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RISS 인기검색어
- 검색키워드
- 저자 : Deng Jiajun (검색결과 3 건)
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Jiajun Shu,Zhengding Deng,Jingzhu Huang,Bingni Wu,Xingqiu Zhang,Huadong Ai 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.5
To investigate the degree of stability deterioration in toppling dangerous rock mass under different action times, firstly, the vibration load generated by train travel in a single action and its decay law were studied, and the theoretical relational equation of rock strength deterioration under the action of vibration loading was obtained by analyzing the increase in half axle expansion of micro fissures inside the rock. Secondly, the freezing stress of the microfissures was determined according to the rock freezing and swelling theory, and the theoretical relational equation of rock strength deterioration under the action of freeze-thaw cycles was obtained by integrating the rock confinement and moisture migration conditions of the fissures wall. The stability coefficient of the toppling dangerous rock mass exhibited a rapid and then slow decreasing trend with increasing number of actions, which was non-linearly negatively correlated with the train running speed and the initial porosity of the rock, and non-linearly positively correlated with the freezing temperature. Controlling the freezing temperature and initial porosity of rock can significantly reduce the degree of deterioration in the stability of dangerous rock mass.
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Yinxin Zhang,Jiajun Deng,Guoxuan Liu,Jianyang Fei,Huaidong Yang 한국광학회 2020 Current Optics and Photonics Vol.4 No.4
Accurate estimation of spatial frequencies and phases for illumination patterns are essential to reconstructing super-resolution images in structured illumination microscopy (SIM). In this manuscript, we propose the improved component cross-correlation (ICC) algorithm, which is based on optimization of the cross-correlation values of the overlapping information between various spectral components. Compared to other algorithms for spatial-frequency and phase determination, the results calculated by the ICC algorithm are more accurate when the modulation depths of the illumination patterns are low. Moreover, the ICC algorithm is able to calculate the spatial frequencies and phases simultaneously. Simulation results indicate that even if the modulation depth is lower than 0.1, the ICC algorithm still estimates the parameters precisely; the images reconstructed by the ICC algorithm are much clearer than those reconstructed by other algorithms. In experiments, our home-built SIM system was used to image bovine pulmonary artery endothelial (BPAE) cells. Drawing support from the ICC algorithm, super-resolution images were reconstructed without artifacts.
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Wang Tingting,Yang Yang,Liu Xinyue,Deng Jiajun,Wu Junqi,Hou Likun,Wu Chunyan,She Yunlang,Sun Xiwen,Xie Dong,Chen Chang 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.4
Objective: To investigate the association between CT imaging features and survival outcomes in patients with primary invasive mucinous adenocarcinoma (IMA). Materials and Methods: Preoperative CT image findings were consecutively evaluated in 317 patients with resected IMA from January 2011 to December 2015. The association between CT features and long-term survival were assessed by univariate analysis. The independent prognostic factors were identified by the multivariate Cox regression analyses. The survival comparison of IMA patients was investigated using the Kaplan-Meier method and propensity scores. Furthermore, the prognostic impact of CT features was assessed based on different imaging subtypes, and the results were adjusted using the Bonferroni method. Results: The median follow-up time was 52.8 months; the 5-year disease-free survival (DFS) and overall survival rates of resected IMAs were 68.5% and 77.6%, respectively. The univariate analyses of all IMA patients demonstrated that 15 CT imaging features, in addition to the clinicopathologic characteristics, significantly correlated with the recurrence or death of IMA patients. The multivariable analysis revealed that five of them, including imaging subtype (p = 0.002), spiculation (p < 0.001), tumor density (p = 0.008), air bronchogram (p < 0.001), emphysema (p < 0.001), and location (p = 0.029) were independent prognostic factors. The subgroup analysis demonstrated that pneumonic-type IMA had a significantly worse prognosis than solitary-type IMA. Moreover, for solitary-type IMAs, the most independent CT imaging biomarkers were air bronchogram and emphysema with an adjusted p value less than 0.05; for pneumonic-type IMA, the tumors with mixed consolidation and ground-glass opacity were associated with a longer DFS (adjusted p = 0.012). Conclusion: CT imaging features characteristic of IMA may provide prognostic information and individual risk assessment in addition to the recognized clinical predictors.
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