In 1928, P. Dirac proposed a new wave equation to describe relativistic electrons 1 . Shortly afterwards, O. Klein solved a simple potential step problem for the Dirac equation and stumbled upon an apparent paradox -the potential becomes transparent when the height is larger than the electron energy. For massless particles, backscattering is completely forbidden in Klein tunneling, leading to perfect transmission through any potential barrier 2,3 . Recent advent of condensed matter systems with Dirac-like excitations, such as graphene and topological insulators (TIs), has opened the possibility of observing the Klein tunneling experimentally 4-6 . In the surface states of TIs, fermions are bound by spin-momentum locking, and are thus immune to backscattering due to time-reversal symmetry. Here we report the

Structural phase transitions serve as the basis for many functional applications including shape memory alloys (SMAs), switches based on metal-insulator transitions (MITs), etc. In such materials, lattice incompatibility between transformed and parent phases often results in a thermal hysteresis, which is intimately tied to degradation of reversibility of the transformation. The non-linear theory of martensite suggests that the hysteresis of a martensitic phase transformation is solely determined by the lattice constants, and the conditions proposed for geometrical compatibility have been successfully applied to minimizing the hysteresis in SMAs. Here, we apply the non-linear theory to a correlated oxide system (V 1− x W x O 2 ), and show that the hysteresis of the MIT in the system can be directly tuned by adjusting the lattice constants of the phases. The results underscore the profound influence structural compatibility has on intrinsic electronic properties, and indicate that the theory provides a universal guidance for optimizing phase transforming materials.

The hydrological response characteristics for the catchments in the Republic of Korea are related to a strong seasonality in the rainfall and streamflow distributions with distinct wet and dry seasons. This study aims to improve a model's ability to predict streamflows by minimizing information loss from the available data during the calibration processes. This study assesses calibration techniques incorporating a multi‐objective approach and seasonal calibration. The lumped conceptual rainfall–runoff model IHACRES was applied to selected catchments in Korea. The model was calibrated based on three different methods: the classical approach using a single performance statistic (the single‐objective method), the multi‐objective approach (the multi‐objective method (I)) and the combined approach incorporating multi‐objective and seasonal calibrations (the multi‐objective method (II)). In the multi‐objective approach, the ‘best fit’ models in the calibration period were selected by considering the trade‐offs among multiple statistics. During seasonal calibration, the calibration period was divided into four seasons to investigate whether these calibrated models can improve the model performance with regards to seasonal climate, rainfall and streamflow distributions. The adequacy of the three different calibration methods was assessed through comparison of the variability of model performance in high and low flows and water balance for the entire period and for each seasonal period. The multi‐objective methods yielded more accurate and consistent predictions for high and low flows and water balance simultaneously, compared to the single‐objective method. In particular, the multi‐objective method (II) produces the best modelling capacity to capture the non‐stationary nature of the hydrological response under different climate conditions. The pattern of improvement with the multi‐objective method (II) was generally consistent through the seasons, with the exception of the winter period in the regions partially affected by snow. This exception is due to a potential limitation of the IHACRES model in reflecting the impact of snow on the catchment hydrology. Copyright © 2013 John Wiley & Sons, Ltd.

This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi-objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over-parameterization and data requirements. The IHACRES rainfall-runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi-objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall-runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi-objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single-objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi-objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single-objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single-objective measure. The parameters calibrated by the single-objective approach decreased the predictability of the regional models.