FAIR_OSCE – Feedback structure for assessment of interactive roleplay in Objective Structured Clinical Exams

Introduction Since the quality of patient portrayal of standardized patients (SPs) during an Objective Structured Clinical Exam (OSCE) has a major impact on the reliability and validity of the exam, quality control should be initiated. Literature about quality control of SPs’ performance focuses on feedback [1, 2] or completion of checklists [3, 4]. Since we did not find a published instrument meeting our needs for the assessment of patient portrayal, we developed such an instrument after being inspired by others [5] and used it in our high-stakes exam. Project description SP trainers from five medical faculties collected and prioritized quality criteria for patient portrayal. Items were revised twice, based on experiences during OSCEs. The final instrument contains 14 criteria for acting (i.e. adequate verbal and non-verbal expression) and standardization (i.e. verbatim delivery of the first sentence). All partners used the instrument during a high-stakes OSCE. SPs and trainers were introduced to the instrument. The tool was used in training (more than 100 observations) and during the exam (more than 250 observations). Outcome High quality of SPs’ patient portrayal during the exam was documented. More than 90% of SP performances were rated to be completely correct or sufficient. An increase in quality of performance between training and exam was noted. For example, the rate of completely correct reaction in medical tests increased from 88% to 95%. Together with 4% of sufficient performances these 95% add up to 99% of the reactions in medical tests meeting the standards of the exam. SP educators using the instrument reported an augmentation of SPs’ performance induced by the use of the instrument. Disadvantages mentioned were the high concentration needed to observe all criteria and the cumbersome handling of the paper-based forms. Discussion We were able to document a very high quality of SP performance in our exam. The data also indicates that our training is effective. We believe that the high concentration needed using the instrument is well invested, considering the observed enhancement of performance. The development of an iPad-based application for the form is planned to address the cumbersome handling of the paper.

An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

A new feedback ANC system approach

We propose a new active noise control (ANC) technique. The technique has a feedback structure to have a simple configuration in practical implementation. In this approach, the secondary path is modelled online to ensure convergence of the system as the secondary paths are practically time varying or non-linear. The proposed method consists of two steps: a noise controller which is based on a modified FxLMS algorithm, and a new variable step size (VSS) LMS algorithm which is used to adapt the modelling filter with the secondary path. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Eliminating continuous injection of the white noise increases the performance of the proposed method significantly and makes it more desirable for practical ANC systems. The computer simulations are presented to show the effectiveness of the proposed method.

Global state synchronization in networks of cyclic feedback systems

This technical note studies global asymptotic state synchronization in networks of identical systems. Conditions on the coupling strength required for the synchronization of nodes having a cyclic feedback structure are deduced using incremental dissipativity theory. The method takes advantage of the incremental passivity properties of the constituent subsystems of the network nodes to reformulate the synchronization problem as one of achieving incremental passivity by coupling. The method can be used in the framework of contraction theory to constructively build a contracting metric for the incremental system. The result is illustrated for a network of biochemical oscillators. © 2011 IEEE.

Design of linear systems for decoupling and disturbance rejection

The question of achieving decoupling and asymptotic disturbance rejection in time-invariant linear multivariable systems subject to unmeasurable arbitrary disturbances of a given class is discussed. A synthesis procedure which determines a feedback structure, incorporating an integral compensator, is presented.

Electrically Pumped Room-Temperature Pulsed InGaAsP-Si Hybrid Lasers Based on Metal Bonding

A pulsed InGaAsP-Si hybrid laser is fabricated using metal bonding. A novel structure in which the optical coupling and metal bonding areas are transversely separated is employed to integrate the silicon waveguide with an InGaAsP multi-quantum well distributed feedback structure. When electrically pumped at room temperature, the laser operates with a threshold current density of 2.9 kA/cm(2) and a slope efficiency of 0.02 W/A. The 1542 nm laser output exits mainly from the Si waveguide.

Design of digital differentiators

A digital differentiator simply involves the derivation of an input signal. This work includes the presentation of first-degree and second-degree differentiators, which are designed as both infinite-impulse-response (IIR) filters and finite-impulse-response (FIR) filters. The proposed differentiators have low-pass magnitude response characteristics, thereby rejecting noise frequencies higher than the cut-off frequency. Both steady-state frequency-domain characteristics and Time-domain analyses are given for the proposed differentiators. It is shown that the proposed differentiators perform well when compared to previously proposed filters. When considering the time-domain characteristics of the differentiators, the processing of quantized signals proved especially enlightening, in terms of the filtering effects of the proposed differentiators. The coefficients of the proposed differentiators are obtained using an optimization algorithm, while the optimization objectives include magnitude and phase response. The low-pass characteristic of the proposed differentiators is achieved by minimizing the filter variance. The low-pass differentiators designed show the steep roll-off, as well as having highly accurate magnitude response in the pass-band. While having a history of over three hundred years, the design of fractional differentiator has become a ‘hot topic’ in recent decades. One challenging problem in this area is that there are many different definitions to describe the fractional model, such as the Riemann-Liouville and Caputo definitions. Through use of a feedback structure, based on the Riemann-Liouville definition. It is shown that the performance of the fractional differentiator can be improved in both the frequency-domain and time-domain. Two applications based on the proposed differentiators are described in the thesis. Specifically, the first of these involves the application of second degree differentiators in the estimation of the frequency components of a power system. The second example concerns for an image processing, edge detection application.

Dynamic grey-box modeling for online monitoring of extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple-fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

On the structure of state-feedback LQG controllers for distributed systems with communication delays

This paper presents explicit solutions for a few distributed LQG problems in which players communicate their states with delays. The resulting control structure is reminiscent of a simple management hierarchy, in which a top level input is modified by newer, more localized information as it gets passed down the chain of command. It is hoped that the controller forms arising through optimization may lend insight into the control strategies of biological and social systems with communication delays. © 2011 IEEE.

Re-conceptualizing the international aid structure: recipient donor interactions and the rudiments of a feedback mechanism

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The mode of action and the structure of a herbicide in complex with its target: binding of activated hydantocidin to the feedback regulation site of adenylosuccinate synthetase.

(+)-Hydantocidin, a recently discovered natural spironucleoside with potent herbicidal activity, is shown to be a proherbicide that, after phosphorylation at the 5' position, inhibits adenylosuccinate synthetase, an enzyme involved in de novo purine synthesis. The mode of binding of hydantocidin 5'-monophosphate to the target enzyme was analyzed by determining the crystal structure of the enzyme-inhibitor complex at 2.6-A resolution. It was found that adenylosuccinate synthetase binds the phosphorylated compound in the same fashion as it does adenosine 5'-monophosphate, the natural feedback regulator of this enzyme. This work provides the first crystal structure of a herbicide-target complex reported to date.