University of Ciego de Ávila

About: University of Ciego de Ávila is a education organization based out in Ciego de Ávila, Cuba. It is known for research contribution in the topics: Recommender system & Shoot. The organization has 122 authors who have published 125 publications receiving 1360 citations. The organization is also known as: University of Ciego de Avila & Universidad de Ciego de Ávila "Máximo Gómez Báez".

Optimization of plantain (Musa AAB) micropropagation by temporary immersion system

Abstract: The positive and reliable effect of temporary immersion systems on in vitroshoot proliferation was already proved for different plant genera and it is now presented as an alternative for plantain micropropagation. Some culture parameters affecting the efficiency of the twin flasks system or temporary immersion bioreactor (Escalona et al., 1999) were investigated. Three different cytokinins (benzyladenine, thidiazuron and meta-topolin) were added to the culture medium and meta-topolin at a concentration of 4.4 μM was proved to be the most efficient. Successive subcultures (28 days per subculture) were performed on medium supplemented with meta-topolin, revealing a decrease in multiplication after the 6th subculture. Multiplication rate was not changed within the ranges of immersion times (4, 12 or 22 min) and frequencies (every 3, 5 or 7 h) tested. The size of the bioreactor (250, 1,000, 5,000 or 10,000 ml) and the volume of medium per inoculum (10, 20 or 30 ml) were also evaluated and appeared to have an influence on the multiplication. A proportion of 25–100 ml of headspace per inoculum and 30 ml of medium per inoculum resulted in a multiplication rate > 13 in 28 days.

Physiology of effects of temporary immersion bioreactors on micropropagated pineapple plantlets

Abstract: Summary Temporary immersion bioreactors are an efficient tool for plant mass propagation because they increase multiplication rate and plant quality. Little knowledge is available on the ecosystem and physiological behavior of plantlets when using this new culture technique. In order to evaluate the effects of the conditions on physiological change of pineapple plantlets, a factorial experiment was conducted, where axillary clusters were cultured under two levels of photosynthetic photon flux (PPF): 80mmol m 22 s 21 (low) and 225mmol m 22 s 21 (high), using two culture methods (conventional micropropagation in liquid medium and a temporary immersion bioreactor) during the elongation phase. CO2 concentration in the headspace volume container was measured during a whole cycle of temporary immersion (3 h). At the time before the next immersion period, the levels of CO2 increased significantly to 14 171mmol mol 21 at high PPF. The maximal photosynthetic rate as well as the maximum quantum yield of photosystem II were low for plantlets cultivated in the temporary immersion bioreactor at high PPF. However, these plantlets showed large increases in sugar and nitrogen uptake and also increases in dry weight and foliar area. These results indicate that shoot growth did not totally depend on the photosynthesis process. In vitro pineapple plantlets appeared to use more nutrients in the culture medium than those from photosynthesis. In summary, temporary immersion bioreactor-derived plantlets showed remarkable nutrient uptake, indicating a higher photo-mixotrophic metabolism.

Correcting noisy ratings in collaborative recommender systems

Abstract: Recommender systems help users to find information that best fits their preferences and needs in an overloaded search space. Most recommender systems research has been focused on the accuracy improvement of recommendation algorithms. Despite this, recently new trends in recommender systems have become important research topics such as, cold start, group recommendations, context-aware recommendations, and natural noise. The concept of natural noise is related to the study and management of inconsistencies in datasets of users’ preferences used in recommender systems. In this paper a novel approach is proposed to detect and correct those inconsistent ratings that might bias recommendations, whose main advantage regarding previous proposals is that it uses only the current ratings in the dataset without needing any additional information. To do so, this proposal detects noisy ratings by characterizing items and users by their profiles, and then a strategy to fix these noisy ratings is carried out to increase the accuracy of such recommender systems. Finally a case study is developed to show the advantage of this proposal to deal with natural noise regarding previous methodologies.

A Food Recommender System Considering Nutritional Information and User Preferences

Abstract: The World Health Organization identifies the overall increasing of noncommunicable diseases as a major issue, such as premature heart diseases, diabetes, and cancer. Unhealthy diets have been identified as the important causing factor of such diseases. In this context, personalized nutrition emerges as a new research field for providing tailored food intake advices to individuals according to their physical, physiological data, and further personal information. Specifically, in the last few years, several types of research have proposed computational models for personalized food recommendation using nutritional knowledge and user data. This paper presents a general framework for daily meal plan recommendations, incorporating as main feature the simultaneous management of nutritional-aware and preference-aware information, in contrast to the previous works which lack this global viewpoint. The proposal incorporates a pre-filtering stage that uses AHPSort as multi-criteria decision analysis tool for filtering out foods which are not appropriate to the current user characteristics. Furthermore, it incorporates an optimization-based stage for generating a daily meal plan whose goal is the recommendation of food highly preferred by the user, not consumed recently, and satisfying his/her daily nutritional requirements. A case study is developed for testing the performance of the recommender system.