Circuit Synthesis of Electrochemical Supercapacitor Models

This paper is concerned with the synthesis of RC electrical circuits from physics-based supercapacitor models describing conservation and diffusion relationships. The proposed synthesis procedure uses model discretisation, linearisation, balanced model order reduction and passive network synthesis to form the circuits. Circuits with different topologies are synthesized from several physical models. This work will give greater understanding to the physical interpretation of electrical circuits and will enable the development of more generalised circuits, since the synthesized impedance functions are generated by considering the physics, not from experimental fitting which may ignore certain dynamics

Identifiability and parameter estimation of the single particle lithium-ion battery model

This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially non-dimensionalising the SPM to determine the maximum expected degrees of freedom in the problem. We discover that, excluding open circuit voltage, there are only six independent parameters. We then examine the structural identifiability by considering whether the transfer function of the linearised SPM is unique. It is found that the model is unique provided that the electrode open circuit voltage functions have a known non-zero gradient, the parameters are ordered, and the electrode kinetics are lumped into a single charge transfer resistance parameter. We then demonstrate the practical estimation of model parameters from measured frequency-domain experimental electrochemical impedance spectroscopy (EIS) data, and show additionally that the parametrised model provides good predictive capabilities in the time domain, exhibiting a maximum voltage error of 20 mV between model and experiment over a 10 minute dynamic discharge.Comment: 16 pages, 9 figures, pre-print submitted to the IEEE Transactions on Control Systems Technolog

Advances in friction stir welding of steel : Project HILDA

A microstructure and property evaluation of friction stir welded DH36 6mm plate has been undertaken. The study examined a wide range of process parameters and, from this, a process parameter envelope has been developed and an initial process parameter set established that gives good welding properties. Thermo-mechanical deformation studies were developed to generate flow stress regimes over a range of stain rates and temperatures and these data will support the on-going local numerical modelling development. A preliminary thermo-fluid model has been developed to predict temperature and material flow during the FSW of steel grade DH36. In this model, materials are considered as highly viscous incompressible fluid. The welded material is flowing around the rotating tool thanks to the modelling of the friction at tool/workpiece interface. In parallel, a global numerical model is being developed to predict the inherent residual stresses and distortion of FSW butt welded assemblies often in excess of 6m long plate

Altering the stability of the Cdc8 overlap region modulates the ability of this tropomyosin to bind cooperatively to actin and regulate myosin.

Tropomyosin (Tm) is an evolutionarily conserved ?-helical coiled-coil protein, dimers of which form end-to-end polymers capable of associating with and stabilising actin-filaments and regulate myosin function. The fission yeast, Schizosaccharomyces pombe, possesses a single essential Tm, Cdc8, which can be acetylated on its amino terminal methionine to increase its affinity for actin and enhance its ability to regulate myosin function. We have designed and generated a number of novel Cdc8 mutant proteins with amino terminal substitutions to explore how stability of the Cdc8-polymer overlap region affects the regulatory function of this Tm. By correlating the stability of each protein, its propensity to form stable polymers, its ability to associate with actin and to regulate myosin, we have shown the stability of the amino terminal of the Cdc8 ?-helix is crucial for Tm function. In addition we have identified a novel Cdc8 mutant with increased amino-terminal stability, dimers of which are capable of forming Tm-polymers significantly longer than the wild-type protein. This protein had a reduced affinity for actin with respect to wild type, and was unable to regulate actomyosin interactions. The data presented here are consistent with acetylation providing a mechanism for modulating the formation and stability of Cdc8 polymers within the fission yeast cell. The data also provide evidence for a mechanism in which Tm dimers form end-to-end polymers on the actin-filament, consistent with a cooperative model for Tm binding to actin

A rapid, chromatography-free route to substituted acridine–isoalloxazine conjugates under microwave irradiation

Microwave irradiation was applied to a sequence of condensation reactions from readily available 9-chloroacridines to provide a range of novel acridine–isoalloxazine conjugates. The combination of these two moieties, both of biological interest, was achieved by a chromatography free route

Disney\u27s Internship Program: More than Hands-On Experience

The vast majority of hospitality management programs require students to participate in a hands-on work experience, which helps bridge the gap between theory and practice, providing the student with an opportunity to practice the theory learned in the classroom. The Walt Disney World Co. developed, implemented, and operates one of the most successful internship programs in the hospitality industry. It recognizes the need for business practitioners to become more involved in the education of future hospitality managers. The authors summarize the company\u27s program and offer suggestions for other employers looking to give interns more than hands-on experience

Reduced-Order Neural Network Synthesis with Robustness Guarantees

In the wake of the explosive growth in smartphones and cyberphysical systems, there has been an accelerating shift in how data is generated away from centralised data towards on-device generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware limited, devices to improve user privacy, reduce latency and be more energy efficient. However, our understanding of how these device orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input/output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semi-definite programme that minimises the worst-case approximation error with respect to the larger network. Worst-case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g. pruning, is the inclusion of the worst-case approximation error directly within the training cost function, which should add robustness. Numerical examples highlight the potential of the proposed approach. The overriding goal of this paper is to generalise recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases