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Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction

International Journal of Mining Science and Technology 2024年第9期34卷 1283-1292页

作者： Eloy Peña-Asensio Josep M.Trigo-Rodríguez jordi Sort jordi ibáñez-insa Albert Rimola Department of Aerospace Science and Technology Politecnico di MilanoMilano 20156Italy Institut de Ciències de l’Espai(ICE-CSIC) Campus UAB08193 Cerdanyola del VallèsSpain Institut d’Estudis Espacials de Catalunya(IEEC) Barcelona 08034Spain Departament de Física Universitat Autònoma de BarcelonaCerdanyola del Vallès E-08193Spain InstitucióCatalana de Recerca i Estudis Avançats(ICREA) Barcelona E-08010Spain Geosciences Barcelona(GEO3BCN-CSIC) E-08028 BarcelonaSpain Departament de Química Universitat Autònoma de BarcelonaBellaterra 08193Spain

Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive testing methods are *** translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition *** study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084,JAH 838,and NWA 11444 lunar meteorites based solely on their atomic percentage *** a prior-data fitted network model,we achieved near-perfect classification scores for meteorites,mineral groups,and individual *** regressor models,notably the KNeighbor model,provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness,reduced Young’s modulus,and elastic *** considerations on the nature and physical properties of the minerals forming these meteorites,including porosity,crystal orientation,or shock degree,are essential for refining *** findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration,which pave the way for new advancements and quick assessments in extraterrestrial mineral mining,processing,and research.

关键词： Meteorites Moon Mineralogy Machine learning Mechanical properties

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Size effects on the tensile strength and fracture toughness of granitic rock in different tests

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第9期15卷 2179-2192页

作者： Ignacio Pérez-Rey Andrea Muñoz-ibáñez Manuel A.González-Fernández Mauro Muñiz-Menéndez Miguel Herbón Penabad Xian Estévez-Ventosa jordi Delgado Leandro RAlejano Laboratorio de Geotecnia CEDEXMadridSpain CINTECX GESSMin GroupDepartment of Natural Resources and Environmental EngineeringUniversidade de VigoVigoSpain School of Civil Engineering Universidade da CoruñaA CoruñaSpain

This study investigates the tensile failure mechanisms in granitic rock samples at different scales by means of different types of *** do that,we have selected a granitic rock type and obtained samples of different sizes with the diameter ranging from 30 mm to 84 *** samples have been subjected to direct tensile strength(DTS)tests,indirect Brazilian tensile strength(BTS)tests and to two fracture toughness testing *** DTS and fracture toughness were found to consistently grow with sample size,this trend was not clearly identified for BTS,where after an initial grow,a plateau of results was *** is a rather complete database of tensile related properties of a single rock *** if similar databases are rare,the obtained trends are generally consistent with previous scatter and partial experimental ***,different observations apply to different types of rocks and experimental *** differences in variability and mean values of the measured parameters at different scales are critically analysed based on the heterogeneity,granular structure and fracture mechanics *** potential relations between parameters are revised and an indication is given on potential sample sizes for obtaining reliable *** this database with different types of rocks is thought to be convenient to advance towards a better understanding of the tensile strength of rock materials.

关键词： Size effect Tensile strength Fracture toughness Granite Finite fracture mechanics

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Non-Axillary Sentinel Node in Breast *** we Staging Correctly?A Multicenter Study

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Advances in Breast Cancer Research 2014年第4期3卷 124-130页

作者： Javier Encinas Méndez Joan Francesc Julián ibánez Manel Cremades Pérez jordi Navinés Josep Verge Schulte-Eversum Manel Fraile López-Amor Manel Armengol Carrasco Consorci Sanitari Garraf.H.Sant Camil Sant Pere de RibesSpain Hospital Germans Trias i Pujol BadalonaSpain H.U.Vall d’Hebron BarcelonaSpain

Purpose: The study of the sentinel lymph node is the best technique to stage, have a prognosis and decide the adequate treatment in breast cancer. The usual technique implies studding the axillary lymph node. Our work tries to identify affected nodes in other regions apart from the axilla and its possible impact in staging and treatment. Methods: The sentinel lymph node technique was performed on 1660 patients included in an observational and multicentric study designed to observe the presence of metastatic cells in axillary and non-axillary lymph nodes. Results: In 19% of the patients the sentinel lymph node was detected in non-axillary regions. In these cases metastatic cells were more frequent which could suppose a change in the stage and/or treatment. As protective factor against non-axillary nodes involvement we found the localization of the cancer in external quadrants while youth and injecting the tracer inside the tumor were found to be risk factors. Conclusions: Detecting and studding non-axillary lymph nodes in breast cancer leads to a more precise staging of the disease which could imply a change in the optimal treatment.

关键词： Breast Neoplasms Sentinel Lymph Node Biopsy Neoplasm Staging

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