Computational simulation of heat transfer from alternate heating sources

The alternant heat transfer induced by particle packet and gas bubbles on an object surface in a gas fluidised bed is computationally studied. The particle packet and bubble are modelled by a DPPM (double particle-layer and Porous Medium) model and a hemispherical model, respectively. Different meshing schemes are applied and different mesh sizes are used in meshing particle packet and heated object and a very large geometrical size difference between them was considered. Two parallel solver processes were proposed to perform the simulation of heat transfer for different purposes and implemented with the Fluent CFD package.

Analyzing inconsistency toward enhancing integration of biological molecular databases

The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.

Interacting amino acid preferences of 3D pattern pairs at the binding sites of transient and obligate protein complexes

To assess the physico-chemical characteristics of protein-protein interactions, protein sequences and overall structural folds have been analyzed previously. To highlight this, discovery and examination of amino acid patterns at the binding sites defined by structural proximity in 3-dimensional (3D) space are essential. In this paper, we investigate the interacting preferences of 3D pattern pairs discovered separately in transient and obligate protein complexes. These 3D pattern pairs are not necessarily sequence-consecutive, but each residue in two groups of amino acids from two proteins in a complex is within certain °A threshold to most residues in the other group. We develop an algorithm called AA-pairs by which every pair of interacting proteins is represented as a bipartite graph, and it discovers all maximal quasi-bicliques from every bipartite graph to form our 3D pattern pairs. From 112 and 2533 highly conserved 3D pattern pairs discovered in the transient and obligate complexes respectively, we observe that Ala and Leu is the highest occuring amino acid in interacting 3D patterns of transient (20.91%) and obligate (33.82%) complexes respectively. From the study on the dipeptide composition on each side of interacting 3D pattern pairs, dipeptides Ala-Ala and Ala-Leu are popular in 3D patterns of both transient and obligate complexes. The interactions between amino acids with large hydrophobicity difference are present more in the transient than in the obligate complexes. On contrary, in obligate complexes, interactions between hydrophobic residues account for the top 5 most occuring amino acid pairings.

Lightweight authentication for recovery in wireless sensor networks

Wireless sensor networks (WSNs) suffer from a wide range of security attacks due to their limited processing and energy capabilities. Their use in numerous mission critical applications, however, requires that fast recovery from such attacks be achieved. Much research has been completed on detection of security attacks, while very little attention has been paid to recovery from an attack. In this paper, we propose a novel, lightweight authentication protocol that can secure network and node recovery operations such as re-clustering and reprogramming. Our protocol is based on hash functions and we compare the performance of two well-known lightweight hash functions, SHA-1 and Rabin. We demonstrate that our authentication protocol can be implemented efficiently on a sensor network test-bed with TelosB motes. Further, our experimental results show that our protocol is efficient both in terms of computational overhead and execution times which makes it suitable for low resourced sensor devices.

Physiological response to extreme fasting in subantarctic fur seal (Arctocephalus tropicalis) pups : metabolic rates, energy reserve utilization, and water fluxes

Physiological response to extreme fasting in subantarctic fur seal (Arctocephalus tropicalis) pups: metabolic rates, energy reserve utilization, and water fluxes. Am J Physiol Regul Integr Comp Physiol 297: R1582–R1592, 2009. First published September 23, 2009; doi:10.1152/ajpregu.90857.2008.— Surviving prolonged fasting requires various metabolic adaptations, such as energy and protein sparing, notably when animals are simultaneously engaged in energy-demanding processes such as growth. Due to the intermittent pattern of maternal attendance, subantarctic fur seal pups have to repeatedly endure exceptionally long fasting episodes throughout the 10-mo rearing period while preparing for nutritional independence. Their metabolic responses to natural prolonged fasting (33.4 ± 3.3 days) were investigated at 7 mo of age. Within 4–6 fasting days, pups shifted into a stage of metabolic economy characterized by a minimal rate of body mass loss (0.7%/day) and decreased resting metabolic rate  (5.9 ± 0.1 ml O₂ ·kg^-1·day^-1) that was only 10% above the level predicted for adult terrestrial mammals. Field metabolic rate (289 ± 10 kJ·kg^-1 ·day^-1) and water influx (7.9 ± 0.9 ml·kg^-1 ·day^-1) were also among the lowest reported for any young otariid, suggesting minimized energy allocation to behavioral activity and thermoregulation. Furthermore, lean tissue degradation was dramatically reduced. High initial adiposity (>48%) and predominant reliance on lipid catabolism likely contributed to the exceptional degree of protein sparing attained. Blood chemistry supported these findings and suggested utilization of alternative fuels, such as β-hydroxybutyrate and de novo synthesized glucose from fat-released glycerol. Regardless of sex and body condition, pups tended to adopt a convergent strategy of extreme energy and lean body mass conservation that appears highly adaptive for it allows some tissue growth during the repeated episodes of prolonged fasting they experience throughout their development.

An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.

The ranking of factors influencing the behaviour of light structures on expansive soils in Victoria, Australia

In this paper a Neural Network Model was used to develop a ranking of the potential damage influences for light structures on expansive soils in Victoria. These influences include geology, Thornthwaite moisture index, vegetation covers, construction foundation type, construction wall type, geographical region and age of building when first inspected. Approximately 400 cases of damage to light structures in Victoria, Australia were considered in this study. Feedforward Backpropagation was adopted to train the data. The ranking of importance was estimated using connection weight approach and then compared to results calculated from sensitivity analysis. From the analysis, the ranking of importance for potential damage factor was noted.