Why is it so difficult to identify a single indicator of water stress in plants? A proposal for a multivariate analysis to assess emergent properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparative proteomic analysis reveals that T3SS, Tfp, and xanthan gum are key factors in initial stages of Citrus sinensis infection by Xanthomonas citri subsp citri

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Plant iononne diagnosis using sound balances: case study with mango (Mangifera Indica)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aquatic plant community in porto primavera reservoir

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identification and sequence analysis of five allexiviruses species infecting garlic crops in Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Assessment of the Removal Capacity, Tolerance, and Anatomical Adaptation of Different Plant Species to Benzene Contamination

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Metabolic profiling by ultra-performance liquid chromatography-mass spectrometry and parallel factor analysis for the determination of disease biomarkers in Eucalyptus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a knowledge-based system for cogeneration plant design: Verification, validation and lessons learned

This paper presents the development of a knowledge-based system (KBS) prototype able to design natural gas cogeneration plants, demonstrating new features for this field. The design of such power plants represents a synthesis problem, subject to thermodynamic constraints that include the location and sizing of components. The project was developed in partnership with the major Brazilian gas and oil company, and involved interaction with an external consultant as well as an interdisciplinary team. The paper focuses on validation and lessons learned, concentrating on important aspects such as the generation of alternative configuration schemes, breadth of each scheme description created by the system, and its module to support economic feasibility analysis. (C) 2014 Elsevier B.V. All rights reserved.

Cogeneration design problem Computational complexity analysis and solution through an expert system

Purpose - The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.Design/methodology/approach - The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.Findings - The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.Originality/value - The expert system approach is not a "blind" method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

ASIAN SOYBEAN RUST INTEGRATED MANAGEMENT POTENTIAL: DISEASE SEVERITY, PLANT DEVELOPMENT AND YIELD COMPONENTS, CULTIVAR MG/BR-46 (CONQUISTA)

Soybean rust caused by Phakopsora pachyrhizi Sydow & P. Sydow is one of the major diseases of the soybean crop. The aim of this study was to evaluate the effects of sowing dates, plant populations and reduced doses of fungicides on soybean rust severity and its effects on plant development and yield, cultivar MG/BR46 (Conquista). Field experiments were conducted in the 2009/2010 and 2010/2011 harvests, under natural rust infestation of soybean rust. As from the appearance of the first disease symptoms, also began the fungicide spraying and the disease severity assessments. To understand the nature and extent of the effects of different treatments, a multivariate analysis of factors was applied. For the majority of the agronomic characters and factors, one-third to two-thirds of their variability can be explained by changes in plant populations or by differences in the fungicide treatments, and the remainder, was explained by sowing date variations. The fungicide treatments and sowing dates are determinants in disease severity and its interference on crop productivity. The characters of plant growth are more dependent on plant population variations. Treatments with azoxystrobina + ciproconazol promoted smaller disease severities, reflecting in productivity increase. The plant populations can be reduced up to 160.000 plants ha(-1) without losses in the disease control and the soybean yield. In general, the earliest sowings provided increase in the plant development, although the rust control was less efficient.

Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.

Terminalia catappa L.: A medicinal plant from the Caribbean pharmacopeia with anti-Helicobacter pylori and antiulcer action in experimental rodent models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Expression Pattern and Promoter Analysis of a Eucalyptus grandis Germin-like Gene

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genotoxicity evaluation of environmental pollutants using analysis of nucleolar alterations

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Deriving vegetation indices for phenology analysis using genetic programming

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)