Potencial fisiológico de sementes de soja enriquecidas com molibdênio no armazenamento e sua influência na atividade de enzimas do metabolismo do nitrogênio

Molybdenum is one of the essential micronutrients for soybeans, acting directly on nitrogen metabolism as enzyme cofactor of nitrogenase. Usually, this nutrient is supplied to the plants through seed treatment or foliar application. The aim of this study was to evaluate the molybdenum effects by foliar in the physiological potential of soybean seeds and verify its interference in the enzyme activities involved in nitrogen metabolism. Soybean seeds of BMX Turbo cultivar were used, produced in Erechim, RS, harvest 2013, from plants treated with the following Mo concentrations: 0; 25; 50 and 75 g ha-1, supplied through two commercial products (Biomol and Molybdate) and stored during 0 and 6 months in uncontrolled conditions. The first experiment was conducted in Seedtes Seed Analysis Laboratory in Pato Branco, PR. The used design was completely randomized in a factorial analysis 4 x 2 x 2 with four replications each. The physiological potential of the seeds was evaluated by the germination test, seedling growth, accelerated aging and emergence on the soil. The second experiment was conducted in a greenhouse, where the seeds derived from treatments with different concentrations of Mo: 0; 25; 50 and 75 g ha-1 supplied through two commercial products (Biomol and Molybdate) were grown in vases. The used design was completely randomized in a factorial analysis 4 x 2 with four replications. Evaluations were performed when the plants reached the R1 phenological stage concerning the nodulation, dry matter of root and shoot of the plants and the determination of the activity of the enzymes glutamine synthetase and glutamate synthetase and the content of total soluble proteins. The data were submitted to variance analysis and when significant they were assessed by Tukey’s test for comparison of products and seed storage and with regression study to the concentrations at 5% probability. Analyses were performed using SISVAR statistical software. The soybean seed storage under uncontrolled conditions affected the seed vigour produced with Mo, regardless of the commercial product used during production. The application of Mo through foliar positively influences the production of soya beans which presented increasing responses in the germination and vigour with the application of Mo above 25 g ha-1 . The enrichment of Mo through foliar did not affect the nodulation of plants of the next generation, however, the use of Mo above 25 g ha-1 provided an increase in the activity of enzymes involved in nitrogen metabolism as well as on the total protein content.

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.