Long-term trends in sunshine duration and its association with schizophrenia birth rates and age at first registration - data from Australia and the Netherlands

Back ground. Based on the well-described excess of schizophrenia births in winter and spring, we hypothesised that individuals with schizophrenia (a) would be more likely to be born during periods of decreased perinatal sunshine, and (b) those born during periods of less sunshine would have an earlier age of first registration. Methods. We undertook an ecological analysis of long-term trends in perinatal sunshine duration and schizophrenia birth rates based on two mental health registers (Queensland. Australia n = 6630; The Netherlands n = 24, 474). For each of the 480 months between 1931 and 1970, the agreement between slopes of the trends in psychosis and long-term sunshine duration series were assessed. Age at first registration was assessed by quartiles of long-term trends in perinatal sunshine duration, Males and females were assessed separately. Results. Both the Dutch and Australian data showed a statistically significant association between falling long-term trends in sunshine duration around the time of birth and rising schizophrenia birth rates for males only. In both the Dutch and Australian data there were significant associations between earlier age of first registration and reduced long-term trends in sunshine duration around the time of birth for both males and females, Conclusions. A measure of long-term trends in perinatal sunshine duration was associated with two epidemiological features of schizophrenia in two separate data sets. Exposures related to sunshine duration warrant further consideration in schizophrenia research. (C) 2002 Elsevier Science B.V. All rights reserved.

Fuzzy logic in web data mining for website assessment

This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.

Edge-to-edge matching and its applications - Part I. Application to the simple HCP/BCC system

The edge-to-edge matching crystallographic model has been used to predict all the orientation relationships (OR) between crystals that have simple hexagonal close packed (HCP) and body-centered cubic (BCC) structures. Using the critical values for the interatomic spacing misfit along the matching directions and the cl-value mismatch between matching planes, the model predicted all the four common ORs, namely the Burgers OR, the Potter OR, the Pitsch-Schrader OR and the Rong Dunlop OR, together with the corresponding habit planes. Taking the c(H)/a(H) and a(H)/a(B) ratios as variables, where H and B denote the HCP and BCC structures respectively, the model also predicted the relationship between these variables and the four ORs. These predictions are perfectly consistent with the published experimental results. As was the case in the FCC/BCC system, the edge-to-edge matching model has been shown to be a powerful tool for predicting the crystallographic features of diffusion-controlled phase transformations. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Edge-to-edge matching and its applications - Part II. Application to Mg-Al, Mg-Y and Mg-Mn alloys

A model for the crystallography and morphology of diffusion-controlled phase transformations - edge-to-edge matching - has been used to predict the orientation relationships (OR) and habit planes of precipitates Mg17Al12 in Mg-Al alloy, Mg24Y5 in Mg-Y alloy and alpha-Mn in Mg-Mn alloy. Based on the crystal structures and lattice parameters only, the model predicts that the possible ORs between Mg17Al12 and Mg matrix are the near Burgers OR, the Potter OR, the Gjonnes-Ostmoe OR and the Crawley OR. In the Mg-Y alloy, the OR between Mg24Y5 precipitates and the Mg matrix is predicted to be the Burgers OR only. The model also predicts that there are no reproducible ORs between alpha-Mn and Mg in the Mg-Mn alloy. Combining the edge-to-edge matching model and W. Zhang's Deltag approach, the habit plane and side facets of the precipitate for each OR can be determined. All the predicted ORs and the corresponding habit planes in Mg-Al and Mg-Y alloys agree very well with the experimental results. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Improvements in the data partitioning approach for frequent itemsets mining

Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

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.

Comparative public support for conserving reptile species is high: Australian evidence and its implications

This paper investigates factors influencing the public’s support for conservation of tropical reptile species in a focal group drawing on Australian data and an experiment involving a sample of the Australian public. The influences of the likeability of the species, their degree of endangerment, ethical considerations as well as knowledge are examined and found to be important. Likeability is found to be much less important than the existing literature suggests. This is highlighted by comparing the likeability of the focal group of reptiles with that for a group of birds and a group of mammals with differences in willingness to pay for their conservation.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Geochemical mass-balance and oxygen-isotope constrains on silcrete formation and its paleoclimatic implications in Southern Australia

Two geographically distinct silcrete associations are present in southern Australia, inland and eastern; these were sampled in central South Australia and central Victoria, respectively, At each site, both silicified and immediately adjacent unsilicified parent material were collected. Analytical data from these pairs were used to construct isocons, assuming Zr immobility, and to calculate the volume change and amount of silica introduced during silicification, These results, together with whole-rock oxygen isotope compositions, were used to determine the delta(18)O of th, introduced silica, The results show that the eastern silcretes in central Victoria are probably linked genetically to the associated basalts, weathering of which supplied the introduced silica, This conclusion is based on the close spatial connection between the two, as well as the substantial amount of introduced silica in the silcretes (greater than in the inland silcretes), resulting in volume increases in some eastern silcretes, Oxygen isotopic calculations for the silcretes indicate that the silica precipitated from groundwaters at temperatures slightly higher than present conditions. Silcrete formation apparently occurred during the Miocene and Pliocene (basalts in Victoria younger than Pliocene lack associated silcrete) and may reflect the much wetter climate in southeastern Australia at that time. The inland silcretes of central South Australia can be divided into pedogenic (the most common) and groundwater varieties. The pedogenic silcretes, which show typical soil features like columnar and nodular textures, contain moderate amounts of introduced silica that precipitated by evaporation from saline groundwaters, For the groundwater silcretes, which have massive textures and formed at or close to the water table, insufficient data are available to determine the mode of formation. The inland pedogenic silcretes have probably been farming from the Eocene-Miocene to the present, implying that conditions of seasonally high evaporation have occurred in central Australia during this time period. Thus silcrete formation depends on a complex interplay between climate and silica supply, and it is impossible to generalize that the presence of silcrete is indicative of a particular climate. Likewise, the elemental composition of silcretes, particularly Ti content, is not necessarily of climatic significance, Nevertheless, detailed geochemical and oxygen isotopic studies of a silcrete and its parent material can elucidate the mechanisms of silcrete formation, and if evaporation is indicated as a major factor in silcrete formation, then the climate at the time was likely to have been at least seasonally arid.

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.