Resultados de investigación

This dataset contains processed audio samples coming from a hydraulic block from a biomedical equipment. The block mounts 3 Thomas SR10/30 DC standard perisltaltic pumps, which were filled with distilled water. Only one pump was running at the same time during these recordings. There are two different predictive maintenance scenarios. In the first one, the cassettes of the pumps were changed before each recording. We used cassettes with 2 different levels of degradation: NEW (unused) and OLD (lifetime already expired). We defined 3 different classes: Class 1 is STOP (no pump running), class 2 is NEW (one pump running with a new cassette), and class 3 is OLD (one pump running with an old cassette). In the second scenario, air bubbles were introduced into the tube. This second scenario also has 3 classes: Class 1 is STOP (no pump running), class 2 is NORMAL (no air bubbles), and class 3 is BUBBLE (air bubbles present). A single microphone was used for all recordings. The .wav audio files were processed using a 64 channel Neuromorphic Auditory Sensor (NAS) into .aedat files, which are the present in this dataset. This neuromorphic audio data were also converted into cochleogram images using the software pyNAVIS, and they are also present in this format (.png files)., There is one folder for each of the two scenarios (aging and bubble). Inside those folders there are 3 subfolders, one for each data type (AEDAT, PNG and WAV). For WAV and AEDAT, each file is a unique sample tagged with one class (1, 2 or 3). At the end of each filename this class is also included. For example, 0008_03.wav is the sample number 8, which corresponds to class 3 tagged data. PNG cochleogram images represent 500ms audio time each and are named after their source AEDAT file followed by their starting time mark in microseconds.

Conjunt editat de fragments sonors de 40 gravacions de regal enviades pel sant de Francesc Daumal el dia 2 d'abril de 2020., La simultània embolcallada per a regal és una obra artística basada en 40 paisatges sonors reals provinents de tot el món, que s’han gravat simultàniament a migdia del dia del meu sant, el 2 d’abril del 2020, en plena pandèmia. La gravació final, de 3 minuts d’extensió en total, té 40 talls sonors, de 4,5 segons cadascún, empalmats de forma consecutiva, respectant l’ordre en que es vàren recepcionar les gravacions originals. Els autors han cedit els seus drets per a poder fer aquest compendi, en el que ha participat molt activament el tècnic de so Hèctor Balart Armengol, compatibilitzant els diferents llenguatges de cada deriva sonora, i fent l’elecció aleatòria dels seus valors estètics més representatius. Aquesta obra s’hapresentat com a primicia el 14 d’octubre 2020, a la Biennal del Pensament de l’Ajuntament de Barcelona, per a la taula rodona Sons, Sorolls, Silencis. Cap a una ecología acústica urbana en l’era digital. Participants: Francesc Daumal i Domènech, Fernando Valdés, Rosa María Alsina, Antonio de Clemente, Vicent Matamoros, Laura Puig, Oriol Server Yerga, Carlos Gómez Caballero, Valentin Martín Fanjul, Belen Casla Herguedas, Pedro Montesinos, Ebe Giovannini, Julia Angiolini, Pedro Alcalde, Riccardo Chinni, Sandra del Rio Bonnin, Marta Monzón, Eduard Puig Solé, Luis Gerald Montero, David Pérez Vallejo, Fernando J. Elizondo-Garza, Miguel Molina-Alarcón, Alicia González Escamilla, José Nocito, Josep Manuel Berenguer Alarcón, Josep Cerdà, Gabriel de Toledo Piza, Jerónimo Vida, José Luís de Carles, Estefanía Aimar, Uli Marchsteiner, Luis Paulino Salas, Ricardo Atienza, Ivana Rossell, Leonel de la Pava, Enric Mir, Cesc Cels i López, Rosa Mª Cibrián Ortiz de Anda., La simultánea envuelta para regalo, es una obra artística basada en 40 paisajes sonoros reales provenientes de todo el mundo, que se han grabado simultáneamente a mediodía del día de mi santo, el 2 de abril del 2020, en plena pandemia. La grabación final, de 3 minutos de extensión en total, contiene 40 cortes sonoros, de 4,5 segundos cada uno, empalmados de forma consecutiva, respetando el orden en que se recepcionaron las grabaciones originales. Los autores han cedido sus derechos para poder hacer este compendio, en el que ha participado muy activamente el técnico de sonido Héctor Balart Armengol, compatibilizando los diferentes lenguajes de cada deriva sonora, y realizando la elección aleatoria de sus valores estéticos más representativos. Esta obra se presentó como primicia el 14 de octubre del 2020 en la Bienal del Pensamiento del Ayuntamiento de Barcelona, para la mesa redonda Sonidos, Ruidos, Silencios. Hacia una ecología acústica urbana en la era digital. Participantes: Francesc Daumal i Domènech, Fernando Valdés, Rosa María Alsina, Antonio de Clemente, Vicent Matamoros, Laura Puig, Oriol Server Yerga, Carlos Gómez Caballero, Valentin Martín Fanjul, Belen Casla Herguedas, Pedro Montesinos, Ebe Giovannini, Julia Angiolini, Pedro Alcalde, Riccardo Chinni, Sandra del Rio Bonnin, Marta Monzón, Eduard Puig Solé, Luis Gerald Montero, David Pérez Vallejo, Fernando J. Elizondo-Garza, Miguel Molina-Alarcón, Alicia González Escamilla, José Nocito, Josep Manuel Berenguer Alarcón, Josep Cerdà, Gabriel de Toledo Piza, Jerónimo Vida, José Luís de Carles, Estefanía Aimar, Uli Marchsteiner, Luis Paulino Salas, Ricardo Atienza, Ivana Rossell, Leonel de la Pava, Enric Mir, Cesc Cels i López, Rosa Mª Cibrián Ortiz de Anda.

Més informació a: / Más información en: https://segues.upc.edu/es/jornadas-segues/segues20

Més informació a: / Más información en: https://segues.upc.edu/es/jornadas-segues/segues20

El Foment Vilanoví organitza el primer Tribuna en format telemàtic amb la col·laboració de l'EPSEVG amb la Dra. Clara Prats. Clara Prats, llicenciada en Física per la Universitat de Barcelona i doctora en Física Aplicada i Simulació en Ciències per la Universitat Politècnica de Catalunya (UPC). Actualment és professora agregada de la UPC. Investigadora del grup de Biologia Computacional i Sistemes Complexos de la UPC, i responsable de models computacionals al Centre de Medicina Comparativa i Bioimatge de l’Institut de Recerca en Ciències de la Salut Germans Trias i Pujol (IGTP). La seva línia de recerca se centra en l’ús de la modelització computacional per a l’estudi de malalties infeccioses, principalment la tuberculosi, però també la malària, la malaltia del Chagas i, els últims mesos, la COVID-19. En col·laboració amb el Diari de Vilanova es va crear Tribuna Vilanova, un espai on dialogar amb persones d’interès rellevant a Vilanova i la Geltrú i Catalunya de diferents àmbits d’activitat: social, jurídic, cívic, artístic, literari, entre d'altres.

Postprint (author's final draft)

Presentación de la sessión Plenaria 4 a cargo de Carles Llop, Director del Congreso. Moderadores de la sesión: Mosé Ricci (Univesidad de Trento) y Manuel Gausa (Universidad de Génova). SAB) y Miquel Martí: Ponentes: Patrizia Gabellii; Arturo Lanzani; Maria Chiara Tosi. (Ponencias en italiano con traducción subtitulada al castellano), Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles

Tessellated cartilage is a distinctive composite tissue forming the bulk of the skeleton of cartilaginous fishes (e.g. sharks and rays), built from unmineralized cartilage covered at the surface by a thin layer of mineralized tiles called tesserae. The finescale structure and composition of elasmobranch tessellated cartilage has largely been investigated with electron microscopy, micro-computed tomography and histology, but many aspects of tissue structure and composition remain uncharacterized. In our study, we demonstrate that the tessellated cartilage of a stingray exhibits a strong and diverse autofluorescence, a native property of the tissue which can be harnessed as an effective label-free imaging technique. The autofluorescence signal was excited using a broad range of wavelengths in confocal and light sheet microscopy, comparing several sample preparations (fresh; demineralized and paraffin-embedded; non-demineralized and plastic-embedded) and imaging the tissue at different scales. Autofluorescence varied with sample preparation with the signal in both plastic- and paraffin-embedded samples strong enough to allow visualization of finescale (=¿1 µm) cellular and matrix structures, such as cell nuclei and current and former mineralization fronts, identifiable by globular mineralized tissue. A defined pericellular matrix (PCM) surrounding chondrocytes was also discernible, described here for the first time in elasmobranchs. The presence of a PCM suggests similarities with mammalian cartilage regarding how chondrocytes interact with their environment, the PCM in mammals acting as a transducer for biomechanical and biochemical signals. A posterior analysis of hyperspectral images by an MCR-ALS unmixing algorithm allowed identification of several distinct fluorescence signatures associated to specific regions in the tissue. Some fluorescence signatures identified could be correlated with collagen type II, the most abundant structural molecule of cartilage. Other fluorescence signatures, however, remained unidentified, spotlighting tissue regions that deserve deeper characterization and suggesting the presence of molecules still unidentified in elasmobranch skeletal cartilage. Our results show that autofluorescence can be a powerful exploratory imaging tool for characterizing less-studied skeletal tissues, such as tessellated cartilage. The images obtained are largely comparable with more commonly used techniques, but without the need for complicated sample preparations or external staining reagents standard in histology and electron microscopy (TEM, SEM)., Postprint (published version)

This dataset contains samples coming from a hydraulic block from a biomedical equipment. The block mounts 3 Thomas SR10/30 DC standard perisltaltic pumps, which were filled with distilled water. Only one pump was running at the same time during these recordings, always at maximum constant speed. The cassettes of the pumps were changed before each recording. We used cassettes with 2 different levels of degradation: NEW (unused) and OLD (lifetime already expired). We defined 3 different classes: Class 1 is STOP (no pump running), class 2 is NEW (one pump running with a new cassette), and class 3 is OLD (one pump running with an old cassette). The classified samples were recorded using several sensors: 3 accelerometers, 1 gyroscope, 1 magnetometer and 1 microphone. All data were recorded at the same time at the maximum available frequency using the device "ST SensorTile.box". The raw data has already been processed into sepparate different .csv files (.wav files for audio) using python code., Each sensor has its own folder. The sensor folder name starts with ACC for accelerometer data, GYRO for gyroscope data, MAG for magnetometer data or MIC for microphone data. After that, the name of the sensor. At the end, sampling frequency in Hz. For example: ACC_LISDW12_1600Hz folder contains data for the accelerometer named LISDW12 at a sampling rate of 1600Hz. Each sensor folder contains 17 .csv files except for the microphone folder, which contains 17 .wav files. Each file is a unique sample tagged with one class (1, 2 or 3). At the end of each filename this class is also included. For example, 0008_03.csv is the sample number 8, which corresponds to class 3 tagged data.