Delft University of Technology

About: Delft University of Technology is a education organization based out in Delft, Zuid-Holland, Netherlands. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 37681 authors who have published 94404 publications receiving 2741710 citations. The organization is also known as: TU-Delft & Technische Hogeschool Delft.

Preparation, optimization, and structures of cross-linked enzyme aggregates (CLEAs).

Abstract: The broad applicability of the cross-linking of enzyme aggregates to the effective immobilisation of enzymes is demonstrated and the influence of many parameters on the properties of the resulting CLEAs is determined. The relative simplicity of the operation ideally lends itself to high-throughput methodologies. The aggregation method was improved up to 100% activity yield for any enzyme. For the first time, the physical structures of CLEAs are elucidated.

The annealing algorithm

Abstract: 1 The Annealing Algorithm: A Preview.- 1.1 Combinatorial optimization.- 1.2 Moves and local minima.- 1.3 Hill climbing.- 1.4 Simulated annealing.- 1.5 Applications.- 1.6 Mathematical model.- 1.7 Discussion.- 2 Preliminaries from Matrix Theory.- 2.1 Matrices. Notation and basic properties.- 2.2 Pseudo-diagonal normal forms.- 2.3 Norms and limits of matrices.- 2.4 Quadratic forms.- 2.5 Discussion.- 3 Chains.- 3.1 Terminology.- 3.2 Linear arrangement, an example.- 3.3 The chain limit theorem.- 3.4 Reversible chains.- 3.5 Discussion.- 4 Chain Statistics.- 4.1 Density Functions.- 4.2 Expected values.- 4.3 Sampling.- 4.4 Maximum likelyhood densities.- 4.5 Aggregate functions.- 4.6 Discussion.- 5 Annealing Chains.- 5.1 Towards low scores.- 5.2 Maximal accessibility.- 5.3 The acceptance function.- 5.4 Properties of annealing chains.- 5.5 Discussion.- 6 Samples from Normal Distributions.- 6.1 Characteristic functions.- 6.2 Quadratic forms and characteristic functions.- 6.3 Sampling distributions.- 6.4 Asymptotic properties of sampling distributions.- 6.5 Discussion.- 7 Score Densities.- 7.1 The density of states.- 7.2 Weak control.- 7.3 Strong control.- 7.4 Three parameter aggregates.- 7.5 Discussion.- 8 The Control Parameter.- 8.1 Initialization.- 8.2 Decrements in the control parameter.- 8.3 A stop criterion.- 8.4 Proper convergence.- 8.5 Discussion.- 9 Finite-Time Behavior of the Annealing Algorithm.- 9.1 Rate of convergence of chains.- 9.2 Minimum number of iterations.- 9.3 Finite-time optimal schedules.- 9.4 Discussion.- 10 The Structure of the State Space.- 10.1 Chain convergence.- 10.2 The topography of the state space.- 10.3 The set of moves.- 10.4 Global convergence.- 10.5 Discussion.- 11 Implementation Aspects.- 11.1 An implementation.- 11.2 The selection function.- 11.3 Other speed-up methods.- References.

Optimal Aluminum-Assisted Mesoporosity Development in MFI Zeolites by Desilication

Abstract: Tetrahedrally coordinated aluminum in MFI zeolite frameworks controls the mechanism of intracrystalline mesopore formation by desilication in alkaline medium. The optimal molar Si/Al ratio in the range of 25−50 enables a controlled mesoporosity development with preserved crystalline and acidic properties, potentially leading to a more efficient zeolite utilization by improved diffusion characteristics.

Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Abstract: Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented.