Explicit derivation of the relativistic mass-energy relation for internal kinetic and potential energies of a composite system

A straightforward derivation of relativistic expressions for the mechanical momentum, kinetic and total energies, and mass-energy equivalence (including potential energy) which does not require any knowledge of the energy-momentum relation for electromagnetic waves or consideration of elastic collisions, but is directly based on Newton's second law and Lorentz's transformations, is presented in this paper. The existence of an invariant force is shown to be important for the validity of the relativistic mechanics.

Review of the ecology of Australian urban fauna: A focus on spatially explicit processes

Cities have a major impact on Australian landscapes, especially in coastal regions, to the detriment of native biodiversity. Areas suitable for urban development often coincide with those areas that support high levels of species diversity and endemism. However, there is a paucity of reliable information available to guide urban conservation planning and management, especially regarding the trade-off between investing in protecting and restoring habitat at the landscape level, and investing in programmes to maintain the condition of remnant vegetation at the local (site) level. We review the literature on Australian urban ecology, focusing on urban terrestrial and aquatic vertebrate and invertebrate fauna. We identify four main factors limiting our knowledge of urban fauna: (i) a lack of studies focusing at multiple ecological levels; (ii) a lack of multispecies studies; (iii) an almost total absence of long-term (temporal) studies; and (iv) a need for stronger integration of research outcomes into urban conservation planning and management. We present a set of key principles for the development of a spatially explicit, long-term approach to urban fauna research. This requires an understanding of the importance of local-level habitat quality and condition relative to the composition, configuration and connectivity of habitats within the larger urban landscape. These principles will ultimately strengthen urban fauna management and conservation planning by enabling us to prioritize and allocate limited financial resources to maximize the conservation return.

World knowledge in the process of translation

The paper illustrates the role of world knowledge in comprehending and translating texts. A short news item, which displays world knowledge fairly implicitly in condensed lexical forms, was translated by students from English into German. It is shown that their translation strategies changed from a first draft which was rather close to the surface structure of the source text to a final version which took situational aspects, texttypological conventions and the different background knowledge of the respective addressees into account. Decisions on how much world knowledge has to be made explicit in the target text, however, must be based on the relevance principle. Consequences for teaching and for the notions of semantic knowledge and world knowledge are discussed.

Knowledge management in the energy sector:review and future directions

Purpose – The purpose of this paper is to examine the state of knowledge management (KM) in the energy sector and more broadly, and consider future directions for research and practice. Design/methodology/approach – The paper reviews the literature on KM and the practice of KM as relevant to the energy sector. Findings – There are many examples of good practice in KM in the sector, and some organisations, especially in the oil industry, are seen as leaders in KM practice. However, other organisations have yet to embark on explicit KM initiatives or projects at all. In addition, some parts of the energy sector discuss KM without any reference to the more general KM literature. Originality/value – Although some parts of the energy sector have justifiably earned a good reputation for KM, other parts are completely unaware of the field, as is apparent from the literature. This review helps to raise awareness and guide future work.

Knowledge management: a review of the field and of OR's contribution

This paper examines the field of knowledge management (KM) and identifies the role of operational research (OR) in key milestones and in KM's future. With the presence of the OR Society journal Knowledge Management Research and Practice and with the INFORMS journal Organization Science, OR may be assumed to have an explicit and a leading role in KM. Unfortunately, the origins and the evidence of recent research efforts do not fully support this assumption. We argue that while OR has been inside many of the milestones there is no explicit recognition of its role and while OR research on KM has considerably increased in the last 5 years, it still forms a rather modest explicit contribution to KM research. Nevertheless, the depth of OR's experience in decision-making models and decision support systems, soft systems with hard systems and in risk management suggests that OR is uniquely placed to lead future KM developments. We suggest that a limiting aspect of whether OR will be seen to have a significant profile will be the extent to which developments are recognized as being informed by OR.

Intelligence agents (knowledge management)

Reports some insights into knowledge management (KM) derived from UK one-day workshops with six businesses, three non-profits and one public sector organization. Lists the four questions posed to participants and discusses the themes which emerged, e.g. the need for a KM strategy to make raw information more useable, KM performance measurement etc. Stresses the need for commitment from a top-level champion and a wide range of employees to make this work and identifies three types of solutions for improving KM strategy: technological (e.g. databases and intranets), people (e.g. motivation, retention, training and networking) and processes (e.g. procedural instructions and balancing formal/informal knowledge sharing methods). Finds that accountants and senior managers do not generally see KM as very important but argues that management accountants are suitable knowledge champions who could develop explicit links between KM and organizational performance.

Approaches to sharing knowledge in group problem structuring

Problem-structuring techniques are an integral aspect of 'Soft-OR'. SSM, SAST, Strategic Choice, and JOURNEY Making, all depend for their success on a group developing a shared view of a problem through some form of explicit modelling. The negotiated problem structure becomes the basis for problem resolution. Implicit to this process is an assumption that members of the group share and build their knowledge about the problem domain. This paper explores the extent to which this assumption is reasonable. The research is based on detailed records from the use of JOURNEY Making, where it has used special purpose Group Support software to aid the group problem structuring. This software continuously tracks the contributions of each member of the group and thus the extent to which they appear to be 'connecting' and augmenting their own knowledge with that of other members of the group. Software records of problem resolution in real organisational settings are used to explore the sharing of knowledge among senior managers. These explorations suggest a typology of knowledge sharing. The implications of this typology for problem structuring and an agenda for future research are considered.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

An investigation into the use of knowledge representation in computer-integrated manufacturing

In a certain automobile factory, batch-painting of the body types in colours is controlled by an allocation system. This tries to balance production with orders, whilst making optimally-sized batches of colours. Sequences of cars entering painting cannot be optimised for easy selection of colour and batch size. `Over-production' is not allowed, in order to reduce buffer stocks of unsold vehicles. Paint quality is degraded by random effects. This thesis describes a toolkit which supports IKBS in an object-centred formalism. The intended domain of use for the toolkit is flexible manufacturing. A sizeable application program was developed, using the toolkit, to test the validity of the IKBS approach in solving the real manufacturing problem above, for which an existing conventional program was already being used. A detailed statistical analysis of the operating circumstances of the program was made to evaluate the likely need for the more flexible type of program for which the toolkit was intended. The IKBS program captures the many disparate and conflicting constraints in the scheduling knowledge and emulates the behaviour of the program installed in the factory. In the factory system, many possible, newly-discovered, heuristics would be awkward to represent and it would be impossible to make many new extensions. The representation scheme is capable of admitting changes to the knowledge, relying on the inherent encapsulating properties of object-centres programming to protect and isolate data. The object-centred scheme is supported by an enhancement of the `C' programming language and runs under BSD 4.2 UNIX. The structuring technique, using objects, provides a mechanism for separating control of expression of rule-based knowledge from the knowledge itself and allowing explicit `contexts', within which appropriate expression of knowledge can be done. Facilities are provided for acquisition of knowledge in a consistent manner.

Consumer price knowledge in the market for apparel

Purpose – The aim of this study is to analyze consumers' price knowledge in the market for apparels. Design/methodology/approach – After reviewing earlier attempts at assessing the construct, the price estimation error “PEE” was used, a measure based on explicit price knowledge stored in long-term memory, as a valid indicator of price knowledge. Findings – The results, including data from about 1,527 consumers on 66 products from the German apparel market, indicate that price knowledge is relatively low. Originality/value – Although, in the literature, there are several studies on price knowledge in the food industry, little is known about price knowledge in other industry sectors. This is quite surprising since pricing strategy is a concept which is vitally important to all retailers. Therefore, this study is a first contribution to extending the concept of behavioral pricing to the apparel market.

Consumer price knowledge in the German retail market

Price knowledge as a construct has been one of the top behavioral pricing themes in the last four decades, especially in the Anglo-American literature. In Germany, scientists have paid relatively little attention to this topic during the last 15 years – with some notable exceptions. Therefore, this study analyzes German consumers' price knowledge and, by doing so, replicates and extends existing international work. After reviewing earlier attempts at assessing the construct, a measure is developed for the price estimation error “PEE”, based on explicit price knowledge stored in long-term memory. Results, including data from about 1,000 consumers on 69 products from a German retail chain, indicate that price knowledge in Germany is relatively low. Based on that observation, implications for the management are discussed.

The effectiveness of explicit individualized phonemic awareness instruction by a speech -language pathologist to preschool children with phonological speech disorders

This study investigated the effects of an explicit individualized phonemic awareness intervention administered by a speech-language pathologist to 4 prekindergarten children with phonological speech sound disorders. Research has demonstrated that children with moderate-severe expressive phonological disorders are at-risk for poor literacy development because they often concurrently exhibit weaknesses in the development of phonological awareness skills (Rvachew, Ohberg, Grawburg, & Heyding, 2003).^ The research design chosen for this study was a single subject multiple probe design across subjects. After stable baseline measures, the participants received explicit instruction in each of the three phases separately and sequentially. Dependent measures included same-day tests for Phase I (Phoneme Identity), Phase II (Phoneme Blending), and Phase III (Phoneme Segmentation), and generalization and maintenance tests for all three phases.^ All 4 participants made substantial progress in all three phases. These skills were maintained during weekly and biweekly maintenance measures. Generalization measures indicated that the participants demonstrated some increases in their mean total number of correct responses in Phase II and Phase III baseline while the participants were in Phase I intervention, and more substantial increases in Phase III baseline while the participants were in Phase II intervention. Increased generalization from Phases II to III could likely be explained due to the response similarities in those two skills (Cooper, Heron, & Heward, 2007).^ Based upon the findings of this study, speech-language pathologists should evaluate phonological awareness in the children in their caseloads prior to kindergarten entry, and should allocate time during speech therapy to enhance phonological awareness and letter knowledge to support the development of both skills concurrently. Also, classroom teachers should collaborate with speech-language pathologists to identify at-risk students in their classrooms and successfully implement evidence-based phonemic awareness instruction. Future research should repeat this study including larger groups of children, children with combined speech and language delays, children of different ages, and ESOL students.^

The Effectiveness of Explicit Individualized Phonemic Awareness Instruction By a Speech-Language Pathologist to Preschool Children With Phonological Speech Disorders

This study investigated the effects of an explicit individualized phonemic awareness intervention administered by a speech-language pathologist to 4 prekindergarten children with phonological speech sound disorders. Research has demonstrated that children with moderate-severe expressive phonological disorders are at-risk for poor literacy development because they often concurrently exhibit weaknesses in the development of phonological awareness skills (Rvachew, Ohberg, Grawburg, & Heyding, 2003). The research design chosen for this study was a single subject multiple probe design across subjects. After stable baseline measures, the participants received explicit instruction in each of the three phases separately and sequentially. Dependent measures included same-day tests for Phase I (Phoneme Identity), Phase II (Phoneme Blending), and Phase III (Phoneme Segmentation), and generalization and maintenance tests for all three phases. All 4 participants made substantial progress in all three phases. These skills were maintained during weekly and biweekly maintenance measures. Generalization measures indicated that the participants demonstrated some increases in their mean total number of correct responses in Phase II and Phase III baseline while the participants were in Phase I intervention, and more substantial increases in Phase III baseline while the participants were in Phase II intervention. Increased generalization from Phases II to III could likely be explained due to the response similarities in those two skills (Cooper, Heron, & Heward, 2007). Based upon the findings of this study, speech-language pathologists should evaluate phonological awareness in the children in their caseloads prior to kindergarten entry, and should allocate time during speech therapy to enhance phonological awareness and letter knowledge to support the development of both skills concurrently. Also, classroom teachers should collaborate with speech-language pathologists to identify at-risk students in their classrooms and successfully implement evidence-based phonemic awareness instruction. Future research should repeat this study including larger groups of children, children with combined speech and language delays, children of different ages, and ESOL students

Bipartite Assertion: A New Account of Assertion, Defined in Terms of Responsibility and Explicit Presentation

Assertion is a speech act that stands at the intersection of the philosophy of language and social epistemology. It is a phenomenon that bears on such wide-ranging topics as testimony, truth, meaning, knowledge and trust. It is thus no surprise that analytic philosophers have devoted innumerable pages to assertion, trying to give the norms that govern it, its role in the transmission of knowledge, and most importantly, what assertion is, or how assertion is to be defined. In this thesis I attempt to show that all previous answers to the question “What is assertion?” are flawed. There are four major traditions in the literature: constitutive norm theories of assertion, accounts that treat assertion as the expression of speaker attitudes, accounts that treat assertion as a proposal to add some proposition to the common ground, and accounts that treat assertion as the taking of responsibility for some claim. Each tradition is explored here, the leading theories within the tradition developed, and then placed under scrutiny to demonstrate flaws within the positions surveyed. I follow the work of G.E. Moore and William P. Alston, whilst drawing on the work of Robert Brandom in order to give a new bipartite theory of assertion. I argue that assertion consists in the explicit presentation of a proposition, along with a taking of responsibility for that proposition. Taking Alston's explicit presentation condition and repairing it in order to deal with problems it faces, whilst combining it with Brandom's responsibility condition, provides, I believe, the best account of assertion.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.