Ion migration in nepheline: a dielectric spectroscopy and computer modelling study

 Using a combination of dielectric spectroscopy and atomistic computer simulation techniques, the dynamical behaviour of the loosely bound (Na⁺ and K⁺) channel ions in nepheline has been investigated. The low-frequency dielectric properties of a natural Bancroft nepheline have been studied from room temperature to 1100 K. At each temperature, the dielectric constant, conductivity and dielectric loss were determined over a range of frequencies from 100 Hz to 10 MHz. At high temperatures a distinct Debye-type relaxation in the dielectric loss spectrum was observed; the activation energy for this process was determined to be 1.38 ± 0.02 eV. Atomistic simulation techniques were used to elucidate the mechanism and energetics of cation migration. A mechanism involving the hopping of Na⁺ ions between oval sites and partially occupied hexagonal (K⁺) sites, via a bottleneck consisting of a distorted sixfold ring of (Al,Si)O₄ tetrahedra, was found to give a calculated energy barrier in very good agreement with the experimentally determined activation energy. These results confirm the nature of the process responsible for the observed dielectric behaviour. Overall, this study demonstrates the intrinsic, microscopic control of cation diffusion processes in rock-forming minerals. Identifying specific energy barriers and preferred diffusion pathways is fundamental to the prediction of diffusion energetics. Received: 8 May 2000 / Accepted: 21 July 2000     

Analysis of radio astronomy bands using CALLISTO spectrometer at Malaysia-UKM station

The e-CALLISTO system is a worldwide network that aims to observe solar radio emission for astronomical science. CALLISTO instruments have been deployed worldwide in various locations that together can provide continuous observation of the solar radio spectrum for 24 h per day year-round. Malaysia-UKM is a strategic equatorial location and can observe the Sun 12 h per day. This paper gives an overview of the spectrum allocation for radio astronomy, which falls in the specified operating frequency band of the CALLISTO spectrometer. The radio astronomy bands are analyzed at the Malaysia-UKM station according to the International Telecommunication Union recommendations. Some observational results are also presented in this paper.     

Metamorphic P–T evolution of the Gotsu blueschists from the Suo metamorphic belt in SW Japan: Implications for tectonic correlation with the Heilongjiang Complex,NE China

Mineralogy and Petrology - In the Gotsu area of the c. 200 Ma high-P/T Suo metamorphic belt in the Inner Zone of southwest Japan, blueschists occur as lenses or layers within pelitic...     

Arsenic incorporation into authigenic pyrite, Bengal Basin sediment, Bangladesh

Sediment from two deep boreholes (∼400 m) approximately 90 km apart in southern Bangladesh was analyzed by X-ray absorption spectroscopy (XAS), total chemical analyses, chemical extractions, and electron probe microanalysis to establish the importance of authigenic pyrite as a sink for arsenic in the Bengal Basin. Authigenic framboidal and massive pyrite (median values 1500 and 3200 ppm As, respectively), is the principal arsenic residence in sediment from both boreholes. Although pyrite is dominant, ferric oxyhydroxides and secondary iron phases contain a large fraction of the sediment-bound arsenic between approximately 20 and 100 m, which is the depth range of wells containing the greatest amount of dissolved arsenic. The lack of pyrite in this interval is attributed to rapid sediment deposition and a low sulfur flux from riverine and atmospheric sources. The ability of deeper aquifers (>150 m) to produce ground water with low dissolved arsenic in southern Bangladesh reflects adequate sulfur supplies and sufficient time to redistribute the arsenic into pyrite during diagenesis.     

Farm income,inequality, and poverty among farm families of a flood-prone area in Bangladesh: climate change vulnerability assessment

GeoJournal - This paper examines the farm income differences, income inequality of farm households, parameters of income variability that ascertain vulnerability levels, and cost-income variability...     

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace(RS) coupled with Artificial Neural Network(ANN), Random Forest(RF), and Support Vector Machine(SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment.The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve(ROC) were employed.The value of the Area Under the Curve(AUC) of ROC was above 0.80 for all models.For flood susceptibility modelling, the Dagging model performs superior, followed by RF,the ANN, the SVM, and the RS, then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Decomposition of wind speed fluctuations at different time scales

Understanding the inherent features of wind speed (variability on different time scales) has become critical for assured wind power availability, grid stability, and effective power management. The study utilizes the wavelet, autocorrelation, and FFT (fast Fourier transform) techniques to analyze and assimilate the fluctuating nature of wind speed data collected over a period of 29–42 years at different locations in the Kingdom of Saudi Arabia. The analyses extracted the intrinsic features of wind speed, including the long-term mean wind speed and fluctuations at different time scales (periods), which is critical for meteorological purposes including wind power resource assessment and weather forecasting. The long-term mean wind speed varied between 1.45 m/s at Mecca station and 3.73 m/s at Taif. The annual variation is the largest (±0.97 m/s) at Taif and the smallest (±0.25 m/s) at Mecca. Similarly, the wind speed fluctuation with different periods was also discussed in detail. The spectral characteristics obtained using FFT reveal that Al-Baha, Najran, Taif and Wadi-Al-Dawasser having a sharp peak at a frequency f = 0.00269 (1/day) retain a more regular annual repetition of wind speed than Bisha, Khamis-Mushait, Madinah, Mecca, and Sharourah. Based on the autocorrelation analysis and FFT results, the stations are divided into three groups: (i) having strong annual modulations (Al-Baha, Najran, Taif and Wadi-Al-Dawasser), (ii) having comparable annual and half-yearly modulations (Bisha, Khamis-Mushait, and Mecca) and (iii) having annual modulation moderately prominent (Madinah and Sharourah).