What is he best known for?
The fields of study he is best known for:
- Statistics
- Biochemistry
- Machine learning
Peter Goos spends much of his time researching Mathematical optimization, Optimal design, Algorithm, Bayesian probability and D optimality.
His study in the fields of Metaheuristic, Variable neighborhood search and Traveling repairman problem under the domain of Mathematical optimization overlaps with other disciplines such as Waiting time.
His studies deal with areas such as Local search and Heuristic as well as Metaheuristic.
His Optimal design study is concerned with the larger field of Statistics.
His Guided Local Search, Tabu search and Generalized least squares study in the realm of Algorithm interacts with subjects such as Restricted randomization.
Peter Goos studied Bayesian probability and Choice set that intersect with Prior probability.
His most cited work include:
- Optimal Design of Experiments: A Case Study Approach (181 citations)
- A Comparison of Criteria to Design Efficient Choice Experiments (176 citations)
- Historical land use change has lowered terrestrial silica mobilization (143 citations)
What are the main themes of his work throughout his whole career to date?
Peter Goos focuses on Mathematical optimization, Optimal design, Statistics, Design of experiments and Algorithm.
He interconnects Focus, Surface and Fractional factorial design in the investigation of issues within Mathematical optimization.
His biological study spans a wide range of topics, including Choice set, Econometrics, Bayesian probability and Generalized least squares.
His Plot, Estimator and Fisher information study in the realm of Statistics connects with subjects such as Respondent.
His work on D optimality as part of general Design of experiments research is frequently linked to Context, bridging the gap between disciplines.
His Local search and Iterated local search study, which is part of a larger body of work in Algorithm, is frequently linked to Split plot, bridging the gap between disciplines.
He most often published in these fields:
- Mathematical optimization (53.61%)
- Optimal design (41.35%)
- Statistics (27.40%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (53.61%)
- Food science (6.01%)
- Design of experiments (30.05%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Food science, Design of experiments, Optimal design and Algorithm.
In Mathematical optimization, Peter Goos works on issues like Surface, which are connected to Regression analysis.
His Food science research is multidisciplinary, incorporating elements of Arabinoxylan, Nannochloropsis and Lipid oxidation.
His Design of experiments research also works with subjects such as
- Linear programming that intertwine with fields like Orthogonality and Factorial experiment,
- Blocking, which have a strong connection to Row and column spaces.
The concepts of his Optimal design study are interwoven with issues in Focus and Nonlinear system.
His Algorithm research incorporates themes from Fractional factorial design and Two factor interaction.
Between 2017 and 2021, his most popular works were:
- Age-related arabinoxylan hydrolysis and fermentation in the gastrointestinal tract of broilers fed wheat-based diets (23 citations)
- The impact of steeping, germination and hydrothermal processing of wheat (Triticum aestivum L.) grains on phytate hydrolysis and the distribution, speciation and bio-accessibility of iron and zinc elements (19 citations)
- Study into the effect of microfluidisation processing parameters on the physicochemical properties of wheat (Triticum aestivum L.) bran (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Biochemistry
- Machine learning
His main research concerns Food science, Mathematical optimization, Optimal design, Nannochloropsis and Tomato puree.
Peter Goos has included themes like Dry matter, Arabinoxylan, Zinc, Germination and Phytase in his Food science study.
His study in the field of Integer programming is also linked to topics like Dirichlet distribution.
His studies deal with areas such as Focus and Nonlinear system as well as Optimal design.
Peter Goos interconnects Lipid oxidation and Carotenoid in the investigation of issues within Nannochloropsis.
His Product research includes themes of Design of experiments and Blocking.
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