Spatialization of Tibraca limbativentris Stål in irrigated rice: a geostatistical approach / Espacialização de Tibraca limbativentris Stål em arroz irrigado: uma abordagem geoestatística

The objective was to identify the dispersion of Tibraca limbativentris (Hemiptera: Pentatomidae) in different phenological phases of irrigated rice. The research was carried out in an area of 20.12 ha, subdivided in four fields of 0.25 to 14.1 ha with the irrigated rice culture, grown under the technical recommendations of the culture. In each field, a sampling grid of 30 × 30 m was generated, with each sampling point corresponding to 1·m-2 (200 plants), sampling was through direct counting. The number of adults of T. limbativentris·m-2 was subjected to descriptive and geostatistical analyzes. Tibraca limbativentris presents border dispersion towards the center of the irrigated rice cultivation area. The highest population densities were estimated in the anthesis and elongation phase.(AU)

O objetivo foi identificar a dispersão de Tibraca limbativentris (Hemiptera: Pentatomidae) em diferentes fases fenológicas do arroz irrigado. A pesquisa foi realizada em uma área de 20,12 ha, subdividida em quatro lavouras de 0,25 a 14,1 ha com a cultura de arroz irrigado, cultivadas sob as recomendações técnicas da cultura. Em cada lavoura, foi gerado um grid de amostragem de 30 × 30 m sendo cada ponto amostral correspondente a 1·m-2 (200 plantas), a amostragem realizada foi através de contagem direta. O número de adultos de T. limbativentris·m-2 foi submetido a análises descritivas e geoestatística. Tibraca limbativentris apresenta dispersão das bordaduras para o centro da área de cultivo de arroz irrigado. As maiores densidades populacionais foram estimadas na fase de antese e elongação.(AU)

Mean square optimal NUFFT approximation for efficient non-Cartesian MRI reconstruction.

The fast evaluation of the discrete Fourier transform of an image at non-uniform sampling locations is key to efficient iterative non-Cartesian MRI reconstruction algorithms. Current non-uniform fast Fourier transform (NUFFT) approximations rely on the interpolation of oversampled uniform Fourier samples. The main challenge is high memory demand due to oversampling, especially when multidimensional datasets are involved. The main focus of this work is to design an NUFFT algorithm with minimal memory demands. Specifically, we introduce an analytical expression for the expected mean square error in the NUFFT approximation based on our earlier work. We then introduce an iterative algorithm to design the interpolator and scale factors. Experimental comparisons show that the proposed optimized NUFFT scheme provides considerably lower approximation errors than the previous designs [1] that rely on worst case error metrics. The improved approximations are also seen to considerably reduce the errors and artifacts in non-Cartesian MRI reconstruction.