Zegbe J.A.,INIFAP Campo Experimental Zacatecas |
Mena-Covarrubias J.,INIFAP Campo Experimental Zacatecas |
Dominguez-Canales V.S.I.,Zacatecas Institute of Technology
Acta Horticulturae | Year: 2015
Annual pruning of cactus pear cladodes provides an opportunity for adding value to this crop by extracting mucilage from which to create edible films and coatings for perishable fruits such as guavas (Psidium guajava L.). The objective of this research was to create mucilage films and assess their effects on quality and shelf life of guava cultivar 'Media China'. Cactus pear cladodes were peeled, cubed, and homogenized in distilled water. Mucilage was precipitated using ethanol, then dried and ground. The experimental films tested were: no films as control (C), mucilage plus glycerol (T1), and mucilage plus glycerol and polyethylene glycol (T2). Two experiments were conducted with two different concentrations of mucilage, glycerol, and polyethylene glycol. Guavas were harvested from local farmers and treated with a fungicide before coating. The treated fruit was stored for eight or six days at room temperature (28°C and 20% RH or 27°C and 20% RH, respectively). In the first trial, the T2 film increased fruit weight loss more than C and TI film. Both films delayed fruit skin colour and maintained higher firmness (F), total soluble solids concentration (TSSC), and dry matter concentration (DMC) than C fruit. In the second trial, T1 and T2 films reduced fruit weight loss and delayed fruit skin colour more than C fruit. Firmness, TSSC, and DMC of fruit were similar among treatments. Overall, the experimental mucilage films showed a tendency to prolong shelf life and maintain some quality attributes of guava. Further research is needed to understand the mucilage potential as an edible film at cold room conditions. © 2015, International Society for Horticultural Science. All rights reserved.
Mejia J.,Unidad University |
Garcia A.,Zacatecas Institute of Technology |
Munoz M.A.,Unidad University
Advances in Intelligent Systems and Computing | Year: 2013
Software development outsourcing is now a reality in both international and national organizations because it represents a competitive strategy. However, even when organizations recognize in the outsourcing competitiveness and business strategy, the software development projects in outsourcing enviroments fails due to the inadequate project management. Therefore, this paper presents a proposal of how to implement TSPi Methodology to manage software project in outsourcing environment. To achieve this, the main failure factors in software development in outsourcing environments are identified and a traceability to identify the adaptability of TSPi to this environment is showed. © 2013 Springer-Verlag.
Esquivel A.,Zacatecas Institute of Technology |
Haya P.,Instituto de Ingenieria del Conocimiento |
Alaman X.,Autonomous University of Madrid
Sensors (Switzerland) | Year: 2015
This paper presents a proof of concept from which the metaphor of “fair trade” is validated as an alternative to manage the private information of users. Our privacy solution deals with user’s privacy as a tradable good for obtaining environmental services. Thus, users gain access to more valuable services as they share more personal information. This strategy, combined with optimistic access control and transaction registry mechanisms, enhances users’ confidence in the system while encouraging them to share their information, with the consequent benefit for the community. The study results are promising considering the user responses regarding the usefulness, ease of use, information classification and perception of control with the mechanisms proposed by the metaphor. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
Galvan-Tejada C.E.,Autonomous University of Zacatecas |
Galvan-Tejada J.I.,Autonomous University of Zacatecas |
Celaya-Padilla J.M.,Autonomous University of Zacatecas |
Delgado-Contreras J.R.,Zacatecas Institute of Technology |
And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016
Due to an increase interest for providing services based on user location, several indoor location approaches based on mobile devices have been proposed recently. This paper focuses on the use of a novel crowdsourcing approach for indoor location of a mobile device that uses social collaboration to improve the accuracy and magnetic field signal as information source using feature extraction and a deterministic method that allows us to include information from new users that improves the fitness of the model. Four phases were included in the methodology: Raw data collection, Data pre-process, Feature extraction and Social collaboration. An experiment was succesfully carried out to test the proposed methodology. On the whole, good results were obtained on computational cost, recalculation time and accuracy improvement. © Springer International Publishing Switzerland 2016.
Delgado-Contreras J.R.,Zacatecas Institute of Technology |
Delgado-Contreras J.R.,Monterrey Institute of Technology |
Garcia-Vazquez J.P.,Autonomous University of Baja California |
Brena R.,Monterrey Institute of Technology
2016 International Conference on Electronics, Communications and Computers, CONIELECOMP 2016 | Year: 2016
One of the many possible sources for identifying a place is environmental sound. Ambient sound can be used by itself or in combination with other methods, like GPS, WiFi, etc. A way of identifying a place with sound is using "fingerprinting", which tries to match features of sound in similar places with the one being registered. Nevertheless, one of the many parameters in this process relates to the length of the audio both for the patterns and for the current recording. Several authors use a given time length (e.g. 10, 15, 30 seconds; however, they fail to provide any justification about the time length of the audio fingerprint for creating their classification models. In this paper, we propose to optimize the time length for classifying an environmental audio signal and for increasing the accuracy of the our classification model that uses support vector machine (SVM) as a classifier, and we perform an experimental evaluation. Our results show that the length of environmental audio signal should be between 30 and 40 seconds to get a model with 94.28 % of accuracy. © 2016 IEEE.