Comment on `Spinning loop black holes' [arXiv:1006.0232]

We review the derivations and conclusions made in Caravelli and Modesto (2010 \textit{Class. Quantum Grav.} \textbf{27} 245022, arXiv:1006.0232) and show that most of the analysis performed there is not valid.Comment: 2 pages, 1 figur

Large Scale Inhomogeneity Versus Source Evolution -- Can We Distinguish Them Observationally?

We reconsider the issue of proving large scale spatial homogeneity of the universe, given isotropic observations about us and the possibility of source evolution both in numbers and luminosities. Two theorems make precise the freedom available in constructing cosmological models that will fit the observations. They make quite clear that homogeneity cannot be proven without either a fully determinate theory of source evolution, or availability of distance measures that are independent of source evolution. We contrast this goal with the standard approach that assumes spatial homogeneity a priori, and determines source evolution functions on the basis of this assumption.Comment: mn style, mn.sty file included, mn.sty file remove

Partially locally rotationally symmetric perfect fluid cosmologies

We show that there are no new consistent cosmological perfect fluid solutions when in an open neighbourhood ${\cal U}$ of an event the fluid kinematical variables and the electric and magnetic Weyl curvature are all assumed rotationally symmetric about a common spatial axis, specialising the Weyl curvature tensor to algebraic Petrov type D. The consistent solutions of this kind are either locally rotationally symmetric, or are subcases of the Szekeres dust models. Parts of our results require the assumption of a barotropic equation of state. Additionally we demonstrate that local rotational symmetry of perfect fluid cosmologies follows from rotational symmetry of the Riemann curvature tensor and of its covariant derivatives only up to second order, thus strengthening a previous result.Comment: 20 pages, LaTeX2.09 (10pt), no figures; shortened revised version, new references; accepted for publication in Classical and Quantum Gravit

Factors influencing the distribution of Chl-a along coastal waters of East Peninsular Malaysia

Determination of chlorophyll-a (Chl-a) distribution in the coastal waters is important to understand the coastal environmental conditions. This study was conducted to understand the spatial and temporal distribution of Chl-a along coastal waters of east Peninsular Malaysia and factors influencing its variability using Chl-a data derived from Aqua MODIS satellite (1 km spatial resolution) from January 2006 to December 2012. Chl-a variability was described using empirical orthogonal function (EOF) analysis. In-situ data (temperature, salinity, density and nitrate) and rainfall data from the Department of Meteorology were analyzed using spatial interpolation to determine factors influencing the distribution of Chl-a. The seasonal progressions of Chl-a showed high value during northeast monsoon along the coast. This variability was described by four modes of the EOF analysis. The first mode (72.08% of total variance) indicated seasonal cycle with high variability along the coast. Second mode (17.03% of variance) explained the northeast monsoon with high variability from river mouth to the south. Third mode (2.39% of variance) indicated variability during southwest monsoon along the coast and much higher to the south. Mode 4 (1.93% of variance) explained the inter-monsoon period observed along the northern and southern coastline. Concentration and distribution of Chl-a were related to availability of nutrient influenced by rainfall. The thermohaline front was also observed to play an important role in accumulation of phytoplankton biomass during northeast and southwest monsoon

Ammonia Concentrations in Different Aquaculture Holding Tanks

Ammonia was measured in collapsible pond, concrete tank, and earthen pond of the same size, volume and containing same fish biomass cultured under intensive system. Ammonia was also evaluated from a natural pond under extensive culture. Ammonia was measured in the afternoons for 12 weeks using Nessler method. Temperature and pH were measured in situ using Portable tester. Unionized ammonia was calculated from total ammonia using spreadsheet computation. Result showed total ammonia ranging from 1.4 to 10 mg/l with highest concentration recorded in collapsible pond and lowest found in natural pond. The unionized ammonia concentrations followed the same pattern with concentrations ranging from 0.002 to 1.13 mg/l. The trend in the total ammonia and unionized ammonia concentrations is: collapsible pond > concrete tank > earthen pond > natural pond. Temperature and pH ranged between 29.1 to 35.9 °C and 6.35 to 8.03 respectively, with the highest temperature and pH recorded in the collapsible pond and lowest temperature and pH found in natural pond. Temperature and pH followed seasonal pattern with lowest and highest temperatures and pH recorded at the end of rainy season and in the dry season respectively. High unionized ammonia recorded in the collapsible and concrete ponds was from excretion of high protein rich feed, decomposition of uneaten feed, high stocking density, low water exchange rates, water source and the alkaline medium of the systems. Low unionized ammonia in earthen pond and natural pond was attributed to the presence of phytoplankton, high water exchanges, feeding system, low acidity and relatively low temperature. Remediating measures such as the use of biofilters, aeration and reduction in feeding, temperature and pH should be employed to reduce the high concentration of unionized ammonia

An integrative cancer classification based on gene expression data

The advent of integrative approach has shifted cancer classification task from purely data-centric to incorporate prior biological knowledge. Integrative analysis of gene expression data with multiple biological sources is viewed as a promising approach to classify and to reveal relevant cancer-specific biomarker genes. The identification of biomarker genes can be used as a powerful tool for understanding the complex biological mechanisms, and also for diagnosing and treatment of cancer diseases. However, most integrative-based classifiers only incorporate a single type of biological knowledge with gene expression data within the same analysis. For instance, gene expression data is normally integrated with functional ontology, metabolic pathways, or protein-protein interaction networks, where they are then analysed separately and not simultaneously. Apart from that, current methods generates a large number of candidate genes, which still require further experiments and testing to identify the potential biomarker genes. Hence, this study aims to resolve the problems by proposing a systematic integrative framework for cancer gene expression analysis to the classification task. The association based framework is capable to integrate and analyse multiple prior biological sources simultaneously. Set of biomarker genes that are relevant to the cancer diseases of interest are identified in order to improve classification performance and its interpretability. In this paper, the proposed approach is tested on a breast cancer microarray dataset and integrated with protein interaction and metabolic pathway data. The results shows that the classification accuracy improved if both protein and pathways information are integrated into the microarray data analysis

Bioactive molecule prediction using extreme gradient boosting

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets

A pressure-based method for monitoring leaks in a pipe distribution system: a review

Leakage from a pipe network possibly poses significant environmental destruction and economic losses due to the release of potential energy. While the pipe network may be planned and constructed to satisfy the requirements of rigorous conditions, it is quite hard to avoid the subsequent appearance of leakages in a pipeline during the system's lifetime. Pressure leak detection enables a fast and reliable action response which is necessary to minimise the damage. Many leak detection approaches have been previously suggested. These methods basically depend on numerical modelling and transient analysis, such as inverse transient analysis, time domain analysis and frequency domain analysis, the negative pressure method, etc. Many methods build upon the analysis of the variation of measured pressure, such as the pressure residual vector method. Hydraulic leak detection has the important advantage of being less costly and has a faster response compared to other leak detection approaches. In this work, various leak detection methods based on pressure are listed and the analysis is reviewed. Both steady state and unsteady state conditions are discussed. The advantages and disadvantages of each approach are mentioned. In addition, methods are included that are suitable for use in both the oil and water industries

An assessment of cost escalation in building construction project

Estimating of cost for building construction projects with minimum error at the conceptual stage of project development is quite  essential for planning. This study seeks to evaluate factors responsible for cost escalation of building construction projects.  Questionnaires were administered to examine and assess these factors. Subsequently, the mean score value of each factor was determined. In addition, Correlation and Linear regression analyses were used to establish the relationship between these factors. Factors responsible for cost escalation in projects were examined as well as the impact of those factors, and occurrence of those factors on project cost. The result of the analysis showed that, the most agreed factors responsible for project cost escalation were; inadequate supervision, irregular payment, and design error, having high mean values of 4.25, 4.20, and 4.15, respectively. Also, correlation analysis result established that the factors responsible for cost escalation and the impact of cost escalation had significant R and R2 of 0.81 and 0.70 respectively. Addressing these factors would go a long way in reducing the escalation of building project cost. Never the less, an effective cost management strategy is absolutely necessary to safeguard and sustain the construction  industry. Keywords: cost escalation, building project, construction, regression analysi