The Experts below are selected from a list of 139902 Experts worldwide ranked by ideXlab platform
Vinyas M. - One of the best experts on this subject based on the ideXlab platform.
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An Optimization Strategy for customized radiotherapy head immobilization masks
'Institute of Electrical and Electronics Engineers (IEEE)', 2019Co-Authors: Craveiro D.s., Vieira, Lina Oliveira, Loja Amélia, Vinyas M.Abstract:Este trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2016 do Instituto Politécnico de Lisboa. Código de referência IPL/2016/SoftImob_ISELAn effective head immobilization is an important requirement in radiotherapy treatment sessions, although it may also be thought in the future as a precious aid in brain medical imaging. Thus, the present work is focused on the stiffness Optimization of a customized head immobilization mask, modelled upon the head reconstruction surface based on computerized tomography images. This paper proposes a Strategy supported by a metaheuristic Optimization technique and a metamodeling approach for the whole mask, illustrated at its most unfavorable region occurring in the gnathion region.info:eu-repo/semantics/publishedVersio
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An Optimization Strategy for customized radiotherapy head immobilization masks
'Institute of Electrical and Electronics Engineers (IEEE)', 2019Co-Authors: Craveiro D. S., Loja M.a.r., Vieira, Lina Oliveira, Vinyas M.Abstract:Project IPL/2016/SoftImob/ISEL. Support of FCT through IDMEC, LAETA, project UID/EMS/50022/2019.An effective head immobilization is an important requirement in radiotherapy treatment sessions, although it may also be thought in the future as a precious aid in brain medical imaging. Thus, the present work is focused on the stiffness Optimization of a customized head immobilization mask, modeled upon the head reconstruction surface based on computerized tomography images. This paper proposes a Strategy supported by a metaheuristic Optimization technique and a metamodeling approach for the whole mask, illustrated at its most unfavorable region occurring in the gnathion region.info:eu-repo/semantics/publishedVersio
Mahitosh Mandal - One of the best experts on this subject based on the ideXlab platform.
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a combined artificial neural network modeling particle swarm Optimization Strategy for improved production of marine bacterial lipopeptide from food waste
Biochemical Engineering Journal, 2014Co-Authors: Gunaseelan Dhanarajan, Mahitosh MandalAbstract:Abstract In the present study, an artificial neural network (ANN) modeling coupled with particle swarm Optimization (PSO) algorithm was used to optimize the process variables for enhanced lipopeptide production by marine Bacillus megaterium , using food waste. In the non-linear ANN model, temperature, pH, agitation and aeration were used as input variables and lipopeptide concentration as the output variable. Further, on application of PSO to the ANN model, the optimum values of the process parameters were as follows: pH = 6.7, temperature = 33.3 °C, agitation rate = 458 rpm and aeration rate = 128 L h −1 . Significant enhancement of lipopeptide production from waste by about 46% (w/v) with 20 times reduction in operating cost compared to the conventional synthetic medium was achieved under optimum conditions. Thus, the novelty of the work lies in the application of combination of ANN–PSO as Optimization Strategy to enhance the yield of a fermentative product like lipopeptide biosurfactant from waste.
Linda M Collins - One of the best experts on this subject based on the ideXlab platform.
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developing a psychological behavioral intervention in cardiac patients using the multiphase Optimization Strategy lessons learned from the field
Annals of Behavioral Medicine, 2019Co-Authors: Jeff C Huffman, Rachel A Millstein, Christopher M Celano, Brian C Healy, Elyse R Park, Linda M CollinsAbstract:BACKGROUND: The Multiphase Optimization Strategy (MOST) is an approach to systematically and efficiently developing a behavioral intervention using a sequence of experiments to prepare and optimize the intervention. PURPOSE: Using a 6 year MOST-based behavioral intervention development project as an example, we outline the results-and resulting decision-making process-related to experiments at each step to display the practical challenges present at each stage. METHODS: To develop a positive psychology (PP) based intervention to promote physical activity after an acute coronary syndrome (N = 255 across four phases), we utilized qualitative, proof-of-concept, factorial design, and randomized pilot experiments, with iterative modification of intervention content and delivery. RESULTS: Through this multiphase approach, we ultimately developed a 12 week, phone-delivered, combined PP-motivational interviewing intervention to promote physical activity. Across stages, we learned several important lessons: (a) participant and interventionist feedback is important, even in later Optimization stages; (b) a thoughtful and systematic approach using all information sources is required when conflicting results in experiments make next steps unclear; and (3) new approaches in the field over a multiyear project should be integrated into the development process. CONCLUSIONS: A MOST-based behavioral intervention development program can be efficient and effective in developing optimized new interventions, and it may require complex and nuanced decision-making at each phase.
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the multiphase Optimization Strategy for engineering effective tobacco use interventions
Annals of Behavioral Medicine, 2011Co-Authors: Linda M Collins, Timothy B Baker, Robin J Mermelstein, Megan E Piper, Douglas E Jorenby, Stevens S Smith, Bruce A Christiansen, Tanya R Schlam, Jessica W Cook, Michael C FioreAbstract:The multiphase Optimization Strategy (MOST) is a new methodological approach for building, optimizing, and evaluating multicomponent interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward steadily and incrementally. MOST can be used to guide the evaluation of research evidence, develop an optimal intervention (the best set of intervention components), and enhance the translation of research findings, particularly type II translation. This article uses an ongoing study to illustrate the application of MOST in the evaluation of diverse intervention components derived from the phase-based framework reviewed in the companion article by Baker et al. (Ann Behav Med, in press, 2011). The article also discusses considerations, challenges, and potential benefits associated with using MOST and similar principled approaches to improving intervention efficacy, effectiveness, and cost-effectiveness. The applicability of this methodology may extend beyond smoking cessation to the development of behavioral interventions for other chronic health challenges.
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the multiphase Optimization Strategy most and the sequential multiple assignment randomized trial smart new methods for more potent ehealth interventions
American Journal of Preventive Medicine, 2007Co-Authors: Linda M Collins, Susan A Murphy, Victor J StrecherAbstract:In this article two new methods for building and evaluating eHealth interventions are described. The first is the Multiphase Optimization Strategy (MOST). It consists of a screening phase, in which intervention components are efficiently identified for inclusion in an intervention or for rejection, based on their performance; a refining phase, in which the selected components are fine tuned and issues such as optimal levels of each component are investigated; and a confirming phase, in which the optimized intervention, consisting of the selected components delivered at optimal levels, is evaluated in a standard randomized controlled trial. The second is the Sequential Multiple Assignment Randomized Trial (SMART), which is an innovative research design especially suited for building time-varying adaptive interventions. A SMART trial can be used to identify the best tailoring variables and decision rules for an adaptive intervention empirically. Both the MOST and SMART approaches use randomized experimentation to enable valid inferences. When properly implemented, these approaches will lead to the development of more potent eHealth interventions.
Pierluigi Pisu - One of the best experts on this subject based on the ideXlab platform.
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an energy Optimization Strategy for power split drivetrain plug in hybrid electric vehicles
Transportation Research Part C-emerging Technologies, 2012Co-Authors: Mashrur Chowdhury, Pierluigi PisuAbstract:Abstract To demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV), an energy Optimization Strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented. In addition, the paper reports an analysis and evaluation of issues related to real time control implementation for the modeled PHEV system, which include the Optimization window sizes and the impact of prediction errors on the energy Optimization Strategy performance. The Optimization time window sizes were identified and validated for different driving cycles under different operating modes and total length of travel. With the identified Optimization windows size, improvements in fuel consumption were realized; the highest improvement was for Urban Dynamometer Driving Schedule (UDDS), with a range of improvement of 14–31%, followed by a 1–15% range of improvement for Highway Fuel Economy Driving Schedule (known as HWFET) and a 1–8% range of improvement for US06 (also known as Supplemental Federal Test Procedure). While no correlation was observed between the error rate and the rate of increased fuel consumption, this PHEV system still yielded energy savings with errors in the speed prediction, which is an indication of robustness of this PHEV model.
Zheng Shu-me - One of the best experts on this subject based on the ideXlab platform.
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Research of Search Engine Optimization Strategy
Journal of Xiangyang Vocational and Technical College, 2014Co-Authors: Zheng Shu-meAbstract:Search engine is the most widely used tool and the main way for users to search for online information and resources. Search engine marketing has become the most important part of network marketing. How to make your own sites included by the major search engines and get a good ranking has become an issue the site builders have to consider. This paper focuses on search engine Optimization Strategy. It firstly elaborates the basic meaning of search engine, then introduces the significance of the search engine Optimization, and finally puts forward the search engine Optimization Strategy.
