Change in the use of and attitude to peak flow measurement among general practitioners in Northern Ireland between 1989 and 1994

In 1994 we repeated a study first performed in 1989 to assess the change in general practitioners' use of and attitudes to peak flow measurement. Of 232 general practitioners surveyed, 199 (86%) and 192 (83%) responded in 1989 and 1994 respectively. The percentage who reported having patients using domiciliary peak flow monitoring rose form 58.3 (95% confidence limits 51.4 to 65.2)% to 97.9 (95.9 to 99.9)%. The percentage who reported 'usually' using peak flow measurements for the diagnosis and management of asthma rose from 81.9 (76.5 to 87.3)% to 93.2 (89.6 to 96.8)% and from 83.3 (78.1 to 88.5)% to 95.8 (92.9 to 98.7)% respectively. An unchanged proportion took peak flow meters on house calls. General practitioners have become more aware of the potential of peak flow measurements but are still unlikely to have a meter available to assess patients seen at home. They are therefore likely to be ill-equipped to manage acute exacerbations of asthma in this setting.

Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal-ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suffers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possibilistic knowledge base as the result of merging. In such a case, we argue that it is not always necessary to keep weighted information after merging. In this paper, we define a merging operator that maps a set of possibilistic knowledge bases and a formula representing the integrity constraints to a classical knowledge base by using lexicographic ordering. We show that it satisfies nine postulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowledge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and establish the advantage of our merging operator over existing semantic merging operators in the propositional case.

Taiwanese Learners’ Motivation and Attitude Towards Completing an Online Distance Education Course During a One Year Industrial Placement

This mixed methods study investigated language learning motivation in an one-year e-learning course for technological university students to bridge the geographical divide between students on industrial placements when studying graded readers using an e-learning course to improve their English competence and to pass the General English Proficiency Test. Data was collected through questionnaires and course feedback. The results of this study extend Gardner’s socio-educational model in an e-learning environment by adding the new category, Computer Attitudes, which was proven to be highly correlated with Motivation. Although the low proficiency English students had good computer skills, their habits of using the computer for entertainment and their lack of the skill of “technological communication efficacy” caused increased anxiety when using computers and thus provided them with a lower computer confidence over time. Consequently, it is recommended that sound e-learning training should be provided to all of the students prior to embarking on an e-leaning course so that these learners can benefit from online language learning in the future.

Out-group trust, intergroup anxiety, and out-group attitude as mediators of the effect of imagined intergroup contact on intergroup behavioural tendencies.

We investigated whether imagining contact with an out-group member would change behavioral tendencies toward the out-group. In Experiment 1, British high school students who imagined talking to an asylum seeker reported a stronger tendency to approach asylum seekers than did participants in a control condition. Path analysis revealed this relationship was mediated by out-group trust and, marginally, by out-group attitude. In Experiment 2, straight undergraduates who imagined an interaction with a gay individual reported a stronger tendency to approach, and a weaker tendency to avoid, gay people. Path analyses showed that these relationships were mediated by out-group trust, out-group attitude, and less intergroup anxiety. These findings highlight the potential practical importance of imagined contact and important mediators of its effects.

Offenders' Risk-Taking Attitude Inside and Outside the Prison Walls

It has long been assumed that risk taking is closely associated with criminal behavior. One reason for placing criminals behind bars-aside from punishment and protecting the public-is to prevent them from engaging in further risky criminal activities. Limited attention has been paid to whether being inside or outside prison affects offenders' risk-taking behaviors and attitudes. We compared risk-taking behaviors and attitudes in five risk domains (ethical, financial, health/safety, recreational, social) among 75 incarcerated offenders (i.e., offenders who are currently in prison) and 45 ex-offenders (i.e., offenders who have just been released from prison). Ex-offenders reported higher likelihood of engaging in risky behavior, driven largely by a willingness to take more risks in the recreational and ethical domains. Benefits attributed to risk taking as well as risk perception did not differ between incarcerated and ex-offenders, indicating that the opportunity to take risks might underlie behavioral risk intentions. Our results also indicate that risk-taking activities are better predicted by the expected benefits rather than by risk perception, aside from the health/safety domain. These results highlight the importance of studying the person and the environment and examining risk taking in a number of content domains.

Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases

There is extensive theoretical work on measures of inconsistency for arbitrary formulae in knowledge bases. Many of these are defined in terms of the set of minimal inconsistent subsets (MISes) of the base. However, few have been implemented or experimentally evaluated to support their viability, since computing all MISes is intractable in the worst case. Fortunately, recent work on a related problem of minimal unsatisfiable sets of clauses (MUSes) offers a viable solution in many cases. In this paper, we begin by drawing connections between MISes and MUSes through algorithms based on a MUS generalization approach and a new optimized MUS transformation approach to finding MISes. We implement these algorithms, along with a selection of existing measures for flat and stratified knowledge bases, in a tool called mimus. We then carry out an extensive experimental evaluation of mimus using randomly generated arbitrary knowledge bases. We conclude that these measures are viable for many large and complex random instances. Moreover, they represent a practical and intuitive tool for inconsistency handling.

A Characteristic Function Approach to Inconsistency Measures for Knowledge Bases.

Knowledge is an important component in many intelligent systems.
Since items of knowledge in a knowledge base can be conflicting, especially if
there are multiple sources contributing to the knowledge in this base, significant
research efforts have been made on developing inconsistency measures for
knowledge bases and on developing merging approaches. Most of these efforts
start with flat knowledge bases. However, in many real-world applications, items
of knowledge are not perceived with equal importance, rather, weights (which
can be used to indicate the importance or priority) are associated with items of
knowledge. Therefore, measuring the inconsistency of a knowledge base with
weighted formulae as well as their merging is an important but difficult task. In
this paper, we derive a numerical characteristic function from each knowledge
base with weighted formulae, based on the Dempster-Shafer theory of evidence.
Using these functions, we are able to measure the inconsistency of the knowledge
base in a convenient and rational way, and are able to merge multiple knowledge
bases with weighted formulae, even if knowledge in these bases may be
inconsistent. Furthermore, by examining whether multiple knowledge bases are
dependent or independent, they can be combined in different ways using their
characteristic functions, which cannot be handled (or at least have never been
considered) in classic knowledge based merging approaches in the literature.