Knowledge base

The methods for transforming real-world problem into simulation models are being increasingly explored with the availability of inexpensive computing power. In general, traditional model building procedures involve a lengthy problem formulation and interaction between the analyst and the client. Furthermore, after the model definition is arrived at, the simulation model is often programmed manually. Recent developments in simulation modeling have focused on automatic model generation employing artificial intelligence (AI) techniques. Such developments focus on the transfer of the user's knowledge about the system into an executable simula, tion model. Current techniques still lack effective knowledge acquisition tools and a global database from which model alternatives can be generated. In this paper, a set of knowledge bases (KBs) will be proposed to aid in the hierarchical model construction.

Inspection of loaded printed circuit boards (PCBs) using 3D laser striping range data is described. The work presented here is intended to assess the feasibility of using 3D depth data for robust identification of PCB defects. A knowledge-based control system interrogates a 2D CAD database for PCB layout information. Test strategies are then implemented with efficient multiresolution data capture. Inspection algorithms use 3D scene models to detect and interpret faults. This paper describes the implementation of the 3D ...

Six years ago at the first MT Summit conference, the field of MT was dominated by approaches which had been established in the late 1970s. These were the systems which had built upon experience gained in what may be called the 'quiet' decade of machine translation, the ten years after the publication of the ALPAC report in 1966 had brought to an end MT research in the United States and had profoundly affected its support elsewhere.

ABSTRACT: In this paper, we briefly discuss the use of alternative urban development proposals (AUDP) as a knowledge resource. The article combines a theoretical reflection with an applied study of urban planning in the Brussels-Capital Region. In the beginning, we present different types of alternative proposals and discuss how they can be evaluated and represented systematically. Afterwards, we test these inferences by applying one specific mapping tool-conceptual graphs- to the context of Brussels-Capital Region and compare the discourses of four urban investigations that were developed for the European Quarter. In conclusion we review the value of alternative development projects as a source of knowledge and make future recommendations for collective analysis, as well as collection and production of different kinds of knowledge in urban policy making.

Surface mount technology (SMT) is one of the most important developments in electronic industry. A surface mount assembly (SMA) has three consecutive manufacturing steps: solder paste stencil printing, component placement, and solder reflow. Stencil printing process (SPP) involves highly operation complexity and has multiple quality characteristics, and averagely accounts for 60% of soldering defects in SMA. This work presents a knowledgebased system for SPP planning and control to upgrade soldering quality level and system performance. A hybrid data set contains a 3 8-3 experimental design and statistical process control (SPC) records was collected firstly, and followed by data processing for removing conflicted, inconsistent, and redundant samples through a fuzzycluster algorithm. The output columns of the clustered data were then transformed using fuzzy quality loss function (FQLF) with respect to the dissatisfaction of printing performance. The neuro-fuzzy technique was adapted to model and learn SPP knowledge from the transformed data set into a SPP knowledge base. Finally, a GUI man-machine interface was developed to help engineers in predicting responses and evaluating the overall SPP performance. The empirical evaluations of soldering quality and productivity demonstrate the effectiveness and efficiency of this proposed system.

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher's Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA.

For the treatment of industrial wastewater a multitude of processes is available. It is the task of the chemical engineer to choose processes and to build up a treatment sequence suitable for the actual problem. Because of the large number of processes and process combinations, a knowledge-based system may be helpful during process selection and sequencing. The introduced knowledge based system delivers a treatment sequence based on heuristics and cost estimation. Depending on the supplied information a more or less detailed treatment sequence is suggested by the program. The article describes the aim and development of the knowledge based system, gives an overview on the program functions and presents the result of an example program consultation.

This paper introduces a novel knowledge based neural network models that incorporate and adapt both existing logistic regression formulas and kernel functions in there structures to improve the learning and adaptation ability of a connectionist model when there is an existing knowledge on the problem in the form of a logistic regression. Different from standard feed-forward neural networks, the proposed

This paper proposes a novel man-machine interface which takes into account the mutual cooperation between the operator and an expert system in order to realize precise decision making and operation. This interface is composed of three modules: a diagnosis module, a supporting module for the operator's decision making and an indication module. The knowledge base in the system described is represented by a fault tree diagram, incorporated to communicate between the operator and the computer. The system constructed enables the diagnosis of not only human errors, but also malfunctions of the process, and increases precision, reliability, and safety during bioprocess operation. © 1997 Elsevier Science B.V.

Collaborative online encyclopedias or knowledge bases such as Wikipedia and PlanetMath are becoming increasingly popular. We demonstrate NNexus, a generalization of the automatic linking engine of PlanetMath.org and the first system that automates the process of linking disparate "encyclopedia" entries into a fully-connected conceptual network. The main challenges of this problem space include: 1) linking quality (correctly identifying which terms to link and which entry to link to with minimal effort on the part of users), 2) efficiency and scalability, and 3) generalization to multiple knowledge bases and web-based information environment. We present NNexus that utilizes subject classification and other metadata to address these challenges and demonstrate its effectiveness and efficiency through multiple real world corpora.

Motivation: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). Results: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the Micro-Array Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The * To whom correspondence should be addressed. † The authors wish it to be known that, in their opinion, these two authors should be regarded as joint First Authors. open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. Availability: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/ SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Alzheimer’s disease (AD) is the major cause of dementia in the elderly. The biochemical changes that precede AD may be present up to 20 years before the clinical manifestation of the disease. The translational development of AD biomarkers may be theoretically achieved via two different strategies: the first strategy can be defined as ‘knowledge-based’ (deductive method), while the second one

The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.

Abstract. Knowledge Sharing is a crucial issue in the Semantic Web: SW ser-vices expose and share knowledge content (expressed through ontologies and related knowledge bases) arising from distinct languages, locales, and personal perspectives; in this scenario, semantic ...

This paper describes how we have developed a knowledge base for a heuristic application in a clinical domain. Firstly, the knowledge base has been modelled using CommonKADS. Later, the knowledge base has been represented in the language DAML+OIL. We will illustrate in some depth how objects, classes and relationships of a medical domain (modelled following an object-oriented approach) can be specified in the Web language DAML+OIL.

Designers of AI educational systems have largely ignored the problem of choosing what information to present and how to present it because systems to date have not been faced with a large space of possible presentations. This paper explains how a student model can provide the knowledge needed to make presentation decisions. Systems that use a student model for this purpose can make problems more interesting, challenging, and hand-tailored to students. We describe DECIDER: a program that improves student's decision-making abilities by constructing an environment where they can confront problems and decide on a plan of action. DECIDER chooses from a database of decision-making problems and varies how it presents those problems based on a model of the student's knowledge of goals, plans, and priorities.

PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. The main purpose of this paper is to sue wireless technology and design a Knowledge Based expert system to tune the parameters of the PID controller without using any mathematical model of the plant. The designed expert system uses pattern recognition techniques to tune the parameters of the controller, as a human expert does. The expert system uses input output to and from the plant under control, using wireless technology. The expert system uses a number of tuning rules to change the parameters of the controller based on the shape of the closed-loop step response to get a desired performance, which has been set by the control engineer. The developed expert system is able to tune the parameters of the controller for fixed, time-varying, time-delayed and noisy plants. The performance of the expert system has been examined using a laboratory ri. .

Objectives: To study how visual anticipatory capabilities develop in high and low-skill tennis players and the role of years of practice (i.e. experience). It was expected that with accumulated experience differences will increase with skill-level, in particular under conditions of fast visual exposure which results in minimal environmental information exposure. Method: Eighty tennis players divided into 4 age categories and 2 skill levels (high and low) were selected according to 4 criterion which ensured appropriate representation of skill-level and experience in tennis. Age categories were 8-11, 11-14, 14-18, and Ͼ18. Years of experience in the game were 2.12, 4.17, 6.5, and 12.4 years respectively. Players observed filmed segments of tennis strokes on a monitor, which varied in temporal occlusion conditions from Ϫ480 ms prior to ball-racquet contact to 320 ms after contact. After each exposure they were asked to indicate the final ball location. The radial, lateral, and depth distances were averaged for age and skill level. Design: A temporal occlusion paradigm was used in this study. The film was prepared with a camera positioned in the receiver's court. Eight strokes which best represent the game of tennis were chosen. Forty-eight segments were viewed and errors were calculated for each exposure. Result: High-skill tennis players gained more from practice and experience in developing visual anticipatory skills, but their perceptual advantage was not consistent across all stroke conditions, being at its greatest number in conditions of maximal temporal constraints. Conclusions: Differences in visual anticipatory capabilities exist between different skill levels at the outset of their development. These differences increase with experience, mainly after 6-7 years. Greater

This special issue on "Knowledge-based Environments and Services in Human-Computer Interaction" aims at presenting some novel environments and services and relevant challenging issues and proposed solutions that allow users to interact with computers more

As simulator-based training systems become more complex, the amount of effort required to generate, monitor, and maintain training exercises multiplies greatly. This has significantly increased the burden on the instructors, potentially making the training experience less efficient as well as less effective. Research on intelligent tutoring systems (ITS) has largely addressed this issue by replacing the instructor with a computer model of the appropriate pedagogical concepts and the domain expertise. While this approach is highly desirable, the effort required to develop and maintain an ITS can be quite significant. A more modest as well as practical alternative to an ITS is the development of intelligent computer-based tools that can support the instructors in their tasks. The advantage of this approach is that various tools can be developed to address the different aspects of the instructor's duties. Moreover, without the burden of having to repace the instructor, these tools are more easily developed and fielded in existing trainers. One aspect of an instructor's task is to assess the students' performance after each training exercise and select the next exercise based on their previous performances. It would clearly be advantageous if this exercise selection process were to be automated, thus relieving the instructor of a significant burden and allowing him to concentrate on other tasks. Therefore, the focus of this paper is the development of a stand-alone system capable of determining exercise progression and remediation automatically during a training session in a simulator-based trainer, on the basis of the students's past performance. Instructional heuristics were developed to carry out the exercise progression process. A prototype was developed and applied to gunnery training on the Army M1 main battle tank. I. PROBLEM STATEMENT AND OBJECTIVE OF RESEARCH NTELLIGENT tutoring systems (ITS) are generally devel-I oped to take the place of the instructor. Ideally, by having no human instructor, the efficiency of an instructor is increased infinitely. Control of the progression of exercises, evaluation of performance, feedback to student, and student record update are some of the functions that can be automated by an ITS. However, the level of effort required to develop and field a full ITS can be quite high and therefore very costly, especially when applied to previously existing simulator-based trainers. This paper presents the concept of an Intelligent instructor Support System (ZZSS) [l] as an alternative to an ITS for simulator-based training. Rather than replace the isntructor, IISS provides the tools for automating various facets of an Manuscript

We report on a case study to assess the use of an advanced knowledge-based software design technique with programmers who have not participatedin the technique's development. We use the KIDS approach to algorithm design to construct two global search algorithms that route baggage through a transportation net. Construction of the second algorithm involves extending the KIDS knowledge base. Experience with the case study leads us to integrate the approach with the spiral and prototyping models of software engineering, and to discuss ways to deal with incomplete design knowledge.

In this paper an operational model for the automatic generation of lifelike gestures of an anthropomorphic virtual agent is described. The biologically motivated approach to controlling the movements of a highly articulated figure provides a transformation of spatiotemporal gesture specifications into an analog representation of the movement from which the animations are directly rendered. To this end, knowledge-based computer animation techniques are combined with appropriate methods for trajectory formation and articulated figure animation.

A lot has been done in the use of Information and Communication Technology (ICT) in business yet very little in solving agricultural problems in developing countries. The problem addressed in this paper is the integration of widely available knowledge, processing and providing effective delivery for countries with low ICT infrastructure. Data was collected from small-scale farmers, meteorologists, agricultural extension officers and a research station and analyzed. From this, a suitable knowledge based model was developed. In order to evaluate the model, a prototype was developed and used in tests with sample data. The results reveal that the best way of disseminating agro-meteorological information to small scale farmers is using an integrated knowledge management system and mobile phone technology.

Several recent ascertaining studies revealed a deep-rooted and almost irresistible tendency among 12-16-year old students to improperly apply the linear or proportional model in word problems involving lengths, areas and volumes of similar plane figures and solids. While these previous studies showed to what extent students' improper use of linear reasoning is affected by different characteristics of the task, it remained largely unclear what aspects of their knowledge base are responsible for the occurrence and strength of this phenomenon and how these aspects relate to other more general misconceptions and buggy rules identified in the literature. This paper reports an in-depth investigation by means of individual semi-standardised interviews aimed at analysing the thinking process underlying students' improper linear reasoning and how this process is affected by their mathematical conceptions, beliefs and habits. During these interviews, students' solution processes were revealed through a number of well-specified questions by the interviewer with respect to one single non-linear application problem, as well as through their reactions to subsequent kinds of cognitive conflict. The interviews provided a lot of information about the actual process of problem solving from students falling into the 'linearity trap' and the mechanism behind it. Although some students seem to really 'believe' that quantities are always linked proportionally, their improper use of linearity often results from superficial and intuitive reasoning, influenced by specific mathematical conceptions, habits and beliefs leading to a deficient modelling process.

Modern decision support systems (DSS) not only store large amounts of decision-relevant data, but also aim at assisting decision-makers to explore the meaning of that data, and to take decisions based on understanding. In transportation domains, a multiagent approach to the construction of DSS is becoming increasingly popular, because it does not only reduce design complexity, but it also adequately supports a dialogue-based stance on decision support interactions. However, despite recent advances in the field of agent-oriented software engineering, a principled approach to the design of multiagent systems for decision support is still to come. In this paper, we outline a design method for the construction of agent-based DSS. Setting out from an organisational and communicative model of decision support environments, we present an abstract

The purpose of this paper is to describe potential improvements in patient safety resulting from design decisions in the development of a computerized decision support system (DSS) for managing opioid therapy for chronic noncancer pain. ATHENA-DSS is an automated decision support system developed in a collaboration between Stanford University and the U.S. Department of Veterans Affairs (VA) to increase guideline-adherent prescribing and to change physician behavior. Based on data in patients' computerized medical record and knowledge of the clinical domain encoded in a knowledge base, the system gives patient-specific recommendations to primary care providers at the point of care. ATHENA-Opioid Therapy is based on a previous system, ATHENA-Hypertension, and is designed to follow the VA/Department of Defense clinical practice guideline for the management of opioid therapy for chronic noncancer pain. We describe the rationale for development of decision support system elements and a graphical user interface to increase patient safety during primary care treatment for chronic pain. The ATHENA-Opioid Therapy system focuses on reducing patient risk in four main ways by: (1) identifying patients with comorbidities or concurrent prescriptions that raise risk for overdose and recommending more conservative dosing; (2) identifying patients with mental health problems that increase risk of medication abuse and recommending referral to psychiatric care and close monitoring; (3) assisting doctors with complex pharmacologic calculations to reduce the risk of mistakes when initiating, titrating, or switching medications; and (4) presenting relevant information to clinicians in an easy-to-use format. We describe a system evaluation plan that we believe is essential to ensure that deployment of ATHENA-Opioid Therapy leads to improvements in patient safety and increases in guideline-concordant prescribing, and we discuss the limitations of this system for patient safety efforts.

Phenolic compounds are major constituents of red wines and impact on certain wine-quality parameters. This study has aimed to increase the knowledge base on the phenolic composition of South African red wines by HPLC quan-tification of 39 individual phenols and 2 polymeric, phenolic groups in 260 South African wines of the cultivars Pinotage, Shiraz and Cabernet Sauvignon. Statistical analysis of the collected data revealed significant cultivar dif-ferences in the levels of many of the analysed compounds, as well as in the ratios in which specific anthocyanins are found in the wines. These data may be helpful in the cultivar authentication of wines of these cultivars. Discriminant analysis of the data showed statistically significant separations of cultivars, based on their polyphenol composition, and also how the use of data from a specific wine-producing area or vintage improved the possibilities for success-ful authentication. The data were collected from both pure cultivar wines...

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patient's medical record and knowledge of available resources, including, where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.

This paper frames juvenile justice as a public health concern and articulates a public health approach to intervention with incarcerated youth. The authors review the founding principles of juvenile justice and examine current practices through the lens of public health concepts. Although the knowledge base is incomplete, existing literature suggests dismal outcomes including high recidivism and low productivity; it also reveals promising evidence-based practices that require ongoing scrutiny and continuing elaboration. The authors' main recommendation concerns the application of a three-tiered prevention logic model that encompasses appropriate, evidence-based interventions, national implementation, and empirical validation. We advocate comprehensive, coordinated planning models that focus on outcomes, operate under shared infrastructure, and incorporate data analysis. The transformation of the juvenile justice system will require careful coordination, improved data systems, and multiple levels of focus. We assert that, despite the numerous challenges facing today's juvenile justice system, incarceration presents an opportunity for youth rehabilitation through effective interventions. Systemic reform has the potential both to improve outcomes for youth and to accrue public benefit through crime reduction and cost efficiencies.

This paper examines the relationship between the coherence of the knowledge base and the innovative productivity of bio-pharmaceutical firms. Using patent technological classes, we develop measures of the coherence and the scope of the firms' knowledge base. These variables constitute two original inputs in the traditional knowledge production function, additional to those of internal research spending and external knowledge flows. Poisson regressions show a strong link between the coherence of the knowledge base and the firms' innovative performance. Pharmaceutical firms experience decreasing returns to scale in research, while intra-sectoral knowledge flows positively nourish the firms' innovation performance. As biotechnology becomes more mature, knowledge flows tend to become uniform throughout different industries. In the meantime, the effect of coherence is significantly increasing, while that of the scope of the knowledge base and of research efforts are substantially decreasing. In the nineties, the coherent organisation of the knowledge base is a prominent condition for superior innovative performance.

‡ This work was partially suported by ISA/DGOT/JNICT contract 28/94. Abstract: A new classification method which permits the integration of expert knowledge in satellite image classification is presented. This approach differs from other knowledge based ones for the following reasons: 1) it is a low-cost procedure; 2) it allows that the available experts explicitly express all their knowledge on the area to be classified, and 3) it does not have to face the problem of rule generalization over the whole area. The method is applied to a real image data set and it is shown that the achieved classification is more accurate than the classification just based on spectral data.

Background: Substances with performance enhancing properties appear on a continuum, ranging from prohibited performance enhancing drugs (PED) through dietary supplements to functional foods (FF). Anti-doping messages designed to dissuade athletes from using PEDs have been typically based on moralising sport competition and/or employing scare campaigns with focus on the negative consequences. Campaigns offering comparable and acceptable alternatives are nonexistent, nor are athletes helped in finding these for themselves. It is timely that social marketing strategies for anti-doping prevention and intervention incorporate media messages that complement the existing approaches by promoting comparable and acceptable alternatives to doping. To facilitate this process, the aim of this study was to ascertain whether a single exposure knowledge-based information intervention led to increased knowledge and subsequently result in changes in beliefs and automatic associations regarding performance enhancements. Methods: In a repeated measure design, 115 male recreational gym users were recruited and provided with a brief information pamphlet on nitrite/nitrate and erythropoietin as a comparison. Measures of knowledge, beliefs and automatic associations were taken before and after the intervention with at least 24 hours between the two assessments. The psychological tests included explicit measures of beliefs and cognitive attitudes toward FF and PED using a self-reported questionnaire and computerised assessments of automatic associations using the modified and shortened version of the Implicit Association Test. Results: The information based intervention significantly increased knowledge (p < 0.001), changed explicit beliefs in specific FF (p < 0.001) and shifted the automatic association of FF with health to performance (p < 0.001). Explicitly expressed beliefs and automatic associations appear to be independent. Conclusion: Evidence was found that even a single exposure to a persuasive positive message can lead to belief change and can create new or alter existing associations -but only in the specific domain. Interventions to change outcome expectations in a positive way could be a rewarding avenue for anti-doping. Effective social marketing campaigns for drug free sport should follow appropriate market segmentation and use targeted messages via promoting the natural form as opposed to the purified form of the main active ingredient.

This article presents the learnings from PASP, the Priority Action Schools Program expressed in the meta-evaluation 'Knowing Makes the Difference'. PASP, jointly supported by the NSW Department of Education and Training and the NSW Teachers Federation, was designed to provide intensive support to 74 schools with concentrations of students from low socio-economic status. PASP schools aimed to: improve student engagement in learning and learning outcomes, reduce disruptive behaviour and suspensions, and improve student attendance and retention. Whole school communities were involved in identifying issues and potential solutions through partnerships and school networks. School Learning Portfolios documented rigorously evaluated strategies with the assistance of academic partners. The most significant feature of PASP is that it is a knowledge based program placing emphasis on the learnings within classrooms, schools and the system.

Organizational knowledge and discourse of diversity in employment Abstract Purpose Evidence suggests that the notion of diversity in employment has failed to meet expectations of increased inclusion and organizational competitiveness in an ever-changing and globalizing economic context. In this paper we consider the use of language of diversity in an organizational context. Methodology/Approach Using discourse analysis, we examine data obtained from semi-structured interviews conducted with Human Resources Managers and Personnel Managers. Participants' descriptions of diversity in relation to one particular group of (potential) employees, namely older jobseekers, are analysed for their function and effects in relation to organizational knowledge and practices.

Agile and flexible manufacturing systems make it mandatory that control programs have features such as agility, flexibility and reusability to meet fast changing, customer demands. To cope with this, a task-oriented model of the control system provides control code, data mapping and generating PLC code according to assembly sequences. The model is defined with the unit functions which can describe

New teachers in the US often are unprepared to meet the needs of culturally and linguistically diverse students. Preparing teachers for diversity has generated widespread interest in mentoring, yet little research has explored a knowledge base for equity-focused mentoring. Drawing on expertise of leading mentor practitioners and a case study, this article builds a framework for what mentors need to know and be able to do to focus new teachers on equity. Mentors need a bi-level and multi-domain knowledge base, targeting both students and teachers. Using a bifocal perspective, mentors view the new teacher up close, but also focus on the larger picture of students. Analyses delineate challenges and tensions in tapping this knowledge base in the action of mentoring.

This paper presents an application of the steady state network equivalents and expert system for voltage and reactive power (VAR) control in large-scale power systems. A steady state network equivalencing technique is used to construct the "three tier" subsystem which is adequate to solve voltage violation problems. An expert system utilises the sensitivity tree method to select the optimal set of control actions to alleviate the voltage problem. The expert system was developed using the VP-EXPERT system shell. VP-EXPERT interacts with the power system analysis software providing analysis of the network sensitivity matrix and data for the knowledge base. Practical application of the developed expert system is demonstrated on the example of the HydroElectric Commission (HEC) power system of Tasmania, however the proposed approach is not limited by the system size.

In everyday life, eye movements enable the eyes to gather the information required for motor actions. They are thus proactive, anticipating actions rather than just responding to stimuli. This means that the oculomotor system needs to know where to look and what to look for. Using examples from table tennis, driving and music reading we show that the information the eye movement system requires is very varied in origin and highly task speci¢c, and it is suggested that the control program or schema for a particular action must include directions for the oculomotor and visual processing systems. In many activities (reading text and music, typing, steering) processed information is held in a memory bu¡er for a period of about a second. This permits a match between the discontinuous input from the eyes and continuous motor output, and in particular allows the eyes to be involved in more than one task.

This paper is about one of the most important export products in Norway, "Norwegian salmon", focusing especially on the innovation system of aquaculture of salmon and trout in Norway, the aim of the paper is to describe the sector in a national and global context, for thereafter highlight in particular how different aquaculture firms operate and carry out innovation by looking at what kind of external relations and interactive learning processes are involved in innovation, and as such suggesting input to policy makers on how to strengthen the sectoral innovation system, a sector with the potential to become even more knowledge intensive and innovative than today. By differentiating aquaculture according to knowledge base and degree of structured and functionally differentiated organisations, the empirical material presented in this paper shows that aquaculture firms have very different approaches to innovation; from antiinnovation strategies to strategies of being in the forefront of innovation in the industry. The empirical material has shown that firms with very different innovation systems exists side-by-side and the overall functioning of the sectoral innovation system of aquaculture is influenced by all the different layers of firm types.