Currere, Illness, and Motherhood: A Dwelling Place for Examining the Self

This dissertation is a pathography, my experience as a mother dwelling with illness which began because of my son\u27s illness. The purpose of this dissertation is two-fold: to examine my Self as a mother dwelling with illness so that I may begin to work through repressed emotions and to further complicate the conversation begun by Marla Morris (2008) by illuminating the ill person\u27s voice as one which is underrepresented in the canon. This dissertation is written autobiographically and analyzed psychoanalytically. The subjects of chaos, the Self, and motherhood are examined as they apply to my illness. In addition to psychoanalysis, this dissertation draws from illness narratives, pathographies, and other stories of illness as a way to collaborate voices within the illness community

Certain types of alimentary neurosis

Certain dysfunctions of the alimentary tract - Achalasia of the Cardia, Pyloric Spasm, ,:egacolon, or liirschprung's Disease - are considered to be part expressions of a,eneraliaJd neurosis of psychogenic origin, in which heredity, various environmental factors, and infection, have a marked influence.The neurosis produces its effects by imbalance of the autonomic nervous system, and is benefited by psychotherapeutic treatment in conjunction with which drugs, physical measures, and surgical treatment have a limited place

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Exploring parental experiences and decision-making processes following a fetal anomaly diagnosis

Often the first indication that something may be wrong in a seemingly normal pregnancy occurs during the first detailed ultrasound appointment between 16 and 20 weeks gestation. Even the most tentative suspicions of fetal anomalies is jarring. Parent’s default reality of a normal pregnancy and a ‘perfect child’ changes to one of risk factors and the possibility of an ‘unhealthy child’. This study begins with the realization of this first loss in a series of losses that follow for parents as they grapple with diagnostic information to be able to make informed medical decisions regarding their fetus and pregnancy. The study aims to explore the gap between clinical/ professional knowledge and the private worldviews of parents when they return home to process the information and make decisions. This study was situated within a Canadian healthcare context that provides prenatal screening and medical care within a socialized medicine system. Using Grounded Theory methodology, this study bridges the disciplinary boundaries of Thanatology, Psychology, Bioethics and Reproductive medicine to explore the lived experience and the processes of personal/emotional decision making of parents, as well as a needs assessment of services. The process of inquiry and the results will be discussed with relevance to scholars and clinicians on the context of end-of-life decision making that occurs within the prenatal context. Theoretical lens also examines the multiple death related and non-death losses as well as the reframed identity of parents and their unborn babies following a diagnosis of fetal anomalies

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

GROK-FPGA: Generating Real on-Chip Knowledge for FPGA Fine-Grain Delays Using Timing Extraction

Circuit variation is one of the biggest problems to overcome if Moore\u27s Law is to continue. It is no longer possible to maintain an abstraction of identical devices without huge yield losses, performance penalties, and energy costs. Current techniques such as margining and grade binning are used to deal with this problem. However, they tend to be conservative, offering limited solutions that will not scale as variation increases. Conventional circuits use limited tests and statistical models to determine the margining and binning required to counteract variation. If the limited tests fail, the whole chip is discarded. On the other hand, reconfigurable circuits, such as FPGAs, can use more fine-grained, aggressive techniques that carefully choose which resources to use in order to mitigate variation. Knowing which resources to use and avoid, however, requires measurement of underlying variation. We present Timing Extraction, a methodology that allows measurement of process variation without expensive testers nor highly invasive techniques, rather, relying only on resources already available on conventional FPGAs. It takes advantage of the fact that we can measure the delay of logic paths between any two registers. Measuring enough paths, provides the information necessary to decompose the delay of each path into individual components-essentially, forming a system of linear equations. Determining which paths to measure requires simple graph transformation algorithms applied to a representation of the FPGA circuit. Ultimately, this process decomposes the FPGA into individual components and identifies which paths to measure for computing the delay of individual components. We apply Timing Extraction to 18 commercially available Altera Cyclone III (65 nm) FPGAs. We measure 22×28 logic clusters and the interconnect within and between cluster. Timing Extraction decomposes this region into 1,356,182 components, classified into 10 categories, requiring 2,736,556 path measurements. With an accuracy of ±3.2 ps, our measurements reveal regional variation on the order of 50 ps, systematic variation from 30 ps to 70 ps, and random variation in the clusters with σ=15 ps and in the interconnect with σ=62 ps