Efecto de la adición de NaOH (hidróxido de sodio) al ensilaje de caña de azúcar, en el comportamiento de toretes cebú

El empleo de la caña de azúcar como forraje de corte está parcialmente limitado por la dificultad de preservar d producto en forma de ensilaje, ya que este último tiene una fermentación alcohólica, lo que se traduce en un bajo consumo voluntario por los rumiantes (James, 1973

Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams

We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.~glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual gravitational-wave signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification.Comment: 15 pages, 14 figure

IMPLEMENTACIÓN DE UN PROGRAMA DE ADMINISTRACIÓN DE LA ENERGÍA EN EL SECTOR INDUSTRIAL

En este trabajo se muestran los pasos a seguir para implementar un programa de administración de la Energía en el sector industrial y los avances al respecto logrados en un ingenio azucarero colombiano

Wireless intelligent sensors based in nanostructures with energy self-sufficiency to study the consequences of high temperatures in combustion motors

In this research are proposed the consequences of high temperatures in Internal Combustion Motors (ICM) as correlation of its performance according to give information of the ICM fault detector, which also can be useful for preventive maintenance. It was possible to achieve the proposed target because of it was designed a smart sensor based in nanostructures prepared over Anodic Aluminum Oxide (AAO) samples, which proportionated short response time and high robustness in the measurement tasks of the smart sensor, as well as, the designed sensor has the possibility to work by energy self-sufficiency and sending the measurement data to external users by wireless. In fact, it is waited that this research could be a support for researchers of ICM enhancement, who could look for new techniques of environment conditions cares in compensation to keep the balance between the useful energy obtained from ICM and the environment conditions, where are developed economical activities such as public transport or mining in Peru

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Determinación del bienestar animal en equinos de tracción a sangre en barrios periféricos de la ciudad de Corrientes, Argentina

In many cities of Argentina, equine-drawn carts represent an economic livelihood for low-income families. These equines are generally in inadequate physical condition, which results in a sub-optimal performance and a serious detriment to their welfare. The aim of the present work was to determine the animal welfare state of working equines in peripheral neighborhoods of the City of Corrientes Capital, Argentina, using direct and indirect indicators. The population (n=42) presented an average age of 6.8 years and within it a 60% were stallions. Forty five percent of the animals presented suboptimal nutritional status, 43% of the cases presented oral alterations, 67% had skin lesions and 74% had inadequate hoof conditions. Almost all of the animals were alert to the approach of the observer, some reacting aggressively and others expressing abnormal behaviors. Equines were usually used during the morning hours, with loads of brick and sand being the most frequently recorded. According to their caretakers, the animals receive veterinary care on a regular basis, registering an alarming tendency to release them in empty lots, public squares or roads during rest hours. The sources of water and food, constituted by river water, alfalfa and corn, added to grazing during the rest, are positioned as the main alternatives. Thus, it is observed that the welfare state of the equines sampled in the city was between regular and bad, being evident the need for training of caretakers on responsible ownership and joint work between veterinarians and public agencies to address the problem.En numerosas ciudades de la Argentina, la tracción animal o también conocida como tracción a sangre (TAS) representa un medio de sustento económico para familias de escasos recursos; los equinos de la actividad generalmente se hallan en condiciones físicas inadecuadas, lo que resulta en un sub-óptimo desempeño y en un serio detrimento de su bienestar. El presente trabajo busca determinar el bienestar animal de los equinos de trabajo en barrios periféricos de la Ciudad de Corrientes Capital, Argentina, utilizando indicadores directos e indirectos. Los primeros se obtuvieron a través del examen físico y comportamental, mientras que los segundos, mediante encuestas a los tutores. La población (n=42) presentó un promedio de edad de 6,8 años y dentro de ella se identificó una subpoblación compuesta por un 60% de machos enteros. Se registró un 45% de animales con estados nutricionales subóptimos, un 43% de pacientes con alteraciones bucales, 67% con lesiones cutáneas y un 74% con condiciones inadecuadas de cascos. Casi la totalidad de los animales se mostraron alerta al acercamiento humano, reaccionando algunos de forma agresiva y otros con conductas anormales. Los animales son usualmente utilizados en horas de la mañana, siendo las cargas de ladrillo y arena las más frecuentemente registradas. Las encuestas revelaron que los animales reciben atención veterinaria, registrándose una alarmante tendencia a la liberación en baldíos, plazas o en la vía pública durante las horas de descanso. Las fuentes de agua y alimentos, constituidas por el agua de río, alfalfa y maíz se posicionan como las principales alternativas. Se observa así que el estado de bienestar de los equinos muestreados en la ciudad es regular a malo, siendo evidente la necesidad de capacitación de los tutores sobre tenencia responsable y el trabajo conjunto entre médicos veterinarios y organismos públicos para abordar la problemática

Ultrafast photochemistry produces superbright short-wave infrared dots for low-dose in vivo imaging

12 p.-5 fig.Optical probes operating in the second near-infrared window (NIR-II, 1,000-1,700 nm), where tissues are highly transparent, have expanded the applicability of fluorescence in the biomedical field. NIR-II fluorescence enables deep-tissue imaging with micrometric resolution in animal models, but is limited by the low brightness of NIR-II probes, which prevents imaging at low excitation intensities and fluorophore concentrations. Here, we present a new generation of probes (Ag2S superdots) derived from chemically synthesized Ag2S dots, on which a protective shell is grown by femtosecond laser irradiation. This shell reduces the structural defects, causing an 80-fold enhancement of the quantum yield. PEGylated Ag2S superdots enable deep-tissue in vivo imaging at low excitation intensities (<10 mW cm-2) and doses (<0.5 mg kg-1), emerging as unrivaled contrast agents for NIR-II preclinical bioimaging. These results establish an approach for developing superbright NIR-II contrast agents based on the synergy between chemical synthesis and ultrafast laser processing.Authors thank Dr A. Benayas (CICECO, U. Aveiro, Portugal), Prof G. Lifante and Prof J. García Sole (UAM) for helpful discussions. This work has been founded by Ministerio de Economı́a y Competitividad-MINECO (MAT2017-83111R and MAT2016-75362-C3-1-R) and the Comunidad de Madrid (B2017/BMD-3867 RENIM-CM) co-financed by European Structural and Investment Fund. D.M.-G. thanks UCM-Santander for a predoctoral contract (CT17/17-CT18/17). We thank the staff at the ICTS-National Centre for Electron Microscopy at the UCM for the help in the electron microscopy studies and C.M. at the beamline BL22-CLAESS of the Spanish synchrotron ALBA for his help in the XANES experiments. We also thank J.G.I at the Ultrafast Laser Laboratory at UCM for his help and fruitful discussion. Y.S. acknowledges the support from the China Scholarship Council (CSC File No. 201806870023). Additional funding was provided by the European Commission Horizon 2020 project NanoTBTech, the Fundación para la Investigación Biomédica del Hospital Universitario Ramón y Cajal project IMP18_38 (2018/0265). Ajoy K. Kar and Mark D. Mackenzie acknowledge support from the UK Engineering and Physical Sciences Research Council (Project CHAMP, EP/M015130/1). C. Jacinto thanks the financial support of the Brazilian agencies: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) through the grants: Projeto Universal Nr. 431736/2018-9 and Scholarship in Research Productivity 1C under the Nr. 304967/20181; FINEP (Financiadora de Estudos e Projetos) through the grants INFRAPESQ-11 and INFRAPESQ-12; FAPEAL (Fundação de Amparo à Pesquisa do Estado de Alagoas) grant Nr. 1209/2016. H. D. A. Santos was supported by a graduate studentship from CNPq and by a sandwich doctoral program (PDSE-CAPES) developed at Universidad Autonoma de Madrid, Spain, Project Nr. 88881/2016-01.Peer reviewe