Understanding phase transition and vibrational mode coupling in ammonium nitrate using 2D correlation Raman spectroscopy

Ammonium nitrate (AN) is an important component of the chemical industry such as an active ingredient in fertilizers, as an oxidizer in explosive compositions and propellants, and as a blasting agent in civil explosives. Numerous accidents have been reported in the past which concerns its thermal instability and poses a big threat to its processing, transportation, and storage. Despite much literature being reported to understand its thermal instability, a mechanistic view remains unclear. In the present work, we have studied the behavior of AN to temperature change using a mathematical approach called 2D correlation (2D Cos) Raman spectroscopy to provide complete insight into the detailed dynamical nature of the interactions between the species (ionic or molecular) occurring with an increase in temperature. We have analyzed various libration and translational modes of nitrate in the low-frequency region using this mathematical tool. It is observed from 2D maps that the phase transition of AN starts with changes in libration modes followed by various nitrate modes and ammonium modes which further precedes low-frequency translational modes. Further, the 2D correlation could differentiate between modes splitting and shifting based on specific 2D Cos pattern. The changes occurring in the N-O deformation modes, symmetric stretching modes as well as anti-symmetric stretching modes which have been attributed to the weakening of the hetero-ionic coupling between the NH₄⁺ and the NO₃^- ions could be clearly distinguished in the 2D synchronous and asynchronous plots. Besides, moving window analysis was performed to visualize the transition temperature at which phase change of AN takes place.

Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index

Environmental problems caused by mines have been increasing. As one of the most serious types of mining damage caused to the eco-environment, open pits have been the focus of monitoring and management. Previous studies have obtained effective results when evaluating the ecological quality of a mining area by using the remote sensing ecological index (RSEI). However, the calculation of RSEI does not consider that the ecological environmental impact is limited under natural conditions. To overcome this shortcoming, this paper proposes an improved RSEI based on a moving window model, namely the moving window-based remote sensing ecological index (MW-RSEI). This improved index is more in agreement with the First Law of Geography than RSEI. This study uses Landsat ETM/OLI/TIRS images to extract MW-RSEI information of a case area in Zhengzhou City, Henan Province, central China, in 2009 and 2018. The results revealed that the average value of MW-RSEI declined from 0.668 to 0.611 from 2009 to 2018, and the main drivers of the deterioration of the eco-environment were land use/cover (LUCC) changes, most of which were derived from urban expansion and mining. The serious impact of open pits on the eco-environment in mining areas is mainly due to their low vegetation cover; therefore, some effectively managed open pits can have a positive impact on the mining environment. The use of MW-RSEI provides valuable information on the eco-environment surrounding the open pit, which can be used for the rapid and effective monitoring of the eco-environment in mining areas.

Temperature prediction for roller kiln based on hybrid first-principle model and data-driven MW-DLWKPCR model

In this paper, a hybrid temperature prediction model is developed for an industrial roller kiln of lithium-ion battery cathode materials, which is based on first-principle model and moving window-double locally weighted kernel principal component regression (DLKWKPCR). First, the mechanism model is built for the roller kiln according to the energy conservation law and heat transfer mechanism. Since the first-principle model is based on some simplified assumptions, it often results in large estimation errors. Thus, a data-driven error compensation model is further constructed with real-time process running data. In order to handle the strongly nonlinear, highly redundant and gradually time-varying characteristics, the error compensation model is built with moving window based DLWKPCR. Finally, a hybrid temperature prediction model is obtained by combining the compensation model and the mechanism model. An industrial roller kiln is utilized to test the effectiveness of the hybrid prediction model, in which the modeling results demonstrate that the developed hybrid prediction model can correctly estimate the roller kiln temperature.

Classifying development stages of primeval European beech forests: is clustering a useful tool?

Background

Old-growth and primeval forests are passing through a natural development cycle with recurring stages of forest development. Several methods for assigning patches of different structure and size to forest development stages or phases do exist. All currently existing classification methods have in common that a priori assumptions about the characteristics of certain stand structural attributes such as deadwood amount are made. We tested the hypothesis that multivariate datasets of primeval beech forest stand structure possess an inherent, aggregated configuration of data points with individual clusters representing forest development stages. From two completely mapped primeval beech forests in Albania, seven ecologically important stand structural attributes characterizing stand density, regeneration, stem diameter variation and amount of deadwood are derived at 8216 and 9666 virtual sampling points (moving window, focal filtering). K-means clustering is used to detect clusters in the datasets (number of clusters (k) between 2 and 5). The quality of the single clustering solutions is analyzed with average silhouette width as a measure for clustering quality. In a sensitivity analysis, clustering is done with datasets of four different spatial scales of observation (200, 500, 1000 and 1500 m², circular virtual plot area around sampling points) and with two different kernels (equal weighting of all objects within a plot vs. weighting by distance to the virtual plot center).

Results

The clustering solutions succeeded in detecting and mapping areas with homogeneous stand structure. The areas had extensions of more than 200 m², but differences between clusters were very small with average silhouette widths of less than 0.28. The obtained datasets had a homogeneous configuration with only very weak trends for clustering.

Conclusions

Our results imply that forest development takes place on a continuous scale and that discrimination between development stages in primeval beech forests is splitting continuous datasets at selected thresholds. For the analysis of the forest development cycle, direct quantification of relevant structural features or processes might be more appropriate than classification. If, however, the study design demands classification, our results can justify the application of conventional forest development stage classification schemes rather than clustering.

Electronic supplementary material

The online version of this article (10.1186/s12898-018-0203-y) contains supplementary material, which is available to authorized users.

Time-varying sensitivity analysis of hydrologic and sediment parameters at multiple timescales: Implications for conservation practices

Environmental models can be used to better understand the hydrologic and sediment behavior in a watershed system. However, different processes may dominate at different time periods and timescales, which highly complicate the model interpretation. The related parameter uncertainty may be significant and needs to be addressed to avoid bias in the watershed management. In this study, we used the time-varying and multi-timescale (TVMT) method to characterize the temporal dynamics of parameter sensitivity at different timescales in hydrologic and sediment modeling. As a case study, the first order sensitivity indices were estimated with the Fourier amplitude sensitivity test (FAST) method for the Hydrological Simulation Program - Fortran (HSPF) model in the Zhangjiachong catchment in the Three Gorge Reservoir Region (TGRR) in China. The results were compared to those of the traditional aggregate method to demonstrate the merits of the TVMT method. The time-varying nature of the hydrologic and sediment parameters was revealed and explained mainly by the variation of hydro-climatic conditions. The baseflow recession parameter, evapotranspiration (ET) parameter for the soil storage, and sediment washoff parameter showed high sensitivities almost across the whole period. However, parameters related to canopy interception and channel sediment scour varied notably over time due to changes in the climate forcing. The timescale-dependent characteristics was observed and was most evident for the baseflow recession parameter and ET parameter. At last, the parameters affecting the sediment export and transport were discussed together with the inferred conservation practices. Reasonable controls for sediment must be storm-dependent. Compared to management practices on the land surface, practices affecting channel process would be more effective during storm events. Our results present one of the first investigations for sediment modeling in terms of the importance of parameter sensitivity in both time periods and evaluation timescales for the model calibration, diagnostic evaluation, and prioritizing efforts for conservation practices.

The Effect of Moving Window on Acoustic Analysis

OBJECTIVE

To investigate the effects of the moving window method on acoustic measures and discrimination ability between normal and disordered voices.

METHODS

Fifty-three normal voices and 50 disordered voices were recruited. Three selection methods, the moving window method, the mid-vowel method, and the whole vowel method, were applied to each raw audio signal to determine the most stable segment of each signal. Acoustic parameters such as percent jitter, percent shimmer, signal-to-noise ratio (SNR), cepstral peak prominence (CPP), and correlation dimension (D2) were calculated. The Wilcoxon test was used to compare the stability of these segments across different methods. An artificial neural network was used for estimating how well disordered voices were discriminated from normal ones.

RESULTS

Segments selected using the moving window method were more stable than those selected using the other two methods, meaning lower perturbation and nonlinear dynamic measurements as well as higher SNR and CPP values. The discrimination accuracy rate for the moving window method was 91.90 ± 8.73%, whereas the mid-vowel method and the whole vowel method were 72.34 ± 12.94% and 70.34 ± 5.24%, respectively.

CONCLUSION

The moving window method is capable of providing a more stable audio segment and can discriminate disordered voices from normal ones more effectively.

4S Peak Filling - baseline estimation by iterative mean suppression

A novel baseline estimation procedure building on previously published works is presented. •The core of the estimation is an iterative spectrum suppression consisting of a moving window minimum replacement (adapted from Friedrichs [1]).•Four, easily understandable, parameters control placement of the baseline relative to the noise band around the signal (adapted from Eilers [2]) and the flexibility in different situations.•The method is especially suited for non-linear baselines with local variations and for resolving peak clusters in qualitative analyses.

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.