Predicted High Thermoelectric Performance of Quasi-Two-Dimensional Compound GeAs Using First-Principles Calculations

The electronic structure of binary quasi-two-dimensional GeAs is investigated using first-principles calculations, and it is found that the anisotropic structure of the layered compound GeAs brings about the anisotropy of the transport properties. Meanwhile, the band structure of GeAs exhibits a relatively large dispersion near the valence-band maximum in the Z –V direction while it is rather flat in the Z –Γ direction, which is highly desirable for good thermoelectric performance. The calculated partial charge density distribution also reveals that GeAs possesses anisotropic electrical conductivity. Based on the semi-classical Boltzmann transport theory, the anisotropic transport properties are observed, and the optimal doping concentrations are estimated. The temperature dependence transport properties of p-type GeAs are compared with the experimental data in good agreement, and the theoretical figure-of-merit ZT has been predicted as well.

Prediction of line-spectrum noise induced by high speed vehicle counter-rotation propellers in water

Line-Spectrum noise of counter-rotation propellers has constructed the main part of the radiated noise of high speed vehicles in water. The line-spectrum noise of the counter-rotation propellers is due to the interaction between fore or aft propeller and wake of the vehicle,and the interaction between fore and aft propeller. Based on a combination of the lifting surface theory and acoustic method, the prediction of line-spectrum noise is presented in this paper.Theoretical calculation method, characteristics and numerical prediction of the line-spectrum noise are detailed too. The effect of different wake and different distance between fore and aft propeller on the propeller noise is also studied by numerical method. The agreement of predicted results compared with existing experimental data is quite satisfactory.

Prediction and datamining of burned areas of forest fires:Optimized data matching and mining algorithm provides valuable insight

An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents.It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables.The total burned area distribution of the 517 burn events in that dataset is highly positively skewed.The model is transparent and avoids regressions and hidden layers.This increases its detailed datamining capabilities.It matches the highest burned-area prediction accuracy achieved for this datasetwith a wide range of traditionalmachine learning algorithms.The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions.Optimizing with mean absolute error(MAE)and root mean square error(RMSE)as separate objective functions provides complementary information with which to data mine each total burnedarea incident.Such insight offers potential agricultural,ecological,environmental and forestry benefits by improving the understanding of the key influences associated with each burn event.Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types.Such prediction accuracy and insight leads to confidence in how each prediction is derived.It provides knowledge to make appropriate responses and mitigate specific burn incidents,as they occur.Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.