Electronic structure methods applied to gas-carbon reactions

A review is given on the fundamental studies of gas-carbon reactions using electronic structure methods in the last several decades. The three types of electronic structure methods including semi-empirical, ab initio and density functional theory, methods are briefly introduced first, followed by the studies on carbon reactions with hydrogen and oxygen-containing gases (non-catalysed and catalysed). The problems yet to solve and possible promising directions are discussed. (C) 2002 Elsevier Science Ltd. All rights reserved.

Refined method of potential enhancement in the equilibria characterization of activated carbon. Comparison with GCMC and DFT

In this paper, we present a technique for equilibria characterization of activated carbon having slit-shaped pores. This method was first developed by Do (Do, D. D. A new method for the characterisation of micro-mesoporous materials. Presented at the International Symposium on New Trends in Colloid and Interface Science, September 24-26, 1998 Chiba, Japan) and applied by his group and other groups for characterization of pore size distribution (PSD) as well as adsorption equilibria determination of a wide range of hydrocarbons. It is refined in this paper and compared with the grand canonical Monte Carlo (GCMG) simulation and density functional theory (DFT). The refined theory results in a good agreement between the pore filling pressure versus pore width and those obtained by GCMG and DFT. Furthermore, our local isotherms are qualitatively in good agreement with those obtained by the GCMC simulations. The main advantage of this method is that it is about 4 orders of magnitude faster than the GCMC simulations, making it suitable for optimization studies and design purposes. Finally, we apply our method and the GCMG in the derivation of the PSD of a commercial activated carbon. It was found that the PSD derived from our method is comparable to that derived from the GCMG simulations.

Referral to rehabilitation following traumatic brain injury: a model for understanding inequities in access

Identifying inequities in access to health care requires critical scrutiny of the patterns and processes of care decisions. This paper describes a conceptual model. derived from social problems theory. which is proposed as a useful framework for explaining patterns of post-acute care referral and in particular, individual variations in referral to rehabilitation after traumatic brain injury (TBI). The model is based on three main components: (1) characteristics of the individual with TBI, (2) activities of health care professionals and the processes of referral. and (3) the contexts of care. The central argument is that access to rehabilitation following TBI is a dynamic phenomenon concerning the interpretations and negotiations of health care professionals. which in turn are shaped by the organisational and broader health care contexts. The model developed in this paper provides opportunity to develop a complex analysis of post-acute care referral based on patient factors, contextual factors and decision-making processes. It is anticipated that this framework will have utility in other areas examining and understanding patterns of access to health care. (C) 2002 Elsevier Science Ltd. All rights reserved.

Local Utility Functions and Local Probability Transformations

This note shows that, under appropriate conditions, preferences may be locally approximated by the linear utility or risk-neutral preference functional associated with a local probability transformation.

Valerian does not appear to reduce symptoms for patients with chronic insomnia in general practice using a series of randomised n-of-I trials

Objectives: To investigate the effectiveness of valerian for the management of chronic insomnia in general practice. Design: Valerian versus placebo in a series of n-of-1 trials, in Queensland, Australia. Results: Of 42 enrolled patients, 24 (57%) had sufficient data for inclusion into the n-of-1 analysis. Response to valerian was fair for 23 (96%) participants evaluating their 'energy level in the previous day' but poor or modest for all 24 (100%) participants' response to 'total sleep time' and for 23 (96%) participants' response to 'number of night awakenings' and 'morning refreshment'. As a group, the proportion of treatment successes ranged from 0.35 (95% CI 0.23, 0.47) to 0.55 (95% CI 0.43, 0.67) for the six elicited outcome sleep variables. There was no significant difference in the number (P = 0.06), distribution (P = 1.00) or severity (P = 0.46) of side effects between valerian and placebo treatments. Conclusions: Valerian was not shown to be appreciably better than placebo in promoting sleep or sleep-related factors for any individual patient or for all patients as a group. (C) 2003 Elsevier Ltd. All rights reserved.

Gender differences in the relationship between offending, self-harm and depression in adolescence and young adulthood

Criminal offending and poor mental health are both recognised as important social problems warranting prevention and intervention efforts. Although there is some evidence for comorbidity between these problems, little research has examined the causal relationship between offending and mental health, particularly for young people. The present investigation addresses these issues by using data from the Sibling Study, a longitudinal investigation of delinquency as self-reported by 731 adolescents and young adults in south-east Queensland, Australia. The results suggest that for young women, but not men, offending behaviours (including the use of illicit drugs) lead to increases in self-harm and depression. Conversely, poor mental health, as indicated by having low self-esteem, a poor future outlook, and a belief that life is very confusing, does not influence subsequent levels of offending for either sex. The implications for prevention and intervention are discussed, with emphasis on the need for the criminal justice system to provide mental health services to young female offenders.

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.

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Socioeconomic causes of loss of animal genetic diversity: analysis and assessment

The proximate causes and processes involved in loss of breeds are outlined. The path-dependent effect and Swanson's dominance-effect are discussed in relation to lock-in of breed selection. These effects help to explain genetic erosion. It is shown that the extension of markets and economic globalisation have contributed significantly to the loss of breeds. The decoupling of animal husbandry from surrounding natural environmental conditions is further eroding the stock of genetic resources. Recent trends in animal husbandry raise serious sustainability issues, apart from animal welfare concerns. The extension of markets and economic globalisation have contributed significantly to the rapid loss of domestic breeds, especially livestock. (C) 2003 Elsevier Science B.V. All rights reserved.