Concept for sleeve induction motor with 1-msec mechanical time constant

Conductive sleeve induction motor having a 1-msec mechanical time constant is used with solid-state devices to control all-electric servo power systems. The servomotor rotor inertia is small compared to the maximum force rating of the servo motion, permitting high no-load acceleration

Improving the monitoring and evaluation of schistosomiasis by determining appropriate targets and utilizing new technologies

The World Health Organization’s framework for the assessment of schistosomiasis morbidity control utilizes the prevalence of heavy-intensity infections in a homogenous ecological zone. The foundational research for the use of heavy-intensity infections is at least 30 years old. Research since then has illuminated the relationship between Schistosoma infection and all morbidity. In addition, severe, chronic, schistosomiasis morbidity is less common due to increasing dissemination of preventive chemotherapy. There are calls for improvements to the monitoring and evaluation framework, especially relating to the measurement of schistosomiasis morbidity. The focus of this thesis was to improve the schistosomiasis monitoring and evaluation framework by evaluating whether those current infection measures are linked to morbidity indicators. For those measures linked to indicators, an attempt was made to calculate programmatic targets linked to morbidity using robust methods. Targets based on microhaematuria prevalence were calculated based on community-level S. haematobium prevalence. For S. mansoni, associations between infection and morbidity were much weaker and it appears unlikely that a reliable target can be found. S. mansoni morbidity control may require changes to accurately measure the S. mansoni morbidity burden in a geographic area. Incorporating new technologies, such as portable, tablet-based ultrasound systems, may allow researchers and control programs to collect schistosomiasis morbidity indicators

RascalC: A Jackknife Approach to Estimating Single and Multi-Tracer Galaxy Covariance Matrices

To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs $\sim 10^4$ times faster than previous codes, computing finely-binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot-noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, RascalC, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input dataset (without a prior 2PCF model). Deviations between large scale model covariances from a mock survey and those from a large suite of mocks are found to be be indistinguishable from noise. In addition, the choice of input mock are shown to be irrelevant for desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization to multi-tracer data-sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.Comment: 29 pages, 8 figures. Accepted by MNRAS. Code is available at http://github.com/oliverphilcox/RascalC with documentation at http://rascalc.readthedocs.io

Using Intelligent Agents to Manage Business Processes

This paper describes work undertaken in the ADEPT (Advanced Decision Environment for Process Tasks) project towards developing an agent-based infrastructure for managing business processes. We describe how the key technology of negotiating, service providing, autonomous agents was realised and demonstrate how this was applied to the BT business process of providing a customer quote for network services

Evaluating the impact of road infrastructure on household income in Papua New Guinea:Spatial data compilation and analysis

As part of the ADB sub‐project ‘Developing Impact Evaluation Methodologies, Approaches, and Capacities in Selected Developing Member Countries’ two closely related research projects were carried out by VU to evaluate the impact of road infrastructure on household income in Papua New Guinea. One project focused on econometric analysis (contract no. 117629‐S85196), while the other developed the spatial database needed for this analysis (contract no. 117642‐S84962). The current report documents the data collection process and provides a concise description of the data that were collected for the project and the analyses that were performed to enrich the available data sources to create meaningful variables for the statistical analysis. It serves as a background document with the scientific paper‐that concisely describes the findings of the two joint research projects

Guidance for Improving Foundation Layers to Increase Pavement Performance on Local Roads

This document is the second of two deliverables for the project Optimizing Pavement Base, Subbase, and Subgrade Layers for Cost and Performance on Local Roads (TR-640). The first deliverable is the 454-page Final Field Data Report. The field data report describes test results and comparative analysis from 16 different portland cement concrete (PCC) pavement sites on local city and county roads in Iowa. At each site the surface conditions of the pavement (i.e., crack survey) and foundation layer strength, stiffness, and hydraulic conductivity properties were documented. The field test results were used to calculate in situ parameters used in pavement design methodologies for AASHTO (1993) and Iowa’s Statewide Urban Design and Specifications (SUDAS). Overall, the results of the study demonstrate how in situ and lab testing can be used to assess the support conditions and design values for pavement foundation layers and how the measurements compare to the assumed design values. This guide summarizes the study results and outlines general guidelines for applying them to optimize pavement bases, subbases, and subgrade layers of local roads with PCC pavements and thus their performance

An in vitro DNA sensor-based assay to measure receptor-specific adhesion forces of eukaryotic cells and pathogens

Motility of eukaryotic cells or pathogens within tissues is mediated by the turnover of specific interactions with other cells or with the extracellular matrix. Biophysical characterization of these ligand-receptor adhesions helps to unravel the molecular mechanisms driving migration. Traction force microscopy or optical tweezers are typically used to measure the cellular forces exerted by cells on a substrate. However, the spatial resolution of traction force microscopy is limited to ~2 μm and performing experiments with optical traps is very time-consuming. Here we present the production of biomimetic surfaces that enable specific cell adhesion via synthetic ligands and at the same time monitor the transmitted forces by using molecular tension sensors. The ligands were coupled to double-stranded DNA probes with defined force thresholds for DNA unzipping. Receptor-mediated forces in the pN range are thereby semi-quantitatively converted into fluorescence signals, which can be detected by standard fluorescence microscopy at the resolution limit (~0.2 μm). The modular design of the assay allows to vary the presented ligands and the mechanical strength of the DNA probes, which provides a number of possibilities to probe the adhesion of different eukaryotic cell types and pathogens and is exemplified here with osteosarcoma cells and Plasmodium berghei Sporozoites

Use of soil moisture information in yield models

There are no author-identified significant results in this report

Model Generation to Support Model-Based Testing Applied on NASA DAT - An Experience Report

Model-based Testing (MBT), where a model of the system under tests (SUT) behavior is used to automatically generate executable test cases, is a promising and versatile testing technology. Nevertheless, adoption of MBT technologies in industry is slow and many testing tasks are performed via manually created executable test cases (i.e. test programs such as JUnit). In order to adopt MBT, testers must learn how to construct models and use these models to generate test cases, which might be a hurdle. An interesting observation in our previous work is that the existing manually created test cases often provided invaluable insights for the manual creation of the testing models of the system. In this paper we present an approach that allows the tester to first create and debug a set of test cases. When the tester is happy with the test cases, the next step is to automatically generate a model from the test cases. The generated model is derived from the test cases, which are actions that the system can perform (e.g. a button clicks) and their expected outputs in form of assert statements (e.g. assert data entered). The model is a Finite State Machine (FSM) model that can be employed with little or no manual changes to generate additional test cases for the SUT. We successfully applied the approach in a feasibility study to the NASA Data Access Toolkit (DAT), which is a web-based GUI. One compelling finding is that the test cases that were generated from the automatically generated models were able to detect issues that were not detected by the original set of manually created test cases. We present the findings from the case study and discuss best practices for incorporating model generation techniques into an existing testing process