Terahertz Radar Cross Section Characterization using Laser Feedback Interferometry with a Quantum Cascade Laser

Radar cross section (RCS) measurements of complex, large objects are usually performed on scale models so that the measurement is carried out in a well-controlled environment. This letter explores the feasibility of RCS measurement using a terahertz quantum cascade laser via laser feedback interferometry. Numerical simulations show that the RCS information embedded in the non-linear interferometric signals obtained from simple targets can be retrieved through numerical fitting of the well-known excess phase equation. The method is validated experimentally using a terahertz quantum cascade laser and the results are well matched with those obtained from numerical simulations

Manufacturing flow line systems: a review of models and analytical results

The most important models and results of the manufacturing flow line literature are described. These include the major classes of models (asynchronous, synchronous, and continuous); the major features (blocking, processing times, failures and repairs); the major properties (conservation of flow, flow rate-idle time, reversibility, and others); and the relationships among different models. Exact and approximate methods for obtaining quantitative measures of performance are also reviewed. The exact methods are appropriate for small systems. The approximate methods, which are the only means available for large systems, are generally based on decomposition, and make use of the exact methods for small systems. Extensions are briefly discussed. Directions for future research are suggested.National Science Foundation (U.S.) (Grant DDM-8914277

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Shape memory alloy tufted composites combining high delamination resistant and crack closure properties

A new type of fibre reinforced polymer composite is presented that uniquely combines high delamination resistance with crack closure properties via the use of tufted shape memory alloy (SMA) filaments. A carbon-epoxy laminate was tufted in the through-thickness direction using thin SMA filaments of Ni-Ti alloy (nitinol). The SMA tufts increase the mode I interlaminar fracture toughness of the laminate by forming a large-scale bridging process zone along the delamination crack. Following crack growth, electrical heating of the SMA tufts activates a shape memory effect that partially closes the delamination. Finite element (FE) analysis reveals that complete crack closure occurs when the SMA tufts are not deformed above the shape memory strain limit. The use of SMA tufts offers the important opportunity to produce a new class of damage tolerant composite material that both resist the opening and aid the closing of delamination cracks formed by overloading, impact or other damaging events