Control of One Percent Yield Offset in Reaction Injection Molding.

Reaction Injection Molding produces parts that must meet property specifications to be useful. For example a car bumper must have a high impact strength to prevent the car from being damaged during a collision. This research presents a control algorithm that can be implemented to control the part properties of reaction injection molded parts. The part property need not be measurable on line if the property is affected by the reaction conditions within the mold. This is an important feature of the algorithm since many important properties, such as yield strength, cannot be measured without destroying the part. The algorithm will of course work with on-line property measurements. The control algorithm implements two empirical models of the process. One model relates the variables that can be manipulated by the operator, such as reactant temperature and reactant ratio, to the desired part property and the other model relates model reaction conditions to the part property. The second model uses coefficients of a simplified reaction temperature profile model as the independent variables. A statistical process control filter developed by R. Rhinehart (Rhinehart, 1991) reduces the variation in the property estimates that are sent to the controller. The controller minimizes the moves in the manipulated variables subject to the estimate of the part property being equal to its set point for the part property. The control algorithm was tested on a RIM unit located in the Chemical Engineering Department at Louisiana State University. The algorithm when subjected to a set point change and a disturbance in the chain extender was able to follow the set point in only eight shots and to compensate for the disturbance by bringing the process back to set point within five shots

Social isolation: A conceptual and measurement proposal

Social isolation is a deprivation of social connectedness. It is a crucial aspect that continues to be named by people as a core impediment for achieving well-being and as a relevant factor for understanding poverty. The notion of social isolation has been discussed within a diversity of theories that have provided rich insights into particular aspects of social isolation. However, there is no agreement on the core components of this social malady or on how to measure it. Although the challenge of conceptualising and measuring social connectedness is daunting, this paper argues that existing research in several fields provides solid ground for a common concept and for the construction of basic internationally comparable indicators that measure specific aspects of social isolation. In particular, this paper aims to contribute to the debate on social connectedness and its measurement in three ways: (1) presenting a working definition that, while doing justice to the rich insights advanced by different theories, stresses relational features in the life experience of people; (2) emphasising the relevance of isolation for poverty analysis; and (3) proposing some indicators to measure social connectedness that could be feasibly incorporated into a multi-topic household survey

Shame, Humiliation and Social Isolation: Missing Dimensions of Poverty and Suffering Analysis

While people living in poverty talk about isolation, shame, and humiliation as being key aspects of their lived experiences of suffering, until recently, there has been no international data on these aspects – making them “missing dimensions” within poverty analysis and within research into suffering. Drawing upon international fieldwork and datasets from Chile and Chad, this chapter examines the relevance of social isolation, shame and humiliation in contexts of poverty, to research on suffering. The chapter suggests that the use of particular indicators of shame, humiliation, and social isolation can better recognize distributions of suffering. It can also help identify individuals and sub-groups within those living in multidimensional poverty – or of the general population at large – that are affected by concrete and particularly hurtful situations. Consequently, they can help to identify levels of suffering which are higher within a specific population. We argue that these types of indicators could form the basis of more refined measures that help generate more concise data on suffering

Geometric K-homology and the Atiyah-Singer index theorem

This thesis presents a proof of the Atiyah{Singer index theorem for twisted Spinc- Dirac operators using (geometric) K-homology. The case of twisted Spinc-Dirac operators is the most important case to resolve, and will proceed as a corollary of the computation that the K-homology of a point is Z. We introduce the topological index of a pair (M;E), indt(M;E) = (ch(E) [ Td(M))[M] and the analytic index inda(M;E) = dim(kerDE)⁺- dim(kerDE)- and show that they agree for a \test computation" on a pair of index 1. The main result is that both inda and indt are well-defined on classes [(M;E)] ∈ K0(·) and that there exists a representative on each class for which the analytic and topological indices agree, proving the index theorem for twisted Spinc-Dirac operators. We also present a description of an analogue the Atiyah{Singer index theorem when a compact Lie group action is introduced to (M;E) and an overview of the steps required prove this result.Thesis (MPhil.) -- University of Adelaide, School of Mathematical Sciences, 201

SAMSA: a comprehensive metatranscriptome analysis pipeline

BackgroundAlthough metatranscriptomics-the study of diverse microbial population activity based on RNA-seq data-is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes.ResultsWorking with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes. Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100 bp) or joined paired-end reads. Each sample needs 40-50 million raw sequences, which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that overall organism transcriptional activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism transcriptional activity and specific cellular functions.ConclusionsBy making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100 bp paired end format with a minimum of 40 million reads per sample

Why Patients Miss Scheduled Outpatient Appointments at Urban Academic Residency Clinics: A Qualitative Evaluation

Introduction Missed outpatient appointments are a common problem for academic residency clinics, and reducing their rate improves office efficiency, income, and resident education. Identifying specific reasons why some patients miss outpatient appointments may provide insight into developing targeted approaches to reducing their rates. This study sought to find reasons associated with patients’ missed appointments at two family medicine residency clinics. Methods The study utilized a qualitative research design involving patients at two urban, university-affiliated family medicine residency outpatient clinics. Twenty-five randomly selected patients who were dismissed from the clinics for missing three or more scheduled appointments during a five-year span (July 2012 to July 2017) were interviewed over the phone about reasons they did not keep their scheduled clinic appointments. The authors, individually and as a group, used an immersion-crystalization approach to analyze the content of the interviews. Results Responses from 25 participants (21 females and four males) are presented. Fifty-two percent of patients were Caucasian, 32% Black, 12% Hispanic, and 4% Asian. Five themes emerged from the data analysis as major reasons the patients missed their scheduled outpatient appointments: forgetfulness, transportation issues, personal health issues, family and employer obligations, and other issues, such as anticipated long clinic wait times, bad weather, and financial problems. Conclusions The findings showed there are several logistical, situational, and clinical reasons for patients’ missed scheduled outpatient appointments