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Effects of Confinement on the Valey Splitting of Si Quantum Structures
D. Ahn,S. N. Ko,J. H. Bae,Y. Y. Lee,Y. H. Moon,J. H. Oh 한국물리학회 2008 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.53 No.6
Valley splitting in SiGe/Si/SiGe heterostructures is calculated with the multi-valley effective mass theory. By examining various quantum structures, such as wells, wires and dots, we show that the valley splitting exhibits an oscillating behavior as a function of geometrical size. The maximum value of the valley splitting is found to be on the order of meV for a typical well-width of about 5 nm, which is consistent with experimental values. We discuss the origins of the valley splitting and the confinement eects in the presence of an electric-field.
Dahn Byun*,이슬기,Hyeyoung Kim,Yunghun You,정재학,Je Ho Jang,Moon-Soo Lee,김창남,Byung Sun Cho,Yoon-Jung Kang 대한외과학회 2022 Annals of Surgical Treatment and Research(ASRT) Vol.103 No.5
Purpose: Although protein-induced vitamin K absence or antagonist II (PIVKA-II) has been used as a diagnostic tool for hepatocellular carcinoma (HCC), its prognostic value remains unclear. Methods: This was a nationwide multicenter study using the database of the Korean Liver Cancer Association. Patients with hepatitis B-related HCC who underwent liver resection as the first treatment after initial diagnosis (2008–2014) were selected randomly. Propensity score matching (1:1) was performed for comparative analysis between those with low and high preoperative PIVKA-II. Univariable and multivariable Cox proportional-hazards regression were used to identify prognostic factors for HCC-specific survival. Results: Among 6,770 patients, 956 patients were included in this study. After propensity score matching, the 2 groups (n = 245, each) were well balanced. The HCC-specific 5-year survival rate was 80.9% in the low PIVKA-II group and 78.7% in the high PIVKA-II group (P = 0.605). In univariable analysis, high PIVKA-II (>106.0 mAU/mL) was not a significant predictor for worse HCC-specific survival (hazard ratio [HR], 1.183; 95% confidence interval [CI], 0.76–1.85; P = 0.461). In multivariable analysis, hyponatremia of <135 mEq/L (HR, 4.855; 95% CI, 1.67–14.12; P = 0.004), preoperative ascites (HR, 4.072; 95% CI, 1.59–10.43; P = 0.003), microvascular invasion (HR, 3.112; 95% CI, 1.69–5.74; P < 0.001), and largest tumor size of ≥5.0 cm (HR, 2.665; 95% CI, 1.65–4.31; P < 0.001), but not preoperative high PIVKA-II, were independent predictors for worse HCC- specific survival. Conclusion: Preoperative PIVKA-II is not an independent prognostic factor for HCC-specific survival after liver resection for hepatitis B-related HCC.
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
Dahne, Sven,Biessmann, Felix,Samek, Wojciech,Haufe, Stefan,Goltz, Dominique,Gundlach, Christopher,Villringer, Arno,Fazli, Siamac,Muller, Klaus-Robert IEEE 2015 Proceedings of the Institute of Electrical and Ele Vol.103 No.9
<P>Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.</P>
박단 ( Dahn Park ) 한국서양사학회 2011 西洋史論 Vol.0 No.111
In France, the term secularization is commonly associated with laicite. However, it is also used to describe Muslim immigrants who were either disinclined for radical Islamism or have acculturated to French society. While emphasizing the incorporation of Muslim immigrants into the French systems, this paper examines the issue of Muslim immigrants` secularization to France from socioeconomic and religious cultural perspectives. It is widely accepted to approach the issue of secularization from a religious cultural perspective. This perspective leads us to easily conclude that many Muslim immigrants were generally secularized with the rapid growth of the second-generation Muslim French. The decreasing participation of regular prayers in the mosque and the increasing number of mixed marriages between the French and Maghrebis are good cases for the phenomenon of secularization. The acculturated Muslims also tend to remain respectful of the principles of the French Republic. However, several problems, such as the high unemployment rate among Muslims and on-going radical Islamism related to terrorism, serve as obstacles to secularization. These obstacles need to be removed for the more favorable process of secularization and the more concrete integration of the Muslim immigrants into French society. The French government must make efforts to solve Muslims` socioeconomic hardship so that radical Islamism cannot exert its influence upon the Muslim community in France.