@article{HeWangHeetal.2022,
  author    = {He, Yushuang and Wang, Feipeng and He, Li and Wang, Qiang and Li, Jian and Qian, Yihua and Gerhard, Reimund and Plath, Ronald},
  title     = {An insight Into the role of Nano-Alumina on DC Flashover-Resistance and surface charge variation of Epoxy Nanocomposites},
  series = {IEEE transactions on dielectrics and electrical insulation},
  volume    = {29},
  journal   = {IEEE transactions on dielectrics and electrical insulation},
  number    = {3},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Piscataway},
  issn      = {1070-9878},
  doi       = {10.1109/TDEI.2022.3173510},
  pages     = {1022 -- 1029},
  year      = {2022},
  abstract  = {The addition of nano-Al2O3 has been shown to enhance the breakdown voltage of epoxy resin, but its flashover results appeared with disputation. This work concentrates on the surface charge variation and dc flashover performance of epoxy resin with nano-Al2O3 doping. The dispersion of nano-Al2O3 in epoxy is characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The dc flashover voltages of samples under either positive or negative polarity are measured with a finger-electrode system, and the surface charge variations before and after flashovers were identified from the surface potential mapping. The results evidence that nano-Al2O3 would lead to a 16.9\% voltage drop for the negative flashovers and a 6.8\% drop for positive cases. It is found that one-time flashover clears most of the accumulated surface charges, regardless of positive or negative. As a result, the ground electrode is neighbored by an equipotential zone enclosed with low-density heterocharges. The equipotential zone tends to be broadened after 20 flashovers. The nano-Al2O3 is noticed as beneficial to downsize the equipotential zone due to its capability on charge migration, which is reasonable to maintain flashover voltage at a high level after multiple flashovers. Hence, nano-Al2O3 plays a significant role in improving epoxy with high resistance to multiple flashovers.},
  language  = {en}
}
@article{TongNikoloski2020,
  author    = {Tong, Hao and Nikoloski, Zoran},
  title     = {Machine learning approaches for crop improvement},
  series = {Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants},
  volume    = {257},
  journal   = {Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants},
  publisher = {Elsevier},
  address   = {M{\"u}nchen},
  issn      = {0176-1617},
  doi       = {10.1016/j.jplph.2020.153354},
  pages     = {13},
  year      = {2020},
  abstract  = {Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.},
  language  = {en}
}