A majority of methods from dynamical system analysis, especially those in applied settings, rely on Poincare's geometric picture that focuses on "dynamics of states." While this picture has fueled our field for a century, it has shown difficulties in handling high-dimensional, ill-described, and uncertain systems, which are more and more common in engineered systems design and analysis of "big data" measurements. This overview article presents an alternative framework for dynamical systems, based on the "dynamics of observables" picture. The central object is the Koopman operator: an infinite-dimensional, linear operator that is nonetheless capable of capturing the full nonlinear dynamics. The first goal of this paper is to make it clear how methods that appeared in different papers and contexts all relate to each other through spectral properties of the Koopman operator. The second goal is to present these methods in a concise manner in an effort to make the framework accessible to researchers who would like to apply them, but also, expand and improve them. Finally, we aim to provide a road map through the literature where each of the topics was described in detail. We describe three main concepts: Koopman mode analysis, Koopman eigenquotients, and continuous indicators of ergodicity. For each concept, we provide a summary of theoretical concepts required to define and study them, numerical methods that have been developed for their analysis, and, when possible, applications that made use of them. The Koopman framework is showing potential for crossing over from academic and theoretical use to industrial practice. Therefore, the paper highlights its strengths in applied and numerical contexts. Additionally, we point out areas where an additional research push is needed before the approach is adopted as an off-the-shelf framework for analysis and design. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4772195
In this work we propose the determination of the maximum downshifting efficiency of any new downshifter applied to photovoltaic (PV) devices in terms of downshifter concentration and the description of the optics applied as the standard procedure if increases in EQE, conversion efficiency and/or I are being reported. A standard procedure is defined for determining the maximum downshifting efficiency, firstly in by reaching the solubility limit of the downshifting precursors and, subsequently, by reaching the maximum luminescent intensity of the downshifter. Also, we consider that a description of the optics applied to the experiment should be included to guarantee the reproducibility of the results. This procedure is successfully applied to poly(methylmethacrilate) (PMMA) films containing the active species [Eu(bphen)(tta) ] (bphen = 4,7-biphenyl-1,10-phenantroline and Htta = thenoltrifluoroacetone). Finally, a hemispherical reflector is applied on the PV device in order to modify the optics applied in the experimental setup and for increasing the EQE of the PV device. This scheme is proposed to be integrated in PV devices for its use in niche applications where concentrator photovoltaics (CPV) offers some advantages in relation to non-concentrator configurations.
An emerging field of plasma medicine is discussed, where non-equilibrium plasmas are shown to be able to initiate, promote, control, and catalyze various complex behaviors and responses in biological systems. More importantly, it will be shown that plasma can be tuned to achieve the desired medical effect, especially in medical sterilization and treatment of different kind of skin diseases. Wound healing and tissue regeneration can be achieved following various types of plasma treatment in a multitude of wound pathologies. Non-equilibrium plasmas will be shown to be non-destructive to tissue, safe, and effective in inactivation of various parasites and foreign organisms.
Plant response to drought is complex, so that traits adapted to a specific drought type can confer disadvantage in another drought type. Understanding which type(s) of drought to target is of prime importance for crop improvement. Modelling was used to quantify seasonal drought patterns for a check variety across the Australian wheatbelt, using 123 yr of weather data for representative locations and managements. Two other genotypes were used to simulate the impact of maturity on drought pattern. Four major environment types summarized the variability in drought pattern over time and space. Severe stress beginning before flowering was common (44% of occurrences), with (24%) or without (20%) relief during grain filling. High variability occurred from year to year, differing with geographical region. With few exceptions, all four environment types occurred in most seasons, for each location, management system and genotype. Applications of such environment characterization are proposed to assist breeding and research to focus on germplasm, traits and genes of interest for target environments. The method was applied at a continental scale to highly variable environments and could be extended to other crops, to other drought-prone regions around the world, and to quantify potential changes in drought patterns under future climates.
We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 degrees. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea-West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.