Approaches to training practitioners in the art and science of plant disease diagnosis

Allowing plant pathology students to tackle fictitious or real crop problems during the course of their formal training not only teaches them the diagnostic process, but also provides for a better understanding of disease etiology. Such a problem-solving approach can also engage, motivate, and enthuse students about plant pathologgy in general. This paper presents examples of three problem-based approaches to diagnostic training utilizing freely available software. The first provides an adventure-game simulation where Students are asked to provide a diagnosis and recommendation after exploring a hypothetical scenario or case. Guidance is given oil how to create these scenarios. The second approach involves students creating their own scenarios. The third uses a diagnostic template combined with reporting software to both guide and capture students' results and reflections during a real diagnostic assignment.

Improvement in measures of psychological distress amongst amphetamine misusers treated with brief cognitive-behavioural therapy (CBT)

This trial of cognitive-behavioural therapy (CBT) based amphetamine abstinence program (n = 507) focused on refusal self-efficacy, improved coping, improved problem solving and planning for relapse prevention. Measures included the Severity of Dependence Scale (SDS), the General Health Questionnaire-28 (GHQ-28) and Amphetamine Refusal Self-Efficacy. Psychiatric case identification (caseness) across the four GHQ-28 sub-scales was compared with Australian normative data. Almost 90% were amphetamine-dependent (SDS 8.15 +/- 3.17). Pretreatment, all GHQ-28 sub-scale measures were below reported Australian population values. Caseness was substantially higher than Australian normative values {Somatic Symptoms (52.3%), Anxiety (68%), Social Dysfunction (46.5%) and Depression (33.7%). One hundred and sixty-eight subjects (33%) completed and reported program abstinence. Program completers reported improvement across all GHQ-28 sub-scales Somatic Symptoms (p < 0.001), Anxiety (p < 0.001), Social Dysfunction (p < 0.001) and Depression (p < 0.001)}. They also reported improvement in amphetamine refusal self-efficacy (p < 0.001). Improvement remained significant following intention-to-treat analyses, imputing baseline data for subjects that withdrew from the program. The GHQ-28 sub-scales, Amphetamine Refusal Self-Efficacy Questionnaire and the SDS successfully predicted treatment compliance through a discriminant analysis function (p

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Integrating runtime assertions with dynamic types: Structuring a derivation from an incomputable specification

An inherent incomputability in the specification of a functional language extension that combines assertions with dynamic type checking is isolated in an explicit derivation from mathematical specifications. The combination of types and assertions (into "dynamic assertion-types" - DATs) is a significant issue since, because the two are congruent means for program correctness, benefit arises from their better integration in contrast to the harm resulting from their unnecessary separation. However, projecting the "set membership" view of assertion-checking into dynamic types results in some incomputable combinations. Refinement of the specification of DAT checking into an implementation by rigorous application of mathematical identities becomes feasible through the addition of a "best-approximate" pseudo-equality that isolates the incomputable component of the specification. This formal treatment leads to an improved, more maintainable outcome with further development potential.