Universal Algorithm for Simulating and Evaluating Cyclic Voltammetry at Macroporous Electrodes by Considering Random Arrays of Microelectrodes

An algorithm for the simulation and evaluation of cyclic voltammetry (CV) at macroporous electrodes such as felts, foams, and layered structures is presented. By considering 1D, 2D, and 3D arrays of electrode sheets, cylindrical microelectrodes, hollow‐cylindrical microelectrodes, and hollowspherical microelectrodes the internal diffusion domains of the macroporous structures are approximated. A universal algorithm providing the timedependent surface concentrations of the electrochemically active species, required for simulating cyclic voltammetry responses of the individual planar, cylindrical, and spherical microelectrodes, is presented as well. An essential ingredient of the algorithm, which is based on Laplace integral transformation techniques, is the use of a modified Talbot contour for the inverse Laplace transformation. It is demonstrated that first‐order homogeneous chemical kinetics preceding and/or following the electrochemical reaction and electrochemically active species with non‐equal diffusion coefficients can be included in all diffusion models as well. The proposed theory is supported by experimental data acquired for a reference reaction, the oxidation of [Fe(CN)6]4− at platinum electrodes as well as for a technically relevant reaction, the oxidation of VO2+ at carbon felt electrodes. Based on our calculation strategy, we provide a powerful open source tool for simulating and evaluating CV data implemented into a Python graphical user interface (GUI)

3D-printed Acoustic Directional Couplers

Acoustic Directional Couplers permit separation of forward and reverse sound pressure waves. This separation opens the way to traceable precision acoustic reflection measurements. In order to span the audio frequency range, multiple couplers will be required, as each operates over a frequency range of slightly more than one octave. To reach 20kHz or above requires vary small, mechanically precise construction. We achieve this by 3D printing techniques. We manufactured two otherwise-identical couplers, one made with a powder-type 3D printer with photopolymer support structure, the other made with an ABS-filament thermoplastic-type 3D printer. We compare the measured acoustic performance of these two couplers. The wavelength of sound at 20 kHz is comparable to that encountered at a microwave frequency of 18 GHz. We expect to be able to fabricate couplers that reach 55 kHz where the wavelength is 6 mm, corresponding to a frequency of 50 GHz in the electromagnetic spectrum

Lipid Metabolism and Comparative Genomics

Unilever asked the Study Group to focus on two problems. The first concerned dysregulated lipid metabolism which is a feature of many diseases including metabolic syndrome, obesity and coronary heart disease. The Study Group was asked to develop a model of the kinetics of lipoprotein metabolism between healthy and obese states incorporating the activities of key enzymes. The second concerned the use of comparative genomics in understanding and comparing metabolic networks in bacterium. Comparative genomics is a method to make inferences on the genome of a new organism using information of a previously charaterised organism. The first mathematical question is how one would quantify such a metabolic map in a statistical sense, in particular, where there are different levels of confidence for presense of different parts of the map. The next and most important question is how one can design a measurement strategy to maximise the confidence in the accuracy of the metabolic map

Bayesian Inference in Estimation of Distribution Algorithms

Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAG c

Spreading dynamics on spatially constrained complex brain networks

The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way