Los claroscuros de la sincronización internacional de los ciclos económicos: evidencia sobre la manufactura de México

Se analiza la sincronización de las fluctuaciones cíclicas de la división manufacturera de México con el ciclo estadounidense. Se utiliza el enfoque tradicional de los ciclos de crecimiento para el periodo 1980-2004 en submuestras de cinco años que se desplazan en el tiempo, lo cual permite estudiar la evolución temporal del proceso. Los resultados sugieren que la sincronización se ha incrementado sustancialmente en el marco del TLCAN, pero ha sido heterogénea, pues el comercio exterior puede haber sido un mecanismo de transmisión fundamental en el proceso. También se muestra que las actividades más integradas al exterior son más vulnerables a los choques externos, lo cual puede constituir una debilidad de la nueva estrategia de desarrollo

Stairs detection with odometry-aided traversal from a wearable RGB-D camera

Stairs are one of the most common structures present in human-made scenarios, but also one of the most dangerous for those with vision problems. In this work we propose a complete method to detect, locate and parametrise stairs with a wearable RGB-D camera. Our algorithm uses the depth data to determine if the horizontal planes in the scene are valid steps of a staircase judging their dimensions and relative positions. As a result we obtain a scaled model of the staircase with the spatial location and orientation with respect to the subject. The visual odometry is also estimated to continuously recover the current position and orientation of the user while moving. This enhances the system giving the ability to come back to previously detected features and providing location awareness of the user during the climb. Simultaneously, the detection of the staircase during the traversal is used to correct the drift of the visual odometry. A comparison of results of the stair detection with other state-of-the-art algorithms was performed using public dataset. Additional experiments have also been carried out, recording our own natural scenes with a chest-mounted RGB-D camera in indoor scenarios. The algorithm is robust enough to work in real-time and even under partial occlusions of the stair

Water Quality of the Poza Honda Dam and Other Water Points Down

The phenomenon of pollution of water basins is eliminating many potential water resources. Most of the pollution in Ecuador comes from household waste and agricultural chemicals, especially along the coast. One of the activities in the management of the water resource is the periodic monitoring of the bodies of water, being able to determine the different changes that occur and to influence through preventive actions that manage to reduce the pollution. The water resource is the articulating axis of all the activities in a territory and therefore of the populations that develop different productive activities that not only depend on the quantity and quality of this resource but also generate alterations to the natural state of the same. In the investigation, the monitoring of the quality of the water in different points of the Poza Honda dam and of the river Portoviejo is carried out. The study aims to manage the pollution processes that occur in the aquifer, due to the depositions of domestic, industrial and agricultural wastewater not controlled to be discharged

Production performance, nutrient digestibility, and milk composition of dairy ewes supplemented with crushed sunflower seeds and sunflower seed silage in corn silage-based diets

This study determined production performance, nutrient digestibility, and milk composition of dairy ewes supplemented with crushed sunflower seeds (Helianthus annuus) and sunflower seed silage in corn silage-based diets. Six ewes were grouped in a double 3 × 3 Latin square design with three periods of 21 days. All treatments were based on ad libitum corn silage. Control diet was based on alfalfa hay (333 g/kg DM), sorghum grain (253 g/kg DM), triticale grain (200 g/kg DM), soybean meal (167 g /kg DM), and vitamin and mineral premix (47 g/kg DM). Sunflower seeds (SF) and sunflower seed silage (SFS) treatments consisted of alfalfa hay (333 g/kg DM), sorghum grain (267 g/kg DM), triticale grain (100 g/kg DM), soybean meal (167 g /kg DM), SF or SFS (87 g/kg DM) and vitamin and mineral premix (47 g/kg DM). Compared to control, SF and SFS increased intake and digestibility of fiber components, such as neutral detergent fiber (NDF) and acid detergent fiber (ADF). Body weight, nitrogen balance, milk yield, milk fat yield, milk protein yield, lactose yield and milk urea N were similar between treatments. Overall, results demonstrated that crushed sunflower seeds and ensiled seeds do not change significantly productive parameters of dairy sheep

ORCA: A Matlab/Octave toolbox for ordinal regression

Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca

ORCA: A Matlab/Octave toolbox for ordinal regression

Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca