Model averaging for linear models with responses missing at random

Annals of the Institute of Statistical Mathematics - In this paper, a model averaging approach is developed for the linear regression models with response missing at random. It is shown that the...     

Optimal design of gears contact interface modification for an objective as minimum impact resistance of initial meshing-in time domain

Meccanica - In gear pair actual alternating meshing process, the comprehensive errors of the transmission system and the thermal elastic deformation of the teeth body cause the gears in the meshing...     

Tantalum Nitride Nanosheets for Photoacoustic Imaging-Guided Photothermal Cancer Therapy

In this study, the synthesis of TaN nanosheets and their application in theranostic agents is reported. After coating polyethylene glycol (PEG) on the TaN nanosheets, the as-synthesized PEG-modified TaN nanosheets (TaN-PEG) show good stability and biocompatibility. Because of their high absorbance in the near-IR region, TaN-PEG can be utilized as photoacoustic imaging contrast agents for tumor imaging. Moreover, TaN-PEG has significant photothermal conversion performance, exhibiting effective laser-induced tumor ablation capability. The TaN-PEG possessing excellent photoacoustic contrast effect and photothermal properties thus have great promise in theranostic applications, especially imaging-guided cancer treatment.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Phase transition and scaling in Kuramoto model with high-order coupling

Nonlinear Dynamics - The classical Kuramoto model serves as a useful tool for studying synchronization transitions in coupled oscillators that is limited to the sinusoidal and pairwise...     

Study on propagation efficiency and fidelity of subthreshold signal in feed-forward hybrid neural network under electromagnetic radiation

Nonlinear Dynamics - The hybrid neural model provides a computationally effective and biophysics-based neuron behavior model, which maintains its simplicity by incorporating the dynamics...     

Quantum Resources in Heisenberg XX Model Under Noisy Environment

International Journal of Theoretical Physics - In this paper, we investigate Einstein-Podolsky-Rosen steering, Bell non-locality, first-order coherence and concurrence in the spin- $\frac {1}{2}$...     

CCSD(T) study on the structures and chemical bonds of AnO molecules (An = Bk–Lr)

The molecular geometries and dissociation energies of AnO (An = Bk–Lr) molecules were first obtained at thecoupled-cluster single-, double-, and perturbative triple-excitations [CCSD(T)] level of theory. Four hybrid functionals,B3LYP, M06-2X, TPSSh, and PBE0, were also employed in the calculations for the sake of comparison. In comparison ofthe CCSD(T) results, B3LYP, TPSSh, and PBE0 functionals can obtain more appropriate results than M06-2X and MP2.The analyses on molecular orbitals show that the 7s, 6d, and 5f atomic orbitals of actinide (An) atoms participate in thebonding of An–O bonds. The partial covalent nature between An and O atoms is revealed by QTAIM analyses.     

基于联合神经网络学习的中文电力计量命名实体识别

随着电力计量业务的不断扩展，迫切需要由业务信息、技术知识、行业标准及其内在联系所组成的电力计量知识图谱，为电网的决策和发展提供更为全面有效的支持。命名实体识别是构建知识图谱的基础。针对电力计量领域需要，结合中文分词技术特点，基于联合学习思想，提出了一种基于联合学习的中文电力计量命名实体识别技术。该技术联合CNN-BLSTM-CRF模型与整合词典知识的分词模型，使其共享实体类别和置信度；同时将2个模型的先后计算顺序改为并行计算，减少了识别误差累积。结果表明，在不需要人工构建特征的情况下，方法的正确率、召回率、F值等均显著优于以往方法。