A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Monitoring dendritic cells in clinical practice using a new whole blood single-platform TruCOUNT assay

Dendritic cells (DC) from distinct DC subsets are essential contributors to normal human immune responses. Despite this, reliable assays that enable DC to be counted precisely have been slow to evolve. We have now developed a new single-platform flow cytometric assay based on TruCOUN(TM) beads and the whole blood Lyse/No-Wash protocol that allows precise counting of the CD14(-) blood DC subsets: CD11c(+)CD16(-) DC, CD11c(+)CD16(+) DC, CD123(hi) DC, CD1c(+) DC and BDCA-3(+) DC. This assay requires 50 mul of whole blood; does not rely on a hematology blood analyser for the absolute DC counts; allows DC counting in EDTA samples 24 It after collection; and is suitable for cord blood and peripheral blood. The data is highly reproducible with intra-assay and inter-assay coefficients of variation less than 3% and 11%, respectively. This assay does not produce the DC-T lymphocyte conjugates that result in DC counting abnormalities in conventional gradient-density separation procedures. Using the TruCOUNT assay, we established that absolute blood DC counts reduce with age in healthy individuals. In preliminary studies, we found a significantly lower absolute blood CD11c(+)CD16(+) DC count in stage III/IV versus stage I/II breast carcinoma patients and a lower absolute blood CD123(hi) DC count in multiple myeloma patients, compared to age-matched controls. These data indicate that scientific progress in DC counting technology will lead to the global standardization of DC counting and allow clinically meaningful data to be obtained. (C) 2003 Elsevier B.V. All rights reserved.

Serum antibodies to oral anaerobic bacteria in patients with rheumatoid arthritis

BACKGROUND: This study was conducted to determine the component that causes the disease in rheumatoid arthritis (RA), which shows great resemblance to periodontitis in a pathologic context. MATERIALS AND METHODS: Within this study, the pathogen-specific IgG levels formed against Porphyromonas gingivalis FDC 381, Prevotella melaninogenica ATCC 25845, Actinobacillus actinomycetemcomitans Y4, Bacteroides forsythus ATCC 43047, and Prevotella intermedia 25611 oral bacteria were researched from the blood serum samples of 30 RA patients and 20 healthy controls with the enzyme-linked immunosorbent assay (ELISA) method. RESULTS: The IgG levels of P gingivalis, P intermedia, P melaninogenica, and B forsythus were found to be significantly higher in RA patients when compared with those of the controls. Of the other bacteria antibodies, A actinomycetemcomitans was not found at greater levels in RA serum samples in comparison with the healthy samples. CONCLUSION: The antibodies formed against P gingivalis, P intermedia, P melaninogenica, and B forsythus could be important to the etiopathogenesis of RA.

Data partitioning for parallel spatial join processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

Dietary trends: Estimates from food supply and survey data

Objective: To illustrate methodological issues involved in estimating dietary trends in populations using data obtained from various sources in Australia in the 1980s and 1990s. Methods: Estimates of absolute and relative change in consumption of selected food items were calculated using national data published annually on the national food supply for 1982-83 to 1992-93 and responses to food frequency questions in two population based risk factor surveys in 1983 and 1994 in the Hunter Region of New South Wales, Australia. The validity of estimated food quantities obtained from these inexpensive sources at the beginning of the period was assessed by comparison with data from a national dietary survey conducted in 1983 using 24 h recall. Results: Trend estimates from the food supply data and risk factor survey data were in good agreement for increases in consumption of fresh fruit, vegetables and breakfast food and decreases in butter, margarine, sugar and alcohol. Estimates for trends in milk, eggs and bread consumption, however, were inconsistent. Conclusions: Both data sources can be used for monitoring progress towards national nutrition goals based on selected food items provided that some limitations are recognized. While data collection methods should be consistent over time they also need to allow for changes in the food supply (for example the introduction of new varieties such as low-fat dairy products). From time to time the trends derived from these inexpensive data sources should be compared with data derived from more detailed and quantitative estimates of dietary intake.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Large scale experiments on gravel and mixed beaches: Experimental procedure, data documentation and initial results

This paper provides information on the experimental set-up, data collection methods and results to date for the project Large scale modelling of coarse grained beaches, undertaken at the Large Wave Channel (GWK) of FZK in Hannover by an international group of researchers in Spring 2002. The main objective of the experiments was to provide full scale measurements of cross-shore processes on gravel and mixed beaches for the verification and further development of cross-shore numerical models of gravel and mixed sediment beaches. Identical random and regular wave tests were undertaken for a gravel beach and a mixed sand/gravel beach set up in the flume. Measurements included profile development, water surface elevation along the flume, internal pressures in the swash zone, piezometric head levels within the beach, run-up, flow velocities in the surf-zone and sediment size distributions. The purpose of the paper is to present to the scientific community the experimental procedure, a summary of the data collected, some initial results, as well as a brief outline of the on-going research being carried out with the data by different research groups. The experimental data is available to all the scientific community following submission of a statement of objectives, specification of data requirements and an agreement to abide with the GWK and EU protocols. (C) 2005 Elsevier B.V. All rights reserved.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

An evaluation of the evidence for linkages between mangroves and fisheries: A synthesis of the literature and identification of research directions

There is a widely held paradigm that mangroves are critical for sustaining production in coastal fisheries through their role as important nursery areas for fisheries species. This paradigm frequently forms the basis for important management decisions on habitat conservation and restoration of mangroves and other coastal wetlands. This paper reviews the current status of the paradigm and synthesises the information on the processes underlying these potential links. In the past, the paradigm has been supported by studies identifying correlations between the areal and linear extent of mangroves and fisheries catch. This paper goes beyond the correlative approach to develop a new framework on which future evaluations can be based. First, the review identifies what type of marine animals are using mangroves and at what life stages. These species can be categorised as estuarine residents, marine-estuarine species and marine stragglers. The marine-estuarine category includes many commercial species that use mangrove habitats as nurseries. The second stage is to determine why these species are using mangroves as nurseries. The three main proposals are that mangroves provide a refuge from predators, high levels of nutrients and shelter from physical disturbances. The recognition of the important attributes of mangrove nurseries then allows an evaluation of how changes in mangroves will affect the associated fauna. Surprisingly few studies have addressed this question. Consequently, it is difficult to predict how changes in any of these mangrove attributes would affect the faunal communities within them and, ultimately, influence the fisheries associated with them. From the information available, it seems likely that reductions in mangrove habitat complexity would reduce the biodiversity and abundance of the associated fauna, and these changes have the potential to cause cascading effects at higher trophic levels with possible consequences for fisheries. Finally, there is a discussion of the data that are currently available on mangrove distribution and fisheries catch, the limitations of these data and how best to use the data to understand mangrove-fisheries links and, ultimately, to optimise habitat and fisheries management. Examples are drawn from two relatively data-rich regions, Moreton Bay (Australia) and Western Peninsular Malaysia, to illustrate the data needs and research requirements for investigating the mangrove-fisheries paradigm. Having reliable and accurate data at appropriate spatial and temporal scales is crucial for mangrove-fisheries investigations. Recommendations are made for improvements to data collection methods that would meet these important criteria. This review provides a framework on which to base future investigations of mangrove-fisheries links, based on an understanding of the underlying processes and the need for rigorous data collection. Without this information, the understanding of the relationship between mangroves and fisheries will remain limited. Future investigations of mangrove-fisheries links must take this into account in order to have a good ecological basis and to provide better information and understanding to both fisheries and conservation managers.

Haemodynamic response to TENS applied to upper thoracic nerve roots in normal subjects

This study investigated the haemodynamic response to the 90-minute application of 85 Hz transcutaneous electrical nerve stimulation (TENS) to the T1 and T5 nerve roots. Comparison was made between 20 healthy subjects who had TENS stimulation and a separate group of 20 healthy subjects who rested for 90 minutes. Pulse and blood pressure were measured just prior to the start of TENS stimulation, after 30 minutes of stimulation, and after 90 minutes of stimulation (immediately after stopping TENS) or at completion of the rest time depending on group allocation. The rate pressure product was calculated from the pulse and systolic blood pressure data. Multivariate repeated measures analysis showed a significant group effect for TENS (p = 0.048). Univariate repeated measures analyses showed a significant group by time effect due to TENS on systolic blood pressure over the 90-minute time period (p = 0.028). Separate group repeated measures ANOVA showed a significant decline in heart rate (p = 0.000), systolic blood pressure (p = 0.013) and rate pressure product (p = 0.000) for the TENS group, while the control resting group showed a significant decline in heart rate only (p = 0.04). The application of 85 Hz TENS to the upper thoracic nerve roots causes no adverse haemodynamic effects in healthy subjects.

Environmental conditions and variation in levels of sun exposure among children in child care

Sun exposure in childhood is I of the risk factors for developing skin cancer, yet little is known about levels of exposure at this age. This is particularly important in countries with high levels of ultraviolet radiation. (UVR) such as Australia. Among 49 children 3 to 5 years of age attending child care centers, UVR exposure was studied under 4 conditions in a repeated measures design; sunny days, cloudy days, teacher's instruction to stay in the shade, and a health professionals instruction to apply sunscreen. Three different data collection methods were employed: (a) completion of questionnaire or diary by parents and researcher, (b) polysulphone dosimeter readings, and (c) observational audits (video recording). Results of this study indicated that more than half the children had been sunburnt (pink or red) and more than a third had experienced painful sunburn (sore or tender) in the last summer. Most wore short sleeve shirts, short skirts or shorts and cap, that do not provide optimal levels of skin protection. However, sunscreen was applied to all exposed parts before the children went out to the playground. Over the period of I hr (9-10 a.m.) the average amount of time children spent in full sun was 22 min. On sunny days there was more variation across children in the amount of sun exposure received. While the potential amount of UVR exposure for young children during the hour they were outside on a sunny day was 1.45 MED (Minimum Erythemal Dose), they received on average 0.35 MED, which is an insufficient amount to result in an erythemal response on fair skin even without the use of sunscreen.