• The use of a new viscous process in constitutive models of polymers

      Sweeney, John; Spencer, Paul E. (2015-06)
      In constitutive models of polymers, there has been a long history of the use of strain-rate dependent viscous processes, such as the Eyring and Argon models. These are combined with elastic elements to generate viscoplastic models that exhibit typical phenomena such as rate dependent yield, creep and stress relaxation. The Eyring process is one of the most frequently used such mechanisms. It has two significant drawbacks: it implies a temperature dependence of mechanical behaviour that is in an opposite sense to that observed; and it predicts a strain rate dependence of yield stress that is less complex than that observed, leading to the requirement for two or more Eyring processes. In recent years, new ideas for amorphous polymers have been developed that lead to an alternative plastic mechanism that addresses these concerns. In this paper a constitutive model that incorporates this mechanism is developed, and its effectiveness in modelling macroscopic mechanical behaviour of polymers is explored with respect to published data.
    • The Use of a Solid Hydrocarbon as a Graphite Substitute for Astaloy CrM Sintered Steel

      Pieczonka, T.; Georgiev, J.; Stoytchev, M.; Mitchell, Stephen C.; Teodosiev, D.; Gyurov, S. (2004)
      Abstract Höganäs Astaloy CrM powder was used to prepare mixtures with 0.3-1.6 % carbon contents, both with and without 1 wt.% manganese additions. The carbon was added in three ways: as a graphite powder, as a solid CnHm hydrocarbon powder, and as a mixture of both. Green compacts were pressed at 300 - 800 MPa and sintered isothermally at temperatures in the range 1170 - 1295°C under flowing high purity nitrogen or nitrogen/hydrogen (9:1) atmosphere. Compressibility of the powder mixtures was investigated. Carbon loss occurring during sintering was carefully monitored. Sintering behaviour of numerous combinations of carbon content was investigated by dilatometry. For high carbon contents and high sintering temperatures, densification resulted from controlled generation of a liquid phase. Advantages of using solid hydrocarbon as a carbon donor and of Mn addition in powder metallurgy processing of steels are indicated.
    • Use of hollowcore flooring in composite steel-concrete construction. Part 2 - Design considerations.

      Lam, Dennis; Uy, B. (28/02/2014)
      This article presents the design procedures for the use of precast hollowcore slabs in steel-concrete composite construction. The paper also summarises the recent and on-going work on the transfer of this knowledge into the Australian construction industry. Whilst it is common practice to use precast concrete planks in Australian building construction, the benefits of composite behaviour with steel beams have not yet been fully realised with these systems, (National Precast Concrete Association of Australia, 2003). The use of precast hollowcore slabs in steel composite construction has seen rapid growth in popularity since it was first developed in the 1990s. The main advantages of this form of construction are that precast hollowcore slabs can span up to 15 metres without propping. The erection of 1.2 metre wide precast concrete units is simple and quick, shear studs can be pre-welded on beams before delivery to site thereby offering the savings associated with shorter construction times.
    • The use of in-situ test method EN 1793-6 for measuring the airborne sound insulation of noise barriers

      Bull, J.; Watts, Gregory R.; Pearse, J. (2017-01-15)
      The in situ measurement of the airborne sound insulation, as outlined in EN 1793-6:2012, is becoming a common means of quantifying the performance of road traffic noise reducing devices. Newly installed products can be tested to reveal any construction defects and periodic testing can help to identify long term weaknesses in a design. The method permits measurements to be conducted in the presence of background noise from traffic, through the use of impulse response measurement techniques, and is sensitive to sound leakage. Factors influencing the measured airborne sound insulation are discussed, with reference to measurements conducted on a range of traffic noise barriers located around Auckland, New Zealand. These include the influence of sound leakage in the form of hidden defects and visible air gaps, signal-to-noise ratio, and noise barrier height. The measurement results are found to be influenced by the presence of hidden defects and small air gaps, with larger air gaps making the choice of measurement position critical. A signal-to-noise ratio calculation method is proposed, and is used to show how the calculated airborne sound insulation varies with signal-to-noise ratio. It is shown that the measurement results are influenced by barrier height, through the need for reduced length Adrienne temporal windows to remove the diffraction components, prohibiting the direct comparison of results from noise barriers with differing heights.
    • The use of multiple mobile sinks in wireless sensor networks for large scale areas

      Al-Behadili, H.; AlWane, S.; Al-Yasir, Yasir I.A.; Ojaroudi Parchin, Naser; Olley, Peter; Abd-Alhameed, Raed A. (IET Digital Library, 2020-07)
      Sensing coverage and network connectivity are two of the most fundamental issues to ensure that there are effective environmental sensing and robust data communication in a WSN application. Random positioning of nodes in a WSN may result in random connectivity, which can cause a large variety of key parameters within the WSN. For example, data latency and battery lifetime can lead to the isolation of nodes, which causes a disconnection between nodes within the network. These problems can be avoided by using mobile data sinks, which travel between nodes that have connection problems. This research aims to design, test and optimise a data collection system that addresses the isolated node problem, as well as to improve the connectivity between sensor nodes and base station, and to reduce the energy consumption simultaneously. In addition, this system will help to solve several problems such as the imbalance of delay and hotspot problems. The effort in this paper is focussed on the feasibility of using the proposed methodology in different applications. More ongoing experimental work will aim to provide a detailed study for advanced applications e.g. transport systems for civil purposes.
    • Use of single-vision distance spectacles improves landing control during step descent in well-adapted multifocal lens-wearers

      Timmis, Matthew A.; Johnson, Louise; Elliott, David B.; Buckley, John G. (2010)
      PURPOSE: Epidemiologic research has shown that multifocal spectacle wearers (bifocal and progressive addition lenses [PALs]) are more than twice as likely to fall than are nonmultifocal spectacle wearers, with this risk further increasing when negotiating stairs. The present study investigated whether step and stair descent safety is improved by using single-vision distance lenses. METHODS: From a stationary standing position on top of a block, 20 long-term multifocal wearers stepped down (from different block heights) onto a lower level wearing bifocal, progressive addition, or single-vision distance lenses. RESULTS: Use of single-vision distance spectacles led to an increased single-limb support time, a reduced ankle and knee angle and vertical center-of-mass velocity at contact with the lower level, and a reduced ankle angular velocity and vertical center-of-mass velocity during initial landing (P < 0.03). These findings indicate that landing occurred in a more controlled manner when the subjects wore single-vision distance spectacles, rather than tending to "drop" onto the lower level as occurred when wearing bifocals or PALs. CONCLUSIONS: Use of single-vision distance spectacles led to improvements in landing control, consistent with individuals' being more certain regarding the precise height of the lower floor level. This enhanced control was attributed to having a view of the foot, step edge, and immediate floor area that was not blurred, magnified, or doubled and that did not suffer from image jump or peripheral distortions. These findings provide further evidence that use of single-vision distance lenses in everyday locomotion may be advantageous for elderly multifocal wearers who have a high risk of falling.
    • The use of thermographic imaging to evaluate therapeutic response in human tumour xenograft models

      Hussain, Nosheen; Connah, David; Ugail, Hassan; Cooper, Patricia A.; Falconer, Robert A.; Patterson, Laurence H.; Shnyder, Steven D. (2016-08-05)
      Non-invasive methods to monitor tumour growth are an important goal in cancer drug development. Thermographic imaging systems offer potential in this area, since a change in temperature is known to be induced due to changes within the tumour microenvironment. This study demonstrates that this imaging modality can be applied to a broad range of tumour xenografts and also, for the first time, the methodology’s suitability to assess anti-cancer agent efficacy. Mice bearing subcutaneously implanted H460 lung cancer xenografts were treated with a novel vascular disrupting agent, ICT-2552, and the cytotoxin doxorubicin. The effects on tumour temperature were assessed using thermographic imaging over the first 6 hours post-administration and subsequently a further 7 days. For ICT-2552 a significant initial temperature drop was observed, whilst for both agents a significant temperature drop was seen compared to controls over the longer time period. Thus thermographic imaging can detect functional differences (manifesting as temperature reductions) in the tumour response to these anti-cancer agents compared to controls. Importantly, these effects can be detected in the first few hours following treatment and therefore the tumour is observable non-invasively. As discussed, this technique will have considerable 3Rs benefits in terms of reduction and refinement of animal use.
    • User Interaction with Linked Data: An Exploratory Search Approach

      Thakker, Dhaval; Yang-Turner, F.; Despotakis, D. (2016)
      It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.
    • User Interface Design within a Mobile Educational Game

      Fotouhi-Ghazvini, Faranak; Earnshaw, Rae A.; Robison, David J.; Moeini, A.; Excell, Peter S. (2011)
      A mobile language learning system is implemented using an adventure game. The primary emphasis is upon graphical design and rich interaction with the user. A wide range of functionalities are described, and an efficient navigation system is proposed that uses contextual information, allowing the players to move seamlessly between mobile real and virtual worlds. The game environment is designed to have consistent graphics, dialogue, screens, and sequences of actions. Quick Response (QR) codes provide the necessary shortcuts for the players and Bluetooth connections automatically send and receive scores between teams. A response for every action is produced depending on the screen type, while keeping the file size manageable. Similar user tasks were kept spatially close together with a clearly designated beginning, middle and end. The main sources of error such as entering and extracting contextual data are predicted and simple error handling is provided. Unexpected events in mobile environments are tolerated and allowed. Internal locus of control is provided by ‘automatic pause’, ‘manual pause’ and ‘save’ commands to help players preserve their data and cognitive progress. The game environment is configurable for novice or expert players. This game is also suitable for students with auditory problems and female students are also specifically addressed.
    • Using a conversational framework in mobile game based learning - assessment and evaluation

      Fotouhi-Ghazvini, Faranak; Earnshaw, Rae A.; Moeini, A.; Robison, David J.; Excell, Peter S. (2011)
      Mobile language learning games usually only focus on spelling or out of context meaning for the entire dictionary, ignoring the role of an authentic environment. ‘Detective Alavi’ is an educational mobile game that provides a shared space for students to work collaboratively towards language learning in a narrative rich environment. This game motivates and preserves a conversation between learners and their teachers, and also between learners and learners, whilst being immersed in the story of the game. A seamless self-assessment scoring system in the game structure provides a less dominating environment for students to expose their weaknesses, and at the same time assists students to judge what skills they have learned and how much. This game has produced improvement in different cognitive processes and a deeper level of learning during the collaborative game play.
    • Using Basic Level Concepts in a Linked Data Graph to Detect User's Domain Familiarity

      Al-Tawil, M.; Dimitrova, V.; Thakker, Dhaval (2015)
      We investigate how to provide personalized nudges to aid a user’s exploration of linked data in a way leading to expanding her domain knowledge. This requires a model of the user’s familiarity with domain concepts. The paper examines an approach to detect user domain familiarity by exploiting anchoring concepts which provide a backbone for probing interactions over the linked data graph. Basic level concepts studied in Cognitive Science are adopted. A user study examines how such concepts can be utilized to deal with the cold start user modelling problem, which informs a probing algorithm.
    • Using computational methods for the prediction of drug vehicles

      Mistry, Pritesh; Palczewska, Anna Maria; Neagu, Daniel; Trundle, Paul R. (2014)
      Drug vehicles are chemical carriers that aid a drug's passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drug's permeability and or solubility, targeting a drug to a specific site or reducing a drug's toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drug's registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drug's toxic effects when no other information about a drug's characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drug's toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.
    • Using DBpedia as a knowledge source for culture-related user modelling questionnaires

      Thakker, Dhaval; Lau, L.; Denaux, R.; Dimitrova, V.; Brna, P.; Steiner, C. (2014)
      In the culture domain, questionnaires are often used to obtain profiles of users for adaptation. Creating questionnaires requires subject matter experts and diverse content, and often does not scale to a variety of cultures and situations. This paper presents a novel approach that is inspired by crowdwisdom and takes advantage of freely available structured linked data. It presents a mechanism for extracting culturally-related facts from DBpedia, utilised as a knowledge source in an interactive user modelling system. A user study, which examines the system usability and the accuracy of the resulting user model, demonstrates the potential of using DBpedia for generating culture-related user modelling questionnaires and points at issues for further investigation.
    • Using deep learning for IoT-enabled smart camera: a use case of flood monitoring

      Mishra, Bhupesh K.; Thakker, Dhaval; Mazumdar, S.; Simpson, Sydney; Neagu, Daniel (IEEE, 2019-07)
      In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy.
    • Using EWGM Method to Optimise the FMEA as a Risk Assessment Methodology

      Almashaqbeh, Sahar; Munive-Hernandez, J. Eduardo; Khan, M. Khurshid (2019-06)
      Failure Modes and Effect Analysis (FMEA) is a proactive, highly structured, and systematic approach for failure analysis. It has been also applied as a risk assessment tool, by ranking potential risks based on the estimation of Risk Priority Numbers (RPNs). This paper develops an improved FMEA methodology for strategic risk analysis. The proposed approach combines the Analytic Hierarchy Process (AHP) technique with the Exponential and Weighted Geometric Mean method (EWGM) to support risk analysis. AHP is applied to estimate the weights of three risk factors: Severity (S), Occurrence (O) and Detection (D), which integrate the RPN for each risk. The EWGM method is applied for ranking RPNs. Combining AHP with EWGM allows avoiding repetition of FMEA results. The results of the developed methodology reveal that duplication of RPNs has been decreased, and facilitating an effective risk ranking by offering a unique value for each risk. The proposed methodology focuses not only on high severity values for risk ranking but also it considers other risk factors (O and D), resulting in an enhanced risk assessment process. Furthermore, the weights of the three risk factors are considered. In this way, the developed methodology offers unique value for each risk in a simple way which makes the risk assessment results more accurate. This methodology provides a practical and systematic approach to support decision-makers in assessing and ranking risks that could affect long-term strategy implementation. The methodology was validated through the case study of a power plant in the Middle East, assessing 84 risks within 9 risk categories. The case study revealed that top management should pay more attention to key risks associated with electricity price, gas emissions, lost-time injuries, bad odor, and production.
    • Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

      Al-Tawil, M.; Dimitrova, V.; Thakker, Dhaval (2019)
      This paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.
    • Using Pareto points for model identification in predictive toxicology

      Palczewska, Anna Maria; Neagu, Daniel; Ridley, Mick J. (2013)
      Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
    • Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

      Mistry, Pritesh; Neagu, Daniel; Trundle, Paul R.; Vessey, J.D. (2016-08)
      Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables.
    • Using real time information for effective dynamic scheduling.

      Cowling, Peter I.; Johansson, M. (2002)
      In many production processes real time information may be obtained from process control computers and other monitoring systems, but most existing scheduling models are unable to use this information to effectively influence scheduling decisions in real time. In this paper we develop a general framework for using real time information to improve scheduling decisions, which allows us to trade off the quality of the revised schedule against the production disturbance which results from changing the planned schedule. We illustrate how our framework can be used to select a strategy for using real time information for a single machine scheduling model and discuss how it may be used to incorporate real time information into scheduling the complex production processes of steel continuous caster planning.