Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as: measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.

In situ gelling systems based on Pluronic F127/Pluronic F68 formulations for ocular drug delivery

This study evaluated the use of Pluronic F127 and Pluronic F68 as excipients for formulating in situ gelling systems for ocular drug delivery. Thermal transitions have been studied in aqueous solutions of Pluronic F127, Pluronic F68 as well as their binary mixtures using differential scanning calorimetry, rheological measurements, and dynamic light scattering. It was established that the formation of transparent gels at physiologically relevant temperatures is observed only in the case of 20 wt % of Pluronic F127. The addition of Pluronic F68 to Pluronic F127 solutions increases the gelation temperature of binary formulation to above physiological range of temperatures. The biocompatibility evaluation of these formulations using slug mucosa irritation assay and bovine corneal erosion studies revealed that these polymers and their combinations do not cause significant irritation. In vitro drug retention study on glass surfaces and freshly excised bovine cornea showed superior performance of 20 wt % Pluronic F127 compared to other formulations. In addition, in vivo studies in rabbits demonstrated better retention performance of 20 wt % Pluronic F127 compared to Pluronic F68. These results confirmed that 20 wt % Pluronic F127 offers an attractive ocular formulation that can form a transparent gel in situ under physiological conditions with minimal irritation.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.