64,994 research outputs found
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
The future of laboratory medicine - A 2014 perspective.
Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine
The Promise of Health Information Technology: Ensuring that Florida's Children Benefit
Substantial policy interest in supporting the adoption of Health Information Technology (HIT) by the public and private sectors over the last 5 -- 7 years, was spurred in particular by the release of multiple Institute of Medicine reports documenting the widespread occurrence of medical errors and poor quality of care (Institute of Medicine, 1999 & 2001). However, efforts to focus on issues unique to children's health have been left out of many of initiatives. The purpose of this report is to identify strategies that can be taken by public and private entities to promote the use of HIT among providers who serve children in Florida
Urban pollution and ecosystem services
Urban pollutants can degrade and inhibit ecological functions and processes. Those natural processes provide vital benefits and services to humans. The âservicesâ range from food and water provision, to aesthetics, cultural benefits, health and recreation opportunities, and also climate regulation (including water and air quality regulation and flood regulation). These services are referred to collectively as ecosystem services (ES). To understand the impacts of urban pollution, and the opportunities for mitigating its effects, it is important to explore the relationship between pollution and ES. This can lead to better decision-making for urban infrastructure and spaces and can provide increased opportunities for gaining multiple ES from urban environments
An Inpatient Rehabilitation Interprofessional Care Pathway for Traumatic Hip Fracture: A Pilot Quality Improvement Project
Background: Each year over 300,000 older adults are hospitalized for hip fracture. The impact of the cost of hip fracture on the US health care system is estimated to be as high as 30,000. Formalized pathways have been developed and successfully utilized for many patient presentations, including hip fracture, in the acute setting. Although this research is important to the comprehensive care of the elderly hip fracture patient, very little research exists that outlines evidence-based best-practice for patients in the post-acute recovery period.
Purpose: The primary aim of this project was to develop an evidence-based, comprehensive, coordinated, and interprofessional care pathway for hip fracture patients in the acute rehabilitation setting to improve the percentage of patients discharging to community settings by 20% from current baseline by the end of the pilot period.
Methods: The design of this project was an observational cohort study. Descriptive statistics will be used to compare intervention groups to controls, including frequencies and distributions.
Results: The hip fracture tool itself had inconclusive results, the impacts of the effects on team work and enhanced coordination of the care team was realized through reducing institutionalized days for hip fracture patients in acute rehabilitation
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Back-propagation algorithm is one of the most widely used and popular
techniques to optimize the feed forward neural network training. Nature
inspired meta-heuristic algorithms also provide derivative-free solution to
optimize complex problem. Artificial bee colony algorithm is a nature inspired
meta-heuristic algorithm, mimicking the foraging or food source searching
behaviour of bees in a bee colony and this algorithm is implemented in several
applications for an improved optimized outcome. The proposed method in this
paper includes an improved artificial bee colony algorithm based
back-propagation neural network training method for fast and improved
convergence rate of the hybrid neural network learning method. The result is
analysed with the genetic algorithm based back-propagation method, and it is
another hybridized procedure of its kind. Analysis is performed over standard
data sets, reflecting the light of efficiency of proposed method in terms of
convergence speed and rate.Comment: 14 Pages, 11 figure
Urban and extra-urban hybrid vehicles: a technological review
Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, âvehicle operating lifeâ is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid âelectric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use
(implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used
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