Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber

Thermal stabilization process of polyacrylonitrile (PAN) is the slowest and the most energy-consuming step in carbon fiber production. As such, in industrial production of carbonfiber, this step is considered as amajor bottleneck in the whole process. Stabilization process parameters are usually many in number and highly constrained, leading to high uncertainty. The goal of this paper is to study and analyze the carbon fiber thermal stabilization process through presenting several effective dynamic models for the prediction of the process. The key point with using dynamic models is that using an evolutionary search technique, the heat of reaction can be optimized. The employed components of the study are Levenberg–Marquardt algorithm (LMA)-neural network (LMA-NN), Gauss–Newton (GN)-curve fitting, Taylor polynomial method, and a genetic algorithm. The results show that the procedure can effectively optimize a given PAN fiber heat of reaction based on determining the proper values of heating rampand temperature

Optimization of laccase production from Trametes versicolor by solid fermentation

The regulation of culture conditions, especially the optimization of substrate constituents, is crucial for laccase production by solid fermentation. To develop an inexpensive optimized substrate formulation to produce high-activity laccase, a uniform design formulation experiment was devised. The solid fermentation of Trametes versicolor was performed with natural aeration, natural substrate pH (about 6.5), environmental humidity of 60% and two different temperature stages (at 37 °C for 3 days, and then at 30 °C for the next 17 days). From the experiment, a regression equation for laccase activity, in the form of a second-degree polynomial model, was constructed using multivariate regression analysis and solved with unconstrained optimization programming. The optimized substrate formulation for laccase production was then calculated. Tween 80 was found to have a negative effect on laccase production in solid fermentation; the optimized solid substrate formulation was 10.8% glucose, 27.7% wheat bran, 9.0% (NH4)2SO4, and 52.5% water. In a scaled-up verification of solid fermentation at a 10 kg scale, laccase activity from T. versicolor in the optimized substrate formulation reached 110.9 IU/g of dry mass.

Isolation and characterization of polyunsaturated fatty acid producing Thraustochytrium species : screening of strains and optimization of omega-3 production

An isolation program targeting Thraustochytrids (marine fungoid protists) from 19 different Atlantic Canadian locations was performed. Sixty-eight isolates were screened for biomass, total fatty acid (TFA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) content. Analysis of fatty acid methyl ester results discerned four distinctive clusters based on fatty acid profiles, with biomass ranging from 0.1 to 2.3 g L−1, and lipid, EPA, and DHA contents ranging from 27.1 to 321.14, 2.97 to 21.25, and 5.18 to 83.63 mg g−1 biomass, respectively. ONC-T18, was subsequently chosen for further manipulations. Identified using 18S rRNA gene sequencing techniques as a Thraustochytrium sp., most closely related to Thraustochytrium striatum T91-6, ONC-T18 produced up to 28.0 g L−1 biomass, 81.7% TFA, 31.4% (w/w biomass) DHA, and 4.6 g L−1 DHA under optimal fermentation conditions. Furthermore, this strain was found to produce the carotenoids and xanthophylls astaxanthin, zeaxanthin, canthaxanthin, echinenone, and β-carotene. Given this strain’s impressive productivity when compared to commercial strains, such as Schizochytrium sp. SR21 (which has only 50% TFA), coupled with its ability to grow at economical nitrogen and very low salt concentrations (2 g L−1), ONC-T18 is seen as an ideal candidate for both scale-up and commercialization.

Optimization of process parameters for the production of naringinase by Aspergillus niger MTCC 1344

Aspergillus niger MTCC 1344 was used to produce extracellular naringinase in a complex (molasses, yeast extract and salts) medium. An initial medium pH 4.5 and cultivation temperature 30 °C were optimal for enzyme production. Among various carbon and organic nitrogen sources used, molasses and peptone were the most effective for enzyme yield. The rate of enzyme production was enhanced when metal ions were added to the medium. Fermentation conditions are described which produced a higher rate of enzyme synthesis. An increase in initial sugar concentration from 6 to 10 g l⁻¹ in the fermentation medium produced decreased naringinase synthesis while cell mass growth increased with the increase of sugar concentration. At a higher sugar level (10 g l⁻¹) the production of cell mass decreased.

Optimization of medium and process parameters for the production of inulinase from a newly isolated Kluyveromyces marxianus YS-1

A newly isolated strain of Kluyveromyces marxianus YS-1 was used for the production of extra cellular inulinase in a medium containing inulin, meat extract, CaCl₂ and sodium dodecyl sulphate (SDS). Fermentation medium pH 6.5, cultivation temperature 30 °C and 5% (v/v) inoculum of 12 h-old culture were optimal for enzyme production (30.8 IU/ml) with a fermentation time of 72 h at shake flask level. Raw inulin (2%, w/v) extracted from dahlia tubers by processing at 15 kg/cm² for 10 min was optimum for bioreactor studies. Maximum enzyme production (55.4 IU/ml) was obtained at an agitation rate of 200 rpm and aeration of 0.75 vvm in a stirred tank reactor with a fermentation time of 60 h.

Investigation into the interactive multimedia production processes of novice multimedia designer /producers in a constructivist environment

The main purpose of this study was to investigate the instructional interactive multimedia (IMI) production processes of adult novice multimedia production course. The study aimed at discovering whether a constructivistic teacher-learning environment facilitated these multimedia novice designer / producers to further develop metacognitivestates of higher-order thinking like schema formation, problem-solving and cognitive construction when producing their interactive multimedia project. To achieve this study examined the facilitative and limiting activities in planning, design and development that have assisted or hindered the NMDPs during their multimedia production work This research utilises a qualitative paradigm and makes extensive use of multiple data sources such as the participants’ proposals, planning aids, logs and final projects for single as well as cross-case analyses and discussion. Three cases were selected for in-depth analysis in the study because they provided interviews and more complete documentation and "thick descriptions" of their multimedia production activities. Findings about the NMDPs multimedia production endeavours showed that they learnt best about multimedia technology for teacing and learning by producing an interactive multimedia project themselves. Factors that enabled some of the NMDPs to flourish in a constructivistic environment included their ability to utilise their new and extended schemata to problem solve, their self regulation and a creative and positive attitude to demanding multimedia work NMDPs who utilised facilitative planning aids and design strategies produced impressive work. The study further indicates that the NMDPs’ holistic multimedia production experience made them aware of the levels of complexity involved and boosted their confidence about multimedia production for educational purposes. Suggestions for further research include examining the production styles of adult multimedia novices and young novices and investigating the implications of designing multimedia for large audience presentation rather than for single or small group learner usage.

Practice chains of production and consumption : mediatized practices across social fields

The argument developed in this paper is that a focus on practice provides some resolutions to methodological problems facing Bourdieuian scholarship in education. In order to develop Bourdieu's work on practice to account for the interactions between practices, this paper presents a conceptualization of practice as chains of production and consumption. The first part of the paper reviews the account of practice offered by Bourdieu both embedded in practice games and as field effects. The second part of the paper introduces practice chains of production and consumption as a way to conceptualize practice by drawing on a case involving print journalists' involvement with policy makers over the course of an Australian policy review. The final section presents a discussion of this conceptualization and highlights the potential of the concept for further research in understanding the processes of educational policy development.

Effectiveness of evolutionary algorithms for optimization of heat exchangers

This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. ε-NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes.

Yield optimization of a heterologously expressed novel mouse macrophage protein

The large scale whole-genome sequencing projects have resulted in large numbers of un-characterized and un-annotated protein sequences. This presents an opportunity and a challenge to characterise these novel protein sequences. Structural biology has become a widely accepted methodology to help assign functions to such proteins, complementing other cellular and biochemical studies. However, most of these studies require the target protein to be produced in large quantities and in a highly pure and soluble state. The present study is an attempt to maximise production of a recombinant mouse macrophage protein (rMMP) over-expressed heterologously in Escherichia coli. Highest production of biomass and total protein (6.6 mg mL-1) was observed at 37 °C. Maximum cell disruption (89%) was observed during freeze-thawing and subsequent ultrasonication.

Scaling relationships of Gaussian processes

Asset returns conforming to a Gaussian random walk are characterised by the temporal independence of the moments of the distribution. Employing currency returns, this note demonstrates the conditions that are necessary for risk to be estimated in this manner.

Will it work? an initial examination of the processes and outcomes of converting course materials to CD-ROMs

The Faculty of Business and Law at Deakin University (Victoria, Australia) decided to dispense of all printed post-graduate learning materials and replace them with CD-ROMs from the commencement of the 2006 academic year. In addition, CD-ROMs were developed for a limited
number of undergraduate units as part of a future delivery plan for this cohort of students. The following paper describes this project, the reasons underpinning it, and the processes the Faculty adopted to implement the project. The project is ongoing and part of a broader agenda for change
that will see an even greater application of electronic technology to teaching and learning within the Faculty. Although only initial findings and observations are possible at this stage, the project provides a basis for longitudinal reporting and, potentially, a guide for other institutions who may
be considering such a move. The paper reports on these observations and on those in the educational development arena and suggests that the Faculty will need to learn from these initial experiences and evaluate the project in greater depth to guarantee a smooth transition for all stakeholders.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

Optimization of MLP parameters on mineral potential mapping tasks

Mineral potential mapping is the process of combining a set of input maps, each representing a distinct geo-scientific variable, to produce a single map which ranks areas according to their potential to host deposits of a particular type. The maps are combined using a mapping function which must be either provided by an expert (knowledge-driven approach), or induced from sample data (data-driven approach). Current data-driven approaches using multilayer perceptrons (MLPs) to represent the mapping function have several inherent problems: they rely heavily on subjective judgment in selecting training data and are highly sensitive to this selection; they do not utilize the contextual information provided by unlabeled data; and, there is no objective interpretation of the values output by the MLP. This paper presents a novel approach which overcomes these three problems.