled peptidoglycan, and in turn regulates peptidoglycan synthesis at multiple steps. We will: a) identify conditions that alter CwlM’s phosphorylation in Mtb; b) characterize the effects of CwlM and CwlM~P on the binding and activity of its interaction partner enzymes; and c) Measure and characterize the function and regulation of the catalytic activity of CwlM.
Aim 3: Determine how DivIVA coordinates polar cell wall metabolism. Our working hypothesis is that DivIVA activates cell wall precursor enzymes to promote growth, and is regulated by phosphorylation. We will: a) determine how DivIVA’s phosphorylation is affected by growth and stress conditions in Mtb; b) identify sites on DivIVA required for its protein interactions, and characterize the phospho-dependence of the interactions; and c) measure the effects of DivIVA and DivIV~P on the activity of their interaction partners.
Upon completion of this work we expect to have a multi-level molecular model of the signaling pathways that control cell wall precursor synthesis in mycobacteria. We will characterize the regulation of the phosphatase PstP, which is a master regulator and a candidate drug target for both antibiotics and anti-tolerance drugs. We will describe the signaling role of the protein interactions between the intermediate regulators CwlM and DivIVA and their enzymatic regulatory targets; these interactions are potential targets for anti-tolerance drugs. This work will lay the groundwork for novel drug screens.
5 miles of City of Lubbock pressure and gravity pipelines of various materials including video inspection, data analysis, and determination of remaining service life, and other related projects (sometimes "Research Services"). University will use reasonable efforts and own facilities and equipment to perform Research Services and deliver deliverables. University does not guarantee specific results. Tasks associated with this Contract will be performed on an as requested basis, and individual task orders may be issued and will be mutually negotiated and executed between the parties for each individual task order.
According to Cisco Visual Networking Index, mobile multimedia traffic is expected to take 79% of themobile data traffic by the end of 2022.
The fast growth of multimedia demands prompts quality ofexperience (QoE), a user-centric quality measurement strategy, to characterize the performance of theseservices. Nonetheless, users play limited roles in improving their own QoE so far. To bridge this gap, wepropose to leverage user context to enhance QoE. Besides, many QoE enhancement schemes, such ascontent pre-caching, adaptive video streaming, and proactive handoff, heavily rely on accurate predictionover user context. We thus aim to study fine-grained context forecasting at the network operator based onhistorical data collected from users. However, there are several critical issues to address. First, contextexhibits complex correlations that are prohibitively hard to model and thus to predict. Second, periodicdata uploading will inevitably cause energy and communication overhead. Third, the data contain privateinformation about the reporter, who may be sensitive about privacy leakage. Given these observations,this project outlines a top-down approach that addresses unique challenges in establishing a cost-efficientprivacy-aware framework that facilitates fine-grained user context prediction.This project involves four inter-dependent research thrusts. In the first thrust, time-dependent graphneural networks will be developed to explore latent spatial-temporal features from historical dataset forprediction, while existing works are either for large-scale prediction or dependent on statistic models. Thesecond thrust seeks to achieve cost efficiency during context sampling. Observing that context is sparseand highly correlated, compressive sensing techniques are applied to extract a small portion of data whilemaintaining high reconstruction accuracy. A unique challenge is to select a proper sampling matrix that isadaptive to environmental dynamics. We thus propose to integrate deep reinforcement learning (DRL) toaccelerate the selection procedure. Since users perceive privacy differently, the third thrust establishes aprivacy trading framework that enables users to decide freely how much privacy to disclose duringcontext provisioning. Particularly, under the framework of differential privacy, a user adds a certainamount of noise to the original context per his privacy requirement. Our design is a novel combination ofdifferential privacy, optimization, and economic theories. It aims to provide theoretical analysis overprivacy-accuracy tradeoffs and interactions between users and the network operator. As a complement tothe proposed theoretical work, the last thrust will thoroughly evaluate the proposed research via acombination of measurement campaigns, experimental studies, simulations, and prototyping efforts.Broader Impacts:The success of this project will unleash the potential of incorporating mobile users and context-awaresensing into QoE improvement in wireless networks. The comprehensive research plan explores apractical framework via novel theoretical analysis and modeling, mechanism and protocol design, andprototype development. The research outcomes will significantly contribute to the networking researchcommunity and benefit numerous wireless multimedia applications. Broader impacts will also result fromthe PI's education and outreach activities, which employ the research results for engineering education.The PI will develop new undergraduate and graduate courses to provide a holistic view of wirelessnetworking and motivate interdisciplinary innovation on mobile computing applications. The PI willcontinuously recruit and mentor female and under-represented minority students for research. She willalso enhance the research experiences for female and K12 students by actively involving them into theeducational programs such as NSF REU, Google explore CSR, and NCWIT.Key words: QoE enhancement, fine-granular context prediction, cost efficiency, trading privacy
The purpose is to produce scholars with interdisciplinary training in special education and social work.
The project will produce 46 scholars: 23 in special education and 23 in social work. The program will produce special education teachers who can connect students and families with necessary social work services and produce social workers who can provide evidence-based instruction to students and families. The project's goals include recruiting and retaining high-quality master's students; building capacity and systems for sustainability; providing high-quality collaborative field experiences and coursework; and evaluating the project's impact on scholars' ability to demonstrate project competencies.
Collaborative Research: Retention, Persistence, and Effectiveness of STEM Teachers in High-need School Districts- An Investigation of NSF Robert Noyce Teacher Scholarship
sponsored by National Science Foundation (NSF)
This Track 4 Noyce Research Proposal has three core research institutions (Texas State University, Florida Atlantic University, and The Brookings Institution) and four collaborating Noyce Institutions (Texas State University, University of Texas at Arlington, University of West Florida, University of North Florida, and Florida International University).
These Noyce collaborators are in two populous states, serve both rural and urban school districts and covers a variety of disciplines across the physical sciences. Three objectives guide the proposed examination. Under Objective 1, the research team will explore and identify malleable factors and policy interventions and their relationship with STEM teacher retention in high-need school districts. The research team will answer the following three broad research questions: What are the characteristics of STEM teachers teaching in high-need school districts and how have these changed over time? What are the factors that are associated with STEM teacher retention and persistence in high-need school districts? What types of district or school programs or policies are associated with stronger STEM workforce measures? Under Objective 2, the research team explores the relationship between the Noyce program and changes in the labor supply of STEM teachers in high-need school districts and related student outcomes. The research team will investigate the following research questions: What is the estimated impact of proximity to the Noyce program on the STEM teacher workforce in high-need districts? Do high-need districts with high shares of Noyce graduates perform better on student outcomes or experience smaller race- or poverty-based gaps than other high-need districts without? Under Objective 3, the research team will conduct a mixed method study to investigate the development of STEM teacher candidates among collaborating Noyce institutions. The following questions will be examined: What are the demographics and qualifications of the STEM teacher candidate pool, and how do they change during the training process? Do different programs have varying levels of success getting candidates (especially high-priority ones) through their programs? How do local high-need districts perceive teachers coming from Noyce institutions? The research team will both utilize secondary data and collect primary data to examine these questions. Secondary data includes several waves of nationally representative surveys on teachers, schools, school districts, and principals administered by the National Center on Education Statistics. New primary data will be generated through interviews at Noyce collaborators and through the administration of an alumni survey. The research team will also work with collaborating Noyce institutions to extract and analyze program data on the selection and matriculation of teacher candidates.
Objective: Develop validated process-to-performance (P2P) methods to predict fatigue life of bonded and fastened structures to reduce cost and schedule impacts during certification.
Scope: Identify damage mechanisms for spectrum loading in 3D textiles and 2D fabrics. Enhance BSAM and VTMS for coupon and sub-element features in the pi-preform joint. Develop and perform calibration and validation experiments. Establish credibility in models and methods using V&V framework.
Background: Reliable bonded primary structures have the potential to improve performance, reduce costs, and in-crease design flexibility in advanced aircraft systems. In order to develop a fail-safe design approach for bonded joints, fasteners are used in critical locations. For fail-safe certification, both spectrum fatigue and residual strength certification test data are needed to characterize the crack arrest capabilities for bonded and fastened joints. Having analysis tools to augment and guide testing is essential to reduce risk and testing costs for certification of bonded composite structures.
Approach: The following tasks are planned to meet these objectives:
Task 1: Enhance BSAM for Damage Evolution in 3D Textiles, 2D Fabric, and Fastened Bonds: A building block approach will be followed to establish a hierarchy of models at various scale levels to develop an efficient and accurate macro level methodology for predicting strength and durability of sub-elements with woven materials and bonded joints. The Rx-FEM methodology will be applied on different scale levels starting from meso-level, where the most accurate representation of the textile morphology is possible in order to compute the effective stiffness and fracture properties for the next scale level. Localized property homogenization with different fidelities will be explored to accurately predict fatigue response. Implementation of additional element types (e.g., tetrahedral, quadratic hexahedron) will be explored for improved accuracy of damage evolution in textile composite structures. A combination of experimental and analytical efforts are required to extend this capability to woven materials and to block and spectrum loading conditions. Recently developed nonlinear damage accumulation laws for non-constant fatigue loading in laminated composites will be leveraged for this effort. A building block approach will be used to develop methodology for predicting durability of woven materials under monotonic fatigue loading, variable R-ratio block loading and spectrum loading.
Task 2: Enhance VTMS for Curvature in Textiles: VTMS capabilities for textile processing, including compaction effects, will be enhanced to support generalized 3D structures. Parallel computing capability will be implemented to efficiently address large data structures. Workflows and documentation for the updated capabilities will be developed.
Task 3: Integrate Cure Process Models with BSAM: The COMPRO to BSAM translator will be enhanced for 3D textile and 2D fabric materials. Workflows and documentation will be developed.
Task 4: Develop and Perform Calibration Experiments: Practical experiments to calibrate model inputs (strengths, properties, etc.) will be developed and performed. Models and probabilistic sensitivity studies will be used to identify important parameters for calibration. A material property calibration toolset will be developed to support future materials, and the workflow will be documented.
Task 5: Verify and Validate P2P Models: The process and damage models will be validated using ICME V&V Best Practices. A V&V plan will be developed and updated periodically. Probabilistic studies will be used throughout the development to identify important sources of uncertainty to support resource allocation. A hierarchical validation approach will be used to validate each part of the P2P framework using controlled experiments. The model predictive capability will be documented using the TML metric.
Task 6: Technology Transfer: Documentation and training material will be developed for using the P2P models and for performing experiments and calibrating model inputs.
Statement of Work
The University of Texas at Arlington is asked to provide a proposal for the following to support this effort:
Develop methods to perform static and fatigue analysis of textile composites.
Develop hierarchical methodologies for representation of textile morphology on different scale levels.
Extend Rx-FEM capability for textile composites for different hierarchical levels and implement in the BSAM software.
Support software V&V for variable R-Ratio block and spectrum fatigue loading capability.
Research and implement methods to improve computational efficiency of BSAM for target problem size of 107 degrees-of-freedom.
Participate in one kickoff meeting, 4 annual review meetings, one final program review meeting, and bi-weekly teleconferences.
Quarterly status reports
Kickoff meeting in Dayton, OH
4 annual review meetings in Dayton, OH
Final program review meeting in Dayton, OH
Interactions with UDRI as needed