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.

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)

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

Postprint (author's final draft)

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.

El aislamiento de base es una técnica de protección sismorresistente para edificaciones, con amplio y exitoso uso a nivel mundial. En Colombia actualmente existen algunos edificios que incorporan esta técnica, los cuales fueron diseñados bajo los requisitos de la normativa americana vigentes en la época, como ASCE 7-10. Dicho documento ha sido recientemente actualizado a su versión 2016, con importantes cambios técnicos respecto a su predecesor. ASCE 7-16 incorpora resultados de las investigaciones más recientes en cuanto a aislamiento sísmico se refiere; en consecuencia, las futuras edificaciones que empleen esta técnica y que sean diseñadas y construidas en el país deberían seguir dicha regulación. Por todo lo anterior, en el presente trabajo se evidencia la necesidad de hacer un análisis de esta nueva estrategia de diseño (para edificaciones con aislamiento de base) y discutir su aplicabilidad en el país., Base isolation is a successful and worldwide used seismic protection technique for buildings. In Colombia there are some buildings that incorporate this technology; were designed by following the current American regulations, namely ASCE 7-10. This document has been recently updated to its 2016 version, with important changes. ASCE 7-16 incorporates the results of the most recent research; thus, apparently, future isolated buildings should follow this document. These considerations highlight the necessity of analyzing the prescriptions of ASCE 7-16 (regarding base isolation of buildings) and discussing its applicability to Colombia., Peer Reviewed, Postprint (published version)

Material docente de introducción a la fisiología digestiva orientado a presentar, de forma global y ordenada, cómo se organiza y cómo funciona el sistema digestivo, así como los mecanismos que coordinan su motilidad, secreción y regulación neuroendocrina y microbiana, El documento ofrece una introducción estructurada a la fisiología digestiva, comenzando por la distinción entre alimentación y nutrición y avanzando hacia la organización funcional del aparato digestivo. Se expone de manera integrada fisiológica e histológica básica del tubo digestivo, las funciones motoras gastrointestinales y las propiedades electromecánicas de la fibra muscular lisa, con especial atención al papel de las células intersticiales de Cajal y a los distintos patrones de motilidad. Además, desarrolla los principales mecanismos de control del sistema digestivo, integrando regulación nerviosa intrínseca y extrínseca, control endocrino mediante hormonas gastrointestinales y la influencia moduladora de la microbiota intestinal dentro del eje intestino-cerebro.