DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Aggregation and caking processes of granular materials:continuum model and simulation with application to sugar

Aggregation and caking of particles are common severe problems in many operations and processing of granular materials, where granulated sugar is an important example. Prevention of aggregation and caking of granular materials requires a good understanding of moisture migration and caking mechanisms. In this paper, the modeling of solid bridge formation between particles is introduced, based on moisture migration of atmospheric moisture into containers packed with granular materials through vapor evaporation and condensation. A model for the caking process is then developed, based on the growth of liquid bridges (during condensation), and their hardening and subsequent creation of solid bridges (during evaporation). The predicted caking strengths agree well with some available experimental data on granulated sugar under storage conditions.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Cathodic protection of reinforced concrete:anodic processes in cements and related electrolytes

The nature and kinetics of electrode reactions and processes occurring for four lightweight anode systems which have been utilised in reinforced concrete cathodic protection systems have been studied. The anodes investigated were flame sprayed zinc, conductive paint and two activated titanium meshes. The electrochemical properties of each material were investigated in rapidly stirred de-oxygenated electrolytes using anodic potentiodynamic polarisation. Conductive coating electrodes were formed on glass microscope slides, whilst mesh strands were immersed directly. Oxygen evolution occurred preferentially for both mesh anodes in saturated Ca (OH)2/CaC12 solutions but was severely inhibited in less alkaline solutions and significant current only passed in chloride solutions. The main reactions for conductive paint was based on oxygen evolution in all electrolytes, although chlorides increased the electrical activity. Self-corrosion of zinc was controlled by electrolyte composition and the experimental set-up, chlorides increasing the electrical activity. Impressed current cathodic protection was applied to 25 externally exposed concrete slabs over a period of 18 months to investigate anode degradation mechanisms at normal and high current densities. Specimen chloride content, curing and reinforcement depth were also variables. Several destructive and non-destructive methods for assessing the performance of anodes were evaluated including a site instrument for quantitative "instant-off- potential measurements. The impact of cathodic protection on the concrete substrate was determined for a number of specimens using appropriate methods. Anodic degradation rates were primarily influenced by current density, followed by cemendtious alkalinity, chloride levels and by current distribution. Degradation of cementitious overlays and conductive paint substrates proceeded by sequential neutralisation of cement phases, with some evidence of paint binder oxidation. Sprayed zinc progressively formed an insulating layer of hydroxide complexes, which underwent pitting_ attack in the presence of sufficient chlorides, whilst substrate degradation was minimal. Adhesion of all anode systems decreased with increasing current density. The influence of anode material on the ionic gradients which can develop during cathodic protection was investigated. A constant current was passed through saturated cement paste prisms containing calcium chloride to central cathodes via anodes applied or embedded at each end. Pore solution was obtained from successive cut paste slices for anion and cation analyses. Various experimental errors reduced the value of the results. Characteristic S-shaped profiles were not observed and chloride ion profiles were ambiguous. Mesh anode specimens were significantly more durable than the conductive coatings in the high humidity environment. Limited results suggested zinc ion migration to the cathode region. Electrical data from each investigation clearly indicated a decreasing order of anode efficiency by specific anode material.

Modeling high-speed network traffic with truncated α-stable processes

It has been reported that high-speed communication network traffic exhibits both long-range dependence (LRD) and burstiness, which posed new challenges in network engineering. While many models have been studied in capturing the traffic LRD, they are not capable of capturing efficiently the traffic impulsiveness. It is desirable to develop a model that can capture both LRD and burstiness. In this letter, we propose a truncated a-stable LRD process model for this purpose, which can characterize both LRD and burstiness accurately. A procedure is developed further to estimate the model parameters from real traffic. Simulations demonstrate that our proposed model has a higher accuracy compared to existing models and is flexible in capturing the characteristics of high-speed network traffic. © 2012 Springer-Verlag GmbH.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.