Objective: To determine if rigid adherence (where medically appropriate) to an algorithm/checklist-based patient care pathway can reduce the duration of hospitalization and complication rates in patients undergoing he...
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Objective: To determine if rigid adherence (where medically appropriate) to an algorithm/checklist-based patient care pathway can reduce the duration of hospitalization and complication rates in patients undergoing head and neck reconstruction with free tissue transfer. Methods: Study design was a retrospective case-control study of patients undergoing major head and neck cancer resections and reconstruction at a tertiary referral centre. The intervention was rigid adherence to a pre-existing care pathway including flow algorithms and multidisciplinary checklists incorporated into patient charting and care orders. 157 patients were enrolled prospectively and were compared to 99 patients in a historical cohort. Patient charts were reviewed and information related to the patient, procedure, and post-operative course was extracted. The two groups were compared for number of major and minor complications (using the Clavien-Dindo system) and length of stay in hospital. Results: Comparing pre- and post-intervention groups, no significant difference was identified in duration of hospital stay (21.5 days vs. 20.5 days, p = 0.750), the rate of major complications was significantly higher in the pre-intervention cohort (25.3 % vs. 14.0 %, p = 0.031), the rate of minor complications was not significantly higher (34.3 % vs 30.8 %, p = 0.610). Conclusion: Rigid adherence to our patient care pathway, and improved charting techniques including flow algorithms and multidisciplinary checklists has improved patient care by showing a significant reduction in the rate of major complications.
Lots of prior models for natural image wavelet coefficients have been proposed in the last two decades. Although most of them belong to the Scale Mixture of Gaussian (GSM) models, they are of obviously different analy...
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Lots of prior models for natural image wavelet coefficients have been proposed in the last two decades. Although most of them belong to the Scale Mixture of Gaussian (GSM) models, they are of obviously different analytical forms. As a result, Bayesian image denoising algorithms based on these prior models are also very different from each other. In this paper, we develop a novel image denoising algorithm by combining the Expectation Maximization (EM) scheme and the properties of the GSM models. The developed algorithm is of a simple iterative form and can converge quickly. It only uses the derivative information of a probability density function and is suitable for all GSM-type prior models that have an analytical probability density function. The developed algorithm can be viewed as a unified Bayesian image denoising framework. As examples, several classical and recently-proposed prior models for natural image wavelet coefficients are tested and some new results are obtained. (c) 2008 Optical Society of America.
A modified Levenberg-Marquardt (MLM) algorithm is proposed to substitute for modified G-S (MGS) algorithm in some situations of phase-diverse phase retrieval wavefront sensing (WFS), such as the obstructed pupil, in w...
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A modified Levenberg-Marquardt (MLM) algorithm is proposed to substitute for modified G-S (MGS) algorithm in some situations of phase-diverse phase retrieval wavefront sensing (WFS), such as the obstructed pupil, in which the second derivative information is specifically employed to eliminate the local minimum stagnation. Experiments have been performed to validate MLM algorithm in WFS accuracy (less than lambda/30 RMS) referring to ZYGO interferometer results and in WFS repeatability (less than lambda/200 RMS), even the dynamic range is more than 7 lambda PV. Moreover, experiments have shown the MLM algorithm is superior to the MGS algorithm both in WFS accuracy and repeatability. (C) 2009 Optical Society of America
A novel, speckle noise reduction algorithm based on the combination of Anisotropic Diffusion (AD) filtering and Interval Type-II fuzzy sets was developed for reducing speckle noise in Optical Coherence Tomography (OCT...
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A novel, speckle noise reduction algorithm based on the combination of Anisotropic Diffusion (AD) filtering and Interval Type-II fuzzy sets was developed for reducing speckle noise in Optical Coherence Tomography (OCT) images. Unlike regular AD, the new Type-II fuzzy AD algorithm considers the uncertainty in the calculated diffusion coefficient and appropriate adjustments to the coefficient are made. The new algorithm offers flexibility in optimizing the trade-off between two of the image metrics: signal-to-noise (SNR) and Edginess, which are directly related to the structure of the imaged object. Application of the Type-II fuzzy AD algorithm to OCT tomograms acquired in-vivo from a human finger tip and human retina show reduction in the speckle noise with very little edge blurring and about 13 dB and 7 dB image SNR improvement respectively. Comparison with Wiener, Adaptive Lee and regular AD filters, applied to the same images, demonstrates the superior performance of the Type-II fuzzy AD algorithm in terms image SNR and edge preservation metrics improvement. (C) 2008 Optical Society of America
作者：
Ismkhan, HassanUniv Bonab
Fac Engn Dept Comp Engn Velayat HighwayPOB *** Bonab East Azerbaijan Iran
The rate of mutation has deep effects on the performance of genetic algorithm (GA). Current mechanisms to control mutation rate (MR) utilize the mathematical functions which usually are monotonic. These mechanisms are...
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The rate of mutation has deep effects on the performance of genetic algorithm (GA). Current mechanisms to control mutation rate (MR) utilize the mathematical functions which usually are monotonic. These mechanisms are too rigid and inflexible. These methods change the MR without enough attending the position of GA. For instance these methods don't attend whether GA is in trap or not. This research proposes a novel mechanism which controls MR by an algorithm which uses a concept of defined local trap. This algorithm probes whether GA is in the local trap or is not. In case of local trap, it changes the MR. This methodology is named MRCA (Mutation Rate Control algorithm). To evaluate performance of MRCA, it is applied to multimodal continuous optimization functions and also a type of combinatorial optimization problem. The results show that MRCA outperforms other state-of-the-art strategy in term of accuracy and speed.
An efficient short-term hydrothermal scheduling algorithm based on the evolutionary programming (EP);technique is proposed. In the algorithm, the thermal generating units in the system are represented by an equivalent...
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An efficient short-term hydrothermal scheduling algorithm based on the evolutionary programming (EP);technique is proposed. In the algorithm, the thermal generating units in the system are represented by an equivalent unit. The power balance constraints, total water discharge constraint, reservoir volume constraints and the constraints on the operation limits of the equivalent thermal and hydro units are fully taken into account. The effectiveness of the proposed algorithm is demonstrated through an example system and the results are compared with those obtained by the classical gradient search and simulated annealing (SA) approaches. Numerical results show that the proposed EP approach provides a cheaper schedule even than the SA approach and hence, has more powerful ability to achieve the global optimum solution than the SA approach. (C) 1999 Elsevier Science S.A. All rights reserved.
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