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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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Perez-Cerdan, A. B., Rubio, B., Gelletly, W., Algora, A., Agramunt, J., Nacher, E., et al. (2013). Deformation of Sr and Rb isotopes close to the N = Z line via beta-decay studies using the total absorption technique. Phys. Rev. C, 88(1), 014324–15pp.
Abstract: A study of the Gamow-Teller strength distributions B(GT) in the beta decay of Sr-78 and Rb-76,Rb-78 has been made using a total absorption spectrometer (TAS). Following the success in deducing the sign of the deformation for Sr-76, a similar approach is adopted for Sr-78 based on a comparison of the measured B(GT) with quasiparticle random-phase approximation calculations. This work confirms its previously expected prolate deformation in the ground state. Conclusions about the structure of the odd-odd Rb-76,Rb-78 isotopes have been drawn based on their measured B(GT) distributions.
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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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Babiano-Suarez, V. et al, Lerendegui-Marco, J., Balibrea-Correa, J., Caballero, L., Calvo, D., Ladarescu, I., et al. (2021). Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques. Eur. Phys. J. A, 57(6), 197–17pp.
Abstract: i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, gamma) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the Au-197(n, gamma) and Fe-56(n, gamma) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of similar to 3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and newanalysis methodologies based on Machine-Learning techniques.
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n_TOF Collaboration(Domingo-Pardo, C. et al), Babiano-Suarez, V., Balibrea-Correa, J., Caballero, L., Ladarescu, I., Lerendegui-Marco, J., et al. (2023). Advances and new ideas for neutron-capture astrophysics experiments at CERN n_TOF. Eur. Phys. J. A, 59(1), 8–11pp.
Abstract: This article presents a few selected developments and future ideas related to the measurement of (n, gamma ) data of astrophysical interest at CERN n_TOF. The MC-aided analysis methodology for the use of low-efficiency radiation detectors in time-of-flight neutron-capture measurements is discussed, with particular emphasis on the systematic accuracy. Several recent instrumental advances are also presented, such as the development of total-energy detectors with gamma- ray imaging capability for background suppression, and the development of an array of small-volume organic scintilla tors aimed at exploiting the high instantaneous neutron-flux of EAR2. Finally, astrophysics prospects related to the intermediate i neutron-capture process of nucleosynthesis are discussed in the context of the new NEAR activation area.
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