Precision comparison of intensity ratios and area ratios in spectral analysis.

The long-debated question in analytical chemistry of which of the area ratio or the intensity ratio is the more precise has yielded no definitive analytical conclusion. To address this issue theoretically, we derived analytical solutions for the lower limits of estimation precision for spectral parameters, including the intensity ratio and area ratio, based on the Cramér-Rao lower bound (CRLB) framework for a Gaussian spectrum. The precisions of spectral parameter estimations from the analytical solutions were consistent with results obtained from Monte Carlo simulations.

Data-Driven Analysis of Nonlinear Heterogeneous Reactions through Sparse Modeling and Bayesian Statistical Approaches.

Heterogeneous reactions are chemical reactions that occur at the interfaces of multiple phases, and often show a nonlinear dynamical behavior due to the effect of the time-variant surface area with complex reaction mechanisms. It is important to specify the kinetics of heterogeneous reactions in order to elucidate the microscopic elementary processes and predict the macroscopic future evolution of the system. In this study, we propose a data-driven method based on a sparse modeling algorithm and sequential Monte Carlo algorithm for simultaneously extracting substantial reaction terms and surface models from a number of candidates by using partial observation data.

Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters.

Discontinuous boundaries of slow slip events beneath the Bungo Channel, southwest Japan.

The down-dip limit of the seismogenic zone and up-dip and down-dip limits of the deep low-frequency tremors in southwest Japan are clearly imaged by the hypocentre distribution. Previous studies using smooth constraints in inversion analyses estimated that long-term slow slip events (L-SSEs) beneath the Bungo Channel are distributed smoothly from the down-dip part of the seismogenic zone to the up-dip part of the tremors. Here, we use fused regularisation, a type of sparse modelling suitable for detecting discontinuous changes in the model parameters to estimate the slip distribution of L-SSEs.

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases.

Free-energy landscape and nucleation pathway of polymorphic minerals from solution in a Potts lattice-gas model.

Metastable minerals commonly form during reactions between water and rock. The nucleation mechanism of polymorphic phases from solution are explored here using a two-dimensional Potts model. The model system is composed of a solvent and three polymorphic solid phases.

High-temperature water-rock interactions and hydrothermal environments in the chondrite-like core of Enceladus.

It has been suggested that Saturn's moon Enceladus possesses a subsurface ocean. The recent discovery of silica nanoparticles derived from Enceladus shows the presence of ongoing hydrothermal reactions in the interior. Here, we report results from detailed laboratory experiments to constrain the reaction conditions.

Extraction of heavy metals characteristics of the 2011 Tohoku tsunami deposits using multiple classification analysis.

Tsunami deposits accumulated on the Tohoku coastal area in Japan due to the impact of the Tohoku-oki earthquake. In the study reported in this paper, we applied principal component analysis (PCA) and cluster analysis (CA) to determine the concentrations of heavy metals in tsunami deposits that had been diluted with water or digested using 1 M HCl. The results suggest that the environmental risk is relatively low, evidenced by the following geometric mean concentrations: Pb, 16 mg kg(-1) and 0.

Ongoing hydrothermal activities within Enceladus.

Detection of sodium-salt-rich ice grains emitted from the plume of the Saturnian moon Enceladus suggests that the grains formed as frozen droplets from a liquid water reservoir that is, or has been, in contact with rock. Gravitational field measurements suggest a regional south polar subsurface ocean of about 10 kilometres thickness located beneath an ice crust 30 to 40 kilometres thick. These findings imply rock-water interactions in regions surrounding the core of Enceladus.

Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits.

Geochemical discrimination has recently been recognised as a potentially useful proxy for identifying tsunami deposits in addition to classical proxies such as sedimentological and micropalaeontological evidence. However, difficulties remain because it is unclear which elements best discriminate between tsunami and non-tsunami deposits. Herein, we propose a mathematical methodology for the geochemical discrimination of tsunami deposits using machine-learning techniques.

Markov-random-field modeling for linear seismic tomography.

We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method.