Análisis de la evaporación en el centro de México: tendencias, auto-afinidad y frecuencias importantes

Históricamente, la mayoría de los estudios confían en el punto de vista tradicional de que las tendencias de evaporación son un reflejo de las tendencias de la evaporación terrestre superficial. La evaporación del agua medida en los tanques evaporímetros ha disminuido en muchas regiones del mundo a partir de la segunda mitad del siglo pasado (Roderick and Farquhar, 2004), lo cual sugiere una disminución reciente en la evaporación terrestre componente del ciclo hidrológico, (Lawrimore and Peterson, 2000). En el hemisferio norte, decrementos en la evaporación de tanque evaporímetro, en promedio de 2 a 4 mm/año, han ocurrido en varias décadas y hasta 1990 (Gifford et al., 2005). A pesar de estos impresionantes hechos, existe la carencia de estudios acerca del comportamiento de la evaporación y su posible relación con fenómenos periódicos en México, aunque se cuenta con una red nacional de estaciones meteorológicas. Generalmente se ha esperado que la evaporación se incremente en el futuro debido al incremento de las temperaturas debido al calentamiento global y a una intensificación del ciclo hidrológico (Huntington 2006). Sin embargo, varios reportes muestran que la tendencia de la evaporación terrestre se está decrementando (Chattopadhyay and Hulme, 1997; Quintana–Gomez, 1998; Linacre, 2004). El conocer el comportamiento de la evaporación local puede ser de gran importancia socioeconómica dado que en las áreas rurales es común asociarlo con valores de coeficientes de cultivos para programación de riego y administración de recursos hidráulicos (Mutziger et al., 2005). Este es el caso de las áreas áridas y semiáridas irrigadas (150,000 ha) del estado mexicano de Zacatecas, donde los acuíferos están sobreexplotados con un déficit de 201,100,000 m3 por año (SEMARNAT, 2008). Adicionalmente a los análisis estadísticos tradicionales, las series de tiempo de evaporación pueden ser analizadas utilizando otros enfoques, por ejemplo, pueden tratarse como perfiles fractales. Por lo tanto, los objetivos de este estudio fueron: 1) Identificar las tendencias de 40 series de largo plazo de evaporación registrada en estaciones meteorológicas en el estado de Zacatecas, México y 2) Identificar frecuencias importantes y su posible conexión con fenómenos periódicos de las series de anomalías de largo plazo de evaporación por medio del análisis de espectro de potencia.Universidad Nacional de La Plat

Análisis de la evaporación en el centro de México: tendencias, auto-afinidad y frecuencias importantes

Históricamente, la mayoría de los estudios confían en el punto de vista tradicional de que las tendencias de evaporación son un reflejo de las tendencias de la evaporación terrestre superficial. La evaporación del agua medida en los tanques evaporímetros ha disminuido en muchas regiones del mundo a partir de la segunda mitad del siglo pasado (Roderick and Farquhar, 2004), lo cual sugiere una disminución reciente en la evaporación terrestre componente del ciclo hidrológico, (Lawrimore and Peterson, 2000). En el hemisferio norte, decrementos en la evaporación de tanque evaporímetro, en promedio de 2 a 4 mm/año, han ocurrido en varias décadas y hasta 1990 (Gifford et al., 2005). A pesar de estos impresionantes hechos, existe la carencia de estudios acerca del comportamiento de la evaporación y su posible relación con fenómenos periódicos en México, aunque se cuenta con una red nacional de estaciones meteorológicas. Generalmente se ha esperado que la evaporación se incremente en el futuro debido al incremento de las temperaturas debido al calentamiento global y a una intensificación del ciclo hidrológico (Huntington 2006). Sin embargo, varios reportes muestran que la tendencia de la evaporación terrestre se está decrementando (Chattopadhyay and Hulme, 1997; Quintana–Gomez, 1998; Linacre, 2004). El conocer el comportamiento de la evaporación local puede ser de gran importancia socioeconómica dado que en las áreas rurales es común asociarlo con valores de coeficientes de cultivos para programación de riego y administración de recursos hidráulicos (Mutziger et al., 2005). Este es el caso de las áreas áridas y semiáridas irrigadas (150,000 ha) del estado mexicano de Zacatecas, donde los acuíferos están sobreexplotados con un déficit de 201,100,000 m3 por año (SEMARNAT, 2008). Adicionalmente a los análisis estadísticos tradicionales, las series de tiempo de evaporación pueden ser analizadas utilizando otros enfoques, por ejemplo, pueden tratarse como perfiles fractales. Por lo tanto, los objetivos de este estudio fueron: 1) Identificar las tendencias de 40 series de largo plazo de evaporación registrada en estaciones meteorológicas en el estado de Zacatecas, México y 2) Identificar frecuencias importantes y su posible conexión con fenómenos periódicos de las series de anomalías de largo plazo de evaporación por medio del análisis de espectro de potencia.Universidad Nacional de La Plat

Cattle Grazing Exclusion Increases Basal, Crown and Mulch Cover in the Sierra de Órganos National Park, Sombrerete, Zacatecas, Mexico

Objetivo: Estimar los efectos de la exclusión del pastoreo de ganado sobre condiciones de suelo y vegetación en el pastizal del Parque Nacional Sierra de Órganos (PNSO), Sombrerete, Zacatecas, México. Diseño/método/aproximación: En el pastizal del PNSO con exclusión del pastoreo se establecieron estratégicamente cuatro transectos. En cada transecto se midieron las coberturas basal, de copa, de mantillo orgánico, de suelo cubierto, de suelo desnudo y la forma de la planta en otoño de 2008, 2010, 2012 y 2014. Resultados: La exclusión del pastoreo en el pastizal del PNSO incrementó las coberturas basal, de copa, de mantillo orgánico, de suelo cubierto, así como de plantas sobredescansadas y plantas decadentes; también, la cobertura de suelo desnudo y el porcentaje de plantas normales disminuyeron. Limitaciones del estudio/implicaciones: El incremento de la cobertura de mantillo orgánico implica acumulación de material combustible y representa un riesgo potencial para que ocurran incendios en el PNSO. Hallazgos/conclusiones: El incremento de las coberturas de mantillo orgánico, plantas sobredescansadas y plantas decadentes demuestra que el pastizal del PNSO transita a un estado ecológico menos estable.Objective: To estimate the effects of cattle grazing exclusion on soil and vegetation conditions in grasslands of Sierra de Órganos National Park (SONP), Sombrerete, Zacatecas, Mexico. Design/Methodology/Approach: Four transects with cattle grazing exclusion were strategically established in SONP grasslands. In each transect the basal, crown and organic mulch cover, soil cover, bare soil, and the form of the autumn plant were measured from 2008, 2010, 2012 and 2014. Results: Cattle grazing exclusion caused an increase in basal, crown and organic mulch cover, soil cover, as well as over- rested plants and deteriorated plants; bare soil cover and the percentage of normal plants decreased. Study Limitations/Implications: Increase of organic mulch cover implies the accumulation of combustible material that represents a potential risk of fire occurring in the SONP. Findings/Conclusions: Increase of organic mulch, over-rested plants and deteriorated plants shows that SONP grasslandsare transiting to a less stable ecological state

Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal …

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possibl

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy

A generalized model for indoor location estimation using environmental sound from human activity recognition

The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (eg, hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set

An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

This work presents a human activity recognition (HAR) model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC). Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source