Caracterização fisio-molecular de cultivares de feijão (Phaseolus vulgaris L.) em resposta à deficiência hídrica

The identification of genotypes for drought tolerance has a great importance in breeding programs. The aim of this study was to characterize genotypes of beans in response to drought tolerance in different reproductive stages through physiologic, agronomic and molecular analysis. The experiment was conducted in greenhouse, using a randomized block design with four replicates; 10 cultivars: ANFC 9, ANFP 110, BRS Esplendor, BRSMG Realce, IPR Siriri, IPR Tangará, IPR Tuiuiu, IPR Uirapuru, IAC Imperador and IAC Milênio under two conditions of irrigation: plants irrigated during their entire life cycle, and plants under irrigation suppression in the reproductive stage (R7) until 16% of field capacity, when the irrigation was restored. In the last four days of stress, the gas exchanges were analyzed, and in the last day of stress was analyzed the percentage of closed stomata in the abaxial surface of the leaves, collected in different times of the day (9h, 12h, 15h and 18h). Additionally, plant samples were collected for the following analysis: fresh and dry mass of leaves, stems and legumes, and proline content in leaves and roots. The plants were harvested at the physiological maturity and the yield components and grain yield were determined. In addition, in order to identify polymorphisms in the sequences of promoters and genes related to drought, seven pairs of primers were tested on the group of genotypes. The drought susceptibility indexes (ISS) ranged from 0.65 to 1.10 in the group of genotypes, which the lowest values observed were for IAC Imperador (0.65) and BRS Esplendor (0.87), indicating the ability of these two genotypes to maintain grain yield under water stress condition. All genotypes showed reduction in yield components under water stress. IAC Imperador (43.4%) and BRS Esplendor (60.6%) had the lowest reductions in productivity and kept about 50% of the stomata closed during all the different times evaluated at last day of irrigation suppression. IAC Imperador showed greater water use efficiency and CO2 assimilation rate under drought stress. IPR Tuiuiú, IPR Tangará and IAC Imperador had the highest proline concentrations in the roots. Under water stress condition, there was a strong positive correlation (0.696) between the percentage of stomata closed with the number of grains per plant (0.696) and the fresh mass of leaves (0.731), the maximum percentage of stomata closed 73.71% in water stress. The accumulation of proline in the root was the character that most contributed to the divergence between the genotypes under water deficit, but not always the genotypes that have accumulated more proline were the most tolerant. The polymorphisms in DNA of coding and promoting sequences of transcription factors studied in this experiment did not discriminate tolerant genotypes from the sensitive ones to water stress.

Diagnóstico automático de eventos e distúrbios em unidades geradoras e usinas utilizando lógica fuzzy

The electric power systems are getting more complex and covering larger areas day by day. This fact has been contribuiting to the development of monitoring techniques that aim to help the analysis, control and planning of power systems. Supervisory Control and Data Acquisition (SCADA) systems, Wide Area Measurement Systems and disturbance record systems. Unlike SCADA and WAMS, disturbance record systems are mainly used for offilne analysis in occurrences where a fault resulted in tripping of and apparatus such as a transimission line, transformer, generator and so on. The device responsible for record the disturbances is called Digital Fault Recorder (DFR) and records, basically, electrical quantities as voltage and currents and also, records digital information from protection system devices. Generally, in power plants, all the DFRs data are centralized in the utility data centre and it results in an excess of data that difficults the task of analysis by the specialist engineers. This dissertation shows a new methodology for automated analysis of disturbances in power plants. A fuzzy reasoning system is proposed to deal with the data from the DFRs. The objective of the system is to help the engineer resposnible for the analysis of the DFRs’s information by means of a pre-classification of data. For that, the fuzzy system is responsible for generating unit operational state diagnosis and fault classification.