Web Crippling of Cold Formed Steel C-and Z- Sections

An experimental investigation of cold formed steel stiffened C-and Z sections subjected to web crippling is presented in this paper. This study was devoted to two loading conditions according to the AISI Specification, namely End Two Flange (ETF) and Interior Two Flange (ITF) loading, with particular emphasis on large inside bend radius to thickness ratios, R, (up to 12) and the specimens being fastened to the support during testing. There is no experimental data available in the literature regarding the web crippling resistance of such members that are fastened to the support and have inside bend radius to thickness ratios greater than 2.7. A total of 72 tests were conducted on C-and Z sections at the University of Waterloo, Waterloo, Canada. Although most of the parameters of the test specimens were beyond the limits specified by the current North American Cold Formed Steel Design Standards (AISI and S136), the test results were compared to the calculated values of the AISI and the S136 web crippling design equations. This was done in an effort to determine the behavior of the current design equations for fastened to the support sections with large inside bend radius ratios. Using the same model of the web crippling design expression currently used in S 136, a nonlinear regression analysis was used to establish the new coefficients for the ETF and ITF loading conditions for both C and Z-sections. The new coefficients showed an excellent agreement with the test results. Furthermore, the S 136 design expression was calibrated for the safety requirements of both the S136-94 Standard and the AISI Specification

Web Crippling of Cold-formed Steel Members

The North American Specification for the Design of Cold-Formed Steel Structural Members (NAS 2001) has recently adopted a new web crippling approach, which is the approach contained in the Canadian S136 Standard (CSA 1994). The objective of this work was to develop new design coefficients for the web crippling strength design expression currently used in the Canadian S136 Standard (CSA 1994) and the North American Specification (NAS 2001). An extensive statistical analysis was performed using published test data up to 1999, from the United States, Canada and Australia. The resulting web crippling coefficients, calibrated resistance factors and respective factors of safety of this study, have been adopted by the North American Specification (NAS 2001). The new coefficients were developed based on section geometry, loading case and two different support conditions, fastened to the support and not fastened to the support during testing. The new coefficients showed excellent agreement with the test data for a wide range of cross section dimensions, yield strengths, bearing lengths and angle of web inclination

Cortical development of AMPA receptor trafficking proteins

AMPA-receptor trafficking plays a central role in excitatory plasticity, especially during development. Changes in the number of AMPA receptors and time spent at the synaptic surface are important factors of plasticity that directly affect long-term potentiation (LTP), long-term depression (LTD), synaptic scaling, and the excitatory-inhibitory (E/I) balance in the developing cortex. Experience-dependent changes in synaptic strength in visual cortex (V1) use a molecularly distinct AMPA trafficking pathway that includes the GluA2 subunit. We studied developmental changes in AMPA receptor trafficking proteins by quantifying expression of GluA2, pGluA2 (GluA2serine880), GRIP1, and PICK1 in rat visual and frontal cortex. We used Western Blot analysis of synaptoneurosome preparations of rat visual and frontal cortex from animals ranging in age from P0 to P105. GluA2 and pGluA2 followed different developmental trajectories in visual and frontal cortex, with a brief period of over expression in frontal cortex. The over expression of GluA2 and pGluA2 in immature frontal cortex raises the possibility that there may be a period of GluA2-dependent vulnerability in frontal cortex that is not found in V1. In contrast, GRIP1 and PICK1 had the same developmental trajectories and were expressed very early in development of both cortical areas. This suggests that the AMPA-interacting proteins are available to begin trafficking receptors as soon as GluA2-containing receptors are expressed. Finally, we used all four proteins to analyze the surface-to-internalization balance and found that this balance was roughly equal across both cortical regions, and throughout development. Our finding of an exquisite surface-to-internalization balance highlights that these AMPA receptor trafficking proteins function as a tightly controlled system in the developing cortex

Semiquantitative Decision Tools for FMD Emergency Vaccination Informed by Field Observations and Simulated Outbreak Data

We present two simple, semiquantitative model-based decision tools, based on the principle of first 14 days incidence (FFI). The aim is to estimate the likelihood and the consequences, respectively, of the ultimate size of an ongoing FMD epidemic. The tools allow risk assessors to communicate timely, objectively, and efficiently to risk managers and less technically inclined stakeholders about the potential of introducing FMD suppressive emergency vaccination. To explore the FFI principle with complementary field data, we analyzed the FMD outbreaks in Argentina in 2001, with the 17 affected provinces as the units of observation. Two different vaccination strategies were applied during this extended epidemic. In a series of 5,000 Danish simulated FMD epidemics, the numbers of outbreak herds at day 14 and at the end of the epidemics were estimated under different control strategies. To simplify and optimize the presentation of the resulting data for urgent decisions to be made by the risk managers, we estimated the sensitivity, specificity, as well as the negative and positive predictive values, using a chosen day-14 outbreak number as predictor of the magnitude of the number of remaining post-day-14 outbreaks under a continued basic control strategy. Furthermore, during an ongoing outbreak, the actual cumulative number of detected infected herds at day 14 will be known exactly. Among the number of epidemics lasting >14 days out of the 5,000 simulations under the basic control scenario, we selected those with an assumed accumulated number of detected outbreaks at day 14. The distribution of the estimated number of detected outbreaks at the end of the simulated epidemics minus the number at day 14 was estimated for the epidemics lasting more than 14 days. For comparison, the same was done for identical epidemics (i.e., seeded with the same primary outbreak herds) under a suppressive vaccination scenario. The results indicate that, during the course of an FMD epidemic, simulated likelihood predictions of the remaining epidemic size and of potential benefits of alternative control strategies can be presented to risk managers and other stakeholders in objective and easily communicable ways

Over the Counter (OTC) analgesic use by Aboriginal people in Adelaide: Report March 2011

de Crespigny C, Wilson C, Chong A, Cusack L, Valadian S and Beshara

Energy flows in structures with compliant nonconservative couplings

SIGLEAvailable from British Library Document Supply Centre-DSC:D195416 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Vibration energy flows between plates with compliant and dissipative couplings

In this work, the transmission of energy through a compliant and dissipative joint between two thin rectangular plates is investigated using a receptance approach. Exact formulae for the spectral density of the energy flow through the joint, the energy dissipated at the joint, the power input by the external excitation and the vibrational energies of the two plates are established when the plates are subject to random ergodic forcing. The more general case of a row of plates which are simply supported along the two longitudinal edges and coupled through compliant and dissipative joints is also investigated. The aim of this study is to examine the effect of joint damping and compliance on the magnitudes of energy flows through the joints and energy levels of the plates. Interest is focused on the energy dissipation at the joints and the conditions for which it is maximised

Talk data to me: Bolstering the communication of data to facilitate data-informed decision making in community colleges

Community colleges are continually being faced with pressures to use data to inform decisions. These pressures arise from a triage of factors, including accountability, accreditation, and student success initiatives. Yet, as these demands continue, research has shown that community colleges struggle to institutionalize data-informed decision making (DIDM) to support student success. In fact, in a 2011 survey of college and university presidents by Inside Higher Ed, only 36.1% of the 344 public community college presidents believed their college was very effective in using data to inform decisions (Green, Jaschik, & Lederman, 2011, p. 19). Through the literature review process, it became evident that open channels of communication and discussions related to data and student success are essential for DIDM (Altose, 2017; Coburn & Turner, 2011; Katz & Ain Dack, 2014; Kerrigan, 2015; McClenney, McClenney, & Peterson, 2007; Peterson, 2007), yet research exploring how these processes take place in community colleges is lacking. As such, this multiple-case study was intended to develop best practices for communicating data related to student success by exploring the communication and presentation of data through the lens of stakeholder and knowledge management theories. Two community colleges were selected based on recommendations from the CEO of Achieving the Dream who affirmed these institutions’ demonstrated efforts in supporting student success through DIDM. Findings showed that executive leadership, administrators, and faculty are the most commonly cited stakeholders in the decision-making process related to student success. Although frequent communication of data exists in both colleges, it was apparent that frequency depends on the stakeholder group. The main method of communicating data occurs in-person. In-person communication can support accurate interpretation of data and the transition of data into information. While participants identified Institutional Research (IR) as the main area helping them to interpret data, in-person conversations with colleagues facilitate bringing meaning and context to the data that are under review. Data are often presented to internal stakeholders in the form of graphs, charts, and tables; however, there was no overall consensus on which presentation is more effective.Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges Alexa M. Beshara-Blauth A Thesis submitted to the Graduate Faculty of the University of Maryland University College in partial Fulfillment of the Requirements for the Doctor of Management Degree Charlene Nunley, Ph.D. Susan McMaster, DM Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges ii Abstract Community colleges are continually being faced with pressures to use data to inform decisions. These pressures arise from a triage of factors, including accountability, accreditation, and student success initiatives. Yet, as these demands continue, research has shown that community colleges struggle to institutionalize data-informed decision making (DIDM) to support student success. In fact, in a 2011 survey of college and university presidents by Inside Higher Ed, only 36.1% of the 344 public community college presidents believed their college was very effective in using data to inform decisions (Green, Jaschik, & Lederman, 2011, p. 19). Through the literature review process, it became evident that open channels of communication and discussions related to data and student success are essential for DIDM (Altose, 2017; Coburn & Turner, 2011; Katz & Ain Dack, 2014; Kerrigan, 2015; McClenney, McClenney, & Peterson, 2007; Peterson, 2007), yet research exploring how these processes take place in community colleges is lacking. As such, this multiple-case study was intended to develop best practices for communicating data related to student success by exploring the communication and presentation of data through the lens of stakeholder and knowledge management theories. Two community colleges were selected based on recommendations from the CEO of Achieving the Dream who affirmed these institutions’ demonstrated efforts in supporting student success through DIDM. Findings showed that executive leadership, administrators, and faculty are the most commonly cited stakeholders in the decision-making process related to student success. Although frequent communication of data exists in both colleges, it was apparent that frequency depends on the stakeholder group. The main method of communicating data occurs in-person. In-person communication can support accurate interpretation of data and the transition of data Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges iii into information. While participants identified Institutional Research (IR) as the main area helping them to interpret data, in-person conversations with colleagues facilitate bringing meaning and context to the data that are under review. Data are often presented to internal stakeholders in the form of graphs, charts, and tables; however, there was no overall consensus on which presentation is more effective. Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges iv © Copyright by Alexa M. Beshara-Blauth 2018 Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges v Dedication My work over the past 3 years and this final dissertation are dedicated to my family; you have provided me with unwavering support and inspiration and believed in me during my most difficult times. Thank you for listening to paper after paper and taking this journey alongside me. To my dad, who instilled in me my love for math and numbers, who taught me perseverance, and who continually pushed me to start working on my doctoral degree. Thank you for not only pushing and nudging me to do what you knew I could, but also for making sure that I was well fed during the past year! To my mom, who has been my rock and so much more throughout this entire process. You have taught me so much and I know that the most important lessons have come not from my books but from watching you. A true role model, I have learned what it means to have dedication, to stand up for what I believe in, and how to put others first. These are all traits that I will carry with me as I move on to my next chapter. To my husband, I don’t quite think you knew what you were getting into when I signed up for this program. For most of our marriage, I have had my nose stuck behind books or was ferociously typing away at my computer. Thank you for loving me, standing by my side, and taking on more responsibilities so that I could focus my time on school. I promise I won’t start on anything crazy within the next few months, so we can truly enjoy our time together. And most importantly, to my son, my heart. You were my motivation even before you were born. I hope that I can be as good of a role model to you as my mother has been to me. I hope that I make you proud, and that when you look back at my journey through this, you remember that anything is possible if you set your mind to it and believe in yourself. Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges vi Acknowledgements The journey through the UMUC DMCCPA program has been one of the most trying yet rewarding experiences I have encountered. I have grown as both a student and leader, pushing myself far beyond what I thought was possible, and I owe that to the dedicated faculty of this program in addition to my cohort. I want to first acknowledge my amazing advisors, Dr. Charlene Nunley and Dr. Susan McMaster. Your patience, feedback, and guidance as I stumbled through this process have allowed me to produce a meaningful addition to community college research. Without your expertise and support, I do not believe I would have gotten through my primary research “on time.” I am forever grateful. Dr. Nunley, your dedication to student success is admirable; it is contagious and invigorating to those who encounter it. It makes me want to be a better leader and it will continue to push me as I pursue my goals. Dr. McMaster, you have such a way with words, even turning dissertation instructions into elaborate stories. Your words of encouragement were powerful and provided me with the reassurance I needed. I am very thankful for all of the grammatical edits you provided. There are so many others from the DMCCPA program whom I wish to acknowledge. Dr. Pat Keir, your instruction for our first class made me realize I had made the right choice in selecting this program. Dr. Ronald Head, I owe my attention to APA format to you. Dr. Gena Glickman, thank you for being a willing participant for some of my research. Monica Graham, I am appreciative of your assistance throughout this program; you were always willing to answer my questions and provided any support that you could. In addition to those in the DMCCPA program, I would like to thank Dr. Karen Stout for her willingness to discuss my dissertation and provide feedback on case study institutions. Your Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges vii assistance in identifying and reaching out to potential participants is truly appreciated. I want to also acknowledge Dr. Paula Pitcher, my mentor and friend who recommended this program to me. Your guidance as I first began my career in higher education has been invaluable. I continue to admire your drive and ambition and look forward to seeing you attain your goals. Last, and certainly not least, I want to acknowledge my cohort. Each and every one of you has motivated me beyond imagination. Your constant support, humor, and commitment to completion kept me sane. I can’t wait to see where this journey takes everyone next! Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges viii Table of Contents Chapter 1 – BACKGROUND ...................................................................................................... 1 Statement of Problem ....................................................................................................... 3 Significance..................................................................................................................... 6 Funding ................................................................................................................ 9 Graduation Rates and Student Success ................................................................ 10 Communication to Facilitate Data Use ................................................................ 13 Purpose ............................................................................................................................. 14 Research Questions .......................................................................................................... 15 Theoretical Context .......................................................................................................... 15 Stakeholder Theory .............................................................................................. 16 Knowledge Management ..................................................................................... 17 Definition of Terms.......................................................................................................... 19 Summary .......................................................................................................................... 21 Chapter 2 – METHODOLOGY ................................................................................................... 23 Research Methodology Selection and Literature Evaluation ........................................... 24 Systematic Review ............................................................................................... 24 Literature Scoping ................................................................................................ 25 Key Sources ......................................................................................................... 27 Multiple-Case Study Methodology .................................................................................. 38 Case Study Site Selection .................................................................................... 39 Interview Guide Development ............................................................................. 41 Pilot ...................................................................................................................... 42 Data Analysis ....................................................................................................... 43 Methodology of Expert Panel Selection .......................................................................... 45 Summary .......................................................................................................................... 48 Chapter 3 – LITERATURE REVIEW ......................................................................................... 50 Accountability, Accreditation, Student Success, and DIDM ........................................... 51 Accountability and DIDM ................................................................................... 51 Accreditation ........................................................................................................ 53 Student Success and the Completion Agenda ...................................................... 55 Historical trends in student success ........................................................... 56 DIDM and student success ........................................................................ 57 Current status of student success ............................................................... 58 Prevalence of Data Use .................................................................................................... 59 Challenges, Barriers, and Influences in DIDM ................................................................ 63 Relevance of Data ................................................................................................ 63 Accessibility and Presentation of Data ................................................................ 65 Trust in Data and DIDM ...................................................................................... 69 Leadership Impact on DIDM ............................................................................... 71 Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges ix Chapter 3 (continued) Communicating Data: The Importance of Channels for Communication and Collaboration ...................................................................................... 73 Institutional Research and DIDM .................................................................................... 78 Function and Size ................................................................................................. 78 Consumers of IR Data .......................................................................................... 78 Structure and Future of IR ................................................................................... 80 The Application of Stakeholder Theory .......................................................................... 82 The Origin of Stakeholder Theory ....................................................................... 83 Identification and Prioritization of Stakeholders ................................................. 84 The Importance of Understanding Stakeholders and DIDM ............................... 87 Knowledge Management Theory ..................................................................................... 88 Concepts of Knowledge Management ................................................................. 89 Connections Between Data, Information, and Knowledge .................................. 91 Knowledge Creation and Sharing ........................................................................ 92 Conceptual Model ............................................................................................................ 95 Elements of the Conceptual Model ...................................................................... 89 Accountability ........................................................................................... 96 Accreditation ............................................................................................. 98 Student success initiative ........................................................................... 98 Pressures on community colleges to use data to inform decisions ............ 99 Community colleges and DIDM ...............................................................100 Stakeholder theory and DIDM ..................................................................100 Data presentation .......................................................................................101 Communication .........................................................................................102 Transformation of data into information ...................................................102 New knowledge created ............................................................................102 Decision making ........................................................................................103 Managing knowledge ................................................................................103 Summary of Conceptual Model ...........................................................................104 Literature Review Summary ............................................................................................104 Chapter 4 – FINDINGS ...............................................................................................................106 Expert Panel Review ........................................................................................................107 Problem Statement and Significance of Problem ................................................109 Relevance of Theories..........................................................................................110 Scope of Research Questions ...............................................................................111 Organization .........................................................................................................113 Quality of Writing ................................................................................................113 Adequacy of References ......................................................................................113 Additional Expert Feedback ................................................................................114 Summary ..............................................................................................................114 Description of Case Study Sites: College A and College B ............................................115 Talk Data to Me: Bolstering the Communication of Data to Facilitate Data-Informed Decision Making in Community Colleges x Chapter 4 (continued) College A .............................................................................................................115 College A’s definition of student success .................................................116 College B .............................................................................................................116 College B’s definition of student success ..................................................117 Interview Guide (IG) Responses and Analysis of Research Questions ...........................118 Overview ..............................................................................................................118 Research Question 1 ............................................................................................119 IG Question 1 (IG1) ..................................................................................119 College A ..........................................................................................119 College B ..........................................................................................120 IG Question 2 (IG2) ..................................................................................121 College A ..........................................................................................121 College B ..........................................................................................121 IG Question 3 (IG3) ..................................................................................122 College A ..........................................................................................122 College B ..........................................................................................123 IG Question 4 (IG4) ..................................................................................123 College A ..........................................................................................123 College B ..........................................................................................124 IG Question 5 (IG5) ...................................................................