| 1. |
- Du, Yaoyao, et al.
(författare)
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Cartilage Oligomeric Matrix Protein Inhibits Vascular Smooth Muscle Calcification by Interacting With Bone Morphogenetic Protein-2.
- 2011
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Ingår i: Circulation Research. - 1524-4571. ; 108, s. 79-917
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Tidskriftsartikel (refereegranskat)abstract
- Rationale: Vascular calcification is a significant contributor to cardiovascular morbidity and mortality. We recently reported that cartilage oligomeric matrix protein (COMP) is pivotal for maintaining the homeostasis of vascular smooth muscle cells (VSMCs). Whether COMP affects the process of vascular calcification is unknown. Objective: We aimed to test whether COMP modulates vascular calcification. Methods and Results: VSMC calcification in vitro was induced by calcifying media containing high inorganic phosphate or calcium. In vivo medial vessel calcification was induced in rats by 5/6 nephrectomy with a high-phosphate diet or by periadventitial application of CaCl(2) to the abdominal aorta. COMP protein level was markedly reduced in both calcified VSMCs and arteries. COMP deficiency remarkably exacerbated VSMC calcification, whereas ectopic expression of COMP greatly reduced calcification. Furthermore, COMP knockdown facilitated osteogenic markers expression by VSMCs even in the absence of calcifying media. By contrast, COMP overexpression significantly inhibited high phosphate- or high calcium-induced VSMC osteochondrogenic transition. Induction of osteogenic marker expression by COMP silencing was reversed by a soluble form of bone morphogenetic protein (BMP)-2 receptor IA, which suggests a BMP-2-dependent mechanism. Our data revealed that COMP bound directly to BMP-2 through the C terminus, inhibited BMP-2 receptor binding, and blocked BMP-2 osteogenic signaling, indicating COMP inhibits osteochondrogenic transition of VSMCs at least partially through inhibiting BMP-2. Conclusions: Our data strongly suggest that COMP is a novel inhibitor of vascular calcification. The imbalance between the effects of COMP and BMP-2 may provide new insights into the pathophysiology of vascular calcification.
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| 2. |
- Liu, Qingbo, et al.
(författare)
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Effects of carbon pre-germanidation implant into Ge on the thermal stability of NiGe films
- 2015
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Ingår i: Microelectronic Engineering. - : Elsevier BV. - 0167-9317 .- 1873-5568. ; 133, s. 6-10
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Tidskriftsartikel (refereegranskat)abstract
- In this work, the effects of carbon pre-germanidation implant into Ge on the properties of NiGe films were systematically investigated. NiGe films with carbon pre-germanidation implant to doses varying from 0 to 6 x 10(15) cm(-2) were characterized by means of sheet resistance measurement, X-ray diffraction (XRD), scanning electron microscopy (SEM), cross-sectional transmission electron microscope (X-TEM) and secondary ion mass spectroscopy (SIMS). The presence of C atoms is proved to significantly enhance the thermal stability of NiGe by about 100 degrees C as well as to change the preferred orientations of polycrystalline NiGe. The homogenous redistribution of C atoms within NiGe films and the segregation of C atoms at the NiGe/Ge interface is responsible for the improved thermal stability of NiGe films.
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| 3. |
- Liu, Qingbo, et al.
(författare)
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Effects of Carbon Pre-Germanidation Implantation on the Thermal Stability of NiGe and Dopant Segregation on Both n- and p-Type Ge Substrate
- 2015
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Ingår i: ECS Journal of Solid State Science and Technology. - : The Electrochemical Society. - 2162-8769 .- 2162-8777. ; 4:5, s. P119-P123
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Tidskriftsartikel (refereegranskat)abstract
- In this work, the effects of carbon pre-geramanidation implantation on the thermal stability of NiGe and dopant segregation on both ntype and p-type Ge substrate were investigated systematically. As-prepared NiGe films with carbon pre-germanidation implantation to different doses were characterized by means of sheet resistance measurement, X-ray diffraction (XRD), scanning electron microscopy (SEM), cross-sectional transmission electron microscope (X-TEM) and secondary ion mass spectroscopy (SIMS). The presence of carbon is proved to improve the thermal stability of NiGe formed on both n-and p-type Ge significantly, as well as to lead to dopant segregation (DS) of P and B at the NiGe/Ge interface. The homogeneous distribution of C within NiGe films and stuffing of C atoms at the NiGe/Ge interface is responsible for the enhanced thermal stability of NiGe and DS of P and B during germanidation process. (C) The Author(s) 2015. Published by ECS. All rights reserved.
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| 4. |
- Liu, Qingbo, et al.
(författare)
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Improvement of the Thermal Stability of Nickel Stanogermanide by Carbon Pre-Stanogermanidation Implant into GeSn Substrate
- 2015
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Ingår i: ECS Journal of Solid State Science and Technology. - : The Electrochemical Society. - 2162-8769 .- 2162-8777. ; 4:3, s. P67-P70
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Tidskriftsartikel (refereegranskat)abstract
- An effective method to improve the thermal stability of Ni(Ge1-xSnx) by carbon pre-stanogermanidation implant into GeSn substrate is investigated systematically. As-prepared samples were characterized by means of sheet resistance measurement, X-ray diffraction (XRD), scanning electron microscopy (SEM), cross-sectional transmission electron microscopy (X-TEM) and secondary ions mass spectroscopy (SIMS). The incorporation of carbon leads to significantly improved thermal stability of Ni(Ge1-xSnx) by about 100 degrees C as well as tends to change the preferred orientations of polycrystalline Ni(Ge1-xSnx). The robust thermal stability can be attributed to the segregation of C in grain boundaries and at Ni(Ge1-xSnx)/GeSn interface after stanogermanidation.
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| 5. |
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| 6. |
- Wen, Qingbo, et al.
(författare)
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Mechanical properties and electromagnetic shielding performance of single-source-precursor synthesized dense monolithic SiC/HfCxN1-x/C ceramic nanocomposites
- 2019
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Ingår i: Journal of Materials Chemistry C. - : Royal Society of Chemistry (RSC). - 2050-7526 .- 2050-7534. ; 7:34, s. 10683-10693
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Tidskriftsartikel (refereegranskat)abstract
- For the first time, single-source-precursor synthesized dense monolithic SiC/HfCxN1-x/C ceramic nanocomposites with outstanding electromagnetic (EM) shielding performance at temperatures up to 600 degrees C are reported. The total shielding effectiveness (SET) of the SiC/HfCxN1-x/C monolith is >40 dB at 600 degrees C, which is superior than most of the reported EM shielding materials under the same conditions. Compared with a Hf-free SiC/C monolith, the SiC/HfCxN1-x/C monolith possesses superior EM shielding performance due to the presence of a highly conductive HfCxN1-x phase. Moreover, the HfCxN1-x-particles are covered by a carbon layer forming core-shell nanoparticles connected with graphite-like carbon ribbons, which result in electrically conductive networks within the semiconducting beta-SiC matrix. In addition, the hardness, Young's modulus and flexural strength of the dense SiC/HfCxN1-x/C monolith are measured to be 29 +/- 4 GPa, 381 +/- 29 GPa and 320 +/- 25 MPa, respectively. The outstanding EM shielding performance combined with excellent mechanical properties of the dense monolithic SiC/HfCxN1-x/C nanocomposites provides a novel strategy to fabricate EM shielding materials for applications in harsh environments and/or under high mechanical load.
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| 7. |
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| 8. |
- Zhu, Qingbo, 1992, et al.
(författare)
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Predicting Electric Vehicle Energy Consumption from Field Data Using Machine Learning
- 2024
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- This study addresses the challenge of accurately forecasting the energy consumption of electric vehicles (EVs), which is crucial for reducing range anxiety and advancing strategies for charging and energy optimization. Despite the limitations of current forecasting methods, including empirical, physics-based, and data-driven models, this paper presents a novel machine learning-based prediction framework. It integrates physics-informed features and combines offline global models with vehicle-specific online adaptation to enhance prediction accuracy and assess uncertainties. Our framework is tested extensively on data from a real-world fleet of EVs. While the leading global model, quantile regression neural network (QRNN), demonstrates an average error of 6.30%, the online adaptation further achieves a notable reduction to 5.04%, with both surpassing the performance of existing models significantly. Moreover, for a 95% prediction interval, the online adapted QRNN improves coverage probability to 91.27% and reduces the average width of prediction intervals to 0.51. These results demonstrate the effectiveness and efficiency of utilizing physics-based features and vehicle-based online adaptation for predicting EV energy consumption.
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