85,029 research outputs found
A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates
HIV dynamic studies have contributed significantly to the understanding of
HIV pathogenesis and antiviral treatment strategies for AIDS patients.
Establishing the relationship of virologic responses with clinical factors and
covariates during long-term antiretroviral (ARV) therapy is important to the
development of effective treatments. Medication adherence is an important
predictor of the effectiveness of ARV treatment, but an appropriate determinant
of adherence rate based on medication event monitoring system (MEMS) data is
critical to predict virologic outcomes. The primary objective of this paper is
to investigate the effects of a number of summary determinants of MEMS
adherence rates on virologic response measured repeatedly over time in
HIV-infected patients. We developed a mechanism-based differential equation
model with consideration of drug adherence, interacted by virus susceptibility
to drug and baseline characteristics, to characterize the long-term virologic
responses after initiation of therapy. This model fully integrates viral load,
MEMS adherence, drug resistance and baseline covariates into the data analysis.
In this study we employed the proposed model and associated Bayesian nonlinear
mixed-effects modeling approach to assess how to efficiently use the MEMS
adherence data for prediction of virologic response, and to evaluate the
predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Model-driven performance evaluation for service engineering
Service engineering and service-oriented architecture as an
integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of
measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised
for the empirical performance evaluation of model-driven developed service systems
Psychometric evaluation of the Disabilities of the Arm, Shoulder and Hand (DASH) with Dupuytren's contracture: validity evidence using Rasch modeling
Background Dupuytren’s contracture is a progressive, fibroproliferative disorder that causes fixed finger contractures and can lead to disability. With the advances of new therapeutic interventions, the necessity to assess the functional repercussions of this condition using valid, reliable and sensitive outcome measures is of growing interest. The Disabilities of the Arm, Shoulder and Hand (DASH) is one frequently used patient-reported outcome measure but its reliability and validity have never been demonstrated specifically for a population affected with Dupuytren’s contracture. The objective of this study was to evaluate the psychometric properties of the DASH, with focus on validity evidence using the Rasch measurement model. Methods Secondary analysis was performed on data collected as part of a randomised clinical trial. One hundred fifty-three participants diagnosed with Dupuytren’s contracture completed the DASH at four time points (pre-op, 3, 6 and 12 months post-op). Baseline data were analysed using traditional analysis and to test whether they adhered to the expectations of the Rasch model. Post-intervention data were subsequently included and analyzed to determine the effect of the intervention on the items. Results DASH scores demonstrated large ceiling effects at all time points. Initial fit to the Rasch model revealed that the DASH did not adhere to the expectations of the Rasch partial credit model (χ2 = 119.92; p < 0.05). Multiple items displayed inadequate response categories and two items displayed differential item functioning by gender. Items were transformed and one item deleted leading to an adequate fit. Remaining items fit the Rasch model but still do not target well the population under study. Conclusions The original version of the 30-item DASH did not display adequate validity evidence for use in a population with Dupuytren’s contracture. Further development is required to improve the DASH for this population
A Supervisor for Control of Mode-switch Process
Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, and adaptive controller could be used. When the operation point changes often, a large number of parameters would have to be adapted over and over again. This makes application of conventional adaptive control unattractive, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are co-ordinated by a ¿supervisor¿. This concept is referred to as a supervisor for control of mode-switch processes. It leads to an adaptive control structure which quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relearn each time. This approach has been tested on experimental set-ups of a flexible beam and of a flexible two-link robot arm, but it is directly applicable to other processes, for instance, in the (petro) chemical industry
Modeling a Sensor to Improve its Efficacy
Robots rely on sensors to provide them with information about their
surroundings. However, high-quality sensors can be extremely expensive and
cost-prohibitive. Thus many robotic systems must make due with lower-quality
sensors. Here we demonstrate via a case study how modeling a sensor can improve
its efficacy when employed within a Bayesian inferential framework. As a test
bed we employ a robotic arm that is designed to autonomously take its own
measurements using an inexpensive LEGO light sensor to estimate the position
and radius of a white circle on a black field. The light sensor integrates the
light arriving from a spatially distributed region within its field of view
weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by
incorporating an accurate model of the light sensor SSF into the likelihood
function of a Bayesian inference engine, an autonomous system can make improved
inferences about its surroundings. The method presented here is data-based,
fairly general, and made with plug-and play in mind so that it could be
implemented in similar problems.Comment: 18 pages, 8 figures, submitted to the special issue of "Sensors for
Robotics
High speed, precision motion strategies for lightweight structures
Research on space telerobotics is summarized. Adaptive control experiments on the Robotic Arm, Large and Flexible (RALF) were preformed and are documented, along with a joint controller design for the Small Articulated Manipulator (SAM), which is mounted on the RALF. A control algorithm is described as a robust decentralized adaptive control based on a bounded uncertainty approach. Dynamic interactions between SAM and RALF are examined. Unstability of the manipulator is studied from the perspective that the inertial forces generated could actually be used to more rapidly damp out the flexible manipulator's vibration. Currently being studied is the modeling of the constrained dynamics of flexible arms
Chalcogenide Glass-on-Graphene Photonics
Two-dimensional (2-D) materials are of tremendous interest to integrated
photonics given their singular optical characteristics spanning light emission,
modulation, saturable absorption, and nonlinear optics. To harness their
optical properties, these atomically thin materials are usually attached onto
prefabricated devices via a transfer process. In this paper, we present a new
route for 2-D material integration with planar photonics. Central to this
approach is the use of chalcogenide glass, a multifunctional material which can
be directly deposited and patterned on a wide variety of 2-D materials and can
simultaneously function as the light guiding medium, a gate dielectric, and a
passivation layer for 2-D materials. Besides claiming improved fabrication
yield and throughput compared to the traditional transfer process, our
technique also enables unconventional multilayer device geometries optimally
designed for enhancing light-matter interactions in the 2-D layers.
Capitalizing on this facile integration method, we demonstrate a series of
high-performance glass-on-graphene devices including ultra-broadband on-chip
polarizers, energy-efficient thermo-optic switches, as well as graphene-based
mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators
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