Old age is often characterised as being associated with neglect, isolation and loneliness, not least since established risks factors for loneliness include widowhood, living alone, depression and being female. Cross-sectional data have challenged the notion that loneliness is especially an old-age phenomenon but longitudinal data on loneliness is scarce. Moreover, an under-represented group in prior studies are the oldest old, those aged 85 years and more. This paper addresses these knowledge gaps using data from the Newcastle 85+ Study, a large population-based cohort aged 85 years at first interview with follow-up interviews at 18 months and three years. At baseline over half (55%) reported being always or often alone, and 41 per cent reported feeling more lonely than ten years previously, although only 2 per cent reported always feeling lonely. Women spent more time alone than men and reported more loneliness both currently and compared to the past. Length of widowhood was a key factor, with those recently widowed having twice the risk of feeling lonely and those widowed for five or more years having a lower risk of reporting increased loneliness. Overall, the findings show that loneliness is a minority experience in the oldest old but is strongly driven by length of widowhood, challenging the notion that loneliness in later life is a static experience.

In a study of domiciliary and out-patient treatment of self-poisoning patients, using a brief problem-orientated approach, it was found that domiciliary treatment resulted in much higher attendance rates but no difference in outcome. Patients who completed out-patient treatment had a better outcome than those who failed to attend treatment sessions. Completion of out-patient treatment was more frequent among higher social class patients. Domiciliary treatment may be more appropriate in some cases for married patients and for those of lower social class. Medical and non-medical staff proved to be equally effective therapists. Future research should be concerned with evaluation of alternative methods of provision of help and further identification of patients who are most likely to benefit from treatment.

The KADS methodology and its successor, CommonKADS, have gained a reputation for being useful approaches to building knowledge-based systems in a manner which is both systematic and well documented. However, these methods require considerable effort to use them completely. It has been suggested that automated support for KADS or CommonKADS users, in the form of “knowledge engineering workbenches”, could be very useful. These tools would provide computerised assistance to knowledge engineers in organising and representing knowledge, in a similar fashion to the support which CASE tools provide for software engineers. To provide support for KADS or CommonKADS, the workbenches should provide specific support for the modelling techniques recommended by these methods, which are very detailed in the representation and analysis stages of knowledge engineering. A good knowledge engineering workbench should also be easy to use, should be robust and reliable, and should generate output in a presentable format.

This paper reports on an evaluation of two commercially available workbenches for supporting the KADS approach: KADS Tool from ILOG and Open KADS Tool from Bull. This evaluation was carried out by AIAI as part of the CATALYST project, funded by the European Community's ESSI programme, which aimed to introduce CommonKADS to two technology-oriented companies. Information is also presented on two other workbenches: the CommonKADS workbench (which will soon become commercially available) and the VITAL workbench. The results show various strengths and weaknesses in each tool.

The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper provided a broad survey of the use of AI in business, summarizing the application of AI in a number of business domains. In addition, it also provided a summary of the use of different forms of knowledge representation in business applications. Part I has a large set of references, including a number of survey papers, focusing on AI in business. Part II of this paper consists of more detailed analysis of particular systems or issues affecting AI in business. It examines technical issues which are central to the construction of business AI systems, and it also examines the commercial contribution made by methods for the development of AI systems. In addition, part II looks at integration between AI and more traditional information systems. AI can be used to add value to many existing information systems, such as database management systems. Particular attention is given to the integration of AI with operations research, which is the one of the primary “competitors” of AI, providing an alternative set of support tools for decision making.

Business organizations are not concerned only with technology issues; there is also concern about the impact of AI on organizations. Further, the evaluation of AI often is based on an economic view of the world. Part II therefore investigates the organizational impact of AI, and the economics of AI, including issues such as value creation. The format of Part II is as follows: Section 8 analyses techniques for improving the performance of AI systems, thus maximizing economic return. Section 9 looks at different forms of uncertainty and ambiguity which must be dealt with by AI systems. It examines the contributions of fuzzy logic and numerical measures of certainty to handling these problems. Section 10 examines the usefulness of different approaches to knowledge acquisition in business situations, and investigates the benefits of methodological approaches to AI applications. It also looks at more recent AI programming techniques which eliminate the need for knowledge elicitation from an expert: neural networks, case-based reasoning and genetic algorithms are discussed. Sections 11 and 12 examine issues of integrating AI systems. Generally, the use of AI in business settings must ultimately be integrated with the broader base of corporate information systems. Section 11 looks at integration with information systems in general, and section 12 looks particularly at integration with operations research. Sections 13 and 14 review the organizational and economic impact of AI. Finally, section 15 provides a brief summary of part II.

The massive acoustic redundancy of minimally contrasting speech sounds, coupled with the auditory integration of psychoacoustically similar acoustic properties produces a highly invariant percept, which cannot be improved by top-down feedback from the lexicon. Contextual effects are also bottom-up but not all entirely auditory and may thus differ in whether they affect sensitivity or only response bias.

Electro-optic devices such as fast (>20 GHz) modulators are one application of ferroelectric-oxide thin-film waveguides. A compact, blue laser source of a few milliwatts power capable of lasting thousands of hours is of great interest as applied to optical data storage and xerography. Ferroelectric-oxide thin films offer several potential advantages over bulk materials for optical waveguides, though no electroceramic thin-film devices have replaced bulk devices yet. Bulk waveguides are defined by ion exchange, which produces only a small index difference. Thin films therefore permit higher intensity per unit power in the guide, and hence larger nonlinear effects and shorter interaction lengths.