Proving Temporal Properties of Z Specifications Using Abstraction

This paper presents a systematic approach to proving temporal properties of arbitrary Z specifications. The approach involves (i) transforming the Z specification to an abstract temporal structure (or state transition system), (ii) applying a model checker to the temporal structure, (iii) determining whether the temporal structure is too abstract based on the model checking result and (iv) refining the temporal structure where necessary. The approach is based on existing work from the model checking literature, adapting it to Z.

Knowledge about a species' conservation status and funding for its preservation: Analysis

Using a species’ population to measure its conservation status, this note explores how an increase in knowledge about this status would change the public’s willingness to donate funds for its conservation. This is done on the basis that the relationship between the level of donations and a species’ conservation status satisfies stated general mathematical properties. This level of donation increases, on average, with greater knowledge of a species’ conservation status if it is endangered, but falls if it is secure. Game theory and other theory is used to show how exaggerating the degree of endangerment of a species can be counterproductive for conservation.

Estimating Income Inequality in China Using Grouped Data and the Generalized Beta Distribution

There are two main types of data sources of income distributions in China: household survey data and grouped data. Household survey data are typically available for isolated years and individual provinces. In comparison, aggregate or grouped data are typically available more frequently and usually have national coverage. In principle, grouped data allow investigation of the change of inequality over longer, continuous periods of time, and the identification of patterns of inequality across broader regions. Nevertheless, a major limitation of grouped data is that only mean (average) income and income shares of quintile or decile groups of the population are reported. Directly using grouped data reported in this format is equivalent to assuming that all individuals in a quintile or decile group have the same income. This potentially distorts the estimate of inequality within each region. The aim of this paper is to apply an improved econometric method designed to use grouped data to study income inequality in China. A generalized beta distribution is employed to model income inequality in China at various levels and periods of time. The generalized beta distribution is more general and flexible than the lognormal distribution that has been used in past research, and also relaxes the assumption of a uniform distribution of income within quintile and decile groups of populations. The paper studies the nature and extent of inequality in rural and urban China over the period 1978 to 2002. Income inequality in the whole of China is then modeled using a mixture of province-specific distributions. The estimated results are used to study the trends in national inequality, and to discuss the empirical findings in the light of economic reforms, regional policies, and globalization of the Chinese economy.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.