Early selection efficiency for fiber dimensions and their relationships with growth and wood quality for Pinus elliotth Engelm.in southern China

A solid understanding of the efficiency of early selection for fiber dimensions is a prerequisite for breeding slash pine(Pinus elliottii Engelm.)with improved properties for pulp and paper products.Genetic correlations between size of fibers,wood quality and growth properties are also important.To accomplish effective early selection for size of fibers and evaluate the impact for wood quality traits and ring widths,core samples were collected from360 trees of 20 open-pollinated Pinus elliottii families from three genetic trials.Cores were measured by SilviScan,and the age trends for phenotypic values,heritability,early-late genetic correlations,and early selection efficiency for fiber dimensions,such as tangential and radial fiber widths,fiber wall thickness and fiber coarseness,and their correlations with microfibril angle(MFA),modulus of elasticity(MOE),wood density and ring width were investigated.Different phenotypic trends were found for tangential and radial fiber widths while fiber coarseness and wall thickness curves were similar.Age trends of heritability based on area-weighted fiber dimensions were different.Low to moderate heritability from pith to bark(~0.5)was found for all fiber dimension across the three sites except for tangential fiber width and wall thickness at the Ganzhou site.Early-late genetic correlations were 0.9 after age of 9 years,and early selection for fiber dimensions could be effective due to strong genetic correlations.Our results showed moderate to strong positive genetic correlations for modulus of elasticity and density with fiber dimensions.The effects on fiber dimensions were weak or moderate when ring width or wood quality traits were selected alone.Estimates of efficiency for early selection indicated that the optimal age for radial fiber width and fiber coarseness was 6-7 years,while for tangential fiber width and wall thickness was 9-10 years.

Assessment of the Tessier and BCR sequential extraction procedures for elemental partitioning of Ca, Fe, Mn, Al,and Ti and their application to surface sediments from Chinese continental shelf

Surface sediments can integrate a wide variety of information of seawater in marginal seas, e.g., the Quaternary sedimentary shelf such as the East China Sea（ECS） and Yellow Sea（YS）. The Tessier and BCR sequential extraction procedures（SEPs） have been widely applied for extraction of various geochemical phases from sediments. To choose a suitable SEP for phase extraction of sediments from the above Quaternary sedimentary shelf, efficiency and selectivity experiments were conducted on typical individual minerals and the applicability of each SEP was assessed for natural sediments（the natural sediment standard GSD-9 and three surface sediment samples）. The geochemical represented elements（Ca, Fe, Mn, Al, and Ti） were measured using both SEPs. Both SEPs have good dissolution efficiency and selectivity for the targeted geochemical phases; the optimized extractant volume for each fraction was determined. The Tessier SEP is particularly recommended for the study of adsorption-desorption process. The application of the Tessier SEP to surface sediments can furnish valuable information, including the productivity conditions（via the reducible fraction Mn） and sedimentary environments（via the carbonate fraction Ca）. These results confirm that the Tessier SEP is suitable for elemental fractionation in sediments from the Chinese continental shelf.

Rapid and controllable in-situ self-assembly of main-group metal nanofilms for highly efficient CO_(2)electroreduction to liquid fuel in flow cells

The electrocatalytic reduction of CO_(2)is a promising pathway to generate renewable fuels and chemicals.However,its advancement is impeded by the absence of electrocatalysts with both high selectivity and stability.Here,we present a scalable in-situ thermal evaporation technique for synthesizing series of Bi,In,and Sn nanofilms on carbon felt(CF)substrates with a high-aspect-ratio structure.The resulting main-group metal nanofilms exhibit a homogeneously distributed and highly exposed catalyst surface with ample active sites,thereby promoting mass transport and ad-/desorption of reaction intermediates.Benefiting from the unique fractal morphology,the Bi nanofilms deposited on CF exhibit optimal catalytic activities for CO_(2)electroreduction among the designed metal nanofilms electrodes,with the highest Faradaic efficiency of 96.9%for formate production at−1.3 V vs.reversible hydrogen electrode(RHE)in H-cell.Under an industrially relevant current density of 221.4 mA·cm−2 in flow cells,the Bi nanofilms retain a high Faradaic efficiency of 81.7%at−1.1 V(vs.RHE)and a good long-term stability for formate production.Furthermore,a techno-economic analysis(TEA)model shows the potential commercial viability of electrocatalytic CO_(2)conversion into formate using the Bi nanofilms catalyst.Our results offer a green and convenient approach for in-situ fabrication of stable and inexpensive thin-film catalysts with a fractal structure applicable to various industrial settings.

Novel and Efficient Randomized Algorithms for Feature Selection

Feature selection is a crucial problem in efficient machine learning,and it also greatly contributes to the explainability of machine-driven decisions.Methods,like decision trees and Least Absolute Shrinkage and Selection Operator(LASSO),can select features during training.However,these embedded approaches can only be applied to a small subset of machine learning models.Wrapper based methods can select features independently from machine learning models but they often suffer from a high computational cost.To enhance their efficiency,many randomized algorithms have been designed.In this paper,we propose automatic breadth searching and attention searching adjustment approaches to further speedup randomized wrapper based feature selection.We conduct theoretical computational complexity analysis and further explain our algorithms’generic parallelizability.We conduct experiments on both synthetic and real datasets with different machine learning base models.Results show that,compared with existing approaches,our proposed techniques can locate a more meaningful set of features with a high efficiency.

The quantized chemical reaction resonantly driven by multiple MIR-photons:From nature to the artificial

Biochemical reactions in vivo occur at the temperature usually lower than that in vitro,however the underlying mechanism still remains a challenge.Inspired by our recent studies of adenosine triphosphate(ATP)releasing photons to resonantly drive DNA replication in a quantum way,we propose a quantized chemical reaction driven by multiple mid-infrared(MIR)photons.The space confinement effect of enzymes on a reactant molecule increases the lifetime of excitation state of its bond vibration,providing a chance for the bond to resonantly absorb multiple photons.Although the energy of each MIR photon is significantly lower than that of chemical bond,the resonant absorption of multiple photons can break the appointed bond of confined molecules.Different from the traditional thermochemistry and photochemistry,the quantized chemical reactions could have a high energy efficiency and ultrahigh selectivity.In addition,we also suggest a quantum driving source for our quantum-confined superfluid reactions proposed previously.The quantized chemical reaction resonantly driven by multiple MIR photons holds great promise to develop novel approaches for the chemical engineering in future.