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Sökning: WFRF:(Gorgen S) > (2020-2023)

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1.
  • Pasqualato, G., et al. (författare)
  • Shape evolution in even-mass 98-104Zr isotopes via lifetime measurements using the γ γ-coincidence technique
  • 2023
  • Ingår i: European Physical Journal A. - : Springer. - 1434-6001 .- 1434-601X. ; 59:11
  • Tidskriftsartikel (refereegranskat)abstract
    • The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first 2(+) state and the increase in the transition strength B(E2; 2(1)(+) -> 0(1)(+) ) going from Zr-98 to Zr-100 has been the first example of "quantum phase transition" in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the gamma gamma-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. This work reports the results of lifetime measurements for the first yrast excited states in Zr98-104 carried out to extract reduced transition probabilities. The new lifetime values in gamma gamma-coincidence and gamma-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree-Fock- Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.
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3.
  • Ivanics, Tommy, et al. (författare)
  • The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator : A Machine Learning Approach
  • 2022
  • Ingår i: Liver transplantation. - : John Wiley & Sons. - 1527-6465 .- 1527-6473. ; 28:4, s. 593-602
  • Tidskriftsartikel (refereegranskat)abstract
    • Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, >= 0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.
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