The cost of recovery protocols in web-based database systems

The cost of recovery protocols is important with respect to system performance during normal operation and failure in terms of overhead, and time taken to recover failed transactions. The cost of recovery protocols for web database systems has not been addressed much. In this paper, we present a quantitative study of cost of recovery protocols. For this purpose, we use an experiment setup to evaluate the performance of two recovery algorithms, namely the, two-phase commit algorithm and log-based algorithm. Our work is a step towards building reliable protocols for web database systems.

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a “spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in “spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Mechanical properties of micro-porous metals produced by space-holding sintering

Micro-porous nickel foams with an open cell structure were fabricated by the space-holding sintering. The average pore size of the micro-porous nickel specimens ranged from 30 μm to 150 μm, and the porosity ranged from 60 % to 80 %. The porous characteristics of the nickel specimens were observed using scanning electron microscopy (SEM). The mechanical properties were studied using compressive tests. For comparison, macro-porous nickel foams prepared by the chemical vapour deposition method with pore sizes of 800 μm and 1300 μm and porosity of 95 % were also presented. Results indicated that the ratio value of 6 and higher for the specimen length to cell size (L/d) is satisfying for obtaining stable compressive properties. The micro-porous nickel specimens exhibited different deformation behaviour and dramatically increased mechanical properties, compared to those of the macro-porous nickel specimens.

Effects of pore morphology on the compressive behaviour of porous metals

In the present study, porous nickel foams with three different porosities (i.e. 50 %, 60 % and 70 %) were fabricated using the space-holding sintering method. Ammonium bicarbonate particles with sizes ranging from 1- 2 mm were chosen as the space-holding material. The anisotropic behaviours of the nickel foam samples were investigated by compressive testing loading at different directions, i.e., in both directions of the major and minor axis of ellipsoidal cells. Electron scanning microscopy (SEM) and Image-Pro Plus was used to characterise the morphological characteristics of the porous nickel foam samples. Results indicated that the porous nickel foam samples exhibited obvious anisotropic mechanical properties. The foam sample shows significantly higher nominal stress for loading in the direction of the major axis of the pores than loading in the direction of the minor axis of pores. The nominal stress increases with the decreasing of the porosity.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Simple characterizations of the Nash and Kalai/Smorodinsky solutions

In this study we introduce two new properties, the Midpoint Outcome on a Linear Frontier (MOLF) and Balanced Focal Point (BFP) properties, to replace the Weak Pareto Optimality (WPO), Symmetry (SYM) and Independence of Equivalent Utility Representations (IEUR) properties in the axiomatic characterizations of the two most prominent solution concepts, namely the Nash and Kalai/Smorodinsky solutions, respectively.

High strain rate tensile properties of rapidly cured woven carbon fibre composites

Tensile tests at high speeds corresponding to automotive crash events were conducted to understand the dynamic properties of rapidly cured woven carbon fiber composites. The High Strain Rate (HSR) experiments were conducted on a servo-hydraulic machine at constant velocities up to a maximum of 25 m/s (82 ft/s). Results from HSR tests were compared with the static results to determine the rate sensitivity of the composite. A high speed camera was used to capture the failure at HSR. The tensile properties of rapidly cured laminate were compared to oven cured laminate to justify its productivity while maintaining the desired properties. The methodology used to achieve constant velocity during HSR tests is discussed in detail. The specimen geometry was specially designed to suit the test rig and to achieve high speeds during tests. All the specimens failed with linear elasticity until sudden brittle fracture. The Scanning Electron Microscopy (SEM) images of the fracture zone were used to identify the failure modes observed at static and high strain rates.

The use of heuristics in valuation practice : implications in a changing market

Despite the existence of prescribed frameworks, valuation remains a cause of much controversy and variety of opinion. It does not matter whether procedures are undertaken in exactly the same way, the conclusion of ‘value’ will vary from valuer to valuer – sometimes considerably. This uncertainty within valuation is founded on property’s heterogeneous nature and the imperfect market that is the property market; in addition to the unpredictability of human behaviour in making judgements (French and Gabrielli 2004). Uncertainty, in valuation is found in the amalgam of locational, physical and legal characteristics and innumerable other forces which control and energise the property market (Whipple 1995). Particular irregular occurrences, or drastic changes in property markets, from either within market evolution or external forces, for example the creation of global financial markets, cause further uncertainty for valuers and provides challenges in identifying ‘market value’ in valuation practice. The praxis of valuation in a commercial sense navigates this complexity using a combination of algorithms and heuristics to identify the value of a property. The application of theoretical mathematical algorithms based on economic theory (Brown 1995), is augmented by valuers’ ability to apply appropriate adjustment based on their knowledge of the market, their ability to analyse, assess and compare the attributes of a property in comparison to its market, and their practical experience (Sliogeriene 2008). Despite the necessity of algorithms, the application of appropriate adjustments and assumptions are important in arriving at a value. This paper is a critical reflection on the basis of valuation practice as guided by standards, methods, and ethics (algorithms), and the use of heuristics in practice. This is important because changes within property markets challenge the inter-relationship between these two aspects of valuation practice. Through the authors’ industry experience and a review of previous research and statements of practice norms this paper provides an analysis of the ability of valuers to address market change in their valuation practices.

Structure and properties of biomedical films prepared from aqueous and acidic silk fibroin solutions

Silk fibroin films are promising materials for a range of biomedical applications. To understand the effects of casting solvents on film properties, we used water (W), formic acid (FA), and trifluoroacetic acid (TFA) as solvents. We characterized molecular weight, secondary structure, mechanical properties, and degradation behavior of cast films. Significant degradation of fibroin was observed for TFA-based film compared to W and TA-based films when analyzed by SDS-PAGE. Fibroin degradation resulted in a significant reduction in tensile strength and modulus of TFA-based films. Compared to water, TFA-based films demonstrated lower water solubility (19.6% vs. 62.5% in 12 h) despite having only a marginal increase in their ß-sheet content (26.9% vs. 23.7%). On the other hand, FA-based films with 34.3% ß-sheet were virtually water insoluble. Following solubility treatment, ß-sheet content in FA-based films increased to 50.9%. On exposure to protease XIV, water-annealed FA-based films lost 74% mass in 22 days compared to only 30% mass loss by ethanol annealed FA films. This study demonstrated that a small variation in the ß-sheet percentage and random coil conformations resulted in a significant change in the rates of enzymatic degradation without alteration to their tensile properties. The film surface roughness changed with the extent of enzymatic hydrolysis.

Beyond the topological map : developing alternate mappings in self organisation

The self organising map is a well established unsupervised
learning technique which is able to form sophisticated representations of an input data set. However, conventional Self Organising Map (SOM) algorithms are limited to the production of topological maps — that is, maps where distance between points on the map have a direct relationship to the Euclidean distance between the training vectors corresponding to those points.

It would be desirable to be able to create maps which form clusters on primitive attributes other than Euclidean distance; for example, clusters based upon orientation or shape. Such maps could provide a novel approach to pattern recognition tasks by providing a new method to associate groups of data.

In this paper, it is shown that the type of map produced by SOM algorithms is a direct consequence of the lateral connection strategy employed. Given this knowledge, a technique is required to establish the feasability of using an alternative lateral connection strategy. Such a technique is presented. Using this technique, it is possible to rule out lateral connection strategies that will not produce output states useful to the organisation process. This technique is demonstrated using conventional Laplacian interconnection as well as a number of novel interconnection strategies.

Improving fiber trapping with a contact surface during the ring twisting of two cotton yarns

In this study, a geometrical model was introduced to improve the hair trapping via a surface contacting the yarn-twisting triangle during ring twisting of two single yarns. The fiber-trapping improvement with the contact surface was analyzed theoretically. Then, single Ne 80 ring cotton yarns were used to produce two-ply yarns under different ring-twisting conditions, namely conventional twisting, dry twisting of yarns with a plane surface, wet twisting of yarns with a plane surface, dry twisting of yarns with a grooved surface, and wet twisting of yarns with a grooved surface. Plied yarn properties, including yarn hairiness, strength, and irregularity, were tested. The Student Newman Keuls (SNK) test and variation analysis were also carried out in the SPSS program to study the effect of different contact surfaces on related yarn properties; the significance level was 0.05 for the SNK test and variation analysis. The hairiness of plied yarns was significantly reduced when twisting with the plane or grooved surface, especially for the wet twisting cases. This corresponds well with our model on improving fiber trapping.

Development of an intelligent system for the solder paste printing process

A neurogenetic-based hybrid framework is developed where the main components within the framework are artificial neural networks (ANNs) and genetic algorithms (GAs). The investigation covers a mode of combination or hybridisation between the two components that is called task hybridisation. The combination between ANNs and GAs using task hybridisation leads to the development of a hybrid multilayer feedforward network, trained using supervised learning. This paper discusses the GA method used to optimize the process parameters, using the ANN developed as the process mode, in a solder paste printing process, which is part of the process in the surface mount technology (SMT) method. The results obtained showed that the GA-based optimization method works well under various optimization criteria

On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models

Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc.

Evaluation of segmentation algorithms for extraction of RNFL in OCT images

The thickness of the retinal nerve fiber layer (RFNL) has become a diagnose measure for glaucoma assessment. To measure this thickness, accurate segmentation of the RFNL in optical coherence tomography (OCT) images is essential. Identification of a suitable segmentation algorithm will facilitate the enhancement of the RNFL thickness measurement accuracy. This paper investigates the performance of six algorithms in the segmentation of RNFL in OCT images. The algorithms are: normalised cuts, region growing, k-means clustering, active contour, level sets segmentation: Piecewise Gaussian Method (PGM) and Kernelized Method (KM). The performance of the six algorithms are determined through a set of experiments on OCT retinal images. An experimental procedure is used to measure the performance of the tested algorithms. The measured segmentation precision-recall results of the six algorithms are compared and discussed.

Transition to motherhood in type 1 diabetes : design of the pregnancy and postnatal well-being in transition questionnaires

Background : Life transitions are associated with high levels of stress affecting health behaviours among people with Type 1 diabetes. Transition to motherhood is a major transition with potential complications accelerated by pregnancy with risks of adverse childbirth outcomes and added anxiety and worries about pregnancy outcomes. Further, preparing and going through pregnancy requires vigilant attention to a diabetes management regimen and detailed planning of everyday activities with added stress on women. Psychological and social well-being during and after pregnancy are integral for good pregnancy outcomes for both mother and baby. The aim of this study is to establish the face and content validity of two novel measures assessing the well-being of women with type 1 diabetes in their transition to motherhood, 1) during pregnancy and 2) during the postnatal period.

Methods : The approach to the development of the Pregnancy and Postnatal Well-being in T1DM Transition questionnaires was based on a four-stage pre-testing process; systematic overview of literature, items development, piloting testing of questionnaire and refinement of questionnaire. The questionnaire was reviewed at every stage by expert clinicians, researchers and representatives from consumer groups. The cognitive debriefing approach confirmed relevance of issues and identified additional items.

Results : The literature review and interviews identified three main areas impacting on the women’s postnatal self-management; (1) psychological well-being; (2) social environment, (3) physical (maternal and fetal) well-being. The cognitive debriefing in pilot testing of the questionnaire identified that immediate postnatal period was difficult, particularly when the women were breastfeeding and felt depressed.

Conclusions : The questionnaires fill an important gap by systematically assessing the psychosocial needs of women with type 1 diabetes during pregnancy and in the immediate postnatal period. The questionnaires can be used in larger data collection to establish psychometric properties. The questionnaires potentially play a key role in prospective research to determine the self-management and psychological needs of women with type 1 diabetes transitioning to motherhood and to evaluate health education interventions.