The molecular weight distribution of coal extracts. Coalification is not a condensation polymerization

The pyridine extracts from Bruceton and Illinois No. 6 coals were fractionated using gel permeation chromatography and the molecular weight of each fraction was measured using vapor pressure osmometry. If, as has been suggested, coalification is a simple condensation polymerization from a mixture of monomers with gel formation, then as the molecular weight of the components increase, their concentration should decrease. Exactly the opposite behavior is observed. The number average molecular weights for the Bruceton and Illinois No. 6 extracts respectively are 860 daltons and 1100 daltons while the corresponding weight average molecular weights calculated from the molecular weight distribution are 2300 daltons and 2700 daltons.     

Damage assessment through structural identification of a three‐story large‐scale precast concrete structure

This paper investigates the damage assessment of a three‐story half‐scale precast concrete building resembling a parking garage through structural identification. The structure was tested under earthquake‐type loading on the NEES large high‐performance outdoor shake table at the University of California San Diego in 2008. The tests provide a unique opportunity to capture the dynamic performance of precast concrete structures built under realistic boundary conditions. The effective modal parameters of the structure at different damage states have been identified from white‐noise and scaled earthquake test data with the assumption that the structure responded in a quasi‐linear manner. Modal identification has been performed using the deterministic‐stochastic subspace identification method based on the measured input–output data. The changes in the identified modal parameters are correlated to the observed damage. In general, the natural frequencies decrease, and the damping ratios increase as the structure is exposed to larger base excitations, indicating loss of stiffness, development/propagation of cracks, and failure in joint connections. The analysis of the modal rotations and curvatures allowed the localization of shear and flexural damages respectively and the checking of the effectiveness of repair actions. Copyright © 2013 John Wiley & Sons, Ltd.     

An analysis of geomagnetic response functions prior to the Tohoku,Japan earthquake

Geomagnetic records from 20 Japanese observatories have been used to yield time series of response function (RF) components for 20 years at periods of between 2.5 and 60 min. Six observatories showed anomalous variations lasting 3–5 years in the short period part of the above range of periods prior to the March 11, 2011 Tohoku earthquake. The variations could have been intermediate-term precursors. We made a detailed analysis of how noise affects the results using coherence criteria, visual control, and the remote-reference technique. We clarified the conditions that make response functions dependent on geomagnetic activity. For 19 observatories we constructed the tensor of the anomalous magnetic field with Kakioka as the base site. An anomaly in electrical conductivity striking WNW–ESE has been identified beneath the Boso Peninsula near Tokyo in the conditions of strong noise. We sought to corroborate the reality of the anomaly by visual control and processing of nighttime records with minimum noise. We advanced idea that precursors can be monitored using the DC noise field in the presence of a shallow conductivity anomaly. We provided a tectonic interpretation of the obtained RF anomalies. The Boso conductivity anomaly is interpreted as being due to a graben-shaped structure of the sediments and possibly to a deeper plate-tectonics structure, that is, the Sagami Trough. We examine similarities and differences between the Boso anomaly and the Avacha anomaly in Kamchatka, and provided recommendations for further study of the Boso anomaly and for using the Avacha anomaly to monitor EM precursors in Kamchatka.     

The performance of SPI and PNPI in analyzing the spatial and temporal trend of dry and wet periods over Iran

Drought monitoring is carried out using various drought indices, including SPI, to generate time series of dry and wet periods. Furthermore, the dispersion of dry and wet periods was embossed with different intensities (high, medium, and low) over the data record years. Although these results were very necessary for planning and predicting future droughts, it appeared that the application of any trend over dry and wet periods could provide more accurate and unbiased or safer predictions in terms of analysis process. Generally, most of the researchers believed that the results of a drought trend analysis have been influenced by short-term persistence or significant autocorrelation with different lags on drought event time series and the mentioned impact should be preferably removed. Accordingly, drought monitoring was accomplished using SPI and PNPI drought indices to extract time series of dry and wet periods in terms of 50-year (1965–2014) annual rainfall data of 40 synoptic stations over Iran. Having used the basic and modified Mann–Kendall nonparametric tests, it was attempted to analyze the trend of dry and wet periods extracted from mentioned indices. The results represent the relative advantage of using the modified Mann–Kendall test in drought trend analysis. Furthermore, it was shown that the trend of dry and wet periods was negative in the majority of selected stations and that this trend was significant at 95% confidence level in northwest of Iran. Also, the results indicated the similar performance of SPI and PNPI indices in trend analysis of dry and wet periods.

     

Comparing different statistical models for assessing Fe-contaminated soils based on VNIR/SWIR spectral data

Two statistical models including partial least squares regression (PLSR) and principal component regression were comparatively utilized to determine the predictive accuracy of visible–near-infrared and short-wave infrared reflectance spectroscopy in quantifying the Fe concentration in contaminated soils. Two scenarios were applied to select the best model: Scenario I included all wavelengths (400–2450 nm) and Scenario II encompassed characteristic bands of Fe. Pre-processing techniques used to select the best model included: first and second derivatives (FD and SD), multiplicative scatter correction (MSC) and standard normal variate. The abilities of the predictive models were evaluated by splitting soil samples into two random groups (80 and 20%). The first group (80%) was used to evaluate calibration and validation sets by employing the cross‐validation method, and the second group (20%) was applied to test the models. The coefficient of determination (R ²), root mean square error and residual prediction deviation were calculated to evaluate the models. Applying Scenario I indicated that the PLSR model with SD pre-processing was a more accurate technique for predicting the Fe concentration, whereas in the Scenario II, the PLSR model with MSC pre-processing had a better performance. Comparing Scenarios I and II indicated that the more reliable models for predicting the soil Fe content could be constructed by the PLSR model with the SD pre-processing techniques and all wavelengths. The modeling results produced by the PLSR model with the SD pre-processing could be used to detect, map and monitor Fe-contaminated soils by proximal and remote sensing in the mining areas.     

Statistical approach to inverse distance interpolation

Inverse distance interpolation is a robust and widely used estimation technique. Variants of kriging are often proposed as statistical techniques with superior mathematical properties such as minimum error variance; however, the robustness and simplicity of inverse distance interpolation motivate its continued use. This paper presents an approach to integrate statistical controls such as minimum error variance into inverse distance interpolation. The optimal exponent and number of data may be calculated globally or locally. Measures of uncertainty and local smoothness may be derived from inverse distance estimates.