Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A GIS Based Assessment of Land Suitable for Growing Hoop Pine in the Atherton, Eacham and Herberton Shires of North Queensland

The area of private land suitable and available for growing hoop pine (Araucaria cunninghamii) on the Atherton Tablelands in North Queensland was modelled using a geographic information system (GIS). In Atherton, Eacham and Herberton shires, approximately 64,700 ha of privately owned land were identified as having a mean annual rainfall and soil type similar to Forestry Plantations Queensland (FPQ) hoop pine growth plots with an approximate growth rate of 20 m3 per annum. Land with slope of over 25° and land covered with native vegetation were excluded in the estimation. If land which is currently used for high-value agriculture is also excluded, the net area of land potentially suitable and available for expansion of hoop pine plantations is approximately 22,900 ha. Expert silvicultural advice emphasized the role of site preparation and weed control in affecting the long-term growth rate of hoop pine. Hence, sites with less than optimal fertility and rainfall may be considered as being potentially suitable for growing hoop pine at a lower growth rate. The datasets had been prepared at various scales and differing precision for their description of land attributes. Therefore, the results of this investigation have limited applicability for planning at the individual farm level but are useful at the regional level to target areas for plantation expansion.

Speciation and absolute bioavailability: risk assessment of arsenic-contaminated sites in a residential suburb in Canberra

Watson is a fully developed suburb of some 30 years in Canberra (the capital city of Australia), A plunge dip using arsenical pesticides for tick control was operated there between 1946 and 1960, Chemical investigations revealed that many soil samples obtained from the study area contained levels of arsenic exceeding the current health-based investigation levels of 100 mg kg(-1) set by the National Health and Medical Research Council in Australia, For the speciation study, nine composite samples of surface and sub-surface soils and a composite sample of rocks were selected. ICP-MS analysis showed that arsenic levels in these samples ranged from 32 to 1597 mg kg(-1), Chemical speciation of arsenic showed that the arsenite (trivalent) components were 0.32-56% in the soil and 44.8% in the rock composite samples. Using a rat model, the absolute bioavailability of these contaminated soils relative to As3+ or As5+ ranged from 1.02 to 9.87% and 0.26 to 2.98%, respectively, An attempt was made to develop a suitable leachate test as an index of bioavailability. However, the results indicated that there was no significant correlation between the bioavailability and leachates using neutral pH water or 1 M HCl. Our results indicate that speciation is highly significant for the interpretation of bioavailability and risk assessment data; the bioavailable fractions of arsenic in soils from Watson are small and therefore the health impact upon the environment and humans due to this element is limited.

Assessment of limb muscle and adipose tissue by dual-energy X-ray absorptiometry using magnetic resonance imaging for comparison

OBJECTIVE: To use magnetic resonance imaging (MRI) to validate estimates of muscle and adipose tissue (AT) in lower limb sections obtained by dual-energy X-ray absorptiometry (DXA) modelling. DESIGN: MRI measurements were used as reference for validating limb muscle and AT estimates obtained by DXA models that assume fat-free soft tissue (FFST) comprised mainly muscle: model A accounted for bone hydration only; model B also applied constants for FFST in bone and skin and fat in muscle and AT; model C was as model B but allowing for variable fat in muscle and AT. SUBJECTS: Healthy men (n = 8) and women (n = 8), ages 41 - 62 y; mean (s.d.) body mass indices (BMIs) of 28.6 (5.4) kg/m(2) and 25.1 (5.4) kg/m2, respectively. MEASUREMENTS: MRI scans of the legs and whole body DXA scans were analysed for muscle and AT content of thigh (20 cm) and lower leg (10 cm) sections; 24 h creatinine excretion was measured. RESULTS: Model A overestimated thigh muscle volume (MRI mean, 2.3 l) substantially (bias 0.36 l), whereas model B underestimated it by only 2% (bias 0.045 l). Lower leg muscle (MRI mean, 0.6 l) was better predicted using model A (bias 0.04 l, 7% overestimate) than model B (bias 0.1 l, 17% underestimate). The 95% limits of agreement were high for these models (thigh,+/- 20%; lower leg,+/- 47%). Model C predictions were more discrepant than those of model B. There was generally less agreement between MRI and all DXA models for AT. Measurement variability was generally less for DXA measurements of FFST (coefficient of variation 0.7 - 1.8%) and fat (0.8 - 3.3%) than model B estimates of muscle (0.5-2.6%) and AT (3.3 - 6.8%), respectively. Despite strong relationships between them, muscle mass was overestimated by creatinine excretion with highly variable predictability. CONCLUSION: This study has shown the value of DXA models for assessment of muscle and AT in leg sections, but suggests the need to re-evaluate some of the assumptions upon which they are based.

Developing a framework for assessment of the environmental determinants of walking and cycling

The focus for interventions and research on physical activity has moved away from vigorous activity to moderate-intensity activities, such as walking. In addition, a social ecological approach to physical activity research and practice is recommended. This approach considers the influence of the environment and policies on physical activity. Although there is limited empirical published evidence related to the features of the physical environment that influence physical activity, urban planning and transport agencies have developed policies and strategies that have the potential to influence whether people walk or cycle in their neighbourhood. This paper presents the development of a framework of the potential environmental influences on walking and cycling based on published evidence and policy literature, interviews with experts and a Delphi study. The framework includes four features: functional, safety, aesthetic and destination; as well as the hypothesised factors that contribute to each of these features of the environment. In addition, the Delphi experts determined the perceived relative importance of these factors. Based on these factors, a data collection tool will be developed and the frameworks will be tested through the collection of environmental information on neighbourhoods, where data on the walking and cycling patterns have been collected previously. Identifying the environmental factors that influence walking and cycling will allow the inclusion of a public health perspective as well as those of urban planning and transport in the design of built environments. (C) 2002 Elsevier Science Ltd., All rights reserved.

Field assessment of thermal after-ripening time for dormancy release prediction in Lolium rigidum seeds

Dormancy release was studied in four populations of annual ryegrass (Lolium rigidum) seeds to determine whether loss of dormancy in the field can be predicted from temperature alone or whether seed water content (WC) must also be considered. Freshly matured seeds were after-ripened at the northern and southern extremes of the Western Australian cereal cropping region and at constant 37degreesC. Seed WC was allowed to fluctuate with prevailing humidity, but full hydration was avoided by excluding rainfall. Dormancy was measured regularly during after-ripening by germinating seeds with 12-hourly light or in darkness. Germination was lower in darkness than in light/dark and dormancy release was slower when germination was tested in darkness. Seeds were consistently drier, and dormancy release was slower, during after-ripening at 37degreesC than under field conditions. However, within each population, the rate of dormancy release in the field (north and south) in terms of thermal time was unaffected by after-ripening site. While low seed WC slowed dormancy release in seeds held at 37degreesC, dormancy release in seeds after-ripened under Western Australian field conditions was adequately described by thermal after-ripening time, without the need to account for changes in WC elicited by fluctuating environmental humidity.