1,529 research outputs found

    Miniature distributed filters for software re-configurable radio applications

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    Mixer linearisation for software defined radio applications

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    A software defined radio receiver test-bed

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    Multiple imputation using linked proxy outcome data resulted in important bias reduction and efficiency gains: a simulation study.

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    BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depends on outcome values), estimates of the effect of an exposure on this outcome are often biased. We investigated the extent of this bias and examined whether the bias can be reduced through incorporating proxy outcomes obtained through linkage to administrative data as auxiliary variables in multiple imputation (MI). METHODS: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) we estimated the association between breastfeeding and IQ (continuous outcome), incorporating linked attainment data (proxies for IQ) as auxiliary variables in MI models. Simulation studies explored the impact of varying the proportion of missing data (from 20 to 80%), the correlation between the outcome and its proxy (0.1-0.9), the strength of the missing data mechanism, and having a proxy variable that was incomplete. RESULTS: Incorporating a linked proxy for the missing outcome as an auxiliary variable reduced bias and increased efficiency in all scenarios, even when 80% of the outcome was missing. Using an incomplete proxy was similarly beneficial. High correlations (> 0.5) between the outcome and its proxy substantially reduced the missing information. Consistent with this, ALSPAC analysis showed inclusion of a proxy reduced bias and improved efficiency. Gains with additional proxies were modest. CONCLUSIONS: In longitudinal studies with loss to follow-up, incorporating proxies for this study outcome obtained via linkage to external sources of data as auxiliary variables in MI models can give practically important bias reduction and efficiency gains when the study outcome is MNAR

    Enabling technologies for software defined radio transceivers

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    Interview of Sidney J. MacLeod, Jr., M.F.A.

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    Sidney MacLeod (often called Sid) was born in 1933 in Chicago, Illinois. He is the oldest of three children and the only boy. He earned his M.S.S. at Saint Mary’s College in Winona, Minnesota and his M.F.A. at Catholic University in Washington, D.C. After graduate school he was drafted into the U.S. Army where he served two years on several domestic military bases. He began working at La Salle in 1959. In 1961 he married his wife, Mary Jane. They have four children (three sons and one daughter). He continues to work at La Salle full-time. When he retires he looks forward to travelling with his wife. According to his biography on the La Salle University Communications Department website (5/6/13), If something isn’t working in the Communication Center, Chicago native Sid MacLeod is usually there to repair, replace, create, paint, or take it apart. When not maintaining everything in the Communication Center, Sid is a huge Broadway fan and he also enjoys gardening, cooking, and cleaning. A Lindback Distinguished Teaching award winner and a Distinguished Lasallian Educator, Sid teaches Media Production. Sid feels that it’s important that students are able to master the equipment, technology, and procedures for successful film, video, and audio work. Sid’s many contributions to La Salle and its students will be forever remembered with the Sid MacLeod Endowed Fund

    Poor Individual Risk Classification From Adverse Childhood Experiences Screening

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    Introduction: Adverse childhood experiences confer an increased risk for physical and mental health problems across the population, prompting calls for routine clinical screening based on reported adverse childhood experience exposure. However, recent longitudinal research has questioned whether adverse childhood experiences can accurately identify ill health at an individual level. // Methods: Revisiting data collected for the Adverse Childhood Experience Study between 1995 and 1997, this study derived approximate area under the curve estimates to test the ability of the retrospectively reported adverse childhood experience score to discriminate between adults with and without a range of common health risk factors and disease conditions. Furthermore, the classification accuracy of a recommended clinical definition for high-risk exposure (≥4 versus 0–3 adverse childhood experiences) was evaluated on the basis of sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios. // Results: Across all health outcomes, the levels of discrimination for the continuous adverse childhood experience score ranged from very poor to fair (area under the curve=0.50–0.76). The binary classification of ≥4 versus 0–3 adverse childhood experiences yielded high specificity (true-negative detection) and negative predictive values (absence of ill health among low-risk adverse childhood experience groups). However, sensitivity (true-positive detection) and positive predictive values (presence of ill health among high-risk adverse childhood experience groups) were low, whereas positive likelihood ratios suggested only minimal-to-moderate increases in health risks among individuals reporting ≥4 adverse childhood experiences versus that among those reporting 0–3. // Conclusions: These findings suggest that screening based on the adverse childhood experience score does not accurately identify those individuals at high risk of health problems. This can lead to both allocation of unnecessary interventions and lack of provision of necessary support
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