Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

High Value of Proton Relaxivity Achieved by Graphene Oxide-Cobalt Ferrite Nanoparticle Composite: A Potential Contrast Agent in Magnetic Resonance Imaging

This work investigates the potential of graphene oxide-cobalt ferrite nanoparticle (GO-CoFe2O4) composite as image contrast enhancing material in Magnetic Resonance Imaging (MRI). In the preset work, GO-CoFe2O4 composites were produced by a two-step synthesis process. In the first step, graphene oxide (GO) was synthesized, and in the second step CoFe2O4 nanoparticles were synthesized in a reaction mixture containing GO to yield graphene GO-CoFe2O4 composite. Proton relaxivity value obtained from the composite was 361 mM(-1)s(-1). This value of proton relaxivity is higher than a majority of reported relaxivity values obtained using several ferrite based contrast agents.

Looking beyond parks: the conservation value of unprotected areas for hornbills in Arunachal Pradesh, Eastern Himalaya

The loss of tropical forests and associated biodiversity is a global concern. Conservation efforts in tropical countries such as India have mostly focused on state-administered protected areas despite the existence of vast tracts of forest outside these areas. We studied hornbills (Bucerotidae), an ecologically important vertebrate group and a flagship for tropical forest conservation, to assess the importance of forests outside protected areas in Arunachal Pradesh, north-east India. We conducted a state-wide survey to record encounters with hornbills in seven protected areas, six state-managed reserved forests and six community-managed unclassed forests. We estimated the density of hornbills in one protected area, four reserved forests and two unclassed forests in eastern Arunachal Pradesh. The state-wide survey showed that the mean rate of encounter of rufous-necked hornbills Aceros nipalensis was four times higher in protected areas than in reserved forests and 22 times higher in protected areas than in unclassed forests. The mean rate of encounter of wreathed hornbills Rhyticeros undulatus was twice as high in protected areas as in reserved forests and eight times higher in protected areas than in unclassed forests. The densities of rufous-necked hornbill were higher inside protected areas, whereas the densities of great hornbill Buceros bicornis and wreathed hornbill were similar inside and outside protected areas. Key informant surveys revealed possible extirpation of some hornbill species at sites in two protected areas and three unclassed forests. These results highlight a paradoxical situation where individual populations of hornbills are being lost even in some legally protected habitat, whereas they continue to persist over most of the landscape. Better protection within protected areas and creative community-based conservation efforts elsewhere are necessary to maintain hornbill populations in this biodiversity-rich region.