Optimization of high-channel count, switch matrices for multinuclear, high-field MRI

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

19F Magnetic Resonance Imaging and Spectroscopy in Neuroscience

¹H magnetic resonance imaging (MRI) has established itself as a key diagnostic technique, affording the visualization of brain anatomy, blood flow, activity and connectivity. The detection of other atoms (e.g. ¹⁹F, ²³Na, ³¹P), so called hetero-nuclear MRI and spectroscopy (MRS), provides investigative avenues that complement and extend the richness of information that can be gained from ¹H MRI. Especially ¹⁹F MRI is increasingly emerging as a multi-nuclear (¹H/¹⁹F) technique that can be exploited to visualize cell migration and trafficking. The lack of a ¹⁹F background signal in the brain affords an unequivocal detection suitable for quantification. Fluorine-based contrast material can be engineered as nanoemulsions, nanocapsules, or nanoparticles to label cells in vitro or in vivo. Fluorinated blood substitutes, typically nanoemulsions, can also carry oxygen and serve as a theranostic in poorly perfused brain regions. Brain tissue concentrations of fluorinated pharmaceuticals, including inhalation anesthetics (e.g. isoflurane) and anti-depressants (e.g. fluoxetine), can also be measured using MRS. However, the low signal from these compounds provides a challenge for imaging. Further methodological advances that accelerate signal acquisition (e.g. compressed sensing, cryogenic coils) are required to expand the applications of ¹⁹F MR imaging to, for instance, determine the regional pharmacokinetics of novel fluorine-based drugs. Improvements in ¹⁹F signal detection and localization, combined with the development of novel sensitive probes, will increase the utility of these multi-nuclear studies. These advances will provide new insights into cellular and molecular processes involved in neurodegenerative disease, as well as the mode of action of pharmaceutical compounds.