Sensory adaptability of foods with added Lupin (Lupinus angustifolius) kernel fibre using pre-set criteria

Fiber-enriched white bread, muffin. pasta, orange juice, and breakfast bar were prepared with lupin (Lupin us angusti/olius) kernel fiber. Consumer panelists (n = 44) determined that all these fiber-enriched foods, except orange juice, fulfilled pre-set acceptability criteria. Fiber enrichment did not change overall acceptability (p> 0.05) of the bread and pasta, but reduced overall acceptability (p < 0.05) of the muffin, orange juice, and breakfast bar. In all fiber-enriched products, flavor was the attribute most highly correlated with overall acceptability (p < 0.05). The lupin kernel fiber used in this study therefore appears to have potential as a 'nonintrusive' ingredient in some processed cereal-based foods_ For other applications, fiber modification appears worthy of investigation to accomplish 'nonintrusive' fiber enrichment.

Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene flourescent in situ by hybridization

Background Changes in the composition of gastrointestinal microbiota by dietary interventions using pro- and prebiotics provide opportunity for improving health and preventing disease. However, the capacity of lupin kernel fiber (LKFibre), a novel legume-derived food ingredient, to act as a prebiotic and modulate the colonic microbiota in humans needed investigation.

Aim of the study The present study aimed to determine the effect of LKFibre on human intestinal microbiota by quantitative fluorescent in situ hybridization (FISH) analysis.

Design A total of 18 free-living healthy males between the ages of 24 and 64 years consumed a control diet and a LKFibre diet (containing an additional 17–30 g/day fiber beyond that of the control—incorporated into daily food items) for 28 days with a 28-day washout period in a single-blind, randomized, crossover dietary intervention design.
Methods Fecal samples were collected for 3 days towards the end of each diet and microbial populations analyzed by FISH analysis using 16S rRNA gene-based oligonucleotide probes targeting total and predominant microbial populations.

Results Significantly higher levels of Bifidobacterium spp. (P = 0.001) and significantly lower levels of the clostridia group of C. ramosum, C. spiroforme and C. cocleatum (P = 0.039) were observed on the LKFibre diet compared with the control. No significant differences between the LKFibre and the control diet were observed for total bacteria, Lactobacillus spp., the Eubacterium spp., the C. histolyticum/C. lituseburense group and the Bacteroides–Prevotella group.
Conclusions Ingestion of LKFibre stimulated colonic bifidobacteria growth, which suggests that this dietary fiber may be considered as a prebiotic and may beneficially contribute to colon health.

A legitimate paradox : neo-liberal reform and the return of the state in Korea

This article examines the neo-liberal reforms that the Kim government implemented in post-crisis Korea. It argues that by embracing the reforms, the state, paradoxically, re-legitimised itself in the national political economy. The process of enacting the reforms completed the power shift from a collusive state-chaebol alliance towards a new alliance based on a more populist social contract - but one that nonetheless generally conformed to the tenets of neo-liberalism. Kim and his closest associates identified the malpractices of the chaebols as the main cause of the crisis, so reforming the chaebols would be the key to economic recovery. Combining populism and neo-liberalism, they drew on support from both domestic and international sources to rein in, rather than nurture, the chaebols.

Pricing segmentation and MBA course choice

The consensus from studies of the price-demand relationship for higher education is that this relationship is negative but small. This paper investigates the circumstances in which demand for an MBA is positive to price increases. A survey of currently enrolled MBA students, and prospective MBA students, found that most students displayed the expected price elasticity in a conjoint analysis of hypothetical MBA course ratings. However, 12 per cent of respondents exhibited “reversal” behaviour regarding price. Profiling these respondents using discriminant analysis suggested that “reversals” seemed prepared to pay more for a course at a high prestige university, if they could study off-campus using print-based materials.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

The culture-based fisheries in small, farmer-managed reservoirs in two provinces of northern Vietnam: an evaluation based on three production cycles

In this paper the results of a study conducted on the culture-based fisheries in small (ranging from 2 to 160 ha), farmer-managed reservoirs in YenBai and ThaiNguyen Provinces in the northern highland region of Vietnam, for the production cycles of 1997/98, 98/99 and 99/00 are presented. The small reservoirs are leased to small farmers by the provincial authorities for fishery activities, and all lessees adopt culture-based fisheries when fingerlings of grass carp (Ctenopharyngodon idella), silver carp (Hypophthalmichthys molitrix), bighead carp (Aristichthys nobilis), common carp (Cyprinus carpio) and mrigal (Cirrihinus mrigala) are stocked between March and mid-April each year and harvested, using large seine nets, after approximately 11-12 months. The mean yields from reservoirs in YenBai and ThaiNguyen Provinces in 97/98, 98/99 and 99/00 production cycles were 251, 332 and 253, and 331, 372 and 210 kg ha⁻¹ respectively. There were major differences in the fish productivity in the reservoirs in the two Provinces, and in a reservoir between culture cycles. The stocking strategies appeared to be rather ad hoc, being determined by the availability of seed stock and the financial status of the lessees. Accordingly, there was no apparent consistent trend in the improvement of yields from the culture-based fishery practice throughout the growth cycles. The fish yields in reservoirs in each Province were significantly related to reservoir area (exponentially) and to mean weight of stocked fish and conductivity (logarithmically). Of the stocked fish, the highest returns were obtained with mrigal and bighead carp, which collectively contributed > 50% to the harvest. The return from common carp was the lowest. The mean growth rate of grass carp (2.7 g day⁻¹), followed by bighead carp (2.0 g day⁻¹) was the highest in reservoirs in YenBai Province, bighead carp (4.0 g day⁻¹) followed by grass carp (3.2 g day⁻¹) was the highest in ThaiNguyen Province. The seed stocked on average accounted for 65% and 48% of the total operating costs in YenBai and ThaiNguyen Provinces, and the mean cost:benefit ratio of the culture-based fishery in the two Provinces was 0.35 and 0.37 respectively. The culture-based fishery on average contributed about 28% to the gross income of a farmer lessee.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

OWDEAH: Online Web Data Extraction based on Access History

Web data extraction systems are the kernel of information mediators between users and heterogeneous Web data resources. How to extract structured data from semi-structured documents has been a problem of active research. Supervised and unsupervised methods have been devised to learn extraction rules from training sets. However, trying to prepare training sets (especially to annotate them for supervised methods), is very time-consuming. We propose a framework for Web data extraction, which logged usersrsquo access history and exploit them to assist automatic training set generation. We cluster accessed Web documents according to their structural details; define criteria to measure the importance of sub-structures; and then generate extraction rules. We also propose a method to adjust the rules according to historical data. Our experiments confirm the viability of our proposal.

Culture-based fisheries in non-perennial reservoirs in Sri Lanka: production and relative performance of stocked species

In Sri Lanka, there is a great potential for the development of culture-based fisheries because of the availability of around 12 000 non-perennial reservoirs in the dry zone (<187 cm annual rainfall) of the island. These reservoirs fill during the north-east monsoonal period in October to December and almost completely dry up during August to October. As these non-perennial reservoirs are highly productive, hatchery-reared fish fingerlings can be stocked to develop culture-based fisheries during the water retention period of 7–9 months. The present study was conducted in 32 non-perennial reservoirs in five administrative districts in Sri Lanka. These reservoirs were stocked with fingerlings of Indian (catla Catla catla Hamilton and rohu Labeo rohita Hamilton) and Chinese (bighead carp Aristichthys nobilis Richardson) major carps, common carp Cyprinus carpio L., genetically improved farmed tilapia (GIFT) strain of Nile tilapia, Oreochromis niloticus (L.) and post-larvae of giant freshwater prawn, Macrobrachium rosenbergii De Man, at three different species combinations and overall stocking densities (SD) ranging from 218 to 3902 fingerlings ha⁻¹, during the 2002–2003 culture cycle. Of the 32 reservoirs stocked, reliable data on harvest were obtained from 25 reservoirs. Fish yield ranged from 53 to 1801 kg ha⁻¹ and the yields of non-perennial reservoirs in southern region were significantly (P < 0.05) higher than those in the northern region. Naturally-recruited snakehead species contributed the catches in northern reservoirs. Fish yield was curvilinearly related to reservoir area (P < 0.05), and a negative second order relationship was evident between SD and yield (P < 0.05). Chlorophyll-a and fish yield exhibited a positive second order relationship (P < 0.01). Bighead carp yield impacted positively on the total yield (P < 0.05), whereas snakehead yield impact was negative. Bighead carp, common carp and rohu appear suitable for poly-culture in non-perennial reservoirs. GIFT strain O. niloticus had the lowest specific growth rate among stocked species and freshwater prawn had a low return.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.

Application of radial return mapping algorithm for finite strain elastoplasticity in a hybrid finite element and particle-in-cell model

The radial return mapping algorithm within the computational context of a hybrid Finite Element and Particle-In-Cell (FE/PIC) method is constructed to allow a fluid flow FE/PIC code to be applied solid mechanic problems with large displacements and large deformations. The FE/PIC method retains the robustness of an Eulerian mesh and enables tracking of material deformation by a set of Lagrangian particles or material points. In the FE/PIC approach the particle velocities are interpolated from nodal velocities and then the particle position is updated using a suitable integration scheme, such as the 4th order Runge-Kutta scheme[1]. The strain increments are obtained from gradients of the nodal velocities at the material point positions, which are then used to evaluate the stress increment and update history variables. To obtain the stress increment from the strain increment, the nonlinear constitutive equations are solved in an incremental iterative integration scheme based on a radial return mapping algorithm[2]. A plane stress extension of a rectangular shape J2 elastoplastic material with isotropic, kinematic and combined hardening is performed as an example and for validation of the enhanced FE/PIC method. It is shown that the method is suitable for analysis of problems in crystal plasticity and metal forming. The method is specifically suitable for simulation of neighbouring microstructural phases with different constitutive equations in a multiscale material modelling framework.

Trading activities and cross-sectional variation in stock expected return : evidence from Kuala Lumpur stock exchange

Trading activity has been considered as one of the possible factor that explains the cross-sectional variation in stock returns. In this study I use trading volume as a possible measure to proxy for liquidity as part of the trading activity. Monthly observations were used over a period 1995 to 2005 to examine the liquidity effect on stock expected returns. Based on findings it is appeared that level of liquidity does matter in explaining the expected stock returns in Malaysian capital market. While Fama-french factors also provide important explanation for stock returns. But none of the second moment variables proxying liquidity appeared to be statistically significant. However, momentum effect apprearently explain ing the cross-sectional variation in stock returns. 

Museum pricing in contemporary museums : a hybrid model

What are the museum pricing strategies in contemporary western museums? A large qualitative study on museum pricing decisions was conducted between 2001 and 2009, based on thirty case studies in Canada, United Kingdom, Italy, Spain, France Australia. Results show that the different strategic motivations of price decisions fonn a hybrid model. The hybrid model varies according to unequal organisational learning of the strategic role of pricing in the international museum community. A discussion about these results enables us to understand how this hybrid pricing model in contemporary museums denotes their hybrid transitional identity.

A novel pseudonoise sequence for time-spread echo based audio watermarking

This paper deals with the problem of digital audio watermarking using echo hiding. Compared to many other methods for audio watermarking, echo hiding techniques exhibit advantages in terms of relatively simple encoding and decoding, and robustness against common attacks. The low security issue existing in most echo hiding techniques is overcome in the timespread echo method by using pseudonoise (PN) sequence as a secret key. In this paper, we propose a novel sequence, in conjunction with a new decoding function, to improve the imperceptibility and the robustness of time-spread echo based audio watermarking. Theoretical analysis and simulation examples illustrate the effectiveness of the proposed sequence and decoding function.