Recent Submissions

  • Design and development of Knowledge Based System for Integrated Maintenance Strategy and Operations

    Milana, M.; Khan, M. Khurshid; Munive-Hernandez, J. Eduardo (2017-03)
    The importance of maintenance has escalated significantly by the increase in automation in manufacturing processes. This condition changed the perspective of maintenance from being considered as an inevitable cost to being seen as a key business function to drive competitiveness. Consequently, maintenance decisions need to be aligned with the business competitive strategy as well as the requirements of manufacturing/quality functions in order to support manufacturing equipment performance. Therefore, it is required to synchronise the maintenance strategy and operations with business and manufacturing/quality aspects. This article presents the design and development of a Knowledge Based System for Integrated Maintenance Strategy and Operations. The developed framework of the Knowledge Based System for Integrated Maintenance Strategy and Operations is elaborated to show how the Knowledge Based System for Integrated Maintenance Strategy and Operations can be applied to support maintenance decisions. The knowledge-based system integrates the Gauging Absences of Prerequisites methodology in order to deal with different decision-making priorities and to facilitate benchmarking with a target performance state. This is a new contribution to this area. The Knowledge Based System for Integrated Maintenance Strategy and Operations is useful in reviewing the existing maintenance system and provides reasonable recommendations for maintenance decisions with respect to business and manufacturing perspectives. In addition, it indicates the roadmap from the current state to the benchmark goals for the maintenance system.
  • Assessing quality management system at a tertiary hospital in Oman using a hybrid knowledge-based system

    Al Khamisi, Yousuf N.; Khan, M. Khurshid; Munive-Hernandez, J. Eduardo (2018-09)
    The cost of medical care is snowballing at an alarming and unmaintainable rate universally. Consequently, the need for a trusted quality management (QM) system at healthcare organizations is a must. Such system will aid the healthcare governance to increase the effectiveness and decrease the cost. It will help in minimizing the risk and enhancing patient safety. Several challenges facing healthcare QM in Oman are creating computerizing monitoring tool and confirming commitment of decision makers at all levels. The Report of Quality and Patient Safety (RQPS) in Oman 2016 highlighted the low level of patient safety and quality culture among staff. It recommended to inaugurate a well-defined organizational chart based on each healthcare organization’s vision and mission. Therefore, it is important to design a national accreditation system that is accredited by an international accreditation body. Such step will help in prioritizing the needs and minimizing the cost of maintaining and upgrading systems. To overcome these challenges, this article is presenting a novel methodology of hybrid knowledge-based (KB) system to assess QM in healthcare environment (QMHE) using gauging absence of prerequisites tool for benchmarking and analytical hierarchy process for prioritizing. The KB-QMHE model can be used as a standard to assess QM at any healthcare organization around the globe. The results showed that 852 questions were answered by the quality managers in a tertiary hospital in Oman; the percentage of bad points in this hospital was 32%. The KB-QMHE model has clearly shown that the priority 1, in level 0, is to focus on the patient-centered dimension in the healthcare quality dimensions submodule. Output, also, suggested a prioritized action plan covering healthcare governance module, healthcare leadership module and healthcare organization’s resources module in level 1–3.
  • Mechanical durability of hydrophobic surfaces fabricated by injection moulding of laser-induced textures

    Romano, J-M.; Gulcur, Mert; Garcia-Giron, A.; Martinez-Solanas, E.; Whiteside, Benjamin R.; Dimov, S.S. (2019-05-15)
    The paper reports an investigation on the mechanical durability of textured thermoplastic surfaces together with their respective wetting properties. A range of laser-induced topographies with different aspect ratios from micro to nanoscale were fabricated on tool steel inserts using an ultrashort pulsed near infrared laser. Then, through micro-injection moulding the topographies were replicated onto polypropylene surfaces and their durability was studied systematically. In particular, the evolution of topographies on textured thermoplastic surfaces together with their wetting properties were investigated after undergoing a controlled mechanical abrasion, i.e. reciprocating dry and wet cleaning cycles. The obtained empirical data was used both to study the effects of cleaning cycles and also to identify cleaning procedures with a minimal impact on textured thermoplastic surfaces and their respective wetting properties. In addition, the use of 3D areal parameters that are standardised and could be obtained readily with any state-of-the-art surface characterisation system are discussed for monitoring the surfaces' functional response.
  • A method for predicting geometric characteristics of polymer deposition during fused-filament-fabrication

    Hebda, Michael; McIlroy, C.; Whiteside, Benjamin R.; Caton-Rose, F.; Coates, Philip D. (2019-05)
    In recent years 3D printing has gained popularity amongst industry professionals and hobbyists alike, with many new types of Fused Filament Fabrication (FFF) apparatus types becoming available on the market. A massively overlooked component of FFF is the requirement for a simple method to calculate the geometries of polymer depositions extruded during the FFF process. Manufacturers have so far achieved adequate methods to calculate tool-paths through so called slicer software packages which calculate the required velocities of extrusion from prior knowledge and data. Presented here is a method for obtaining a series of equations for predicting height, width and cross-sectional area values for given processing parameters within the FFF process for initial laydown on to a glass surface.
  • Performance analysis of contending customer equipment in wireless networks

    Afzal, H.; Awan, Irfan U.; Mufti, M.R.; Sheriff, Ray E. (2016-07)
    Initial ranging is the primary and important process in wireless networks for the customer premise equipments (CPEs) to access the network and establish their connections with the base station. Contention may occur during the initial ranging process. To avoid contention, the mandatory solution defined in the standards is based on a truncated binary exponential random backoff (TBERB) algorithm with a fixed initial contention window size. However, the TBERB algorithm does not take into account the possibility that the number of contended CPEs may change dynamically over time, leading to a dynamically changing collision probability. To the best of our knowledge, this is the first attempt to address this issue. There are three major contributions presented in this paper. First, a comprehensive analysis of initial ranging mechanisms in wireless networks is provided and initial ranging request success probability is derived based on number of contending CPEs and the initial contention window size. Second, the average ranging success delay is derived for the maximum backoff stages. It is found that the collision probability is highly dependent on the size of the initial contention window and the number of contending CPEs. To achieve the higher success probability or to reduce the collision probability among CPEs, the BS needs to adjust the initial contention window size. To keep the collision probability at a specific value for the particular number of contending CPEs, it is necessary for the BS to schedule the required size of the initial contention window to facilitate the maximum number of CPEs to establish their connections with reasonable delay. In our third contribution, the initial window size is optimized to provide the least upper bound that meets the collision probability constraint for a particular number of contending CPEs. The numerical results validate our analysis.
  • Failure Prediction using Machine Learning in a Virtualised HPC System and application

    Bashir, Mohammed; Awan, Irfan U.; Ugail, Hassain; Muhammad, Y. (2019)
    Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classi cation and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This f inding implies that our method can effectively predict all possible future system and application failures within the system.
  • Corneal confocal microscopy detects a reduction in corneal endothelial cells and nerve fibres in patients with acute ischemic stroke

    Khan, A.; Kamran, S.; Akhtar, N.; Ponirakis, G.; Al-Muhannadi, H.; Petropoulos, I.N.; Al-Fahdawi, Shumoos; Qahwaji, Rami S.R.; Sartaj, F.; Babu, B.; Wadiwala, M.F.; Shuaib, A.; Mailk, R.A. (2018-11)
    Endothelial dysfunction and damage underlie cerebrovascular disease and ischemic stroke. We undertook corneal confocal microscopy (CCM) to quantify corneal endothelial cell and nerve morphology in 146 patients with an acute ischemic stroke and 18 age-matched healthy control participants. Corneal endothelial cell density was lower (P<0.001) and endothelial cell area (P<0.001) and perimeter (P<0.001) were higher, whilst corneal nerve fbre density (P<0.001), corneal nerve branch density (P<0.001) and corneal nerve fbre length (P=0.001) were lower in patients with acute ischemic stroke compared to controls. Corneal endothelial cell density, cell area and cell perimeter correlated with corneal nerve fber density (P=0.033, P=0.014, P=0.011) and length (P=0.017, P=0.013, P=0.008), respectively. Multiple linear regression analysis showed a signifcant independent association between corneal endothelial cell density, area and perimeter with acute ischemic stroke and triglycerides. CCM is a rapid non-invasive ophthalmic imaging technique, which could be used to identify patients at risk of acute ischemic stroke.
  • A smartphone camera reveals an ‘invisible’ Parkinsonian tremor: a potential pre-motor biomarker?

    Williams, S.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Patel, P.; Graham, C.D. (2018)
    There are a wide variety of ways to objectively detect neurological signs, but these either require special hard-ware (such as wearable technology) or patient behaviour change (such as engagement with smartphone tasks) [2]. Neither constraint applies to the technology of computer vision, which is the processing of single or multiple camera images by computer to automatically derive useful information. The only equipment involved is ubiquitous: camera and computer.We report a computer vision-enhanced video sequence from a 68-year-old man, diagnosed with idiopathic Parkinson’s disease 2 years previously.
  • Data quality and governance in a UK social housing initiative: Implications for smart sustainable cities

    Duvier, Caroline; Anand, Prathivadi B.; Oltean-Dumbrava, Crina (2018)
    Smart Sustainable Cities (SSC) consist of multiple stakeholders, who must cooperate in order for SSCs to be successful. Housing is an important challenge and in many cities, therefore, a key stakeholder are social housing organisations. This paper introduces a qualitative case study of a social housing provider in the UK who implemented a business intelligence project (a method to assess data networks within an organisation) to increase data quality and data interoperability. Our analysis suggests that creating pathways for different information systems within an organisation to ‘talk to’ each other is the first step. Some of the issues during the project implementation include the lack of training and development, organisational reluctance to change, and the lack of a project plan. The challenges faced by the organisation during this project can be helpful for those implementing SSCs. Currently, many SSC frameworks and models exist, yet most seem to neglect localised challenges faced by the different stakeholders. This paper hopes to help bridge this gap in the SSC research agenda.
  • Data quality challenges in the UK social housing sector

    Duvier, Caroline; Neagu, Daniel; Oltean-Dumbrava, Crina; Dickens, D. (2018)
    The social housing sector has yet to realise the potential of high data quality. While other businesses, mainly in the private sector, reap the benefits of data quality, the social housing sector seems paralysed, as it is still struggling with recent government regulations and steep revenue reduction. This paper offers a succinct review of relevant literature on data quality and how it relates to social housing. The Housing and Development Board in Singapore offers a great example on how to integrate data quality initiatives in the social housing sector. Taking this example, the research presented in this paper is extrapolating cross-disciplinarily recommendations on how to implement data quality initiatives in social housing providers in the UK.
  • The Three Winds of Albert Lamorisse

    Goodall, Mark D. (2018)
    This text discusses the films of French director Albert Lamorisse in relation to the poetics of cinema. It focuses on three of Lamorisse’s films, Crin Blanc (1953), Le Ballon Rouge (1956) and Le Vent des Amoureux (1978), in order to examine his fascination with wind, a force of nature, due to its invisibility, that is virtually impossible to capture on film. Certain French theorists, however, have tried to explain the power of the wind, most notably Gaston Bachelard, whose works are quoted here as part of the analysis, while a few distinguished filmmakers, such as Joris Ivens and Andrei Tarkovsky, have used wind in interesting ways. But only Lamorisse had what could be described as a sustained obsession. Despite early success (the great French film theorist André Bazin was praiseworthy about his short films), Lamorisse has been somewhat neglected in recent years. Thus, this essay highlights the unique skills of a ‘forgotten man’ of French post-war cinema.
  • Multiresolution discrete finite difference masks for rapid solution approximation of the Poisson's equation

    Jha, R.K.; Ugail, Hassan; Haron, H.; Iglesias, A. (2018)
    The Poisson's equation is an essential entity of applied mathematics for modelling many phenomena of importance. They include the theory of gravitation, electromagnetism, fluid flows and geometric design. In this regard, finding efficient solution methods for the Poisson's equation is a significant problem that requires addressing. In this paper, we show how it is possible to generate approximate solutions of the Poisson's equation subject to various boundary conditions. We make use of the discrete finite difference operator, which, in many ways, is similar to the standard finite difference method for numerically solving partial differential equations. Our approach is based upon the Laplacian averaging operator which, as we show, can be elegantly applied over many folds in a computationally efficient manner to obtain a close approximation to the solution of the equation at hand. We compare our method by way of examples with the solutions arising from the analytic variants as well as the numerical variants of the Poisson's equation subject to a given set of boundary conditions. Thus, we show that our method, though simple to implement yet computationally very efficient, is powerful enough to generate approximate solutions of the Poisson's equation.
  • Experiments on deep face recognition using partial faces

    Elmahmudi, A.A.M.; Ugail, Hassan (2018)
    Face recognition is a very current subject of great interest in the area of visual computing. In the past, numerous face recognition and authentication approaches have been proposed, though the great majority of them use full frontal faces both for training machine learning algorithms and for measuring the recognition rates. In this paper, we discuss some novel experiments to test the performance of machine learning, especially the performance of deep learning, using partial faces as training and recognition cues. Thus, this study sharply differs from the common approaches of using the full face for recognition tasks. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the forehead. In this study, we use a convolutional neural network based architecture along with the pre-trained VGG-Face model to extract features for training. We then use two classifiers namely the cosine similarity and the linear support vector machine to test the recognition rates. We ran our experiments on the Brazilian FEI dataset consisting of 200 subjects. Our results show that the cheek of the face has the lowest recognition rate with 15% while the (top, bottom and right) half and the 3/4 of the face have near 100% recognition rates.
  • Computational analysis of smile weight distribution across the face for accurate distinction between genuine and posed smiles

    Al-Dahoud, A.; Ugail, Hassan (2018)
    In this paper, we report the results of our recent research into the understanding of the exact distribution of a smile across the face, especially the distinction in the weight distribution of a smile between a genuine and a posed smile. To do this, we have developed a computational framework for the analysis of the dynamic motion of various parts of the face during a facial expression, in particular, for the smile expression. The heart of our dynamic smile analysis framework is the use of optical flow intensity variation across the face during a smile. This can be utilised to efficiently map the dynamic motion of individual regions of the face such as the mouth, cheeks and areas around the eyes. Thus, through our computational framework, we infer the exact distribution of weights of the smile across the face. Further, through the utilisation of two publicly available datasets, namely the CK+ dataset with 83 subjects expressing posed smiles and the MUG dataset with 35 subjects expressing genuine smiles, we show there is a far greater activity or weight distribution around the regions of the eyes in the case of a genuine smile.
  • New Media Archaeologies

    Roberts, B; Goodall, Mark D. (2019-01)
  • The Ghosts of Media Archaeology

    Goodall, Mark D. (2019-01)
  • Load capacity predictions of continuous concrete deep beams reinforced with GFRP bars

    Zinkaah, Othman H.; Ashour, Ashraf F. (2019-06)
    Nine continuous concrete deep beams reinforced with glass fibre reinforced polymer (GFRP) bars were experimentally tested to failure. Three main parameters were investigated, namely, shear span-to-overall depth ratio, web reinforcement and size effect. The experimental results confirmed the impacts of web reinforcement and size effect that were not considered by the strut-and-tie method (STM) of the only code provision, the Canadian S806-12, that addressed such elements. The experimental results were employed to evaluate the applicability of the methods suggested by the American, European and Canadian codes as well as the previous studies to predict the load capacities of continuous deep beams reinforced with GFRP bars. It was found that these methods were unable to reflect the influences of size effect and/or web reinforcement, the impact of which has been confirmed by the current experimental investigation. Therefore, a new effectiveness factor was recommended to be used with the STM. Additionally, an upper-bound analysis was developed to predict the load capacity of the tested specimens considering a reduced bond strength of GFRP bars. A good agreement between the predicted results and the experimental ones was obtained with the mean and coefficient of variation values of 1.02 and 5.9%, respectively, for the STM and 1.03 and 8.6%, respectively, for the upper-bound analysis.
  • Performance and emissions study of diesel and waste biodiesel blends with nanosized CZA2 of high oxygen storage capacity

    Pimenidou, Panagiota; Shanmugapriya, N.; Shah, N. (2019)
    In this work, the effect of the nanosized CZA2 (cerium-zirconium-aluminium) on the performance and emissions in a two- cylinder indirect injection (IDI) diesel engine, was studied. CZA2 was dispersed in diesel (D100) and waste cooking oil and tallow origin biodiesel-diesel blends (B10, B20, B30) and tested at different engine loads and constant speed. The nanocatalyst (CZA2) increased the brake specific fuel consumption (BSFC) and decreased the brake thermal efficiency (BTE, %) of all tested fuels, at all loads, except B20 at the lowest load. CZA2 reduced nitrogen oxides (NOx) from D100 at low and high engine loads, as well as carbon monoxide (CO) and unburned hydrocarbons (HC) at medium and high tested loads. The dispersion of CZA2 promoted the combustion of the biodiesel blends by almost eliminating HC while reducing NOx and CO emissions at various loads. Thermogravimetric analysis (TGA) coupled with Attenuated Total Reflectance- Fourier Transform Infrared (ATR-FTIR) spectroscopy revealed that the addition of CZA2 in diesel and biodiesel under pyrolysis and oxidation conditions resulted in the presence of saturated species like ketones and final oxidation products such as CO2, supporting their improved combustion and emissions’ reduction in the engine tests.
  • Flexural performance of hybrid GFRP-steel reinforced concrete continuous beams

    Araba, A.M.; Ashour, Ashraf F. (2018-12-01)
    This paper presents the experimental results of five large-scale hybrid glass fiber reinforced polymer (GFRP)-steel reinforced concrete continuous beams compared with two concrete continuous beams reinforced with either steel or GFRP bars as reference beams. In addition, two simply supported concrete beams reinforced with hybrid GFRP/steel were tested. The amount of longitudinal GFRP, steel reinforcements and area of steel bars to GFRP bars were the main investigated parameter in this study. The experimental results showed that increasing the GFRP reinforcement ratio simultaneously at the sagging and hogging zones resulted in an increase in the load capacity, however, less ductile behaviour. On the other hand, increasing the steel reinforcement ratio at critical sections resulted in more ductile behaviour, however, less load capacity increase after yielding of steel. The test results were compared with code equations and available theoretical models for predicting the beam load capacity and load-deflection response. It was concluded that Yoon's model reasonably predicted the deflection of the hybrid beams tested, whereas, the ACI.440.1R-15 equation underestimated the hybrid beam deflections. It was also shown that the load capacity prediction for hybrid reinforced concrete continuous beams based on a collapse mechanism with plastic hinges at mid-span and central support sections was reasonably close to the experimental failure load.
  • Behaviour of interlocking mortarless hollow block walls under in-plane loading

    Safiee, N.A.; Nasir, N.A.M.; Ashour, Ashraf F.; Bakar, N.A. (2018-01)
    Experimental study of five full scale masonry wall panels subjected to prescibed pre-compressive vertical loading and increasing in-plane lateral loading is discussed. All five walls were constructed using interlocking mortarless load bearing hollow concrete blocks. The behaviour of wall in term of deflections along the wall height, shear strength, mortarless joint behaviour and local and overall failures under increasing in-plane lateral loading and pre-compressive vertical loading are reported and analysed. Simple strut-and-tie models are also developed to estimate the ultimate in-plane lateral capacity of the panel walls tested. The results indicate that, as the pre-compressive load increases, the in-plane lateral load capacity of walls increases. All walls tested failed due to diagonal shear and/or moderate toe crushing depending on the level of the pre-compressive load. The proposed strut-and-tie models were able to give reasonable predictions of the walls tested.

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