THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Mitigation and Adaptation Strategies for Global Change

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

http://www.soft-computing.de/SECPublished.pdf

Surrogate-assisted evolutionary computation: Recent advances and future challenges

This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics

Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

As the name suggests, multi-objective optimisation involves optimising a number of objectives simultaneously. The problem becomes challenging when the objectives are of conflicting characteristics to each other, that is, the optimal solution of an objective function is different from that of the other. In the course of solving such problems, with or without the presence of constraints, these problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solutions. Because of the multiplicity in solutions, these problems were proposed to be solved suitably using evolutionary algorithms using a population approach in its search procedure. Starting with parameterised procedures in early 90s, the so-called evolutionary multi-objective optimisation (EMO) algorithms is now an established field of research and application with many dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this chapter, we provide a brief introduction to its operating principles and outline the current research and application studies of evolutionary multi-objective optmisation (EMO).

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Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction

Saccharification is the most crucial and cost-intensive process in second generation biofuel production. The deficiency of β-glucosidase in commercial enzyme leads to incomplete biomass hydrolysis. The decomposition of biomass at high temperature environments leads us to isolate thermotolerant microbes with β-glucosidase production potential. A total of 11 isolates were obtained from compost and cow dung samples that were able to grow at 50 °C. On the basis of qualitative and quantitative estimation of β-glucosidase enzyme production, Bacillus subtilis RA10 was selected for further studies. The medium components and growth conditions were optimized and β-glucosidase enzyme production was enhanced up to 19.8-fold. The β-glucosidase from B. subtilis RA10 retained 78% of activity at 80 °C temperature and 68.32% of enzyme activity was stable even at 50 °C after 48 h of incubation. The supplementation of β-glucosidase from B. subtilis RA10 into commercial cellulase enzyme resulted in 1.34-fold higher glucose release. Furthermore, β-glucosidase was also functionally elucidated by cloning and overexpression of full length GH1 family β-glucosidase gene from B. subtilis RA10. The purified protein was characterized as thermostable β-glucosidase enzyme. The thermostable β-glucosidase enzyme from B. subtilis RA10 would facilitate efficient saccharification of cellulosic biomass into fermentable sugar. Consequently, after saccharification, thermostable β-glucosidase enzyme would be recovered and reused to reduce the cost of overall bioethanol production process.

/pdf/bioprospecting-of-novel-thermostable-b-glucosidase-from-1wd8a4s1yo.pdf

Bioprospecting of novel thermostable β-glucosidase from Bacillus subtilis RA10 and its application in biomass hydrolysis.

Parthenium sp. is a noxious weed which threatens the environment and biodiversity due to its rapid invasion. This lignocellulosic weed was investigated for its potential in biofuel production by subjecting it to mild alkali pretreatment followed by enzymatic saccharification which resulted in significant amount of fermentable sugar yield (76.6%). Optimization of enzymatic hydrolysis variables such as temperature, pH, enzyme, and substrate loading was carried out using central composite design (CCD) in response to surface methodology (RSM) to achieve the maximum saccharification yield. Data obtained from RSM was validated using ANOVA. After the optimization process, a model was proposed with predicted value of 80.08% saccharification yield under optimum conditions which was confirmed by the experimental value of 85.80%. This illustrated a good agreement between predicted and experimental response (saccharification yield). The saccharification yield was enhanced by enzyme loading and reduced by temperature and substrate loading. This study reveals that under optimized condition, sugar yield was significantly increased which was higher than earlier reports and promises the use of Parthenium sp. biomass as a feedstock for bioethanol production.

/pdf/optimization-of-enzymatic-saccharification-of-alkali-4jte4m1gph.pdf

Optimization of Enzymatic Saccharification of Alkali Pretreated Parthenium sp. Using Response Surface Methodology

This paper presents a concept of combining genetic algorithms (GAs) with an approximate evaluation technique to achieve a computationally effective search and optimization procedure. The major objective of this work is to enable the use of GAs on computationally expensive problems, while retaining their basic robust search capabilities. Starting with a coarse approximation model of the problems, GAs successively use finer models, thereby allowing the proposed algorithm to find the optimal or a near-optimal solution of computationally expensive problems faster. A general methodology is proposed for combining any approximating technique with GA. The proposed methodology is also tested in conjunction with one particular approximating technique, namely the artificial neural network, on a B-spline curve fitting problem successfully. Savings in the exact function evaluation up to 32% are achieved. The computational advantage demonstrated here should encourage the use of the proposed approach to more complex and computationally demanding real-world problems.

Computationally effective search and optimization procedure using coarse to fine approximations

The conversion of cellulosic biomass into monomeric sugars is the most costly and energy intensive process in bioethanol production. Cold active cellulases produced by psychrophilic microorganisms can counter this limitation by hydrolyzing the biomass at low temperature. Enrichment of soil samples collected from the Himalayan region of India, at 5 °C and molecular identification revealed the prominence of phylum ascomycota. The hydrolytic potential of all isolates in terms of cellulases and xylanases were tested at different temperatures and isolate A. niger SH3 which exhibited significantly higher enzyme production at low temperature. Thereafter, experimental mixture design was employed to study the influence of different carbon sources ( e.g . wheat bran, wheat straw and paddy straw). A. niger SH3 gave highest titer of endoglucanase (655.94 IU/g), β -glucosidase (540.57 IU/g), FPase (31.48 IU/g) and xylanase (1951.63 IU/g) under the optimized conditions after 7 days of solid state fermentation. Hydrolysis of pretreated paddy straw using the crude hydrolytic enzymes (15 FPU/gds) of A. niger SH3 at 40 °C resulted in maximum sugar yield (375.84 ± 0.23 mg/gds). This study proves the potential of A. niger SH3 secretome for efficient biomass hydrolysis at low temperature and its suitability for developing an energy efficient biofuel production process.

Pawan Kumar Singh Nain

Papers

Bioprospecting of novel thermostable β-glucosidase from Bacillus subtilis RA10 and its application in biomass hydrolysis.

Optimization of Enzymatic Saccharification of Alkali Pretreated Parthenium sp. Using Response Surface Methodology

Computationally effective search and optimization procedure using coarse to fine approximations

Cold active holocellulase cocktail from Aspergillus niger SH3: process optimization for production and biomass hydrolysis

An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks