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Thomas W. Griffin - One of the best experts on this subject based on the ideXlab platform.
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Fast Neutron Radiation therapy.
1992Co-Authors: Thomas W. GriffinAbstract:Fast Neutron Radiation therapy was first used as a cancer treatment tool by Robert Stone at the Lawrence Berkeley Laboratory in 1938 [l]. Using the Berkeley cyclotron, Dr. Stone treated a series of patients with advanced malignancies in various locations to high doses. Almost all of the long-term survivors from that clinical trial had severe Radiation sequelae in their normal tissues. This result was initially thought to be due to an increased relative biological effectiveness (RBE) of Neutrons for late effects, and deterred further clinical investigation of fast Neutrons for approximately 20 years. Later experiments, however, showed that the
Possamai Bastos Rodrigo - One of the best experts on this subject based on the ideXlab platform.
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Effects of thermal Neutron Radiation on a hardware-implemented machine learning algorithm
2021Co-Authors: Garay Trindade M., Benevenuti Fabio, Letiche M., Beaucour J., Kastensmidt F., Possamai Bastos RodrigoAbstract:International audienceHardware-implemented machine learning algorithms are finding their way in various domains, including safety-critical applications. This has demanded these algorithms to perform correctly even in harsh environmental conditions, such as in avionics altitudes. Support Vector Machine (SVM) is an important Machine Learning that has been target of hardware implementation in recent years. This is the first work to asses both Binary and Multiclass SVMs under thermal Neutron Radiation, a type of particle noticeably present in high altitudes. A fault injection campaign along with a Radiation test with the D50 thermal Neutron source, at the Intitut Laue-Languevin, has been performed. The results show a high intrinsic fault tolerance for both varieties of the SVM algorithm, especially for the Multiclass SVM
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Assessment of a Hardware-Implemented Machine Learning Technique under Neutron IrRadiation
2019Co-Authors: Garay Trindade M., Coelho A., Valadares C., Camponogara-viera, Raphael Andreoni, Rey S., Cheymol B., Baylac M., Velazco R., Possamai Bastos RodrigoAbstract:International audienceHardware-implemented intelligent systems running autonomous functions and decisions are today becoming more and more ubiquitous in many fields of applications, demanding reliable operation even under harsh conditions as in nuclear power plants and avionics altitudes. Support vector machine (SVM) is a prominent machine learning solution to optimize hardware-implemented autonomous systems. This paper is the first to assess the operation of a field-programmable gate array (FPGA)-designed SVM architecture under Radiation effects. A fault emulation campaign along with Radiation test experiments with a 14-MeV Neutron generator has been performed, and the results show that 27% of the Neutron Radiation-induced errors in the target SVM architecture provoked critical failures
Jasjit K Dillon - One of the best experts on this subject based on the ideXlab platform.
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the efficacy of Neutron Radiation therapy in treating salivary gland malignancies
2019Co-Authors: Marialina Timoshchuk, George E. Laramore, Jay J Liao, Preston Dekker, Daniel S Hippe, Upendra Parvathaneni, Jasjit K DillonAbstract:Abstract Objectives Radiation therapy is commonly used to treat head and neck malignancies. While there is abundant research regarding photon Radiation therapy, literature on Neutron radiotherapy (NRT) and oral complications is limited. This study aims to determine: (1) the 6-year and 10-year locoregional control and survival rates, (2) factors associated with locoregional control and survival and (3) the frequency of oral complications in patients undergoing NRT for salivary gland malignancies. Materials and methods This is a retrospective cohort study. The sample was composed of patients with salivary gland malignancies treated with NRT between 1997 and 2010. Data were extracted from patient charts, telephone surveys, and social security records. Multivariate competing risk and Cox regression models were used to assess predictors of locoregional control and survival. Results The sample was composed of 545 subjects with a mean age of 54.2 years (±16). The predominant tumor and location were adenoid cystic carcinoma (47%) and the parotid (56%). Multivariate analysis indicated that positive surgical margins, biopsied/inoperable malignancies, neck involvement, and lymphovascular invasion were prognostic risk factors associated with decreased survival. The 6- and 10-year locoregional control rates were 84% and 79%. The 6- and 10-year survival rates were 72% and 62%. Osteoradionecrosis developed in 3.4% of subjects. Conclusions The 6- and 10-year locoregional control and survival rates compare favorably to rates reported for conventional photon Radiation. Osteoradionecrosis rates were comparable to that of photon Radiation treatment (2–7%). Given the potential benefits of NRT, healthcare professionals should be educated regarding its indications and oral complications.
Garay Trindade M. - One of the best experts on this subject based on the ideXlab platform.
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Effects of thermal Neutron Radiation on a hardware-implemented machine learning algorithm
2021Co-Authors: Garay Trindade M., Benevenuti Fabio, Letiche M., Beaucour J., Kastensmidt F., Possamai Bastos RodrigoAbstract:International audienceHardware-implemented machine learning algorithms are finding their way in various domains, including safety-critical applications. This has demanded these algorithms to perform correctly even in harsh environmental conditions, such as in avionics altitudes. Support Vector Machine (SVM) is an important Machine Learning that has been target of hardware implementation in recent years. This is the first work to asses both Binary and Multiclass SVMs under thermal Neutron Radiation, a type of particle noticeably present in high altitudes. A fault injection campaign along with a Radiation test with the D50 thermal Neutron source, at the Intitut Laue-Languevin, has been performed. The results show a high intrinsic fault tolerance for both varieties of the SVM algorithm, especially for the Multiclass SVM
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Assessment of a Hardware-Implemented Machine Learning Technique under Neutron IrRadiation
2019Co-Authors: Garay Trindade M., Coelho A., Valadares C., Camponogara-viera, Raphael Andreoni, Rey S., Cheymol B., Baylac M., Velazco R., Possamai Bastos RodrigoAbstract:International audienceHardware-implemented intelligent systems running autonomous functions and decisions are today becoming more and more ubiquitous in many fields of applications, demanding reliable operation even under harsh conditions as in nuclear power plants and avionics altitudes. Support vector machine (SVM) is a prominent machine learning solution to optimize hardware-implemented autonomous systems. This paper is the first to assess the operation of a field-programmable gate array (FPGA)-designed SVM architecture under Radiation effects. A fault emulation campaign along with Radiation test experiments with a 14-MeV Neutron generator has been performed, and the results show that 27% of the Neutron Radiation-induced errors in the target SVM architecture provoked critical failures
Wenbao Jia - One of the best experts on this subject based on the ideXlab platform.
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high performance piezoelectric nanogenerator based on microstructured p vdf trfe bnnts composite for energy harvesting and Radiation protection in space
2019Co-Authors: Can Cheng, Xiaoming Chen, Xiaoliang Chen, Jinyou Shao, Jie Zhang, Hongmiao Tian, Wenbao JiaAbstract:Abstract Stable and durable piezoelectric nanogenerators (PENGs) with good flexibility, high performance and superior Radiation resistance under harsh environments are promising for space exploration. Here, a novel PENG based on P(VDF-TrFE)/boron nitride nanotubes (BNNTs) nanocomposite micropillar arrays with enhanced performance and excellent Neutron Radiation shielding is prepared by a reliable nanoimprint lithography. The PENG comprised of a microstructured P(VDF-TrFE)/0.3 wt% BNNTs nanocomposite demonstrates an outstanding output voltage of 22 V and a sensitivity of 55 V/MPa under the pressure of 0.4 MPa, which are 11-fold higher than those of pristine P(VDF-TrFE) film. This dramatic enhancement in performance is ascribed to synergistic contributions from strong piezoelectric BNNTs and a strain confinement effect of the nanocomposite microstructure. In practice, the PENG is capable of scavenging various mechanical and biomechanical energy for lighting up commercial LEDs, an LCD screen and a digital watch. More importantly, the as-obtained PENG exhibited 9% Neutron Radiation shielding with Neutron cross section increase reaching 260% when compared to the film without BNNTs. Moreover, the high output is retained after 2 h of Neutron Radiation exposure. Overall, the as-prepared microstructured nanocomposites look promising for high-efficiency piezoelectric nanogenerators for the self-powered and wearable electronic devices, in particularly, under the extreme space environments.