The purpose of this paper was to determine the economic, sociopolitical, and other related factors which account for the variation in state expenditures across time. Utah was selected as the test state and data were collected from school records, political rosters, employment statistics, and a variety of federal government documents. Particular emphasis was placed on three areas: the cause-effect relationships between variables, relating the model to a body of economic theory, and demonstrating how the model may be applied in forecasting state expenditure needs. Supply-and-demand analysis was the underlying economic theory. A simultaneous-equation model consisting of four equations--demand for state expenditures, supply of state expenditures, federal grants to states, and an equilibrium condition--was constructed and tested . The paper also discusses the problems of serial correlation and mulli-collinearity.

Philosophiae Doctor - PhD / Most poverty studies ignore the dimension of time and are merely concerned if an individual meets certain money-metric or non-income welfare (e.g., access to services and asset ownership) criteria. They fail to recognise the limited time (24hours per day) available to complete tasks and the added difficulties they have even though there is an abundance of money-metric and asset-related non-money-metric poverty studies. (Kim et al. 2014:1). For example, individuals/households deemed poor by standard measures cannot afford market alternatives to assist them with non-market work (like childcare). Therefore, they find themselves spending all their time in market and non-market work without taking time for rest and improving themselves.

Philosophiae Doctor - PhD / Most poverty studies ignore the dimension of time and are merely concerned if an individual meets certain money-metric or non-income welfare (e.g., access to services and asset ownership) criteria. They fail to recognise the limited time (24hours per day) available to complete tasks and the added difficulties they have even though there is an abundance of money-metric and asset-related non-money-metric poverty studies. (Kim et al. 2014:1). For example, individuals/households deemed poor by standard measures cannot afford market alternatives to assist them with non-market work (like childcare). Therefore, they find themselves spending all their time in market and non-market work without taking time for rest and improving themselves. Recognising non-market work and the allocation of time allows for a greater understanding into the role of women and Africans whose non-market work are unrecognised by standard economic measures such as GDP (Ferrant 2014:1). There are also only a few in-depth studies on time poverty, but they fail to utilise the most current data. Therefore, this study seeks to provide insights into how household production impacts on South African welfare. It explores the income, time poor and the extent of time allocation differences for various personal characteristics. It estimates the likelihood of time poverty based on an individual’s time schedule and the factors which most likely results in time poverty. In this study, various time concepts and measures were explored adding to the scarcely found South African time poverty studies which lack in-depth exploration. At the same time the study highlighted household production, an aspect closely linked to time poverty which affects certain groups of people more (females and Africans), and its welfare implications completely ignored by standard measures of the economy. The study also aimed to examine the relationship between time and income poverty. The study utilised the 2000 and 2010 South African Time Use Survey data by focusing on two main themes: time use patterns (to better understand household production) and time poverty (to measure it and understands its relationship with income poverty). The descriptive results revealed that both mean SNA (System of National Accounts internationally agreed standard for production)) and non-SNA production time increased over time at the cost of the non-productive time. Also, mean paid and unpaid work increased over time.

No description available.

No description available.

No description available.

No description available.

M. S.

Yes / Model checking has been extensively used to verify various systems. However, this usually has been done by experts who have a good understanding of model checking and who are familiar with the syntax of both modelling and property specification languages. Unfortunately, this is not an easy task for nonexperts to learn description languages for modelling and formal logics/languages for property specification. In particular, property specification is very daunting and error-prone for nonexperts. In this paper, we present a methodology to facilitate probabilistic model checking for nonexperts. The methodology helps nonexpert users model their systems and express their requirements without any knowledge of the modelling and property specification languages.

No / Many industrial systems such as aircrafts, ships, manufacturing systems, etc. are required to perform several missions with finite breaks between missions. Maintenance is only available within the breaks. Due to the limitation of resources, all components in the system may not be maintained as desired. The selective maintenance problem helps the decision makers figure out what critical components to select and how to perform maintenance on these components. This paper studies the selective maintenance for multi-state series-parallel systems with the benefit of repairing multiple components simultaneously. Both time and cost savings can be acquired when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount from the repair of multiple identical components. A non-linear optimization model is developed to find the most reliable system subjected to time and cost constraints. Genetic algorithm is used to solve the optimization model. An illustrative example will be provided. / Natural Sciences and Engineering Research Council of Canada (NSERC) and Vietnam International Education Development (VIED)