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Item type:Publication, Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at √sNN = 5.02 TeV(Elsevier B.V., 2024-02-01)The ALICE Collaboration reports a differential measurement of inclusive jet suppression using pp and Pb–Pb collision data at a center-of-mass energy per nucleon–nucleon collision sNN=5.02 TeV. Charged-particle jets are reconstructed using the anti-kT algorithm with resolution parameters R=0.2, 0.3, 0.4, 0.5, and 0.6 in pp collisions and R=0.2, 0.4, 0.6 in central (0–10%), semi-central (30–50%), and peripheral (60–80%) Pb–Pb collisions. A novel approach based on machine learning is employed to mitigate the influence of jet background. This enables measurements of inclusive jet suppression in new regions of phase space, including down to the lowest jet pT≥40 GeV/c at R=0.6 in central Pb–Pb collisions. This is an important step for discriminating different models of jet quenching in the quark–gluon plasma. The transverse momentum spectra, nuclear modification factors, derived cross section, and nuclear modification factor ratios for different jet resolution parameters of charged-particle jets are presented and compared to model predictions. A mild dependence of the nuclear modification factor ratios on collision centrality and resolution parameter is observed. The results are compared to a variety of jet-quenching models with varying levels of agreement. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Informal urban growth monitoring in earthquake-prone areas using SAR satellite images(International Association for Earthquake Engineering, 2024-01-01)In recent decades, Peru’s primary mode of urban growth has been the informal occupation of bare lands. These urban areas are characterized by their lack of essential services, such as electricity and water. With the lack of suitable land for urban development, informal urban has grown into unsafe areas against earthquakes. Due to the lack of resources in developing countries, detecting recent informal occupations in unsafe areas cannot be performed, which is an important task for relocation purposes. This article reports the performance of machine learning applied in synthetic aperture radar (SAR) satellite images for the early detection of informal settlements in hazardous areas. The methodology uses a set of temporally SAR images of a specific area, binary pixel classification, and post-processing techniques to improve the prediction performance. Two informal occupations that occurred in the districts of Chorrillos and Villa el Salvador, Lima, Peru, in April 2021 were used as experimental evaluation. A set of SAR images of the constellation Sentinel-1 was used with a resolution of 10m. The results show that time series analysis of SAR images can identify recent informal occupations. However, the geometrical distortions in SAR images reduce the accuracy of the spatial extent of the occupations. We conclude that SAR images are a valuable source for a sustainable informal urban growth monitoring system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Diagnosis of Pneumoconiosis with Machine Learning(IEEE, 2024-12-17)Pneumoconiosis encompasses a group of lung diseases caused by inhaling dust particles. Frequently recognized as an occupational disease, it primarily affects workers in the mining industry. This paper details the use of machine learning algorithms to aut...4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of solar panel technology and photovoltaic cell status applying machine learning to electroluminescence images(IEEE, 2024-07-30)Photovoltaic energy, being renewable and environmentally friendly, significantly contributes to reducing greenhouse gas emissions. Its popularity and swift technological advances have facilitated the widespread commercialization of solar panels across va...1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A robust five-unknowns higher-order deformation theory optimized via machine learning for functionally graded plates(Taylor & Francis, 2024-04-25)This article presents a new kind of higher-order deformation theory, called Parametric Higher-order Deformation Theory (PHDT), for the static analysis of functionally graded plates (FGPs). The novelty of the PHDT is the use of strain shape functions that...2