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The estimation and compensation of processes with time delays (1996)

O'Dwyer, Aidan

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Distributed optimization algorithm for discrete-time heterogeneous multi-agent systems with nonuniform stepsizes (2019)

Mo, L.; Li, J.; Huang, Jian

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Learning from Time Series: Supervised Aggregative Feature Extraction (2012)

Schirru, Andrea; Susto, Gian Antonio; Pampuri, Simone; McLoone, Sean F.

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Measurement of air exchange time in a mushroom tunnel (1999)

Martin, D.P.; Ringwood, John; Grant, J.J.

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An Efficient Phasiness Reduction Technique for Moderate Audio Time-scale Modification (2004)

Dorran, David; Coyle, Eugene; Lawlor, Bob

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Nonlinear dynamic transformer time-domain identification for power converter applications (2014)

Trong Vu, T.; O'Driscoll, S.; Ringwood, John

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Multiscaled cross-correlation dynamics in financial time series (2009)

Conlon, Thomas; Ruskin, Heather J.; Crane, Martin

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Turning time from enemy into an ally using the Pomodoro technique (2010)

Wang, Xiaofeng; Gobbo, Federico; Lane, Michael

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Providing Hard Real-Time Guarantees in Context-Aware Applications: Challenges and Requirements (2005)

MEIER, RENE; CAHILL, VINNY

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Accelerated Entry Point Search Algorithm for Real Time Ray Tracing (2009)

COLLINS, STEVEN; MANZKE, MICHAEL; FOWLER, COLIN

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Impact of speed change on estimated journey time: Failure of drivers to appreciate relevance of initial speed (2009)

GORMLEY, MICHAEL

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Nonlinear Damage Identification in Fiber-reinforced Cracked Composite Beams through Time-space Wavelet Analysis (2014)

BRODERICK, BRIAN; BASU, BISWAJIT

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Sodium-23 Magnetic Resonance Imaging has potential for improving penumbra detection but not for estimating stroke onset time (2014)

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Compile-time optimisation of store usage in lazy functional programs (1993)

Hamilton, Geoffrey

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Time- and momentum-resolved probe of heat transport in photo-excited bismuth (2013)

Chen, J.; Trigo, M.; Fahy, Stephen B.; Murray, Éamonn D.; Sheu, Y. M.; Graber, T.; Henn...

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Time and technology in Orlando (2014)

O'Sullivan, James

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Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis. (2017)

Ajnakina, Olesya; Lally, John; Di Forti, Marta; Stilo, Simona A; Kolliakou, Anna; Gardn...

Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis. (2017)

Ajnakina, Olesya; Lally, John; Di Forti, Marta; Stilo, Simona A; Kolliakou, Anna; Gardner-Sood, Poonam; Dazzan, Paola; Pariante, Carmine; Reis Marques, Tiago; Mondelli, Valeria; MacCabe, James; Gaughran, Fiona; David, Anthony S; Stamate, Daniel; Murray, Robin M; Fisher, Helen L

Abstract:

<p>The original article is available at https://www.sciencedirect.com </p>
<p>There has been much recent debate concerning the relative clinical utility of symptom dimensions versus conventional diagnostic categories in patients with psychosis. We investigated whether symptom dimensions rated at presentation for first-episode psychosis (FEP) better predicted time to first remission than categorical diagnosis over a four-year follow-up. The sample comprised 193 FEP patients aged 18-65years who presented to psychiatric services in South London, UK, between 2006 and 2010. Psychopathology was assessed at baseline with the Positive and Negative Syndrome Scale and five symptom dimensions were derived using Wallwork/Fortgang's model; baseline diagnoses were grouped using DSM-IV codes. Time to start of first remission was ascertained from clinical records. The Bayesian Information Criterion (BIC) was used to find the best fitting accelerated failure time model of ...