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Algora, A. et al, Jordan, D., Tain, J. L., Rubio, B., Agramunt, J., Perez-Cerdan, A. B., et al. (2010). Reactor Decay Heat in Pu-239: Solving the gamma Discrepancy in the 4-3000-s Cooling Period. Phys. Rev. Lett., 105(20), 202501–4pp.
Abstract: The beta feeding probability of Tc-102,Tc- 104,Tc- 105,Tc- 106,Tc- 107, Mo-105, and Nb-101 nuclei, which are important contributors to the decay heat in nuclear reactors, has been measured using the total absorption technique. We have coupled for the first time a total absorption spectrometer to a Penning trap in order to obtain sources of very high isobaric purity. Our results solve a significant part of a long-standing discrepancy in the gamma component of the decay heat for Pu-239 in the 4-3000 s range.
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Caballero, L., Rubio, B., Kleinheinz, P., Yates, S. W., Algora, A., Dewald, A., et al. (2010). Two-phonon octupole excitation in Gd-146. Phys. Rev. C, 81(3), 031301–4pp.
Abstract: Based on experimental evidence from the Sm-144(alpha,2n) reaction, the 3484.7- keV 6(+) state in Gd-146 is identified as the highest-spin member of the 3(-) circle times 3(-) two-phonon octupole quartet. A previously unknown gamma line of 1905.8 keV and E3 character feeding the 3(-) octupole state has been observed. These results represent the first observation of a 6(+) -> 3(-) -> 0(+) cascade of two E3 transitions in an even-even nucleus and provide strong support for the interpretation of the 6(+) state as a two-phonon octupole excitation.
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Labiche, M. et al, Caballero, L., & Rubio, B. (2010). TIARA: A large solid angle silicon array for direct reaction studies with radioactive beams. Nucl. Instrum. Methods Phys. Res. A, 614(3), 439–448.
Abstract: A compact, quasi-4 pi position sensitive silicon array. TIARA, designed to study direct reactions induced by radioactive beams in inverse kinematics is described here. The Transfer and Inelastic All-angle Reaction Array (TIARA) consists of 8 resistive charge division detectors forming an octagonal barrel around the target and a set of double-sided silicon-strip annular detectors positioned at each end of the barrel. The detector was coupled to the gamma-ray array EXOGAM and the spectrometer VAMOS at the GANIL Laboratory to demonstrate the potential of such an apparatus with radioactive beams. The N-14(d,p)N-15 reaction, well known in direct kinematics, has been carried out in inverse kinematics for that purpose. The observation of the N-15 ground state and excited states at 7.16 and 7.86 MeV is presented here as well as the comparison of the measured proton angular distributions with DWBA calculations. Transferred l-values are in very good agreement with both theoretical calculations and previous experimental results obtained in direct kinematics.
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Schaffter, T. et al, Albiol, F., & Caballero, L. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw. Open, 3(3), e200265–15pp.
Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
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Jordan, D. et al, Algora, A., Tain, J. L., Rubio, B., Agramunt, J., Perez-Cerdan, A. B., et al. (2013). Total absorption study of the beta decay of Tc-102,Tc-104,Tc-105. Phys. Rev. C, 87(4), 044318–14pp.
Abstract: The beta-feeding probabilities for three important contributors to the decay heat in nuclear reactors, namely Tc-102,Tc-104,Tc-105, have been measured using the total absorption spectroscopy technique. For the measurements, sources of very high isobaric purity have been obtained using a Penning trap (JYFLTRAP). A detailed description of the data analysis is given and the results are compared with high-resolution measurements and theoretical calculations. DOI: 10.1103/PhysRevC.87.044318
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