In this paper was made a characterization of the hydrological drought periods that have been historically recorded for the mexican section of the Rio Grande basin, from the analysis of the water contributions to the storage dams located on the main mexican tributaries of this river. The results indicate that in more than 60 % of the analyzed years there was a drought in a certain degree, with contributions lower than the historical average decreasing up to -97 %, and drought periods ranging from one to thirteen consecutive years. It is concluded that in most part of the basin occurred an extraordinary drought period, which lasted for 14 years (1992 – 2005), and it is the most severe and prolonged of which there is record.

The current research was conducted in a greenhouse of 20 m2 area located at the University of Chapingo, Mexico, in hydroponic system (NFT) with recirculating nutrient solution, three treatments were applied (100, 150 and 200 ppm) of calcium concentration to study effects on fruit quality and yield of tomato (Lycopersicum esculentum Mill.). The experiment was conducted from September 30, 2010 to February 4, 2011 and random samples of fruits were collected for three consecutive weeks, where each week were harvested fruits of the first to the third cluster of each plant. Treatment two showed the highest yield and it was obtained in the second cluster with 57.1 %, in treatment one on the third week was 37.5 % and yield treatment three during the second and third week was 38.6 % with respect to the total. The fruits quality was good because tomatoes showed good acidity and salt content as recommended. However the sugar content was low and statistically different compared with previous reported studies. The average yield was low and different in all cases due to the effect of extreme temperatures outside the range of 10-25 °C and these results cannot be asigned to the concentration of calcium because there were not significant differences among the treatments.

Weathering experienced by rocks and minerals Denganthza Hill deposits have been studied by fluorescence analysis in order to assess and quantify the geochemical weathering profiles. Weathering intensity was quantified using different alteration rates (PWR, Potential Weathering Rate, WR, Weathering Rate, AR, Alteration Rate, CAR, Chemical Alteration Rate) The geochemical evolution suffered by the samples were analyzed using the Chesworth (1979, 2000) triangular diagrams and A-CN-K, the binary variation diagrams or Harker and alkali-silica (Le Maitre, 1989). Na2O, K2O, Fe2O3, TiO2 concentrations are greatly reduced in the weathering products, and instead the Al2O3, CaO y MgO concentrations increase indicating an early stage in development of soils, reflecting a poor material alteration. SiO2 versus K2O + Na2O content indicate that the samples of Denganthza Hill are dacite, andesite and mostly picrobasalto foiditas.

The aim of this study was to evaluate the quality and the possible formation of trans fatty acid (TFA) in avocado oil, when it is exposed to electric field (EF) and stored at different times. In order to analyze the oils, chemical values (acidity, iodine and peroxide) were used, and for the identification of TFA, Fourier transform infrared spectroscopy (FTIR) in the mid region of 400-4000 cm-1 was used. For the study of the effect of the EF (voltage 9 kV cm-1, frequency 720 Hz and an exposure time of 3 minutes) two strategies were utilized, first the oil was extracted and then treated with the EF, (Oil 1), second the pulp was first treated with the EF and the oil was extracted, (Oil 2). The oil treated with EF was deposited in a closed container and was stored at room temperature in a box (4, 13, 28, 90 and 365 days). In agreement with the chemical values, the oils had smaller modification in the unsaturated fatty acids and was corroborated by FTIR, and identified a trans double bonds in all samples studied (968 cm-1), with an average peak height of 0.2 mm.

Artificial Intelligence Techniques or Soft Computing, such as Fuzzy Logic (FL) and Artificial Neural Networks (ANN) has gained significant im¬portance in scientific research, due to its ability to solve complex and non-linear problems that conventional methods cannot solve. The research undertaken in the last decade has emphasized the application of Soft Computing techniques for the development of intelligent algorithms to identify, predict, optimize and control the climate in controlled environments (greenhouses) and to generate near optimal climatic conditions in indoor biosys¬tems for growth and crop development, and to obtain higher yields and product quality. The current paper presents a review of Artificial Intelligence Techniques applied in agricultural engineering, mainly to modeling and control of greenhouse environment.

Evolutionary Algorithms, Bio-inspired Algorithms and Hybrid Algorithms, considered as Artificial Intelligence techniques have gained great importance due to their capacity to solve complex and nonlinear problems that conventional methods cannot solve. In the last decade the application of these techniques has been applied to the development of intelligent algorithms to identify, predict, optimize and control the weather for crops in protected environments (greenhouses) and to generate near optimal climatic conditions for biosystems for growth and crop development, and to obtain higher yields and product quality. The current paper shows a review of Artificial Intelligence Techniques applied in agricultural engineering, mainly to modeling and control of greenhouse environment, especially those algorithms inspired by the collective behavior of biological systems, evolution and natural genetics.