Elemental analysis of particulate matter in a metal workshop and of biological samples from exposed workers

During metal welding and cutting, large amounts of particulate matter (PM) are produced that might represent a significant health risk for the exposed workers. In the present pilot study, we performed an elemental analysis of fine PM collected in a metal workshop. Also, elemental analysis of the hair and nail samples collected from workers exposed to the workshop dust and control group was done. Concentrations of 15 elements in PM were measured with X‐Ray Fluorescence (XRF) and Particle Induced X‐ray Emission (PIXE), whereas inductively coupled plasma mass spectrometry (ICP‐MS) was used to determine 12 elements in hair and nail samples. Mean 8‐hr concentrations of PM_2.5, Fe, and Mn in the vicinity of welders were up to 1803, 860, and 30 μg/m³, respectively, whereas in the nearby city, daily PM_2.5 concentrations are on average 11 μg/m³. We found that several elements, especially Fe and Mn, had substantially higher concentrations in hair and nail samples of exposed workers than in the control group, which indicates the accumulation of metals in workers' tissues, although limit values were not exceeded.     

Lower Bounds for the Robustness of Multilevel Coherence

International Journal of Theoretical Physics - Quantum coherence, coming from quantum superposition, occupies a significant position in the field of physics. We put forward a lower bound of the...     

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.     

Development of an improved divergence‐free‐condition compensated coupled framework to solve flow problems with time‐varying geometries

In this article a coupled version of the improved divergence‐free‐condition compensated method will be proposed to simulate time‐varying geometries by direct forcing immersed boundary method. The proposed method can be seen as a quasi‐multi‐moment framework due to the fact that the momentum equations are discretized by both cell‐centered and cell‐face velocity. For simulating time‐varying geometries, a semi‐implicit iterative method is proposed for calculating the direct forcing terms. Treatments for suppressing spurious force oscillations, calculating drag/lift forces, and evaluating velocity and pressure for freshly cells will also be addressed. In order to show the applicability and accuracy, analytical as well as benchmark problems will be investigated by the present framework and compared with other numerical and experimental results.     

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。     

Nonlinear compressed sensing-based adaptive modal shapes selection approach for efficient dynamic response analysis of flexible multibody system

Nonlinear Dynamics - The modal truncation is usually used in the dynamic response analysis of flexible multibody systems, in which, appropriate selection of modal shapes is very critical to achieve...     

The Synergistic Photocatalytic Effect of H3PW12O40 and Pt Nanoparticles Comodified UiO-66-NH2 on the Reduction of Aqueous Cr(VI) under Original pH

Kinetics and Catalysis - A ternary synergistic photocatalyst H3PW12O40/Pt/UiO-66-NH2 was successfully fabricated for the first time through a simple solvothermal synthetic strategy. We found that...     

Fully Chemical Recyclable Poly(γ-butyrolactone)-based Copolymers with Tunable Structures and Properties

Chinese Journal of Polymer Science - The emerging chemical recyclable polymers, such as poly(γ-butyrolactone) (PGBL) and poly((R)-3,4-trans six-membered ring-fused GBL) (P((R)-M)), provide a...