Intelligent agents and distributed models for cooperative task support

Purpose – To describe some research done, as part of an EPSRC funded project, to assist engineers working together on collaborative tasks. Design/methodology/approach – Distributed finite state modelling and agent techniques are used successfully in a new hybrid self-organising decision making system applied to collaborative work support. For the particular application, analysis of the tasks involved has been performed and these tasks are modelled. The system then employs a novel generic agent model, where task and domain knowledge are isolated from the support system, which provides relevant information to the engineers. Findings – The method is applied in the despatch of transmission commands within the control room of The National Grid Company Plc (NGC) – tasks are completed significantly faster when the system is utilised. Research limitations/implications – The paper describes a generic approach and it would be interesting to investigate how well it works in other applications. Practical implications – Although only one application has been studied, the methodology could equally be applied to a general class of cooperative work environments. Originality/value – One key part of the work is the novel generic agent model that enables the task and domain knowledge, which are application specific, to be isolated from the support system, and hence allows the method to be applied in other domains.

Maximising concurrency and scalability in a consistent, causal, distributed virtual reality system, whilst minimising the effect of network delays

The development of large scale virtual reality and simulation systems have been mostly driven by the DIS and HLA standards community. A number of issues are coming to light about the applicability of these standards, in their present state, to the support of general multi-user VR systems. This paper pinpoints four issues that must be readdressed before large scale virtual reality systems become accessible to a larger commercial and public domain: a reduction in the effects of network delays; scalable causal event delivery; update control; and scalable reliable communication. Each of these issues is tackled through a common theme of combining wall clock and causal time-related entity behaviour, knowledge of network delays and prediction of entity behaviour, that together overcome many of the effects of network delay.

Maximising concurrency and scalability in a consistent, causal, distributed virtual reality system, whilst minimising the effect of network delays

The development of large scale virtual reality and simulation systems have been mostly driven by the DIS and HLA standards community. A number of issues are coming to light about the applicability of these standards, in their present state, to the support of general multi-user VR systems. This paper pinpoints four issues that must be readdressed before large scale virtual reality systems become accessible to a larger commercial and public domain: a reduction in the effects of network delays; scalable causal event delivery; update control; and scalable reliable communication. Each of these issues is tackled through a common theme of combining wall clock and causal time-related entity behaviour, knowledge of network delays and prediction of entity behaviour, that together overcome many of the effects of network delays.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Knowledge and perceptions regarding sexual and reproductive health among high school students in Kathmandu, Nepal

Background: Nepal recently began teaching sexual education in the school system and has established youth friendly services in order to meet the need of increased sexual and reproductive knowledge among the youth. Objective: To examine the sexual and reproductive knowledge and perceptions among young people attending schools in Kathmandu. Method: A written questionnaire was distributed to 160 students, in a classroom environment, in four schools in Kathmandu. Results: Two thirds of the females and nearly 60% of the males knew that it was possible to get sexually transmitted infection (STI) during one sexual encounter and more than half of the students knew when in the menstrual cycle conception was more likely to occur . One third of the participants did not know that it was possible to become pregnant after having intercourse once. The males demonstrated less knowledge than the females regarding every aspect of sex and reproduction, with the exception of pregnancy prevention. Conclusion and clinical implications: For the youths in this study, it was more important to prevent unwanted pregnancies than to protect oneself from STIs. Establishment of a hotline on the internet, where personalized and confidential counselling can be offered may complement the comprehensive sexual education in schools.

Understanding the critical distance in sparse distributed memory

Modelos de tomada de decisão necessitam refletir os aspectos da psi- cologia humana. Com este objetivo, este trabalho é baseado na Sparse Distributed Memory (SDM), um modelo psicologicamente e neuro- cientificamente plausível da memória humana, publicado por Pentti Kanerva, em 1988. O modelo de Kanerva possui um ponto crítico: um item de memória aquém deste ponto é rapidamente encontrado, e items além do ponto crítico não o são. Kanerva calculou este ponto para um caso especial com um seleto conjunto de parâmetros (fixos). Neste trabalho estendemos o conhecimento deste ponto crítico, através de simulações computacionais, e analisamos o comportamento desta “Critical Distance” sob diferentes cenários: em diferentes dimensões; em diferentes números de items armazenados na memória; e em diferentes números de armazenamento do item. Também é derivada uma função que, quando minimizada, determina o valor da “Critical Distance” de acordo com o estado da memória. Um objetivo secundário do trabalho é apresentar a SDM de forma simples e intuitiva para que pesquisadores de outras áreas possam imaginar como ela pode ajudá-los a entender e a resolver seus problemas.

Ethnoveterinary knowledge and practices at Colares island, Para state, eastern Amazon, Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Evaluating the knowledge of sports participants regarding dental emergency procedures

Dental trauma is a common consequence of sports practice to which emergency treatment is critical. The purpose of this study was to evaluate the knowledge of sports participants about dental trauma procedures, particularly tooth avulsion. A specific questionnaire concerning concepts, experiences and behaviors after dental trauma and the use of mouthguard was standardized and validated with 80 people. The validated questionnaire was then distributed to 310 sports participants. The results showed that 28.4% had experienced a kind of dental trauma; 42.6% would look for a dentist for treatment; 51.7% reimplanted or would reimplant the avulsed tooth; 6.5% would maintain the avulsed tooth in milk. Although 47.4% of the participants were aware of the possibility of accidents during sports practice, only 13.9% reported to use a mouthguard. This study showed an overall lack of knowledge of sportsmen and sportswomen with regards to tooth avulsion, thus reinforcing the need for educational campaigns to improve the immediate emergency treatment of tooth avulsion.

Knowledge of general dentists in the current guidelines for emergency treatment of avulsed teeth and dental trauma prevention

A high prevalence of dental trauma exists and its effects on function and esthetics deserve the attention of general dentists. The aim of this study was to assess the level of general dental practitioners' (GDPs) knowledge about guidelines for dental avulsion and its prevention using a questionnaire. The 21-item questionnaire was distributed among 264 GDPs and the survey was realized between August-November 2006. The data obtained were statistically analyzed using descriptive analysis and Pearson's Chi-square test to determine associations between knowledge regarding emergency treatment and dentists from public or private dental schools and years of experience. The results showed that the participants exhibited appropriate knowledge concerning procedures in cases of tooth avulsion and its prevention. The number of correct answers was low in relation to recommended treatment at the site of injury. Storage medium, preparation of the alveolus and splint time for receiving the avulsed tooth received a high number of correct answers. One statistically significant association between years of experience and recommended treatment at the site of the injury in the case an avulsed tooth (KH2 = 9.384, P = 0.009). In conclusion, this survey showed appropriate knowledge of dental avulsion management and its prevention among the surveyed dentists. The findings also showed that communication between dentists and the population is deficient, especially concerning practitioners of high risk and contact sports.

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

Edentulous patients' knowledge of dental hygiene and care of prostheses

Objective: The aim of this study was to analyse denture users' oral care habits with regard to the use of their prostheses. Background: Rehabilitative treatment is only successful when patients are motivated and aware of correct prosthesis use and hygiene. Materials and methods: Questionnaires were distributed to 150 complete denture users at the Federal University of Bahia School of Dentistry, the Esmeralda Natividade Health Center, the Bahian Science Development Foundation and a Salvador nursing home. The questionnaire included information on gender, age, length of prosthesis use, cleaning methods and materials, etc. The data were analysed using EpiInfo version 6 software. The chi-squared test was used for statistical analysis, with a significance level of 5%. Results: Questionnaire results showed that 78% of the subjects, with an average age of 67.3 years, had used the same complete denture for over 5 years. 64% slept with their prostheses and 44% removed them from the mouth only for cleaning. None of the patients interviewed knew anything about brushes designed specifically for complete dentures. 37.3% had a restricted diet and 44% believed that a complete denture would last for more than 10 years. Conclusion: Within the limitations of this study, it was concluded that the edentulous patients surveyed had limited awareness of prosthetic hygiene and long-term oral care despite extended periods of denture use. © 2008 The Authors.

Knowledge on dental trauma and orthodontic tooth movement held by a group of orthodontists

The objective of this work was to assess the knowledge about orthodontic tooth movement and dental trauma held by a group of orthodontists in specific areas of Brazil. For this purpose, 166 questionnaires with 15 objective questions about this subject were distributed. One hundred and five questionnaires were properly filled and collected after 30 days. It was concluded that, except for avulsion, the knowledge on dental injuries held by the professionals interviewed was considered unsatisfactory, and about 40% of them were not acquainted with the recommendations for the orthodontic movement of traumatized teeth.