Magnetothermoelectric DC conductivities from holography models with hyperscaling factor in Lifshitz spacetime

We investigate an Einstein-Maxwell-Dilaton-Axion holographic model and obtain two branches of a charged black hole solution with a dynamic exponent and a hyperscaling violation factor when a magnetic field presents. The magnetothermoelectric DC conductivities are then calculated in terms of horizon data by means of holographic principle. We find that linear temperature dependence resistivity and quadratic temperature dependence inverse Hall angle can be achieved in our model. The well-known anomalous temperature scaling of the Nernst signal and the Seebeck coefficient of cuprate strange metals are also discussed.Comment: 1+23 pages, 4 figures, references adde

Optimal experimental design for an enzymatic biodiesel production system

Two optimal experimental design (OED) problems for an enzymatic biodiesel production system are investigated to improve parameter estimation quality. An orthogonalized sensitivity analysis method is firstly implemented to select important parameters. Next the design of measurement set and sampling strategy is developed in the form of two convex optimization problems which are solved by the interior-point algorithm and the Powell’s method, respectively. Simulation results demonstrate the function of OED in reducing parameter estimation errors. The biodiesel concentration is identified to be the most valuable state variable observation, and the parameter estimation accuracy can be improved through optimal sampling design

A two-loop optimization strategy for multi-objective optimal experimental design

A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both observation design and input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of input intensity is calculated in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain maximum production rate by taking into account of the experimental cost

Integrated time sampling design and measurement set selection for biochemical systems

The optimal experimental design (OED) for observation strategy is investigated in this paper to collect the most informative experimental data for parameter estimation. The aim is to determine the best sampling time points and also select the most valuable measurement state variables through OED. The two design objectives are integrated together as a single-objective optimisation problem in which the variables and their sampling times are weighted in an expanded time sampling framework. Three optimisation methods, i.e., the Powell’s method, the sequen- tial selection method, and the sequential quadratic programming method, are employed to solve the optimisation problem. Their computation efficiencies are compared using a biodiesel case study system simulation. Simulation results demonstrate the effectiveness of the proposed method in reducing parameter estimation uncertainties as well as reducing parameter correlations. It can also be observed that the integrated OED doesn’t cost extra computation efforts when variable selection is considered together with the time sampling task