Supplementary document for 1.7 THz tuning range pivot-point-independent mode-hop-free external cavity diode laser - 6106380.pdf

This supplemental document describes details of the optimum relationship between etalon angle, periscope angle, cavity length, and periscope length, and details of the maximum MHF tuning range for the designed periscope

A modified surface kinetic model for calcium and strontium isotope fractionation during calcite precipitation

The Ca and Sr isotope fractionation factors ($\mathrm{\Delta^{44/40}Ca}$ and $\mathrm{\Delta^{88/86}Sr}$), as well as the bulk Sr partition coefficient ($K$), depend on calcite precipitation rates in both experimental and natural settings. These processes are expected to be controlled by surface kinetics. Ten years ago, Depaolo (2011) proposed a surface reaction model that successfully explained the kinetic effects on $\mathrm{\Delta^{44/40}Ca}$ and $K$. With new observations of $\mathrm{\Delta^{88/86}Sr}$, the limitation of this model emerges. Here, we develop a modified surface reaction model with separate contributions from two underlying precipitation mechanisms, namely spiral growth and surface nucleation. We derive the most representative model by synthesizing general forms with simple but justifiable assumptions of surface reaction kinetics. Our modified model successfully explains observations of the precipitation rate dependence of $\mathrm{\Delta^{44/40}Ca}$, $\mathrm{\Delta^{88/86}Sr}$, and $K$, as well as their correlations. Our findings suggest that surface reactions during calcite precipitation solely regulate these parameters, regardless of diffusion. The revised model can be readily integrated with existing stoichiometric models and offers important implications for interpreting carbonate stable Ca and Sr isotope compositions, as well as the partitioning and isotope fractionation of other trace elements during carbonate precipitation

Application of an Improved ABC Algorithm in Urban Land Use Prediction

Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (ABC) algorithm based on the mutation of inferior solutions (MHABC) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHABC-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHABC-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHABC-CA algorithm in the prediction field of urban land use changes

Study on Spontaneous Combustion Tendency of Coals with Different Metamorphic Grade at Low Moisture Content Based on TPO-DSC

In the environments of various open coal storage sites, mining-affected coalbeds, and goafs, etc., some coal bodies are often affected by external environmental factors. They are highly prone to spontaneous combustion in low moisture content (≤8%). In order to examine the effect of low moisture content on the spontaneous combustion tendency of coals with different metamorphic grade, we conducted a temperature programmed oxidation (TPO) experiment and differential scanning calorimetry (DSC) experiment to study the spontaneous combustion characteristics of coals with different metamorphic grade at four different low moisture contents. The change laws of the characteristic parameters of four different metamorphic grade coals at four different low moisture contents were comparatively analyzed. The experimental results indicate that: (1) Compared other low moisture content, anthracite and fat coal at a low moisture content of 1.2 % show a stronger tendency for spontaneous combustion, and long flame coal and lignite at a low moisture content of 3.4% and 5.6% are more prone to spontaneous combustion. (2) Four different metamorphic grade coals at a low moisture content of 7.8% are less prone to spontaneous combustion

Landslide Displacement Prediction Based on a Two-Stage Combined Deep Learning Model under Small Sample Condition

The widely distributed “Step-type” landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of local geological hazard early warning systems for the realization of scientific disaster prevention and mitigation. However, the number of observed data like landslide displacement, rainfall, and reservoir water level in this area is very small, which results in difficulties for the training of advanced deep learning model to obtain more accurate prediction results. To solve the above problems, a Two-stage Combined Deep Learning Dynamic Prediction Model (TC-DLDPM) for predicting the typical “Step-type” landslides in the TGR area under the condition of small samples is proposed. The establishment process of this method is as follows: (1) the Dynamic Time warping (DTW) method is used to enhance the small samples of cumulative displacement data obtained by the Global Positioning System (GPS); (2) A Difference Decomposition Method (DDM) based on sequence difference is proposed, which decomposes the cumulative displacement into trend displacement and periodic displacement, and then the cubic polynomial fitting method is used to predict the trend displacement; (3) the periodic displacement component is predicted by the proposed TC-DLDPM model combined with external environmental factors such as rainfall and reservoir water level. The TC-DLDPM model combines the advantages of Convolutional Neural Network (CNN), Attention mechanism, and Long Short-term Memory network (LSTM) to carry out two-stage learning and parameter transfer, which can effectively realize the construction of a deep learning model for high-precision under the condition of small samples. A variety of advanced prediction models are compared with the TC-DLDPM model, and it is verified that the proposed method can accurately predict landslide displacement, especially in the case of drastic changes in external factors. The TC-DLDPM model can capture the spatio-temporal characteristics and dynamic evolution characteristics of landslide displacement, reduce the complexity of the model, and the number of model training calculations. Therefore, it provides a better solution and exploration idea for the prediction of landslide displacement under the condition of small samples