Comportamiento espacial de las partículas suspendidas PM 10 y estrategias de gestión Ambiental del Aire en la Zona Metropolitana de Toluca, México

En esta investigación se identifican los patrones espaciales de las partículas suspendidas (PM10) en la Zona Metropolitana de Toluca, en el periodo 2011 - 2013, la información se obtuvo de la Red Automática de Monitoreo Atmosférico de Toluca (RAMAT), la modelación se realizó a través del módulo Geostatical Analyst integrado a la plataforma del software Arcgis 10.1 . Los modelos espaciales indican que la calidad del aire se mantiene de mala a regular, por ello es necesario avanzar en la cultura de la prevención a través de un Programa de Alerta Temprana, que informe a la población sobre los riesgos que se presentan en las épocas seca y cálida del año, y las medidas de atención en condiciones de contingencia atmosférica

Effects of whey protein concentrate on shelf life of cookies using corn and sunflower oils

The objective of this work was to study the effect of whey protein concentrate (WPC) on shelf life of cookies using corn and sunflower oils as fat source. Wheat flour was partially replaced by WPC with levels of 5, 7.5, 10 and 15 %. A User Defined Design was used and the three following responses were measured: peroxide index (meqO2/kg), flavour (score from 1-10) and rancidity (detectable and non-detectable) at 0, 7, 14, 21 and 70 days of storage. Results show that during storage peroxide index (PI) increases in all cookies samples. However a significant decrease was found when corn oil was used instead of sunflower oil and when the level of WPC goes from 0% to 15%. Regarding sensory evaluation, all cookies were evaluated as acceptable (scored flavour ≥ 6) and no rancid flavour was perceived, except for two samples which were assigned with 5 and rancid flavour was considered as detectable; these samples correspond to cookies evaluated at day 70, elaborated with sunflower oil and the lowest dose of WPC. No significant differences were found in cookies flavour prepared with different oils. On the other hand and relative to the level of WPC replacement, differences were found by the panel at days 0 and 7, a reduction in assigned score when WPC dose was increased, however this difference was not significant in the subsequent assessment days. According to research results, an increase in stability was clearly obtained in cookies elaborated with corn oil and the highest concentration of WPC.Fil: Erben, Melina. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Sanchez, Hugo Diego. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; ArgentinaFil: Osella, Carlos Alberto. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Learning disentangled representations of satellite image time series in a weakly supervised manner

Cette thèse se focalise sur l'apprentissage de représentations de séries temporelles d'images satellites via des méthodes d'apprentissage non supervisé. Le but principal est de créer une représentation qui capture l'information la plus pertinente de la série temporelle afin d'effectuer d'autres applications d'imagerie satellite. Cependant, l'extraction d'information à partir de la donnée satellite implique de nombreux défis. D'un côté, les modèles doivent traiter d'énormes volumes d'images fournis par les satellites. D'un autre côté, il est impossible pour les opérateurs humains d'étiqueter manuellement un tel volume d'images pour chaque tâche (par exemple, la classification, la segmentation, la détection de changement, etc.). Par conséquent, les méthodes d'apprentissage supervisé qui ont besoin des étiquettes ne peuvent pas être appliquées pour analyser la donnée satellite. Pour résoudre ce problème, des algorithmes d'apprentissage non supervisé ont été proposés pour apprendre la structure de la donnée au lieu d'apprendre une tâche particulière. L'apprentissage non supervisé est une approche puissante, car aucune étiquette n'est nécessaire et la connaissance acquise sur la donnée peut être transférée vers d'autres tâches permettant un apprentissage plus rapide avec moins d'étiquettes. Dans ce travail, on étudie le problème de l'apprentissage de représentations démêlées de séries temporelles d'images satellites. Le but consiste à créer une représentation partagée qui capture l'information spatiale de la série temporelle et une représentation exclusive qui capture l'information temporelle spécifique à chaque image. On présente les avantages de créer des représentations spatio-temporelles. Par exemple, l'information spatiale est utile pour effectuer la classification ou la segmentation d'images de manière invariante dans le temps tandis que l'information temporelle est utile pour la détection de changement. Pour ce faire, on analyse plusieurs modèles d'apprentissage non supervisé tels que l'auto-encodeur variationnel (VAE) et les réseaux antagonistes génératifs (GANs) ainsi que les extensions de ces modèles pour effectuer le démêlage des représentations. Considérant les résultats impressionnants qui ont été obtenus par les modèles génératifs et reconstructifs, on propose un nouveau modèle qui crée une représentation spatiale et une représentation temporelle de la donnée satellite. On montre que les représentations démêlées peuvent être utilisées pour effectuer plusieurs tâches de vision par ordinateur surpassant d'autres modèles de l'état de l'art. Cependant, nos expériences suggèrent que les modèles génératifs et reconstructifs présentent des inconvénients liés à la dimensionnalité de la représentation, à la complexité de l'architecture et au manque de garanties sur le démêlage. Pour surmonter ces limitations, on étudie une méthode récente basée sur l'estimation et la maximisation de l'informations mutuelle sans compter sur la reconstruction ou la génération d'image. On propose un nouveau modèle qui étend le principe de maximisation de l'information mutuelle pour démêler le domaine de représentation. En plus des expériences réalisées sur la donnée satellite, on montre que notre modèle est capable de traiter différents types de données en étant plus performant que les méthodes basées sur les GANs et les VAEs. De plus, on prouve que notre modèle demande moins de puissance de calcul et pourtant est plus efficace. Enfin, on montre que notre modèle est utile pour créer une représentation qui capture uniquement l'information de classe entre deux images appartenant à la même catégorie. Démêler la classe ou la catégorie d'une image des autres facteurs de variation permet de calculer la similarité entre pixels et effectuer la segmentation d'image d'une manière faiblement supervisée.This work focuses on learning data representations of satellite image time series via an unsupervised learning approach. The main goal is to enforce the data representation to capture the relevant information from the time series to perform other applications of satellite imagery. However, extracting information from satellite data involves many challenges since models need to deal with massive amounts of images provided by Earth observation satellites. Additionally, it is impossible for human operators to label such amount of images manually for each individual task (e.g. classification, segmentation, change detection, etc.). Therefore, we cannot use the supervised learning framework which achieves state-of-the-art results in many tasks.To address this problem, unsupervised learning algorithms have been proposed to learn the data structure instead of performing a specific task. Unsupervised learning is a powerful approach since no labels are required during training and the knowledge acquired can be transferred to other tasks enabling faster learning with few labels.In this work, we investigate the problem of learning disentangled representations of satellite image time series where a shared representation captures the spatial information across the images of the time series and an exclusive representation captures the temporal information which is specific to each image. We present the benefits of disentangling the spatio-temporal information of time series, e.g. the spatial information is useful to perform time-invariant image classification or segmentation while the knowledge about the temporal information is useful for change detection. To accomplish this, we analyze some of the most prevalent unsupervised learning models such as the variational autoencoder (VAE) and the generative adversarial networks (GANs) as well as the extensions of these models to perform representation disentanglement. Encouraged by the successful results achieved by generative and reconstructive models, we propose a novel framework to learn spatio-temporal representations of satellite data. We prove that the learned disentangled representations can be used to perform several computer vision tasks such as classification, segmentation, information retrieval and change detection outperforming other state-of-the-art models. Nevertheless, our experiments suggest that generative and reconstructive models present some drawbacks related to the dimensionality of the data representation, architecture complexity and the lack of disentanglement guarantees. In order to overcome these limitations, we explore a recent method based on mutual information estimation and maximization for representation learning without relying on image reconstruction or image generation. We propose a new model that extends the mutual information maximization principle to disentangle the representation domain into two parts. In addition to the experiments performed on satellite data, we show that our model is able to deal with different kinds of datasets outperforming the state-of-the-art methods based on GANs and VAEs. Furthermore, we show that our mutual information based model is less computationally demanding yet more effective. Finally, we show that our model is useful to create a data representation that only captures the class information between two images belonging to the same category. Disentangling the class or category of an image from other factors of variation provides a powerful tool to compute the similarity between pixels and perform image segmentation in a weakly-supervised manner

Effectiveness of the National Program of Complementary Feeding for older adults in Chile on vitamin B12 status in older adults; secondary outcome analysis from the CENEX Study (ISRCTN48153354).

BACKGROUND: Older people are at increased risk of vitamin B12 deficiency and the provision of fortified foods may be an effective way to ensure good vitamin B12 status in later life. AIM: To evaluate the effectiveness of a vitamin B12 fortified food provided by a national program of complementary food for older people on plasma vitamin B12 levels. SUBJECTS AND METHODS: A random sub-sample of 351 subjects aged 65-67 y from a large cluster randomised controlled trial provided blood samples at baseline and after 24 months of intervention. The intervention arm (10 clusters 186 participants) received a vitamin B12 fortified food designed to deliver 1.4 μg/day, while the control arm did not receive complementary food (10 clusters, 165 participants). Serum vitamin B12 and folate levels determined by radioimmunoassay were used to estimate the effect of intervention on vitamin B12 levels, adjusting for baseline levels and sex. RESULTS: Attrition at 24 months was 16.7% and 23.6% in the intervention and control arms respectively (p = 0.07). Over 24 months of intervention, mean (95% CI) serum vitamin B12 decreased from 392 (359-425) pmol/dL to 357 (300-414) pmol/dL (p < 0.07) in the intervention arm and from 395 (350-440) pmol/dL to 351 (308-395) pmol/dL in the control arm. There was no significant effect of the intervention on folate status. DISCUSSION: Our findings suggest that foods fortified with 1.4 μg/daily vitamin B12 as provided by Chile's national programme for older people are insufficient to ensure adequate vitamin B12 levels in this population. Chile has a long and successful experience with nutrition intervention programs; however, the country's changing demographic and nutritional profiles require a constant adjustment of the programs

Rheological Behavior of an OBM Sample of the GOM under xHPHT Conditions

The scope of this research is to study the rheological behavior of an oil based mud (OBM) sample from the Mexican side of the Gulf of Mexico (GOM) under extreme conditions of High Pressure High Temperature (HPHT). In the coming years many HPHT wells are going to be drilled in this area of the GOM. Currently Mexican Oil and Gas industry is already open to international operators because the Mexican energy reform has been approved, so it is important to study the possible drilling fluids that will be used. These fluids can be within any of these 3 tiers of HPHT classification: HPHT, ultra (uHPHT) or extreme (xHPHT). The sample was submitted to extreme HPHT conditions, by using the state-of-the-art Model 7600 HPHT Viscometer that is capable of measuring drilling fluid properties up to 40,000 psi and 600 °F. During the laboratory tests performance, it was noticed that erroneous results were obtained by several mechanical failures. It should be noted that the spare parts take a long time to arrive-around 3 weeks. One of the failures was that the pivot of the spring assembly got inside the device, so the bob was spinning nonstop. For this reason the readings of the dial went well beyond the allowed range; another mechanical failure was that the spring of the spring assembly was loose, which did not allow us to obtain a correct reading of shear stress at high pressures and low temperatures; also the baffle does not separate the pressurizing oil from the sample, mixing these two fluids and obviously affecting the properties of the sample. This was noticed by running one test with baffle and another without it getting very similar results. The rheological behavior of the sample showed that the viscosity is inversely proportional to temperature and directly proportional to pressure, noticing a failure point at 300 °F, because of sample degradation. Moreover the rheogram’s curves obtained are quite similar to a second degree polynomial function, with R-squared values ranging from 0.95 to 0.99; hence an equation can be adjusted in the future by extrapolating different pressure and temperature values