Estimating Load Distributions for Retaining Structures Subjected to Railroad Live Loads

Retaining structures in proximity to railroads experience a variety of lateral loading intensities depending on the live load surcharge. Current design guidelines recommend using the Boussinesq solution for computing lateral loads due to stress influence of the surcharge load. Although this method provides a conservative approach for design of retaining structures, it employs various assumptions that are no longer valid in the cases of non-uniform soil conditions and structures with flexible responses to loading. As a result, a model that captures a closer estimate of soil-structure interaction behavior is desired. An analytical model derived from beam theory is presented in this thesis. This model implements the method of initial parameters to solve for a beam equation for a 3rd-order distributed load. A program was written in Python to solve for the coefficients in the beam equation using a least squares regression. The inputs for this program are strain measurements to be obtained from a test site, and the outputs are the regression coefficients and stresses associated with the input strains. The motivation behind this approach is to analyze future experimental data for a retaining wall constructed at a test site in proximity to a railroad. Analysis of sample strain values produced a regression curve that closely matched the expected distributions associated with strain values. It was also found that the order of the regression could be adjusted if needed to reduce error of the resulting curves. Ultimately, the model produced in this research can be used for estimating loads on a full-scale test wall

Privacy-Preserving Screen Capture: Closing the Loop for Medical Informatics Usability

As information technology permeates healthcare (particularly provider-facing systems), maximizing system effectiveness requires the ability to document and analyze tricky or troublesome usage scenarios. However, real-world medical applications are typically replete with privacy-sensitive data regarding patients, diagnoses, clinicians, and EMR user interface details; any instrumentation for screen capture (capturing and recording the scenario depicted on the screen) needs to respect these privacy constraints. Furthermore, real-world medical informatics systems are typically composed of modules from many sources, mission-critical and often closed-source; any instrumentation for screen capture cannot rely on access to structured output or software internals. In this paper, we present a solution: a system that combines keyboard video mouse (KVM) capture with automatic text redaction (and interactively selectable unredaction) to produce precise technical content that can enrich stakeholder communications and improve end-user influence on system evolution. KVM-based capture makes our system both application and operating-system independent because it eliminates software-interface dependencies on capture targets. Using a corpus of EMR screenshots, we present empirical measurements of redaction effectiveness and processing latency to demonstrate system performances. We discuss how these techniques can translate into instrumentation systems that improve real-world medical informatics deployments

Preparing prospective teachers on the web

This is the publisher's version, collected from: http://www.cec.sped.org/Part of a special issue on the use of the World Wide Web in special education. The writers describe how they have started to integrate the Internet and World Wide Web into preservice teacher education in the hope of expanding the instructional use of technology in special education classrooms

Peer Learning in Virtual Schools

This is the published version. Copyright 2014 Canadian Network for Innovation in EducationThis article is about peer-to-peer learning amongst students within K–12 virtual schools. This issue is examined through a case study of experiences of three students with disabilities enrolled in one virtual school and that of their parents, teachers, and school administrators. The article is framed around variability in learners’ aptitudes for peer-to-peer learning, in the design of the learning environment and what it affords for interpersonal interactions, and in the context where that learning design is implemented (and whether or not it promotes peer-to-peer learning). Each of these areas of variability impacted whether or not peer-to-peer learning occurred

Teachers Learn About ADHD on the Web: An Online Graduate Special Education Course

This is the publisher's version, also found here: www.sped.orgDescribes several possibilities for using Internet-based applications to enhance teacher preparation to better serve students with attention-deficit/hyperactivity disorder (ADHD). Features of the Web-enhanced course, The Learner with ADHD which offers a general introduction to characteristics, treatment and education of students with ADHD. INSETS: Perspectives on distance learning; WebCT-an online course tool

The Scaled Arrival of K-12 Online Education: Emerging Realities and Implications for the Future of Education

Bioinfo PublicationsDramatic increases in K—12 online education for all students, including those in traditionally underserved populations, necessi2 tate a reconceptualization in the way educators plan and implement instruction. In this article the authors examine the complex array of variables and implementation models that must be accounted for during the pivot from a purely brick-and-mortar educational sys2 tem to one that makes use of both virtual and blended environ2 ments. The authors call for enhanced emphasis on instructional goals and design principles, rather than the capabilities of available technology. They conclude that educational leaders and researchers must play a role in three key areas: using technology to enhance the accessibility and usability of curricular materials to meet the needs of different types of learners, advancing the understanding and practices of in-service and pre-service teachers through preparation that focuses on online learning, and fostering collaboration between educational researchers and technology innovators and developers to build a research base that will inform K—12 online education

Invited In: Measuring UDL in Online Learning

This report on K-12 blended and fully online lessons employs the Universal Design for Learning framework to explore and evaluate the appropriateness of online content and instruction, especially for the struggling learner and those with disabilities. Invited In seeks to offer information to administrators, teachers, and parents to help them understand the online materials used by K-12 students and be able to determine how to foster optimal K-12 online learning options for struggling learners and students with disabilities

The impact of instruction- and experience-based evaluative learning on IAT performance : a quad model perspective

Learning procedures such as mere exposure, evaluative conditioning, and approach/avoidance training have been used to establish evaluative responses as measured by the Implicit Association Test (IAT). In this paper, we used the Quad model to disentangle the processes driving IAT responses instantiated by these evaluative learning procedures. Half of the participants experienced one of these three procedures whereas the other half only received instructions about how the procedure would work. Across three experiments (total n = 4231), we examined the extent to which instruction-based versus experience-based evaluative learning impacted Quad estimates of the Activation of evaluative information in IAT responses. Relative to a control condition, both instruction- and experience-based evaluative learning procedures influenced Activation. Moreover, and contrary to what prevailing models of implicit evaluations would predict, in no instance did experience-based procedures influence (positive or negative) Activation more strongly than instruction-based procedures. This was true for analyses which combined procedures and also when testing all three procedures individually. Implications for the processes that mediate evaluative learning effects and the conditions under which those processes operate are discussed

GROM-RD: Resolving Genomic Biases to Improve Read Depth Detection of Copy Number Variants

Amplifications or deletions of genome segments, known as copy number variants (CNVs), have been associated with many diseases. Read depth analysis of next-generation sequencing (NGS) is an essential method of detecting CNVs. However, genome read coverage is frequently distorted by various biases of NGS platforms, which reduce predictive capabilities of existing approaches. Additionally, the use of read depth tools has been somewhat hindered by imprecise breakpoint identification. We developed GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. We found non-uniform variance across distinct GC regions after using existing GC bias correction methods and developed a novel approach to normalize such variance. Although complex and repetitive genome segments complicate CNV detection, GROM-RD adjusts for repeat bias and uses a two-pipeline masking approach to detect CNVs in complex and repetitive segments while improving sensitivity in less complicated regions. To overcome a typical weakness of RD methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. We compared our method to two widely used programs based on read depth methods, CNVnator and RDXplorer, and observed improved CNV detection and breakpoint accuracy for GROM-RD. GROM-RD is available a

Coordinated river infrastructure decisions improve net social-ecological benefits

We explore the social, ecological, economic, and technical dimensions of sustainable river infrastructure development and the potential benefits of coordinating decisions such as dam removal and stream crossing improvement. Dam removal is common practice for restoring river habitat connectivity and ecosystem health. However, stream crossings such as culverts are often 15 times more abundant than dams and may pose similar ecological impacts. Using multi-objective optimization for a model system of 6100 dams and culverts in Maine, USA, we demonstrate substantial benefit-cost improvements provided by coordinating habitat connectivity decisions. Benefit-cost efficiency improves by two orders of magnitude when coordinating more decisions across wider areas, but this approach may cause inequitable resource distribution. Culvert upgrades improve roadway safety and habitat connectivity, creating cost-effective opportunities for coordinating and cost-sharing projects between conservationists and safety managers. Benefit-cost trends indicate significant overlaps in habitat and safety goals, encouraging flexible stakeholder collaborations and cost-sharing strategies