Elements of a model of effective teachers

Much research in teacher education has concentrated on individual elements of effective teaching such as the best way to teach content. There has been less emphasis on understanding the complex process of effective teaching in its entirety. Teacher educators are in the business of creating effective teachers and as such need a clear, evidence based model of an effective teacher. We believe that current models of effective teachers are limited because they fail to give sufficient emphasis to many important aspects of effective teachers and fail to integrate these components into a coherent whole and so provide a language for discussion of and a conceptual framework for developing teacher education.

This paper discusses the elements needed for a model of an effective teacher. This model emphasises not only the domains of effective teaching which receive most of the attention in teacher education and evaluation, namely content knowledge, pedagogical knowledge and, more recently, pedagogical content knowledge but also takes into account the teacher's personal knowledge and knowledge of context. We suggest that it is not just this knowledge that teachers have in these domains but the way this knowledge overlaps and interacts both within the teacher and with the teacher's physical, social, intellectual and emotional environment.

An examination of the effective teacher challenges not only teacher educators to rethink the way we educate both preservice and inservice teachers; but also the way we assess, judge and reward teachers.

An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.

Data-based dynamic haptic interaction model with deformable 3D objects

The data-based modeling of the haptic interaction simulation is a growing trend in research. These techniques offer a quick alternative to parametric modeling of the simulation. So far, most of the use of the data-based techniques was applied to static simulations. This paper introduces how to use data-based model in dynamic simulations. This ensures realistic behavior and produce results that are very close to parametric modeling. The results show that a quick and accurate response can be achieved using the proposed methods.

A sequence based dynamic SOM model for text clustering

Text clustering can be considered as a four step process consisting of feature extraction, text representation, document clustering and cluster interpretation. Most text clustering models consider text as an unordered collection of words. However the semantics of text would be better captured if word sequences are taken into account.

In this paper we propose a sequence based text clustering model where four novel sequence based components are introduced in each of the four steps in the text clustering process.

Experiments conducted on the Reuters dataset and Sydney Morning Herald (SMH) news archives demonstrate the advantage of the proposed sequence based model, in terms of capturing context with semantics, accuracy and speed, compared to clustering of documents based on single words and n-gram based models.

Towards extended machine translation model for next generation world wide web

In this paper, we proposed a Data Translation model which potentially is a major promising web service of the next generation world wide web. This technique is somehow analogy to the technique of traditional machine translation but it is far beyond what we understand about machine translation in the past and nowadays in terms of the scope and the contents. To illustrate the new concept of web services based data translation, a multilingual machine translation electronic dictionary system and its web services based model including generic services, multilingual translation services are presented. This proposed data translation model aims at achieving better web services in easiness, convenience, efficiency, and higher accuracy, scalability, self-learning, self-adapting.

Multi-agent model for robotic assembly system

In this paper, a multi-agent based model for a robotic assembly system is presented. Firstly, an organization model is used to construct the multi-agent model. Secondly, a dynamic self-organizing method is then put forward for the multi-agent robotic system to bid and contract the operations. Thirdly, a real multi-agent robotic system is built and assembly experiments are carried out. Finally, the experimental results confirm that the present multi-agent robotic system has flexibility, adaptation and stability.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Selection of working correlation structure and best model in GEE analyses of longitudinal data

The Generalized Estimating Equations (GEE) method is one of the most commonly used statistical methods for the analysis of longitudinal data in epidemiological studies. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method. However, statistical criteria for selecting the best correlation structure and the best subset of explanatory variables in GEE are only available recently because the GEE method is developed on the basis of quasi-likelihood theory. Maximum likelihood based model selection methods, such as the widely used Akaike Information Criterion (AIC), are not applicable to GEE directly. Pan (2001) proposed a selection method called QIC which can be used to select the best correlation structure and the best subset of explanatory variables. Based on the QIC method, we developed a computing program to calculate the QIC value for a range of different distributions, link functions and correlation structures. This program was written in Stata software. In this article, we introduce this program and demonstrate how to use it to select the most parsimonious model in GEE analyses of longitudinal data through several representative examples.

Climate change impacts and adaptation in Bangladesh : an agent-based approach

Bangladesh exemplifies the complex challenges facing densely populated coastal regions. The
pressures on the country are immense: around 145 million people live within an area of just 145,000 sq-km at
the confluence of three major river systems: the Ganges, the Brahmaputra and the Meghna. While progress
has been made, poverty remains widespread, with around 39% of children under five malnourished. Most of
its land-mass lies below 10m above sea level with considerable areas at sea level, leading to frequent and
prolonged flooding during the monsoons. Sea level rise is leading to more flooding as storm surges rise off
higher sea levels, pushing further inland. Higher sea levels also result in salt-water intrusion into freshwater
coastal aquifers and estuaries, contaminating drinking water and farmland. Warmer ocean waters are also
expected to lead to an increase in the intensity of tropical storms.
Bangladesh depends on the South Asian summer monsoon for most of its rainfall which is expected to
increase, leading to more flooding. Climate scientists are also concerned about the stability of monsoon and
the potential for it to undergo a nonlinear phase shift to a drier regime. Bangladesh faces an additional
hydrological challenge in that the Ganges and Brahmaputra rivers both rise in the Himalaya-Tibetan Plateau
region, where glaciers are melting rapidly. The Intergovernmental Panel on Climate Change (IPCC)
concluded that rapid melting is expected to increase river flows until around the late-2030s, by which time
the glaciers are expected to have shrunk from their 1995 extent of 500,000 sq-km to an expected 100,000 sqkm.
After the 2030s, river flows could drop dramatically, turning the great glacier-fed rivers of Asia into
seasonal monsoon-fed rivers. The IPCC concluded that as a result, water shortages in Asia could affect more
than a billion people by the 2050s. Over the same period, crop yields are expected to decline by up to 30% in
South Asia due to a combination of drought and crop heat stress. Bangladesh is therefore likely to face
substantial challenges in the coming decades.
In order to adequately understand the complex, dynamic, spatial and nonlinear challenges facing Bangladesh,
an integrated model of the system is required. An agent-based model (ABM) permits the dynamic
interactions of the economic, social, political, geographic, environmental and epidemiological dimensions of
climate change impacts and adaptation policies to be integrated via a modular approach. Integrating these
dimensions, including nonlinear threshold events such as mass migrations, or the outbreak of conflicts or
epidemics, is possible to a far greater degree with an ABM than with most other approaches.
We are developing a prototype ABM, implemented in Netlogo, to examine the dynamic impacts on poverty,
migration, mortality and conflict from climate change in Bangladesh from 2001 to 2100. The model employs
GIS and sub-district level census and economic data and a coarse-graining methodology to allow model
statistics to be generated on a national scale from local dynamic interactions. This approach allows a more
realistic treatment of distributed spatial events and heterogeneity across the country. The aim is not to
generate precise predictions of Bangladesh’s evolution, but to develop a framework that can be used for
integrated scenario exploration. This paper represents an initial report on progress on this project. So far the
prototype model has demonstrated the desirability and feasibility of integrating the different dimensions of
the complex adaptive system and, once completed, is intended to be used as the basis for a more detailed
policy-oriented model.

3D particle-based cell modelling for haptic microrobotic cell injection

Introducing haptic interface to conduct microrobotic intracellular injection has many beneficial implications. In particular, the haptic device provides force feedback to the bio-operator's hand. This paper introduces a 3D particle-based model to simulate the deformation of the cell membrane and corresponding cellular forces during microrobotic cell injection. The model is based on the kinematic and dynamic of spring – damper multi particle joints considering visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during the microinjection. The model is verified using experimental data of zebrafish embryo microinjection. The results demonstrate that the developed cell model is capable of estimating zebrafish embryo deformation and force feedback accurately.

Strong population genetic structure and its management implications in the mud carp Cirrhinus molitorella, an indigenous freshwater species subject to an aquaculture and culture-based fishery

This study investigated population genetic structure and diversity of mud carp Cirrhinus molitorella, a species widely used in aquaculture and culture-based fisheries in China and Mekong River riparian countries. Seven newly developed and one published microsatellite DNA markers were used to analyse samples from six wild locations, four hatchery broodstocks and one farmed site from the Mekong, Red and Pearl Rivers. Significant genetic structure was detected in C. molitorella, with isolation-by-distance being a strong force in the Mekong. Pair-wise F_ST, Fisher's exact tests for population differentiation, permutation tests and individual-based structure analysis all support the recognition of a sample originating from Toul Krasaing Lake (Cambodia) and one between Kratie and Stung Treng (Cambodia) as distinct from the remainder of the sampled range. Samples from the main upper Mekong and the Nam Khan River were significantly differentiated, but on a time scale inferred to be short (i.e. by genetic drift, not sufficient for evolution of new microsatellite alleles). The Mekong stock of C. molitorella was strongly differentiated from those from the Red and Pearl Rivers, inferred to be on an evolutionary time scale. Finer-scale sampling is warranted to further improve the understanding of genetic interactions among fish from the Mekong and its tributaries. Detailed studies on the ecology of C. molitorella (e.g. migration pathways and preferred spawning habitats) would provide useful information to explain the patterns of genetic structure detected here, and deepen insights about evolutionary distinctiveness of the population units.

Tuberculosis associated with household crowding in a developed country

Background: Tuberculosis (TB) remains an important infectious disease in New Zealand (NZ) and globally, but risk factors for transmission are still poorly understood. This research aimed to identify whether household crowding contributes to TB transmission in NZ.

Methods: This ecological study used TB surveillance and census data to calculate TB incidence rates by census area unit (CAU). Census data were used to determine CAU characteristics including proportion of household crowding (a bedroom deficit of one or more), proportion of population who are migrants born in high-TB-incidence countries, median household income, and deprivation level. A negative binomial regression model was used to estimate the association between TB incidence and household crowding.

Results: The analysis included 1898 notified TB cases for the 2000–4 period. Univariate analysis showed TB incidence at the CAU level was associated with household crowding, for the total population and for all ethnic and age groups. After adjusting for the covariates of household income, existing TB burden, and proportion of migrants from high-TB-incidence countries, multivariate analysis showed statistically significant associations between TB incidence and household crowding. The incidence rate ratio (IRR) was 1.05 (95% CI 1.02 to 1.08) in the total population and 1.08 (95% CI 1.04 to 1.12) for NZ-born people <40 years.

Conclusion: At the CAU level, TB incidence in NZ is associated with household crowding. An individual-based study (e.g. case–control) in recently infected cases (detected by molecular epidemiology techniques) is suggested to complement these findings. Reducing or eliminating household crowding could decrease TB incidence in NZ and globally.

The impact of divergent historical and cultural factors on convergence in global communication practice

Communication practice is increasingly converging around globally consistent approaches and techniques shaped by both globalisation and globalising communications technologies. However, this paper argues, national and regional practice histories and cultural characteristics have shaped, and continue to shape, practice in individual markets. The paper analyses the extent of that these divergent histories and cultures have shaped the structure and practices of the public relations industry in Australia and other countries. The paper challenges the common assumptions about public relations development and industry practice having developed from a predominantly US-based model progressively disseminated globally. It traces the history of public relations in Australia, counter-pointing its distinctive origins, to the US-origin thesis. It also examines the impact of demography and diverse national culture on industry shape and practice, comparing the Australian industry to that of other industries around the world. It uses mini-case studies of campaigns in specific countries to assess the extent to which they are culturally-bound by historical and cultural differences and the extent to which they are capable of being transferred or adapted to individual markets. For instance, assumptions about globally-consistent brand identities are contradicted by McDonalds’ branding practices in markets such as Canada and Japan. The paper also discusses how emerging market PR industries are being shaped by distinctive and divergent cultures and development paths and may create new structural and practice models as the emerging economies becoming dominant internationally. The authors suggest that history and cultural diversity continue, and will continue to, shape national and regional practices.

Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

Formulation and simulation of a 3D mechanical model of embryos for microinjection

The understanding of cell manipulation, for example in microinjection, requires an accurate model of the cells. Motivated by this important requirement, a 3D particlebased mechanical model is derived for simulating the deformation of the fish egg membrane and the corresponding cellular forces during microrobotic cell injection. The model is formulated based on the kinematic and dynamic of spring- damper configuration with multi-particle joints considering the visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during microinjection. A preliminary simulation study is conducted with different parameter configurations. The results indicate that the proposed particle-based model is able to provide similar deformation profiles as observed from a real microinjection experiment of the zebrafish embryo published in the literature. As a generic modelling approach is adopted, the proposed model also has the potential in applications with different types of manipulation such as micropipette cell aspiration.