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342 results on '"Sanchez Galan A"'

1. Reconstruction of 400 GeV/c proton interactions with the SHiP-charm project

Author

SHiP Collaboration, C. Ahdida, A. Akmete, R. Albanese, A. Alexandrov, F. Alicante, J. Alt, S. Aoki, G. Arduini, J. J. Back, F. Baaltasar Dos Santos, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, I. Boiarska, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, A. Brignoli, V. Büscher, A. Buonaura, S. Buontempo, S. Cadeddu, M. Calviani, M. Campanelli, M. Casolino, D. Centanni, N. Charitonidis, P. Chau, J. Chauveau, K.-Y. Choi, A. Chumakov, V. Cicero, M. Climescu, A. Conaboy, L. Congedo, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, R. de Asmundis, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, A. Di Crescenzo, L. Di Giulio, C. Dib, H. Dijkstra, L. A. Dougherty, V. Drohan, A. Dubreuil, O. Durhan, M. Ehlert, E. Elikkaya, F. Fabbri, F. Fedotovs, M. Ferrillo, M. Ferro-Luzzi, R. A. Fini, H. Fischer, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, T. Fukuda, G. Galati, J. Gall, L. Gatignon, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golutvin, P. Gorbounov, V. Gorkavenko, A. L. Grandchamp, E. Graverini, J.-L. Grenard, D. Grenier, A. M. Guler, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, E. van Herwijnen, C. Hessler, A. Hollnagel, B. Hosseini, G. Iaselli, A. Iuliano, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, B. Kaiser, C. Kamiscioglu, K. Kershaw, G. Khoriauli, Y. G. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, D. I. Kolev, M. Komatsu, A. Kono, S. Kormannshaus, I. Korol, A. Korzenev, V. Kostyukhin, E. Koukovini Platia, S. Kovalenko, H. M. Lacker, M. Lamont, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, N. Leonardo, J.-M. Lévy, V. P. Loschiavo, L. Lopes, E. Lopez Sola, F. Lyons, V. Lyubovitskij, J. Maalmi, A.-M. Magnan, Y. Manabe, M. Manfredi, S. Marsh, A. M. Marshall, P. Mermod, A. Miano, S. Mikado, A. Mikulenko, D. A. Milstead, A. Montanari, M. C. Montesi, K. Morishima, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, P. Ninin, A. Nishio, S. Ogawa, J. Osborne, M. Ovchynnikov, N. Owtscharenko, P. H. Owen, P. Pacholek, B. D. Park, A. Pastore, M. Patel, A. Perillo-Marcone, G. L. Petkov, K. Petridis, J. Prieto Prieto, A. Prota, A. Quercia, A. Rademakers, A. Rakai, T. Rawlings, F. Redi, A. Reghunath, S. Ricciardi, M. Rinaldesi, Volodymyr Rodin, Viktor Rodin, P. Robbe, A. B. Rodrigues Cavalcante, H. Rokujo, T. Rovelli, O. Ruchayskiy, T. Ruf, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, O. Sato, J. S. Schliwinski, W. Schmidt-Parzefall, M. Schumann, N. Serra, S. Sgobba, O. Shadura, M. Shaposhnikov, L. Shchutska, H. Shibuya, L. Shihora, S. Shirobokov, S. B. Silverstein, S. Simone, R. Simoniello, G. Soares, J. Y. Sohn, A. Sokolenko, E. Solodko, L. Stoel, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, N. Tosi, D. Treille, R. Tsenov, G. Vankova-Kirilova, F. Vannucci, P. Venkova, V. Venturi, S. Vilchinski, Heinz Vincke, Helmut Vincke, C. Visone, S. van Waasen, R. Wanke, P. Wertelaers, O. Williams, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, and J. Zimmerman

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The SHiP-charm project was proposed to measure the associated charm production induced by 400 GeV/c protons in a thick target, including the contribution from cascade production. An optimisation run was performed in July 2018 at CERN SPS using a hybrid setup. The high resolution of nuclear emulsions acting as vertex detector was complemented by electronic detectors for kinematic measurements and muon identification. Here we present first results on the analysis of nuclear emulsions exposed in the 2018 run, which prove the capability of reconstructing proton interaction vertices in a harsh environment, where the signal is largely dominated by secondary particles produced in hadronic and electromagnetic showers within the lead target.

Published
2024
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2. Unsupervised identification of Greater Caribbean manatees using Scattering Wavelet Transform and Hierarchical Density Clustering from underwater bioacoustics recordings

Author

Fernando Merchan, Kenji Contreras, Héctor Poveda, Hector M. Guzman, and Javier E. Sanchez-Galan

Subjects
Greater Caribbean manatee, bioacoustics, scattering wavelet transform, Manifold Learning, density-based clustering, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

IntroductionThis work presents an unsupervised learning-based methodology to identify and count unique manatees using underwater vocalization recordings.MethodsThe proposed approach uses Scattering Wavelet Transform (SWT) to represent individual manatee vocalizations. A Manifold Learning approach, known as PacMAP, is employed for dimensionality reduction. A density-based algorithm, known as Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), is used to count and identify clusters of individual manatee vocalizations. The proposed methodology is compared with a previous method developed by our group, based on classical clustering methods (K-Means and Hierarchical clustering) using Short-Time Fourier Transform (STFT)-based spectrograms for representing vocalizations. The performance of both approaches is contrasted by using a novel vocalization data set consisting of 23 temporally captured Greater Caribbean manatees from San San River, Bocas del Toro, in western Panama as input.ResultsThe proposed methodology reaches a mean percentage of error of the number of individuals (i.e., number of clusters) estimation of 14.05% and success of correctly grouping a manatee in a cluster of 83.75%.DiscussionThus having a better performances than our previous analysis methodology, for the same data set. The value of this work lies in providing a way to estimate the manatee population while only relying on underwater bioacoustics.

Published
2024
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3. Vocalizations of wild West Indian manatee vary across subspecies and geographic location

Author

Jessica D. Reyes-Arias, Beth Brady, Eric A. Ramos, Yann Henaut, Delma Nataly Castelblanco-Martínez, Maria Maust-Mohl, Linda Searle, Gabriela Pérez-Lachaud, Héctor M. Guzmán, Héctor Poveda, Fernando Merchan, Kenji Contreras, Javier E. Sanchez-Galan, Kristi A. Collom, and Marcelo O. Magnasco

Subjects
Medicine, Science
Abstract

Abstract Geographic variation in the vocal behavior of manatees has been reported but is largely unexplored. Vocalizations of wild West Indian manatees (Trichechus manatus) were recorded with hydrophones in Florida from Florida manatees (Trichechus manatus latirostris), and in Belize and Panama from Antillean manatees (Trichechus manatus manatus) to determine if calls varied between subspecies and geographic regions. Calls were visually classified into five categories: squeaks, high squeaks, squeals, squeak-squeals, and chirps. From these five categories, only three call types (squeaks, high squeaks and squeals) were observed in all three populations. Six parameters from the temporal and frequency domains were measured from the fundamental frequency of 2878 manatee vocalizations. A repeated measures PERMANOVA found significant differences for squeaks and high squeaks between each geographic location and for squeals between Belize and Florida. Almost all measured frequency and temporal parameters of manatee vocalizations differed between and within subspecies. Variables that may have influenced the variation observed may be related to sex, body size, habitat and/or other factors. Our findings provide critical information of manatee calls for wildlife monitoring and highlight the need for further study of the vocal behavior of manatees throughout their range.

Published
2023
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4. Deep metric learning for the classification of MALDI-TOF spectral signatures from multiple species of neotropical disease vectors

Author

Fernando Merchan, Kenji Contreras, Rolando A. Gittens, Jose R. Loaiza, and Javier E. Sanchez-Galan

Subjects
MALDI-TOF, Machine learning, Metric learning, Culicidae, Ixodidae, Molecular taxonomy, Science (General), Q1-390
Abstract

Deep Learning techniques have significant advantages for mass spectral classification, such as parallelized signal correction and feature extraction. Deep Metric Learning models combine Metric Learning to determine the degree of similarity or difference between a set of mass spectra with the generalization power of Deep Learning to improve feature extraction even further. The two most popular of these models combine multiple neural networks with identical architectures and are commonly called Siamese (SNN) and Triplet Neural Networks (TNN). Herein, using both SNNs and TNNs, we intended to taxonomically categorize two sets of previously-validated mass spectra that corresponded to 30 species of Neotropical arthropods in the Culicidae and Ixodidae families, some of which are disease vectors. The effectiveness of SNNs and TNNs to correctly classify 826 spectra from 12 mosquito species and 310 spectra from 18 species of hard ticks was highly effective, with both algorithms performing with minimal average loss during cross-validation. SNNs produced accuracy rates for ticks and mosquitoes of 91.22% and 94.46%, respectively, while accuracy rates of 93% and 99% were obtained with TNNs. Our results indicate that Deep Metric Learning is a practical machine learning tool for quickly and precisely classifying MALDI-TOF-generated mass spectra of Neotropical and public-health-relevant arthropod species.

Published
2023
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6. Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images

Author

Jorge Serrano Reyes, José Ulises Jiménez, Evelyn Itzel Quirós-McIntire, Javier E. Sanchez-Galan, and José R. Fábrega

Subjects
rice crops, phenology, crop performance, leaf area index, Panama, reflectance, Agriculture (General), S1-972, Engineering (General). Civil engineering (General), TA1-2040
Abstract

This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI (R2 = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama.

Published
2023
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9. Desarrollo de un prototipo de sistema ciber-físico para el control administrativo del suministro de bebidas y refrescos

Author

Arturo A. Castro Rodríguez and Javier Sanchez-Galan

Subjects
sistema experto, industria 4.0, abastecimiento de bebidas, sostenibilidad, Electronic computers. Computer science, QA75.5-76.95
Abstract

Este proyecto se centra en demostrar la viabilidad de la implementación de nuevas tecnologías, principalmente herramientas de inteligencia artificial, para el control de suministro de bebidas de forma automatizada, pero también aplicable a diferentes tipos de productos alimenticios. Esto es posible ya que, en áreas de automatización, las herramientas de inteligencia artificial han propiciado cambios profundos en las industrias y el comercio. Esto debido fundamentalmente al avance de la tecnología y a que las empresas producen información masiva que marcan riesgos e incertidumbre a la hora de la toma de decisiones empresariales, por lo que es de suma importancia organizar dicha información de forma eficiente. Este proyecto básicamente acerca del de desarrollo de un sistema experto que calcule la cantidad de bebida y/o refresco que se necesita a la semana siguiente en un local de restaurante de comida rápida, que trabaja junto con un prototipo de máquina expendedora de bebida controlada por Arduino (aunque puede ser cualquier otro micro-controlador). En este documento se muestran aspectos para diseño, elaboración y desarrollo de aplicaciones e instrumentos que cumplan con este objetivo, además se describe el porqué del proyecto y como esto pretende aportar al sector al cual está enfocado. Al final se muestra una mejor manera de administrar el abastecimiento de bebidas en restaurantes de comida rápida y se concluye que la implementación de este proyecto en este tipo de negocios, puede generar ahorros en productos y puede contribuir a evitar en gran medida la contaminación del medio ambiente, todo enmarcado dentro de la industria 4.0.

Published
2022
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10. Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks

Author

Alvaro A. Teran-Quezada, Victor Lopez-Cabrera, Jose Carlos Rangel, and Javier E. Sanchez-Galan

Subjects
hand gesture recognition, sign language recognition, convolutional neural networks, object detection, transfer learning, machine learning, Technology
Abstract

Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning.

Published
2024
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12. Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R0 for the Republic of Panama in the 1999-2022 period

Author

Vicente Alonso Navarro Valencia, Yamilka Díaz, Jose Miguel Pascale, Maciej F. Boni, and Javier E. Sanchez-Galan

Subjects
ODE, PSO, SIR, SEIR, SIR-SI, R0, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Nowadays, the ability to make data-driven decisions in public health is of utmost importance. To achieve this, it is necessary for modelers to comprehend the impact of models on the future state of healthcare systems. Compartmental models are a valuable tool for making informed epidemiological decisions, and the proper parameterization of these models is crucial for analyzing epidemiological events. This work evaluated the use of compartmental models in conjunction with Particle Swarm Optimization (PSO) to determine optimal solutions and understand the dynamics of Dengue epidemics. The focus was on calculating and evaluating the rate of case reproduction, R0, for the Republic of Panama. Three compartmental models were compared: Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected-Recovered (SEIR), and Susceptible-Infected-Recovered Human-Susceptible-Infected Vector (SIR Human-SI Vector, SIR-SI). The models were informed by demographic data and Dengue incidence in the Republic of Panama between 1999 and 2022, and the susceptible population was analyzed. The SIR, SEIR, and SIR-SI models successfully provided R0 estimates ranging from 1.09 to 1.74. This study provides, to the best of our understanding, the first calculation of R0 for Dengue outbreaks in the Republic of Panama.

Published
2023
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13. The SHiP experiment at the proposed CERN SPS Beam Dump Facility

Author

C. Ahdida, A. Akmete, R. Albanese, J. Alt, A. Alexandrov, A. Anokhina, S. Aoki, G. Arduini, E. Atkin, N. Azorskiy, J. J. Back, A. Bagulya, F. Baaltasar Dos Santos, A. Baranov, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, A. Y. Berdnikov, Y. A. Berdnikov, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, I. Boiarska, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, A. Brignoli, V. Büscher, A. Buonaura, S. Buontempo, S. Cadeddu, M. Calviani, M. Campanelli, M. Casolino, N. Charitonidis, P. Chau, J. Chauveau, A. Chepurnov, M. Chernyavskiy, K.-Y. Choi, A. Chumakov, M. Climescu, A. Conaboy, L. Congedo, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, R. de Asmundis, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, L. Dedenko, P. Dergachev, A. Di Crescenzo, L. Di Giulio, C. Dib, H. Dijkstra, V. Dmitrenko, L. A. Dougherty, A. Dolmatov, S. Donskov, V. Drohan, A. Dubreuil, O. Durhan, M. Ehlert, E. Elikkaya, T. Enik, A. Etenko, O. Fedin, F. Fedotovs, M. Ferrillo, M. Ferro-Luzzi, K. Filippov, R. A. Fini, H. Fischer, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, T. Fukuda, G. Galati, J. Gall, L. Gatignon, G. Gavrilov, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golovatiuk, V. Golovtsov, D. Golubkov, A. Golutvin, P. Gorbounov, D. Gorbunov, S. Gorbunov, V. Gorkavenko, M. Gorshenkov, V. Grachev, A. L. Grandchamp, E. Graverini, J.-L. Grenard, D. Grenier, V. Grichine, N. Gruzinskii, A. M. Guler, Yu. Guz, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, E. van Herwijnen, C. Hessler, A. Hollnagel, B. Hosseini, M. Hushchyn, G. Iaselli, A. Iuliano, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, B. Kaiser, C. Kamiscioglu, D. Karpenkov, K. Kershaw, M. Khabibullin, E. Khalikov, G. Khaustov, G. Khoriauli, A. Khotyantsev, Y. G. Kim, V. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, A. Kolesnikov, D. I. Kolev, V. Kolosov, M. Komatsu, A. Kono, N. Konovalova, S. Kormannshaus, I. Korol, I. Korol’ko, A. Korzenev, E. Koukovini Platia, S. Kovalenko, I. Krasilnikova, Y. Kudenko, E. Kurbatov, P. Kurbatov, V. Kurochka, E. Kuznetsova, H. M. Lacker, M. Lamont, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, N. Leonardo, J.-M. Lévy, V. P. Loschiavo, L. Lopes, E. Lopez Sola, F. Lyons, V. Lyubovitskij, J. Maalmi, A.-M. Magnan, V. Maleev, A. Malinin, Y. Manabe, A. K. Managadze, M. Manfredi, S. Marsh, A. M. Marshall, A. Mefodev, P. Mermod, A. Miano, S. Mikado, Yu. Mikhaylov, A. Mikulenko, D. A. Milstead, O. Mineev, M. C. Montesi, K. Morishima, S. Movchan, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, S. Nasybulin, P. Ninin, A. Nishio, B. Obinyakov, S. Ogawa, N. Okateva, J. Osborne, M. Ovchynnikov, N. Owtscharenko, P. H. Owen, P. Pacholek, B. D. Park, A. Pastore, M. Patel, D. Pereyma, A. Perillo-Marcone, G. L. Petkov, K. Petridis, A. Petrov, D. Podgrudkov, V. Poliakov, N. Polukhina, J. Prieto Prieto, M. Prokudin, A. Prota, A. Quercia, A. Rademakers, A. Rakai, F. Ratnikov, T. Rawlings, F. Redi, A. Reghunath, S. Ricciardi, M. Rinaldesi, Volodymyr Rodin, Viktor Rodin, P. Robbe, A. B. Rodrigues Cavalcante, T. Roganova, H. Rokujo, G. Rosa, O. Ruchayskiy, T. Ruf, V. Samoylenko, V. Samsonov, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, O. Sato, E. S. Savchenko, J. S. Schliwinski, W. Schmidt-Parzefall, M. Schumann, N. Serra, S. Sgobba, O. Shadura, A. Shakin, M. Shaposhnikov, P. Shatalov, T. Shchedrina, L. Shchutska, V. Shevchenko, H. Shibuya, L. Shihora, S. Shirobokov, A. Shustov, S. B. Silverstein, S. Simone, R. Simoniello, M. Skorokhvatov, S. Smirnov, G. Soares, J. Y. Sohn, A. Sokolenko, E. Solodko, N. Starkov, L. Stoel, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, P. Teterin, S. Than Naing, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, D. Treille, R. Tsenov, S. Ulin, E. Ursov, A. Ustyuzhanin, Z. Uteshev, L. Uvarov, G. Vankova-Kirilova, F. Vannucci, P. Venkova, V. Venturi, I. Vidulin, S. Vilchinski, Heinz Vincke, Helmut Vincke, C. Visone, K. Vlasik, A. Volkov, R. Voronkov, S. van Waasen, R. Wanke, P. Wertelaers, O. Williams, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, Yu. Zaytsev, A. Zelenov, J. Zimmerman, and SHiP Collaboration

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The Search for Hidden Particles (SHiP) Collaboration has proposed a general-purpose experimental facility operating in beam-dump mode at the CERN SPS accelerator to search for light, feebly interacting particles. In the baseline configuration, the SHiP experiment incorporates two complementary detectors. The upstream detector is designed for recoil signatures of light dark matter (LDM) scattering and for neutrino physics, in particular with tau neutrinos. It consists of a spectrometer magnet housing a layered detector system with high-density LDM/neutrino target plates, emulsion-film technology and electronic high-precision tracking. The total detector target mass amounts to about eight tonnes. The downstream detector system aims at measuring visible decays of feebly interacting particles to both fully reconstructed final states and to partially reconstructed final states with neutrinos, in a nearly background-free environment. The detector consists of a 50 $$\mathrm { \,m}$$ m long decay volume under vacuum followed by a spectrometer and particle identification system with a rectangular acceptance of 5 m in width and 10 m in height. Using the high-intensity beam of 400 $$\,\mathrm {GeV}$$ GeV protons, the experiment aims at profiting from the $$4\times 10^{19}$$ 4 × 10 19 protons per year that are currently unexploited at the SPS, over a period of 5–10 years. This allows probing dark photons, dark scalars and pseudo-scalars, and heavy neutral leptons with GeV-scale masses in the direct searches at sensitivities that largely exceed those of existing and projected experiments. The sensitivity to light dark matter through scattering reaches well below the dark matter relic density limits in the range from a few $${\mathrm {\,MeV\!/}c^2}$$ MeV / c 2 up to 100 MeV-scale masses, and it will be possible to study tau neutrino interactions with unprecedented statistics. This paper describes the SHiP experiment baseline setup and the detector systems, together with performance results from prototypes in test beams, as it was prepared for the 2020 Update of the European Strategy for Particle Physics. The expected detector performance from simulation is summarised at the end.

Published
2022
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14. Recognition of Multiple Panamanian Watermelon Varieties Based on Feature Extraction Analysis

Author

Sánchez-Galán, Javier E., Henry, Anel, Rangel, Fatima, Sáez, Emmy, Jo, Kang-Hyun, Cáceres-Hernández, Danilo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, Li, Jianqiang, editor, Gribova, Valeriya, editor, and Hussain, Abir, editor

Published
2021
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15. LHC Upgrades in preparation of Run 3

Author

Arduini, G., primary, Baglin, V., additional, Bartosik, H., additional, Bottura, L., additional, Bracco, C., additional, Bradu, B., additional, Bregliozzi, G., additional, Brodzinski, K., additional, Bruce, R., additional, Calviani, M., additional, Chiggiato, P., additional, Cruikshank, P., additional, Claudet, S., additional, Delikaris, D., additional, Fartoukh, S., additional, Garion, C., additional, Himmerlich, M., additional, Hostettler, M., additional, Iadarola, G., additional, Kostoglou, S., additional, Le Naour, S., additional, Lechner, A., additional, Lefevre, T., additional, Mether, L., additional, Papaphilippou, Y., additional, Petit, V., additional, Pojer, M., additional, Poyet, A., additional, Redaelli, S., additional, Rodriguez Mateos, F., additional, Rumolo, G., additional, Salvant, B., additional, Sanchez Galan, F., additional, Siemko, A., additional, Solfaroli-Camillocci, M., additional, Sterbini, G., additional, Taborelli, M., additional, Tavian, L., additional, Timko, H., additional, Tock, J.-Ph., additional, Verweij, A., additional, Wendt, M., additional, Wenninger, J., additional, Wollmann, D., additional, and Yin Vallgren, Ch., additional

Published
2024
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17. Identification and classification of road traffic incidents in Panama City through the analysis of a social media stream and machine learning

Author

Lucia Liu, Ameth Guevara, and Javier E. Sanchez-Galan

Subjects
Twitter analysis, Geocoding, Intelligent transportation system, Natural language processing, Text mining, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95
Abstract

In Panama City, Panama, as in many cities, the large number of cars on the roads and random traffic events produce constant and extensive traffic jams. These issues are usually not solved even with the construction of more traffic lanes. This work proposes the development of a system that allows the visualization of information published on social media about traffic incidents. Feature engineering methods, such as: Count Vectors and TF-IDF, were applied to process the tweets into structured data. Machine Learning models were created for the classification of traffic related tweets using SVM, Naïve Bayes, Random Forest and XGBoost. The prediction models resulted in two: a classification model that detects incident or non-incident tweets and a categorization model which determines the type of incident (accident, danger or obstacle). Results show that there were approximately 200,000 tweets reporting, traffic incidents since 2014 to 2022. In terms of the classification model, a precision of over 92% was achieved and for categorization over 97%. The best results were obtained with the use of Count Vectors and a Random Forest model. Finally, a graphical interface was developed to show the results of the obtained data and the streaming of live tweets, deployed at the website http://www.traficoya-pty.com/. This system has advantages such as speeding up the detection and visualization of traffic incidents, which can be of great help to the country’s traffic authorities and the general public.

Published
2022
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20. Sensitivity of the SHiP experiment to dark photons decaying to a pair of charged particles

Author

SHiP Collaboration, C. Ahdida, A. Akmete, R. Albanese, A. Alexandrov, A. Anokhina, S. Aoki, G. Arduini, E. Atkin, N. Azorskiy, J. J. Back, A. Bagulya, F. Baaltasar Dos Santos, A. Baranov, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, G. Bencivenni, A. Y. Berdnikov, Y. A. Berdnikov, M. Bertani, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, I. Boiarska, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, V. Büscher, A. Buonaura, S. Buontempo, S. Cadeddu, A. Calcaterra, M. Calviani, M. Campanelli, M. Casolino, N. Charitonidis, P. Chau, J. Chauveau, A. Chepurnov, M. Chernyavskiy, K.-Y. Choi, A. Chumakov, P. Ciambrone, V. Cicero, L. Congedo, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, R. de Asmundis, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, L. Dedenko, P. Dergachev, A. Di Crescenzo, L. Di Giulio, N. Di Marco, C. Dib, H. Dijkstra, V. Dmitrenko, L. A. Dougherty, A. Dolmatov, D. Domenici, S. Donskov, V. Drohan, A. Dubreuil, O. Durhan, M. Ehlert, E. Elikkaya, T. Enik, A. Etenko, F. Fabbri, O. Fedin, F. Fedotovs, G. Felici, M. Ferrillo, M. Ferro-Luzzi, K. Filippov, R. A. Fini, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, T. Fukuda, G. Galati, J. Gall, L. Gatignon, G. Gavrilov, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golovatiuk, V. Golovtsov, D. Golubkov, A. Golutvin, P. Gorbounov, D. Gorbunov, S. Gorbunov, V. Gorkavenko, M. Gorshenkov, V. Grachev, A. L. Grandchamp, E. Graverini, J.-L. Grenard, D. Grenier, V. Grichine, N. Gruzinskii, A. M. Guler, Yu. Guz, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, E. van Herwijnen, C. Hessler, A. Hollnagel, B. Hosseini, M. Hushchyn, G. Iaselli, A. Iuliano, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, B. Kaiser, C. Kamiscioglu, D. Karpenkov, K. Kershaw, M. Khabibullin, E. Khalikov, G. Khaustov, G. Khoriauli, A. Khotyantsev, Y. G. Kim, V. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, A. Kolesnikov, D. I. Kolev, V. Kolosov, M. Komatsu, A. Kono, N. Konovalova, S. Kormannshaus, I. Korol, I. Korol’ko, A. Korzenev, V. Kostyukhin, E. Koukovini Platia, S. Kovalenko, I. Krasilnikova, Y. Kudenko, E. Kurbatov, P. Kurbatov, V. Kurochka, E. Kuznetsova, H. M. Lacker, M. Lamont, G. Lanfranchi, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, N. Leonardo, J.-M. Lévy, V. P. Loschiavo, L. Lopes, E. Lopez Sola, V. Lyubovitskij, J. Maalmi, A.-M. Magnan, V. Maleev, A. Malinin, Y. Manabe, A. K. Managadze, M. Manfredi, S. Marsh, A. M. Marshall, A. Mefodev, P. Mermod, A. Miano, S. Mikado, Yu. Mikhaylov, D. A. Milstead, O. Mineev, A. Montanari, M. C. Montesi, K. Morishima, S. Movchan, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, S. Nasybulin, P. Ninin, A. Nishio, B. Obinyakov, S. Ogawa, N. Okateva, B. Opitz, J. Osborne, M. Ovchynnikov, N. Owtscharenko, P. H. Owen, P. Pacholek, A. Paoloni, B. D. Park, A. Pastore, M. Patel, D. Pereyma, A. Perillo-Marcone, G. L. Petkov, K. Petridis, A. Petrov, D. Podgrudkov, V. Poliakov, N. Polukhina, J. Prieto Prieto, M. Prokudin, A. Prota, A. Quercia, A. Rademakers, A. Rakai, F. Ratnikov, T. Rawlings, F. Redi, S. Ricciardi, M. Rinaldesi, Volodymyr Rodin, Viktor Rodin, P. Robbe, A. B. Rodrigues Cavalcante, T. Roganova, H. Rokujo, G. Rosa, T. Rovelli, O. Ruchayskiy, T. Ruf, V. Samoylenko, V. Samsonov, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, A. Saputi, O. Sato, E. S. Savchenko, J. S. Schliwinski, W. Schmidt-Parzefall, N. Serra, S. Sgobba, O. Shadura, A. Shakin, M. Shaposhnikov, P. Shatalov, T. Shchedrina, L. Shchutska, V. Shevchenko, H. Shibuya, S. Shirobokov, A. Shustov, S. B. Silverstein, S. Simone, R. Simoniello, M. Skorokhvatov, S. Smirnov, G. Soares, J. Y. Sohn, A. Sokolenko, E. Solodko, N. Starkov, L. Stoel, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, P. Teterin, S. Than Naing, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, N. Tosi, D. Treille, R. Tsenov, S. Ulin, E. Ursov, A. Ustyuzhanin, Z. Uteshev, L. Uvarov, G. Vankova-Kirilova, F. Vannucci, V. Venturi, I. Vidulin, S. Vilchinski, Heinz Vincke, Helmut Vincke, C. Visone, K. Vlasik, A. Volkov, R. Voronkov, S. van Waasen, R. Wanke, P. Wertelaers, O. Williams, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, Yu. Zaytsev, A. Zelenov, and J. Zimmerman

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Dark photons are hypothetical massive vector particles that could mix with ordinary photons. The simplest theoretical model is fully characterised by only two parameters: the mass of the dark photon m $$_{\gamma ^{\mathrm {D}}}$$ γ D and its mixing parameter with the photon, $$\varepsilon $$ ε . The sensitivity of the SHiP detector is reviewed for dark photons in the mass range between 0.002 and 10 GeV. Different production mechanisms are simulated, with the dark photons decaying to pairs of visible fermions, including both leptons and quarks. Exclusion contours are presented and compared with those of past experiments. The SHiP detector is expected to have a unique sensitivity for m $$_{\gamma ^{\mathrm {D}}}$$ γ D ranging between 0.8 and 3.3 $$^{+0.2}_{-0.5}$$ - 0.5 + 0.2 GeV, and $$\varepsilon ^2$$ ε 2 ranging between $$10^{-11}$$ 10 - 11 and $$10^{-17}$$ 10 - 17 .

Published
2021
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21. Sensitivity of the SHiP experiment to light dark matter

Author

The SHiP collaboration, C. Ahdida, A. Akmete, R. Albanese, A. Alexandrov, A. Anokhina, S. Aoki, G. Arduini, E. Atkin, N. Azorskiy, J. J. Back, A. Bagulya, F. Baaltasar Dos Santos, A. Baranov, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, G. Bencivenni, A. Y. Berdnikov, Y. A. Berdnikov, M. Bertani, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, I. Boiarska, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, V. Büscher, A. Buonaura, L. Buonocore, S. Buontempo, S. Cadeddu, A. Calcaterra, M. Calviani, M. Campanelli, M. Casolino, N. Charitonidis, P. Chau, J. Chauveau, A. Chepurnov, M. Chernyavskiy, K.-Y. Choi, A. Chumakov, P. Ciambrone, V. Cicero, L. Congedo, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, R. de Asmundis, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, L. Dedenko, P. Dergachev, A. Di Crescenzo, L. Di Giulio, N. Di Marco, C. Dib, H. Dijkstra, V. Dmitrenko, L. A. Dougherty, A. Dolmatov, D. Domenici, S. Donskov, V. Drohan, A. Dubreuil, O. Durhan, M. Ehlert, E. Elikkaya, T. Enik, A. Etenko, F. Fabbri, O. Fedin, F. Fedotovs, G. Felici, M. Ferrillo, M. Ferro-Luzzi, K. Filippov, R. A. Fini, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, C. Frugiuele, T. Fukuda, G. Galati, J. Gall, L. Gatignon, G. Gavrilov, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golovatiuk, V. Golovtsov, D. Golubkov, A. Golutvin, P. Gorbounov, D. Gorbunov, S. Gorbunov, V. Gorkavenko, M. Gorshenkov, V. Grachev, A. L. Grandchamp, E. Graverini, J.-L. Grenard, D. Grenier, V. Grichine, N. Gruzinskii, A. M. Guler, Yu. Guz, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, E. van Herwijnen, C. Hessler, A. Hollnagel, B. Hosseini, M. Hushchyn, G. Iaselli, A. Iuliano, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, B. Kaiser, C. Kamiscioglu, D. Karpenkov, K. Kershaw, M. Khabibullin, E. Khalikov, G. Khaustov, G. Khoriauli, A. Khotyantsev, Y. G. Kim, V. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, A. Kolesnikov, D. I. Kolev, V. Kolosov, M. Komatsu, A. Kono, N. Konovalova, S. Kormannshaus, I. Korol, I. Korol’ko, A. Korzenev, V. Kostyukhin, E. Koukovini Platia, S. Kovalenko, I. Krasilnikova, Y. Kudenko, E. Kurbatov, P. Kurbatov, V. Kurochka, E. Kuznetsova, H. M. Lacker, M. Lamont, G. Lanfranchi, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, J.-M. Lévy, V. P. Loschiavo, L. Lopes, E. Lopez Sola, V. Lyubovitskij, J. Maalmi, A. Magnan, V. Maleev, A. Malinin, F. Maltoni, Y. Manabe, A. K. Managadze, M. Manfredi, S. Marsh, A. M. Marshall, O. Mattelaer, A. Mefodev, P. Mermod, A. Miano, S. Mikado, Yu. Mikhaylov, D. A. Milstead, O. Mineev, A. Montanari, M. C. Montesi, K. Morishima, S. Movchan, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, S. Nasybulin, P. Ninin, A. Nishio, A. Novikov, B. Obinyakov, S. Ogawa, N. Okateva, B. Opitz, J. Osborne, M. Ovchynnikov, N. Owtscharenko, P. H. Owen, P. Pacholek, A. Paoloni, B. D. Park, A. Pastore, M. Patel, D. Pereyma, A. Perillo-Marcone, G. L. Petkov, K. Petridis, A. Petrov, D. Podgrudkov, V. Poliakov, N. Polukhina, J. Prieto Prieto, M. Prokudin, A. Prota, A. Quercia, A. Rademakers, A. Rakai, F. Ratnikov, T. Rawlings, F. Redi, S. Ricciardi, M. Rinaldesi, Volodymyr Rodin, Viktor Rodin, P. Robbe, A. B. Rodrigues Cavalcante, T. Roganova, H. Rokujo, G. Rosa, T. Rovelli, O. Ruchayskiy, T. Ruf, V. Samoylenko, V. Samsonov, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, A. Saputi, O. Sato, E. S. Savchenko, J. S. Schliwinski, W. Schmidt-Parzefall, N. Serra, S. Sgobba, O. Shadura, A. Shakin, M. Shaposhnikov, P. Shatalov, T. Shchedrina, L. Shchutska, V. Shevchenko, H. Shibuya, S. Shirobokov, A. Shustov, S. B. Silverstein, S. Simone, R. Simoniello, M. Skorokhvatov, S. Smirnov, J. Y. Sohn, A. Sokolenko, E. Solodko, N. Starkov, L. Stoel, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, P. Teterin, S. Than Naing, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, N. Tosi, F. Tramontano, D. Treille, R. Tsenov, S. Ulin, E. Ursov, A. Ustyuzhanin, Z. Uteshev, L. Uvarov, G. Vankova-Kirilova, F. Vannucci, V. Venturi, S. Vilchinski, Heinz Vincke, Helmut Vincke, C. Visone, K. Vlasik, A. Volkov, R. Voronkov, S. van Waasen, R. Wanke, P. Wertelaers, O. Williams, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, Yu. Zaytsev, A. Zelenov, and J. Zimmerman

Subjects
Beyond Standard Model, Dark matter, Fixed target experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Dark matter is a well-established theoretical addition to the Standard Model supported by many observations in modern astrophysics and cosmology. In this context, the existence of weakly interacting massive particles represents an appealing solution to the observed thermal relic in the Universe. Indeed, a large experimental campaign is ongoing for the detection of such particles in the sub-GeV mass range. Adopting the benchmark scenario for light dark matter particles produced in the decay of a dark photon, with α D = 0.1 and m A′ = 3m χ , we study the potential of the SHiP experiment to detect such elusive particles through its Scattering and Neutrino detector (SND). In its 5-years run, corresponding to 2 · 1020 protons on target from the CERN SPS, we find that SHiP will improve the current limits in the mass range for the dark matter from about 1 MeV to 300 MeV. In particular, we show that SHiP will probe the thermal target for Majorana candidates in most of this mass window and even reach the Pseudo-Dirac thermal relic.

Published
2021
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22. The LBNO long-baseline oscillation sensitivities with two conventional neutrino beams at different baselines

Author

Collaboration, LAGUNA-LBNO, Agarwalla, S. K., Agostino, L., Aittola, M., Alekou, A., Andrieu, B., Antoniou, F., Asfandiyarov, R., Autiero, D., Bésida, O., Balik, A., Ballett, P., Bandac, I., Banerjee, D., Bartmann, W., Bay, F., Biskup, B., Blebea-Apostu, A. M., Blondel, A., Bogomilov, M., Bolognesi, S., Borriello, E., Brancus, I., Bravar, A., Buizza-Avanzini, M., Caiulo, D., Calin, M., Calviani, M., Campanelli, M., Cantini, C., Cata-Danil, G., Chakraborty, S., Charitonidis, N., Chaussard, L., Chesneanu, D., Chipesiu, F., Crivelli, P., Dawson, J., De Bonis, I., Declais, Y., Sanchez, P. Del Amo, Delbart, A., Di Luise, S., Duchesneau, D., Dumarchez, J., Efthymiopoulos, I., Eliseev, A., Emery, S., Enqvist, T., Enqvist, K., Epprecht, L., Erykalov, A. N., Esanu, T., Franco, D., Friend, M., Galymov, V., Gavrilov, G., Gendotti, A., Giganti, C., Gilardoni, S., Goddard, B., Gomoiu, C. M., Gornushkin, Y. A., Gorodetzky, P., Haesler, A., Hasegawa, T., Horikawa, S., Huitu, K., Izmaylov, A., Jipa, A., Kainulainen, K., Karadzhov, Y., Khabibullin, M., Khotjantsev, A., Kopylov, A. N., Korzenev, A., Kosyanenko, S., Kryn, D., Kudenko, Y., Kuusiniemi, P., Lazanu, I., Lazaridis, C., Levy, J. -M., Loo, K., Maalampi, J., Margineanu, R. M., Marteau, J., Martin-Mari, C., Matveev, V., Mazzucato, E., Mefodiev, A., Mineev, O., Mirizzi, A., Mitrica, B., Murphy, S., Nakadaira, T., Narita, S., Nesterenko, D. A., Nguyen, K., Nikolics, K., Noah, E., Novikov, Yu., Oprima, A., Osborne, J., Ovsyannikova, T., Papaphilippou, Y., Pascoli, S., Patzak, T., Pectu, M., Pennacchio, E., Periale, L., Pessard, H., Popov, B., Ravonel, M., Rayner, M., Resnati, F., Ristea, O., Robert, A., Rubbia, A., Rummukainen, K., Saftoiu, A., Sakashita, K., Sanchez-Galan, F., Sarkamo, J., Saviano, N., Scantamburlo, E., Sergiampietri, F., Sgalaberna, D., Shaposhnikova, E., Slupecki, M., Smargianaki, D., Stanca, D., Steerenberg, R., Sterian, A. R., Sterian, P., Stoica, S., Strabel, C., Suhonen, J., Suvorov, V., Toma, G., Tonazzo, A., Trzaska, W. H., Tsenov, R., Tuominen, K., Valram, M., Vankova-Kirilova, G., Vannucci, F., Vasseur, G., Velotti, F., Velten, P., Venturi, V., Viant, T., Vihonen, S., Vincke, H., Vorobyev, A., Weber, A., Wu, S., Yershov, N., Zambelli, L., and Zito, M.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

The proposed Long Baseline Neutrino Observatory (LBNO) initially consists of $\sim 20$ kton liquid double phase TPC complemented by a magnetised iron calorimeter, to be installed at the Pyh\"asalmi mine, at a distance of 2300 km from CERN. The conventional neutrino beam is produced by 400 GeV protons accelerated at the SPS accelerator delivering 700 kW of power. The long baseline provides a unique opportunity to study neutrino flavour oscillations over their 1st and 2nd oscillation maxima exploring the $L/E$ behaviour, and distinguishing effects arising from $\delta_{CP}$ and matter. In this paper we show how this comprehensive physics case can be further enhanced and complemented if a neutrino beam produced at the Protvino IHEP accelerator complex, at a distance of 1160 km, and with modest power of 450 kW is aimed towards the same far detectors. We show that the coupling of two independent sub-MW conventional neutrino and antineutrino beams at different baselines from CERN and Protvino will allow to measure CP violation in the leptonic sector at a confidence level of at least $3\sigma$ for 50\% of the true values of $\delta_{CP}$ with a 20 kton detector. With a far detector of 70 kton, the combination allows a $3\sigma$ sensitivity for 75\% of the true values of $\delta_{CP}$ after 10 years of running. Running two independent neutrino beams, each at a power below 1 MW, is more within today's state of the art than the long-term operation of a new single high-energy multi-MW facility, which has several technical challenges and will likely require a learning curve., Comment: 21 pages, 12 figures

Published
2014

23. Optimised sensitivity to leptonic CP violation from spectral information: the LBNO case at 2300 km baseline

Author

Collaboration, LAGUNA-LBNO, Agarwalla, S. K., Agostino, L., Aittola, M., Alekou, A., Andrieu, B., Antoniou, F., Asfandiyarov, R., Autiero, D., Bésida, O., Balik, A., Ballett, P., Bandac, I., Banerjee, D., Bartmann, W., Bay, F., Biskup, B., Blebea-Apostu, A. M., Blondel, A., Bogomilov, M., Bolognesi, S., Borriello, E., Brancus, I., Bravar, A., Buizza-Avanzini, M., Caiulo, D., Calin, M., Calviani, M., Campanelli, M., Cantini, C., Cata-Danil, G., Chakraborty, S., Charitonidis, N., Chaussard, L., Chesneanu, D., Chipesiu, F., Crivelli, P., Dawson, J., De Bonis, I., Declais, Y., Sanchez, P. Del Amo, Delbart, A., Di~Luise, S., Duchesneau, D., Dumarchez, J., Efthymiopoulos, I., Eliseev, A., Emery, S., Enqvist, T., Enqvist, K., Epprecht, L., Erykalov, A. N., Esanu, T., Franco, D., Friend, M., Galymov, V., Gavrilov, G., Gendotti, A., Giganti, C., Gilardoni, S., Goddard, B., Gomoiu, C. M., Gornushkin, Y. A., Gorodetzky, P., Haesler, A., Hasegawa, T., Horikawa, S., Huitu, K., Izmaylov, A., Jipa, A., Kainulainen, K., Karadzhov, Y., Khabibullin, M., Khotjantsev, A., Kopylov, A. N., Korzenev, A., Kosyanenko, S., Kryn, D., Kudenko, Y., Kuusiniemi, P., Lazanu, I., Lazaridis, C., Levy, J. -M., Loo, K., Maalampi, J., Margineanu, R. M., Marteau, J., Martin-Mari, C., Matveev, V., Mazzucato, E., Mefodiev, A., Mineev, O., Mirizzi, A., Mitrica, B., Murphy, S., Nakadaira, T., Narita, S., Nesterenko, D. A., Nguyen, K., Nikolics, K., Noah, E., Novikov, Yu., Oprima, A., Osborne, J., Ovsyannikova, T., Papaphilippou, Y., Pascoli, S., Patzak, T., Pectu, M., Pennacchio, E., Periale, L., Pessard, H., Popov, B., Ravonel, M., Rayner, M., Resnati, F., Ristea, O., Robert, A., Rubbia, A., Rummukainen, K., Saftoiu, A., Sakashita, K., Sanchez-Galan, F., Sarkamo, J., Saviano, N., Scantamburlo, E., Sergiampietri, F., Sgalaberna, D., Shaposhnikova, E., Slupecki, M., Smargianaki, D., Stanca, D., Steerenberg, R., Sterian, A. R., Sterian, P., Stoica, S., Strabel, C., Suhonen, J., Suvorov, V., Toma, G., Tonazzo, A., Trzaska, W. H., Tsenov, R., Tuominen, K., Valram, M., Vankova-Kirilova, G., Vannucci, F., Vasseur, G., Velotti, F., Velten, P., Venturi, V., Viant, T., Vihonen, S., Vincke, H., Vorobyev, A., Weber, A., Wu, S., Yershov, N., Zambelli, L., and Zito, M.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

One of the main goals of the Long Baseline Neutrino Observatory (LBNO) is to study the $L/E$ behaviour (spectral information) of the electron neutrino and antineutrino appearance probabilities, in order to determine the unknown CP-violation phase $\delta_{CP}$ and discover CP-violation in the leptonic sector. The result is based on the measurement of the appearance probabilities in a broad range of energies, covering t he 1st and 2nd oscillation maxima, at a very long baseline of 2300 km. The sensitivity of the experiment can be maximised by optimising the energy spectra of the neutrino and anti-neutrino fluxes. Such an optimisation requires exploring an extended range of parameters describing in details the geometries and properties of the primary protons, hadron target and focusing elements in the neutrino beam line. In this paper we present a numerical solution that leads to an optimised energy spectra and study its impact on the sensitivity of LBNO to discover leptonic CP violation. In the optimised flux both 1st and 2nd oscillation maxima play an important role in the CP sensitivity. The studies also show that this configuration is less sensitive to systematic errors (e.g. on the total event rates) than an experiment which mainly relies on the neutrino-antineutrino asymmetry at the 1st maximum to determine the existence of CP-violation., Comment: 25 pages, 20 figures

Published
2014

24. Unsupervised identification of Greater Caribbean manatees using Scattering Wavelet Transform and Hierarchical Density Clustering from underwater bioacoustics recordings.

Author

Merchan, Fernando, Contreras, Kenji, Poveda, Héctor, Guzman, Hector M., and Sanchez-Galan, Javier E.

Subjects
WAVELET transforms, HIERARCHICAL clustering (Cluster analysis), MANATEES, K-means clustering, BIOACOUSTICS, FOURIER transforms
Abstract

Introduction: This work presents an unsupervised learning-based methodology to identify and count unique manatees using underwater vocalization recordings. Methods: The proposed approach uses Scattering Wavelet Transform (SWT) to represent individual manatee vocalizations. A Manifold Learning approach, known as PacMAP, is employed for dimensionality reduction. A density-based algorithm, known as Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), is used to count and identify clusters of individual manatee vocalizations. The proposed methodology is compared with a previous method developed by our group, based on classical clustering methods (K-Means and Hierarchical clustering) using Short-Time Fourier Transform (STFT)-based spectrograms for representing vocalizations. The performance of both approaches is contrasted by using a novel vocalization data set consisting of 23 temporally captured Greater Caribbean manatees from San San River, Bocas del Toro, in western Panama as input. Results: The proposed methodology reaches a mean percentage of error of the number of individuals (i.e., number of clusters) estimation of 14.05% and success of correctly grouping a manatee in a cluster of 83.75%. Discussion: Thus having a better performances than our previous analysis methodology, for the same data set. The value of this work lies in providing a way to estimate the manatee population while only relying on underwater bioacoustics. [ABSTRACT FROM AUTHOR]

Published
2024
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25. The SHiP experiment at the proposed CERN SPS Beam Dump Facility

Author

Ahdida, C., Akmete, A., Albanese, R., Alt, J., Alexandrov, A., Anokhina, A., Aoki, S., Arduini, G., Atkin, E., Azorskiy, N., Back, J. J., Bagulya, A., Baaltasar Dos Santos, F., Baranov, A., Bardou, F., Barker, G. J., Battistin, M., Bauche, J., Bay, A., Bayliss, V., Berdnikov, A. Y., Berdnikov, Y. A., Betancourt, C., Bezshyiko, I., Bezshyyko, O., Bick, D., Bieschke, S., Blanco, A., Boehm, J., Bogomilov, M., Boiarska, I., Bondarenko, K., Bonivento, W. M., Borburgh, J., Boyarsky, A., Brenner, R., Breton, D., Brignoli, A., Büscher, V., Buonaura, A., Buontempo, S., Cadeddu, S., Calviani, M., Campanelli, M., Casolino, M., Charitonidis, N., Chau, P., Chauveau, J., Chepurnov, A., Chernyavskiy, M., Choi, K.-Y., Chumakov, A., Climescu, M., Conaboy, A., Congedo, L., Cornelis, K., Cristinziani, M., Crupano, A., Dallavalle, G. M., Datwyler, A., D’Ambrosio, N., D’Appollonio, G., de Asmundis, R., De Carvalho Saraiva, J., De Lellis, G., de Magistris, M., De Roeck, A., De Serio, M., De Simone, D., Dedenko, L., Dergachev, P., Di Crescenzo, A., Di Giulio, L., Dib, C., Dijkstra, H., Dmitrenko, V., Dougherty, L. A., Dolmatov, A., Donskov, S., Drohan, V., Dubreuil, A., Durhan, O., Ehlert, M., Elikkaya, E., Enik, T., Etenko, A., Fedin, O., Fedotovs, F., Ferrillo, M., Ferro-Luzzi, M., Filippov, K., Fini, R. A., Fischer, H., Fonte, P., Franco, C., Fraser, M., Fresa, R., Froeschl, R., Fukuda, T., Galati, G., Gall, J., Gatignon, L., Gavrilov, G., Gentile, V., Goddard, B., Golinka-Bezshyyko, L., Golovatiuk, A., Golovtsov, V., Golubkov, D., Golutvin, A., Gorbounov, P., Gorbunov, D., Gorbunov, S., Gorkavenko, V., Gorshenkov, M., Grachev, V., Grandchamp, A. L., Graverini, E., Grenard, J.-L., Grenier, D., Grichine, V., Gruzinskii, N., Guler, A. M., Guz, Yu., Haefeli, G. J., Hagner, C., Hakobyan, H., Harris, I. W., van Herwijnen, E., Hessler, C., Hollnagel, A., Hosseini, B., Hushchyn, M., Iaselli, G., Iuliano, A., Jacobsson, R., Joković, D., Jonker, M., Kadenko, I., Kain, V., Kaiser, B., Kamiscioglu, C., Karpenkov, D., Kershaw, K., Khabibullin, M., Khalikov, E., Khaustov, G., Khoriauli, G., Khotyantsev, A., Kim, Y. G., Kim, V., Kitagawa, N., Ko, J.-W., Kodama, K., Kolesnikov, A., Kolev, D. I., Kolosov, V., Komatsu, M., Kono, A., Konovalova, N., Kormannshaus, S., Korol, I., Korol’ko, I., Korzenev, A., Koukovini Platia, E., Kovalenko, S., Krasilnikova, I., Kudenko, Y., Kurbatov, E., Kurbatov, P., Kurochka, V., Kuznetsova, E., Lacker, H. M., Lamont, M., Lantwin, O., Lauria, A., Lee, K. S., Lee, K. Y., Leonardo, N., Lévy, J.-M., Loschiavo, V. P., Lopes, L., Lopez Sola, E., Lyons, F., Lyubovitskij, V., Maalmi, J., Magnan, A.-M., Maleev, V., Malinin, A., Manabe, Y., Managadze, A. K., Manfredi, M., Marsh, S., Marshall, A. M., Mefodev, A., Mermod, P., Miano, A., Mikado, S., Mikhaylov, Yu., Mikulenko, A., Milstead, D. A., Mineev, O., Montesi, M. C., Morishima, K., Movchan, S., Muttoni, Y., Naganawa, N., Nakamura, M., Nakano, T., Nasybulin, S., Ninin, P., Nishio, A., Obinyakov, B., Ogawa, S., Okateva, N., Osborne, J., Ovchynnikov, M., Owtscharenko, N., Owen, P. H., Pacholek, P., Park, B. D., Pastore, A., Patel, M., Pereyma, D., Perillo-Marcone, A., Petkov, G. L., Petridis, K., Petrov, A., Podgrudkov, D., Poliakov, V., Polukhina, N., Prieto Prieto, J., Prokudin, M., Prota, A., Quercia, A., Rademakers, A., Rakai, A., Ratnikov, F., Rawlings, T., Redi, F., Reghunath, A., Ricciardi, S., Rinaldesi, M., Rodin, Volodymyr, Rodin, Viktor, Robbe, P., Rodrigues Cavalcante, A. B., Roganova, T., Rokujo, H., Rosa, G., Ruchayskiy, O., Ruf, T., Samoylenko, V., Samsonov, V., Sanchez Galan, F., Santos Diaz, P., Sanz Ull, A., Sato, O., Savchenko, E. S., Schliwinski, J. S., Schmidt-Parzefall, W., Schumann, M., Serra, N., Sgobba, S., Shadura, O., Shakin, A., Shaposhnikov, M., Shatalov, P., Shchedrina, T., Shchutska, L., Shevchenko, V., Shibuya, H., Shihora, L., Shirobokov, S., Shustov, A., Silverstein, S. B., Simone, S., Simoniello, R., Skorokhvatov, M., Smirnov, S., Soares, G., Sohn, J. Y., Sokolenko, A., Solodko, E., Starkov, N., Stoel, L., Stramaglia, M. E., Sukhonos, D., Suzuki, Y., Takahashi, S., Tastet, J. L., Teterin, P., Than Naing, S., Timiryasov, I., Tioukov, V., Tommasini, D., Torii, M., Treille, D., Tsenov, R., Ulin, S., Ursov, E., Ustyuzhanin, A., Uteshev, Z., Uvarov, L., Vankova-Kirilova, G., Vannucci, F., Venkova, P., Venturi, V., Vidulin, I., Vilchinski, S., Vincke, Heinz, Vincke, Helmut, Visone, C., Vlasik, K., Volkov, A., Voronkov, R., van Waasen, S., Wanke, R., Wertelaers, P., Williams, O., Woo, J.-K., Wurm, M., Xella, S., Yilmaz, D., Yilmazer, A. U., Yoon, C. S., Zaytsev, Yu., Zelenov, A., and Zimmerman, J.

Published
2022
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28. Measurement of the muon flux from 400 GeV/c protons interacting in a thick molybdenum/tungsten target

Author

C. Ahdida, A. Akmete, R. Albanese, A. Alexandrov, A. Anokhina, S. Aoki, G. Arduini, E. Atkin, N. Azorskiy, J. J. Back, A. Bagulya, F. Baaltasar Dos Santos, A. Baranov, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, G. Bencivenni, A. Y. Berdnikov, Y. A. Berdnikov, M. Bertani, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, I. Boiarska, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, V. Büscher, A. Buonaura, S. Buontempo, S. Cadeddu, A. Calcaterra, M. Calviani, M. Campanelli, M. Casolino, N. Charitonidis, P. Chau, J. Chauveau, A. Chepurnov, M. Chernyavskiy, K.-Y. Choi, A. Chumakov, P. Ciambrone, V. Cicero, L. Congedo, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, R. de Asmundis, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, L. Dedenko, P. Dergachev, A. Di Crescenzo, L. Di Giulio, N. Di Marco, C. Dib, H. Dijkstra, V. Dmitrenko, S. Dmitrievskiy, L. A. Dougherty, A. Dolmatov, D. Domenici, S. Donskov, V. Drohan, A. Dubreuil, O. Durhan, M. Ehlert, E. Elikkaya, T. Enik, A. Etenko, F. Fabbri, O. Fedin, F. Fedotovs, G. Felici, M. Ferrillo, M. Ferro-Luzzi, K. Filippov, R. A. Fini, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, T. Fukuda, G. Galati, J. Gall, L. Gatignon, G. Gavrilov, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golovatiuk, D. Golubkov, A. Golutvin, P. Gorbounov, D. Gorbunov, S. Gorbunov, V. Gorkavenko, M. Gorshenkov, V. Grachev, A. L. Grandchamp, E. Graverini, J.-L. Grenard, D. Grenier, V. Grichine, N. Gruzinskii, A. M. Guler, Yu. Guz, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, E. van Herwijnen, C. Hessler, A. Hollnagel, B. Hosseini, M. Hushchyn, G. Iaselli, A. Iuliano, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, B. Kaiser, C. Kamiscioglu, D. Karpenkov, K. Kershaw, M. Khabibullin, E. Khalikov, G. Khaustov, G. Khoriauli, A. Khotyantsev, Y. G. Kim, V. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, A. Kolesnikov, D. I. Kolev, V. Kolosov, M. Komatsu, A. Kono, N. Konovalova, S. Kormannshaus, I. Korol, I. Korol’ko, A. Korzenev, V. Kostyukhin, E. Koukovini Platia, S. Kovalenko, I. Krasilnikova, Y. Kudenko, E. Kurbatov, P. Kurbatov, V. Kurochka, E. Kuznetsova, H. M. Lacker, M. Lamont, G. Lanfranchi, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, J.-M. Lévy, V. P. Loschiavo, L. Lopes, E. Lopez Sola, V. Lyubovitskij, J. Maalmi, A. Magnan, V. Maleev, A. Malinin, Y. Manabe, A. K. Managadze, M. Manfredi, S. Marsh, A. M. Marshall, A. Mefodev, P. Mermod, A. Miano, S. Mikado, Yu. Mikhaylov, D. A. Milstead, O. Mineev, A. Montanari, M. C. Montesi, K. Morishima, S. Movchan, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, S. Nasybulin, P. Ninin, A. Nishio, A. Novikov, B. Obinyakov, S. Ogawa, N. Okateva, B. Opitz, J. Osborne, M. Ovchynnikov, N. Owtscharenko, P. H. Owen, P. Pacholek, A. Paoloni, B. D. Park, A. Pastore, M. Patel, D. Pereyma, A. Perillo-Marcone, G. L. Petkov, K. Petridis, A. Petrov, D. Podgrudkov, V. Poliakov, N. Polukhina, J. Prieto Prieto, M. Prokudin, A. Prota, A. Quercia, A. Rademakers, A. Rakai, F. Ratnikov, T. Rawlings, F. Redi, S. Ricciardi, M. Rinaldesi, Volodymyr Rodin, Viktor Rodin, P. Robbe, A. B. Rodrigues Cavalcante, T. Roganova, H. Rokujo, G. Rosa, T. Rovelli, O. Ruchayskiy, T. Ruf, V. Samoylenko, V. Samsonov, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, A. Saputi, O. Sato, E. S. Savchenko, J. S. Schliwinski, W. Schmidt-Parzefall, N. Serra, S. Sgobba, O. Shadura, A. Shakin, M. Shaposhnikov, P. Shatalov, T. Shchedrina, L. Shchutska, V. Shevchenko, H. Shibuya, L. Shihora, S. Shirobokov, A. Shustov, S. B. Silverstein, S. Simone, R. Simoniello, M. Skorokhvatov, S. Smirnov, J. Y. Sohn, A. Sokolenko, E. Solodko, N. Starkov, L. Stoel, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, P. Teterin, S. Than Naing, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, N. Tosi, D. Treille, R. Tsenov, S. Ulin, E. Ursov, A. Ustyuzhanin, Z. Uteshev, G. Vankova-Kirilova, F. Vannucci, V. Venturi, S. Vilchinski, Heinz Vincke, Helmut Vincke, C. Visone, K. Vlasik, A. Volkov, R. Voronkov, S. van Waasen, R. Wanke, P. Wertelaers, O. Williams, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, Yu. Zaytsev, and J. Zimmerman

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The SHiP experiment is proposed to search for very weakly interacting particles beyond the Standard Model which are produced in a 400 GeV/c proton beam dump at the CERN SPS. About $$10^{11}$$ 1011 muons per spill will be produced in the dump. To design the experiment such that the muon-induced background is minimized, a precise knowledge of the muon spectrum is required. To validate the muon flux generated by our Pythia and GEANT4 based Monte Carlo simulation (FairShip), we have measured the muon flux emanating from a SHiP-like target at the SPS. This target, consisting of 13 interaction lengths of slabs of molybdenum and tungsten, followed by a 2.4 m iron hadron absorber was placed in the H4 400 GeV/c proton beam line. To identify muons and to measure the momentum spectrum, a spectrometer instrumented with drift tubes and a muon tagger were used. During a 3-week period a dataset for analysis corresponding to $$(3.27\pm 0.07)~\times ~10^{11}$$ (3.27±0.07)×1011 protons on target was recorded. This amounts to approximatively 1% of a SHiP spill.

Published
2020
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31. ETS1, ELK1, and ETV4 Transcription Factors Regulate Angiopoietin-1 Signaling and the Angiogenic Response in Endothelial Cells

Author

Sharon Harel, Veronica Sanchez, Alaa Moamer, Javier E. Sanchez-Galan, Mohammad N. Abid Hussein, Dominique Mayaki, Mathieu Blanchette, and Sabah N. A. Hussain

Subjects
angiogenesis, angiopoietins, endothelial cells, cell signaling, transcription factors, cell migration, Physiology, QP1-981
Abstract

BackgroundAngiopoietin-1 (Ang-1) is the main ligand of Tie-2 receptors. It promotes endothelial cell (EC) survival, migration, and differentiation. Little is known about the transcription factors (TFs) in ECs that are downstream from Tie-2 receptors.ObjectiveThe main objective of this study is to identify the roles of the ETS family of TFs in Ang-1 signaling and the angiogenic response.MethodsIn silico enrichment analyses that were designed to predict TF binding sites of the promotors of eighty-six Ang-1-upregulated genes showed significant enrichment of ETS1, ELK1, and ETV4 binding sites in ECs. Human umbilical vein endothelial cells (HUVECs) were exposed for different time periods to recombinant Ang-1 protein and mRNA levels of ETS1, ELK1, and ETV4 were measured with qPCR and intracellular localization of these transcription factors was assessed with immunofluorescence. Electrophoretic mobility shift assays and reporter assays were used to assess activation of ETS1, ELK1, and ETV4 in response to Ang-1 exposure. The functional roles of these TFs in Ang-1-induced endothelial cell survival, migration, differentiation, and gene regulation were evaluated by using a loss-of-function approach (transfection with siRNA oligos).ResultsAng-1 exposure increased ETS1 mRNA levels but had no effect on ELK1 or ETV4 levels. Immunostaining revealed that in control ECs, ETS1 has nuclear localization whereas ELK1 and ETV4 are localized to the nucleus and the cytosol. Ang-1 exposure increased nuclear intensity of ETS1 protein and enhanced nuclear mobilization of ELK1 and ETV4. Selective siRNA knockdown of ETS1, ELK1, and ETV4 showed that these TFs are required for Ang-1-induced EC survival and differentiation of cells, while ETS1 and ETV4 are required for Ang-1-induced EC migration. Moreover, ETS1, ELK1, and ETV4 knockdown inhibited Ang-1-induced upregulation of thirteen, eight, and nine pro-angiogenesis genes, respectively.ConclusionWe conclude that ETS1, ELK1, and ETV4 transcription factors play significant angiogenic roles in Ang-1 signaling in ECs.

Published
2021
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32. Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks.

Author

Teran-Quezada, Alvaro A., Lopez-Cabrera, Victor, Rangel, Jose Carlos, and Sanchez-Galan, Javier E.

Subjects
ARTIFICIAL neural networks, SIGN language, RECURRENT neural networks, CONVOLUTIONAL neural networks, SPANISH language
Abstract

Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning. [ABSTRACT FROM AUTHOR]

Published
2024
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36. New optics design of the Cherenkov threshold detectors at CERN

Author

Buesa Orgaz, Jan, primary, Schwinzerl, Martin, additional, Bernhard, Johannes, additional, Brugger, Markus, additional, Charitonidis, Nikolaos, additional, Folch, Ramon, additional, Göktürk, Berare, additional, Kadi, Yacine, additional, Nevay, Laurence, additional, Ortega Ruiz, Inaki, additional, Romagnoli, Giulia, additional, Sanchez Galan, Francisco, additional, and Van Dijk, Maarten, additional

Published
2023
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38. High infestation of invasive Aedes mosquitoes in used tires along the local transport network of Panama

Author

Kelly L. Bennett, Carmelo Gómez Martínez, Alejandro Almanza, Jose R. Rovira, W. Owen McMillan, Vanessa Enriquez, Elia Barraza, Marcela Diaz, Javier E. Sanchez-Galan, Ari Whiteman, Rolando A. Gittens, and Jose R. Loaiza

Subjects
Aedes mosquitoes, Arboviral vectors, Human-assisted transport, Vector surveillance, Disease control, Panama, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The long-distance dispersal of the invasive disease vectors Aedes aegypti and Aedes albopictus has introduced arthropod-borne viruses into new geographical regions, causing a significant medical and economic burden. The used-tire industry is an effective means of Aedes dispersal, yet studies to determine Aedes occurrence and the factors influencing their distribution along local transport networks are lacking. To assess infestation along the primary transport network of Panama we documented all existing garages that trade used tires on the highway and surveyed a subset for Ae. aegypti and Ae. albopictus. We also assess the ability of a mass spectrometry approach to classify mosquito eggs by comparing our findings to those based on traditional larval surveillance. Results Both Aedes species had a high infestation rate in garages trading used tires along the highways, providing a conduit for rapid dispersal across Panama. However, generalized linear models revealed that the presence of Ae. aegypti is associated with an increase in road density by a log-odds of 0.44 (0.73 ± 0.16; P = 0.002), while the presence of Ae. albopictus is associated with a decrease in road density by a log-odds of 0.36 (0.09 ± 0.63; P = 0.008). Identification of mosquito eggs by mass spectrometry depicted similar occurrence patterns for both Aedes species as that obtained with traditional rearing methods. Conclusions Garages trading used tires along highways should be targeted for the surveillance and control of Aedes-mosquitoes and the diseases they transmit. The identification of mosquito eggs using mass spectrometry allows for the rapid evaluation of Aedes presence, affording time and cost advantages over traditional vector surveillance; this is of importance for disease risk assessment.

Published
2019
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39. Sensitivity of the SHiP experiment to Heavy Neutral Leptons

Author

The SHiP collaboration, C. Ahdida, R. Albanese, A. Alexandrov, A. Anokhina, S. Aoki, G. Arduini, E. Atkin, N. Azorskiy, F. Baaltasar Dos Santos, J. J. Back, A. Bagulya, A. Baranov, F. Bardou, G. J. Barker, M. Battistin, J. Bauche, A. Bay, V. Bayliss, G. Bencivenni, Y. A. Berdnikov, A. Y. Berdnikov, I. Berezkina, M. Bertani, C. Betancourt, I. Bezshyiko, O. Bezshyyko, D. Bick, S. Bieschke, A. Blanco, J. Boehm, M. Bogomilov, K. Bondarenko, W. M. Bonivento, J. Borburgh, A. Boyarsky, R. Brenner, D. Breton, R. Brundler, M. Bruschi, V. Büscher, A. Buonaura, S. Buontempo, S. Cadeddu, A. Calcaterra, M. Calviani, M. Campanelli, M. Casolino, N. Charitonidis, P. Chau, J. Chauveau, A. Chepurnov, M. Chernyavskiy, K.-Y. Choi, A. Chumakov, P. Ciambrone, K. Cornelis, M. Cristinziani, A. Crupano, G. M. Dallavalle, A. Datwyler, N. D’Ambrosio, G. D’Appollonio, L. Dedenko, P. Dergachev, J. De Carvalho Saraiva, G. De Lellis, M. de Magistris, A. De Roeck, M. De Serio, D. De Simone, C. Dib, H. Dijkstra, P. Dipinto, A. Di Crescenzo, N. Di Marco, V. Dmitrenko, S. Dmitrievskiy, A. Dolmatov, D. Domenici, S. Donskov, L. A. Dougherty, V. Drohan, A. Dubreuil, J. Ebert, T. Enik, A. Etenko, F. Fabbri, L. Fabbri, A. Fabich, O. Fedin, F. Fedotovs, M. Ferro-Luzzi, G. Felici, K. Filippov, R. A. Fini, P. Fonte, C. Franco, M. Fraser, R. Fresa, R. Froeschl, T. Fukuda, G. Galati, J. Gall, L. Gatignon, G. Gavrilov, V. Gentile, B. Goddard, L. Golinka-Bezshyyko, A. Golovatiuk, D. Golubkov, A. Golutvin, P. Gorbounov, S. Gorbunov, D. Gorbunov, V. Gorkavenko, Y. Gornushkin, M. Gorshenkov, V. Grachev, A. L. Grandchamp, G. Granich, E. Graverini, J.-L. Grenard, D. Grenier, V. Grichine, N. Gruzinskii, Yu. Guz, G. J. Haefeli, C. Hagner, H. Hakobyan, I. W. Harris, C. Hessler, A. Hollnagel, B. Hosseini, M. Hushchyn, G. Iaselli, A. Iuliano, V. Ivantchenko, R. Jacobsson, D. Joković, M. Jonker, I. Kadenko, V. Kain, C. Kamiscioglu, K. Kershaw, M. Khabibullin, E. Khalikov, G. Khaustov, G. Khoriauli, A. Khotyantsev, Y. G. Kim, V. Kim, S. H. Kim, N. Kitagawa, J.-W. Ko, K. Kodama, A. Kolesnikov, D. I. Kolev, V. Kolosov, M. Komatsu, N. Kondrateva, A. Kono, N. Konovalova, S. Kormannshaus, I. Korol, I. Korol’ko, A. Korzenev, V. Kostyukhin, E. Koukovini Platia, S. Kovalenko, I. Krasilnikova, Y. Kudenko, E. Kurbatov, P. Kurbatov, V. Kurochka, E. Kuznetsova, H. M. Lacker, M. Lamont, G. Lanfranchi, O. Lantwin, A. Lauria, K. S. Lee, K. Y. Lee, J.-M. Lévy, L. Lopes, E. Lopez Sola, V. P. Loschiavo, V. Lyubovitskij, A. M. Guler, J. Maalmi, A. Magnan, V. Maleev, A. Malinin, Y. Manabe, A. K. Managadze, M. Manfredi, S. Marsh, A. M. Marshall, A. Mefodev, P. Mermod, A. Miano, S. Mikado, Yu. Mikhaylov, D. A. Milstead, O. Mineev, A. Montanari, M. C. Montesi, K. Morishima, S. Movchan, Y. Muttoni, N. Naganawa, M. Nakamura, T. Nakano, S. Nasybulin, P. Ninin, A. Nishio, A. Novikov, B. Obinyakov, S. Ogawa, N. Okateva, B. Opitz, J. Osborne, M. Ovchynnikov, P. H. Owen, N. Owtscharenko, P. Pacholek, A. Paoloni, R. Paparella, B. D. Park, S. K. Park, A. Pastore, M. Patel, D. Pereyma, A. Perillo-Marcone, G. L. Petkov, K. Petridis, A. Petrov, D. Podgrudkov, V. Poliakov, N. Polukhina, J. Prieto Prieto, M. Prokudin, A. Prota, A. Quercia, A. Rademakers, A. Rakai, F. Ratnikov, T. Rawlings, F. Redi, S. Ricciardi, M. Rinaldesi, P. Robbe, Viktor Rodin, Volodymyr Rodin, A. B. Rodrigues Cavalcante, T. Roganova, H. Rokujo, G. Rosa, T. Rovelli, O. Ruchayskiy, T. Ruf, V. Samoylenko, V. Samsonov, F. Sanchez Galan, P. Santos Diaz, A. Sanz Ull, A. Saputi, O. Sato, E. S. Savchenko, W. Schmidt-Parzefall, N. Serra, S. Sgobba, O. Shadura, A. Shakin, M. Shaposhnikov, P. Shatalov, T. Shchedrina, L. Shchutska, V. Shevchenko, H. Shibuya, S. Shirobokov, A. Shustov, S. B. Silverstein, S. Simone, R. Simoniello, M. Skorokhvatov, S. Smirnov, J. Y. Sohn, A. Sokolenko, E. Solodko, N. Starkov, L. Stoel, B. Storaci, M. E. Stramaglia, D. Sukhonos, Y. Suzuki, S. Takahashi, J. L. Tastet, P. Teterin, S. Than Naing, I. Timiryasov, V. Tioukov, D. Tommasini, M. Torii, N. Tosi, D. Treille, R. Tsenov, S. Ulin, A. Ustyuzhanin, Z. Uteshev, G. Vankova-Kirilova, F. Vannucci, E. van Herwijnen, S. van Waasen, P. Venkova, V. Venturi, S. Vilchinski, M. Villa, Heinz Vincke, Helmut Vincke, C. Visone, K. Vlasik, A. Volkov, R. Voronkov, R. Wanke, P. Wertelaers, J.-K. Woo, M. Wurm, S. Xella, D. Yilmaz, A. U. Yilmazer, C. S. Yoon, P. Zarubin, I. Zarubina, and Yu. Zaytsev

Subjects
Beyond Standard Model, Fixed target experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O $$ \mathcal{O} $$ (50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL’s benchmark models and provide a way to calculate the SHiP’s sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.

Published
2019
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40. Application of matrix-assisted laser desorption/ionization mass spectrometry to identify species of Neotropical Anopheles vectors of malaria

Author

Jose R. Loaiza, Alejandro Almanza, Juan C. Rojas, Luis Mejía, Norma D. Cervantes, Javier E. Sanchez-Galan, Fernando Merchán, Arnaud Grillet, Matthew J. Miller, Luis F. De León, and Rolando A. Gittens

Subjects
Anopheles mosquito, Taxonomic identification, MALDI, Mass spectrometry, Malaria vector, Panama, Arctic medicine. Tropical medicine, RC955-962, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Malaria control in Panama is problematic due to the high diversity of morphologically similar Anopheles mosquito species, which makes identification of vectors of human Plasmodium challenging. Strategies by Panamanian health authorities to bring malaria under control targeting Anopheles vectors could be ineffective if they tackle a misidentified species. Methods A rapid mass spectrometry identification procedure was developed to accurately and timely sort out field-collected Neotropical Anopheles mosquitoes into vector and non-vector species. Matrix-assisted laser desorption/ionization (MALDI) mass spectra of highly-abundant proteins were generated from laboratory-reared mosquitoes using different extraction protocols, body parts, and sexes to minimize the amount of material from specimen vouchers needed and optimize the protocol for taxonomic identification. Subsequently, the mass spectra of field-collected Neotropical Anopheles mosquito species were classified using a combination of custom-made unsupervised (i.e., Principal component analysis—PCA) and supervised (i.e., Linear discriminant analysis—LDA) classification algorithms. Results Regardless of the protocol used or the mosquito species and sex, the legs contained the least intra-specific variability with enough well-preserved proteins to differentiate among distinct biological species, consistent with previous literature. After minimizing the amount of material needed from the voucher, one leg was enough to produce reliable spectra between specimens. Further, both PCA and LDA were able to classify up to 12 mosquito species, from different subgenera and seven geographically spread localities across Panama using mass spectra from one leg pair. LDA demonstrated high discriminatory power and consistency, with validation and cross-validation positive identification rates above 93% at the species level. Conclusion The selected sample processing procedure can be used to identify field-collected Anopheles species, including vectors of Plasmodium, in a short period of time, with a minimal amount of tissue and without the need of an expert mosquito taxonomist. This strategy to analyse protein spectra overcomes the drawbacks of working without a reference library to classify unknown samples. Finally, this MALDI approach can aid ongoing malaria eradication efforts in Panama and other countries with large number of mosquito’s species by improving vector surveillance in epidemic-prone sites such as indigenous Comarcas.

Published
2019
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42. Mechanical and thermal design of the target neutral beam absorber for the High-Luminosity LHC upgrade

Author

P. Santos Díaz, F. Sanchez Galan, R. De Maria, E. Kepes, P. Steinberg, R. Longo, S. Mazzoni, F. Cerutti, M. Sabate Gilarte, A. Infantino, B. Salvant, M. Murray, O. Boettcher, J. Hansen, V. Baglin, N. Zelko, E. Bravin, H. Mainaud, P. Bestmann, A. Herty, P. Fessia, and M. Gonzalez de la Aleja Cabana

Subjects
Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The High-Luminosity target neutral beam absorber (TAXN) is the radiation absorber of the neutral particle debris generated at the beam collisions carried out in the ATLAS and CMS detectors at the Large Hadron Collider (LHC). The new absorber will protect the superconducting magnets from this radiation to prevent their quenching at a design luminosity of 7.5×10^{34} cm^{−2} s^{−1}. This article describes the mechanical design of the TAXN assembly and the recombination chamber together with the thermal computations performed for the water-cooling and bakeout system prepared for the engineering design review held in January, 2021. It takes into account the internal integration of the zero degree calorimeter and beam rate of neutrals luminosity detector as well as their physics case overview. In addition, it provides the beam aperture breakdown and the heat deposition expected in the absorber.

Published
2022
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44. Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.

Author

Rolando A Gittens, Alejandro Almanza, Kelly L Bennett, Luis C Mejía, Javier E Sanchez-Galan, Fernando Merchan, Jonathan Kern, Matthew J Miller, Helen J Esser, Robert Hwang, May Dong, Luis F De León, Eric Álvarez, and Jose R Loaiza

Subjects
Arctic medicine. Tropical medicine, RC955-962, Public aspects of medicine, RA1-1270
Abstract

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.

Published
2020
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45. Vocalizations of wild West Indian manatee vary across subspecies and geographic location

Author

Reyes-Arias, Jessica D., primary, Brady, Beth, additional, Ramos, Eric A., additional, Henaut, Yann, additional, Castelblanco-Martínez, Delma Nataly, additional, Maust-Mohl, Maria, additional, Searle, Linda, additional, Pérez-Lachaud, Gabriela, additional, Guzmán, Héctor M., additional, Poveda, Héctor, additional, Merchan, Fernando, additional, Contreras, Kenji, additional, Sanchez-Galan, Javier E., additional, Collom, Kristi A., additional, and Magnasco, Marcelo O., additional

Published
2023
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46. 130: FULMINANT EVOLUTION OF A POST-TRANSPLANT LYMPHOPROLIFERATIVE DISEASE AS A COMPLICATION OF MULTIVISCERAL TRANSPLANT

Author

Gonzalez Sacristan, Rocio, primary, Alcolea Sanchez, Alida, additional, Pilar Serrano Fernandez, Maria, additional, Sarria Visa, Marta, additional, García Jiménez, Antonio, additional, Sanchez Galan, Alba, additional, Miren Andres Moreno, Ane, additional, Serradilla Rodriguez, Javier, additional, Hernandez Oliveros, Francisco, additional, and Ramos Boluda, Esther, additional

Published
2023
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47. 127: CHRONIC GRAFT-VERSUS-HOST DISEASE AS THE MAIN COMPLICATION OF MULTIVISCERAL TRANSPLANT IN A PATIENT WITH TRICOHEPATOENTERIC SYNDROME TYPE II

Author

Gonzalez Sacristan, Rocio, primary, Alcolea Sanchez, Alida, additional, Pilar Serrano Fernandez, Maria, additional, Sarria Visa, Marta, additional, Garcia Jimenez, Antonio, additional, Sanchez Galan, Alba, additional, Miren Andres Moreno, Ane, additional, Serradilla Rodriguez, Javier, additional, Talayero Gimenez, Paloma, additional, Hernandez Oliveros, Francisco, additional, and Ramos Boluda, Esther, additional

Published
2023
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