Optimal Sampling and Christoffel Functions on General Domains

The explicit engineering of bacterial populations requires that we know which organisms perform which tasks. The comparison of the bacterial diversity of activated sludge plants may give important information about the functions of different bacteria. This difficult task may be made easier by the use of technologies based on 16S rRNA based techniques. In this study we have used denaturing gradient gel electrophoresis (DGGE) to determine the optimal sampling regime for comparative studies and used cluster analysis to show how plants may be quantitatively compared. We sought evidence of spatial, diurnal and intrasample variation in a number of sites. No evidence for variation was found in the plants studied and we concluded that a single sample of an activated sludge plant was sufficient for a plant to plant comparison. The cluster analysis was able to distinguish between plants, though further work is required to find the most appropriate basis for such comparisons. We found organisms from raw sewage in the mixed liquor samples, these organisms may have no functional significance in the treatment process and thus complicate plant to plant comparisons as will the probable presence of heteroduplex rDNA products. Nevertheless we believe that these drawbacks do not outweigh the advantages of being able to take and compare relatively large numbers of samples.

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Optimal sampling of dynamical large deviations via matrix product states

Physical Review E ◽

10.1103/physreve.103.062144 ◽

2021 ◽

Vol 103 (6) ◽

Author(s):

Luke Causer ◽

Mari Carmen Bañuls ◽

Juan P. Garrahan

Keyword(s):

Large Deviations ◽

Matrix Product ◽

Optimal Sampling ◽

Matrix Product States

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Geometric computation of Christoffel functions on planar convex domains

Journal of Approximation Theory ◽

10.1016/j.jat.2021.105603 ◽

2021 ◽

pp. 105603

Author(s):

A. Prymak

Keyword(s):

Convex Domains ◽

Christoffel Functions ◽

Planar Convex

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Optimal Sampling Pattern for the Free Final Time Linear Quadratic Regulator

IEEE Transactions on Control of Network Systems ◽

10.1109/tcns.2021.3074561 ◽

2021 ◽

pp. 1-1

Author(s):

Pedro Balaguer ◽

Carlos Arino

Keyword(s):

Linear Quadratic Regulator ◽

Optimal Sampling ◽

Linear Quadratic ◽

Final Time ◽

Time Linear ◽

Sampling Pattern

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Log-concave polynomials IV: approximate exchange, tight mixing times, and near-optimal sampling of forests

Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing ◽

10.1145/3406325.3451091 ◽

2021 ◽

Author(s):

Nima Anari ◽

Kuikui Liu ◽

Shayan Oveis Gharan ◽

Cynthia Vinzant ◽

Thuy-Duong Vuong

Keyword(s):

Optimal Sampling ◽

Mixing Times

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Learning an Optimal Sampling Distribution for Efficient Motion Planning

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9341245 ◽

2020 ◽

Author(s):

Richard Cheng ◽

Krishna Shankar ◽

Joel W. Burdick

Keyword(s):

Motion Planning ◽

Sampling Distribution ◽

Optimal Sampling

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A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland

Agriculture ◽

10.3390/agriculture11070600 ◽

2021 ◽

Vol 11 (7) ◽

pp. 600

Author(s):

Darren J. Murphy ◽

Michael D. Murphy ◽

Bernadette O’Brien ◽

Michael O’Donovan

Keyword(s):

Remote Sensing ◽

Precise Measurement ◽

Production Systems ◽

Measurement Techniques ◽

Grassland Management ◽

Management Systems ◽

Sampling Techniques ◽

Optimal Sampling ◽

Labour Costs ◽

Grassland Production

The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.

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