Method and truth in MÄ dhyamika

Abstract The study was undertaken to identify what motivates registered nurses to participate in continuing education activities. The primary questions were whether basic nursing education, employment status, clinical area, and position, as well as readiness for selfdirected learning influenced Canadian nurses' motivational orientations when deciding to participate in continuing education activities. Other individual differences (e.g., age) were also examined. The sample included 142 registered nurses employed at an urban community hospital. Three instruments were used for data collection: the Education Participation Scale, the Self-Directed Learning Readiness Scale, and a nursing survey consisting of demographic questions. Basic nursing education and employment status did not effect motivational orientation or self-directed learning readiness. Clinical area and level of position significantly influenced nurses' decisions to participate in continuing education activities. Motivational orientation had a significant relationship with selfdirected learning readiness. Implications for practice as a result of this study involves program planning and delivery. The identification of the motivational orientations of participants may assist in the development and delivery of continuing education programs that are beneficial, relevant, and address the identified learning needs of participants. Implications for future research also exist in relation to studying different groups of nurses, for example, registered nursing assistants, and investigating related issues, for example, what are the deterrents to participation in continuing education?

Generating Relation Algebras for Qualitative Spatial Reasoning

Basic relationships between certain regions of space are formulated in natural language in everyday situations. For example, a customer specifies the outline of his future home to the architect by indicating which rooms should be close to each other. Qualitative spatial reasoning as an area of artificial intelligence tries to develop a theory of space based on similar notions. In formal ontology and in ontological computer science, mereotopology is a first-order theory, embodying mereological and topological concepts, of the relations among wholes, parts, parts of parts, and the boundaries between parts. We shall introduce abstract relation algebras and present their structural properties as well as their connection to algebras of binary relations. This will be followed by details of the expressiveness of algebras of relations for region based models. Mereotopology has been the main basis for most region based theories of space. Since its earliest inception many theories have been proposed for mereotopology in artificial intelligence among which Region Connection Calculus is most prominent. The expressiveness of the region connection calculus in relational logic is far greater than its original eight base relations might suggest. In the thesis we formulate ways to automatically generate representable relation algebras using spatial data based on region connection calculus. The generation of new algebras is a two pronged approach involving splitting of existing relations to form new algebras and refinement of such newly generated algebras. We present an implementation of a system for automating aforementioned steps and provide an effective and convenient interface to define new spatial relations and generate representable relational algebras.