• Nano-Scale Observations of Tattoo Pigments in Skin by Atomic Force Microscopy

      Grant, Colin A.; Twigg, Peter C.; Tobin, Desmond J. (2015-03-26)
      In this study, we have shown how particles in carbon black tattoo ink accumulate in the human skin dermis using fine-resolution atomic force microscopy, with which a single ink particle in the collagenous network can be imaged. This information further demonstrates that tattoo inks are nano-particles. Further, we have deposited a commercially available tattoo ink on a glass slide and calculated a range of volumes for single ink particles.
    • Nano-scale temperature dependent visco-elastic properties of polyethylene terephthalate (PET) using atomic force microscope (AFM)

      Grant, Colin A.; Al-Fouzan, Abdulrahman M.; Gough, Timothy D.; Twigg, Peter C.; Coates, Philip D. (2013)
      Visco-elastic behaviour at the nano-level of a commonly used polymer (PET) is characterised using atomic force microscopy (AFM) at a range of temperatures. The modulus, indentation creep and relaxation time of the PET film (thickness=100 mum) is highly sensitive to temperature over an experimental temperature range of 22-175 degrees C. The analysis showed a 40-fold increase in the amount of indentation creep on raising the temperature from 22 degrees C to 100 degrees C, with the most rapid rise occurring above the glass-to-rubber transition temperature (T(g)=77.1 degrees C). At higher temperatures, close to the crystallisation temperature (T(c)=134.7 degrees C), the indentation creep reduced to levels similar to those at temperatures below T(g). The calculated relaxation time showed a similar temperature dependence, rising from 0.6s below T(g) to 1.2s between T(g) and T(c) and falling back to 0.6s above T(c). Whereas, the recorded modulus of the thick polymer film decreases above T(g), subsequently increasing near T(c). These visco-elastic parameters are obtained via mechanical modelling of the creep curves and are correlated to the thermal phase changes that occur in PET, as revealed by differential scanning calorimetry (DSC).
    • Nano/micro-structures and mechanical properties of ultra-high performance concrete incorporating graphene with different lateral sizes

      Dong, S.; Wang, Y.; Ashour, Ashraf F.; Han, B.; Ou, J. (Elsevier, 2020-10)
      The performance of cement-based materials can be controlled and tailored by adjusting the characteristics of reinforced nano inclusions. Therefore, the lateral size effect of graphene on the nano/micro-structures of ultra-high performance concrete (UHPC) was explored, and then the mechanical properties were investigated to analyze the structure–property correlation of composites in this paper. The test results show that due to nucleation site effect and the formation of core–shell elements, incorporating graphene with lateral size of > 50 µm improves the polymerization degree and mean molecule chain length of C-S-H gel by 242.6% and 56.3%, respectively. Meanwhile, the porosity and average pore volume of composites is reduced by 41.4% and 43.4%. Furthermore, graphene can effectively inhibit the initiation and propagation of cracks by crack-bridging, crack-deflection, pinning and being pulled-out effect, and the wrinkling characteristic is conductive to the enhancement of pinning effect. These improvements on nano- and micro- structures result in that the compressive strength, compressive toughness and three-point bending modulus of UHPC are increased by 43.5%, 95.7% and 39.1%, respectively, when graphene with lateral size of > 50 µm and dosage of 0.5% is added. Compared to graphene with lateral size of > 50 µm, graphene with average lateral size of 10 µm has less folds and larger effective size, then reducing the distance between core–shell elements. Hence, the addition of graphene with average lateral size of 10 µm leads to 21.1% reduction for Ca(OH)2 crystal orientation index, as well as 30.0% increase for three-point bending strength. It can be, therefore, concluded that the lateral size of graphene obviously influences the nano/micro-structures of UHPC, thus leading to the significantly different reinforcing effects of graphene on mechanical behaviors of UHPC.
    • Nanoindentation analysis of oriented polypropylene: Influence of elastic properties in tension and compression

      Vgenopoulos, D.; Sweeney, John; Grant, Colin A.; Thompson, Glen P.; Spencer, Paul E.; Caton-Rose, Philip D.; Coates, Philip D. (2018-08)
      Polypropylene has been oriented by solid-phase deformation processing to draw ratios up to ∼16, increasing tensile stiffness along the draw direction by factors up to 12. Nanoindentation of these materials showed that moduli obtained for indenter tip motion along the drawing direction (3) into to 1–2 plane (axial indentation) were up to 60% higher than for indenter tip motion along the 2 direction into the 1–3 plane (transverse indentation). In static tests, tensile and compressive determinations of elastic modulus gave results differing by factors up to ∼5 for strain along the draw direction. A material model incorporating both orthotropic elasticity and tension/compression asymmetry was developed for use with Finite Element simulations. Elastic constants for the oriented polypropylene were obtained by combining static testing and published ultrasonic data, and used as input for nanoindentation simulations that were quantitatively successful. The significance of the tension/compression asymmetry was demonstrated by comparing these predictions with those obtained using tensile data only, which gave predictions of indentation modulus higher by up to 70%.
    • Nanoparticle enhanced eutectic reaction during diffusion brazing of aluminium to magnesium

      Akhtar, T.S.; Cooke, Kavian O.; Khan, Tahir I.; Shar, M.S. (2019-03)
      Diffusion brazing has gained much popularity as a technique capable of joining dissimilar lightweight metal alloys and has the potential for a wide range of applications in aerospace and transportation industries, where microstructural changes that will determine the mechanical and chemical properties of the final joint must be controlled. This study explores the effect of Al2O3 nanoparticles on the mechanical and microstructural properties of diffusion brazed magnesium (AZ31) and aluminium (Al-1100) joints. The results showed that the addition of Al2O3 nanoparticle to the electrodeposited Cu coating increased the volume of eutectic liquid formed at the interface which caused a change to the bonding mechanism and accelerated the bonding process. When the Cu/Al2O3 nanocomposite coatings were used as the interlayer, a maximum bond strength of 46 MPa was achieved after 2 min bonding time while samples bonded using pure-Cu interlayers achieved maximum strength after 10 min bonding time. Chemical analysis of the bond region confirmed that when short bonding times are used, the intermetallic compounds formed at the interface are limited to the compounds consumed in the eutectic reaction.
    • Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective

      Panesar, Kulvinder (Vernon Press, 2020-03)
      This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.
    • Natural organic matter (NOM) and turbidity removal by plant-based coagulants: A review

      Okoro, B. U.; Sharifi, S.; Jesson, M. A.; Bridgeman, John (Elsevier, 2021)
      NOM deteriorates water quality by forming taste, clarification, colour, and odour problems. It also increases coagulant and chlorine consumption which can initiate disinfection by-products harmful to human health. The coagulation-flocculation (CF) technique is an established method commonly employed to remove NOM in water treatment. Plant-based coagulant products (PCPs) derived from plants like the Moringa oleifera (MO) Strychnos potatorum Linn and Opuntia ficus indica, have been studied and proposed as sustainable alternatives to chemical coagulant, like, aluminium sulphate due to their abundant availability, low cost, low sludge volume and disposal cost, and biodegradability. This review paper provides an overview of the most widely studied plant-based coagulants and discusses their NOM and turbidity removal. It investigates recent analytical tools applied in their characterisation and floc morphological studies. The paper also investigates the effects of operating parameters such as coagulant dose, temperature, and pH, on NOM and turbidity removal. It also reviews up-to-date PCPs biophysical properties and CF mechanism and examines the efficiency of their extraction methods in reducing NOM. Finally, it discusses and suggests ways to overcome commercialisation draw-back caused by nutrient addition.
    • Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration

      Wood, Clive; Alwati, Abdolati; Halsey, S.A.; Gough, Timothy D.; Brown, Elaine C.; Kelly, Adrian L.; Paradkar, Anant R. (2016-09-10)
      The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen – nicotinamide (IBU-NIC) and 1:1 carbamazepine – nicotinamide (CBZ-NIC) has been evaluated. A Partial Least Squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals.
    • Near Infrared Investigation of Polypropylene-Clay Nanocomposites for Further Quality Control Purposes-Opportunities and Limitations

      Witschnigg, A.; Laske, S.; Holzer, C.; Patel, Rajnikant; Khan, Atif H.; Benkreira, Hadj; Coates, Philip D. (2015)
      Polymer nanocomposites are usually characterized using various methods, such as small angle X-ray diffraction (XRD) or transmission electron microscopy, to gain insights into the morphology of the material. The disadvantages of these common characterization methods are that they are expensive and time consuming in terms of sample preparation and testing. In this work, near infrared spectroscopy (NIR) spectroscopy is used to characterize nanocomposites produced using a unique twin-screw mini-mixer, which is able to replicate, at ~25 g scale, the same mixing quality as in larger scale twin screw extruders. We correlated the results of X-ray diffraction, transmission electron microscopy, G′ and G″ from rotational rheology, Young’s modulus, and tensile strength with those of NIR spectroscopy. Our work has demonstrated that NIR-technology is suitable for quantitative characterization of such properties. Furthermore, the results are very promising regarding the fact that the NIR probe can be installed in a nanocomposite-processing twin screw extruder to measure inline and in real time, and could be used to help optimize the compounding process for increased quality, consistency, and enhanced product properties
    • Near-trapping effect of wave-cylinders interaction on pore water pressure and liquefaction around a cylinder array

      Lin, Z.; Pokrajac, D.; Guo, Yakun; Liao, C.; Tang, T. (2020-12-15)
      The near-trapping effects on wave-induced dynamic seabed response and liquefaction close to a multi-cylinder foundation in storm wave conditions are examined. Momentary liquefaction near multi-cylinder structures is simulated using an integrated wave-structure-seabed interaction model. The proposed model is firstly validated for the case of interaction of wave and a four-cylinder structure, with a good agreement with available experimental measurements. The validated model is then applied to investigate the seabed response around a four-cylinder structure at 0° and 45° incident angles. The comparison of liquefaction potential around individual cylinders in an array shows that downstream cylinder is well protected from liquefaction by upstream cylinders. For a range of incident wave parameters, the comparison with the results for a single pile shows the amplification of pressure within the seabed induced by progressive wave. This phenomenon is similar to the near-trapping phenomenon of free surface elevation within a cylinder array.
    • NetClust: A Framework for Scalable and Pareto-Optimal Media Server Placement

      Yin, H.; Zhang, X.; Zhan, T.Y.; Zhang, Y.; Min, Geyong; Wu, D.O. (2013)
      Effective media server placement strategies are critical for the quality and cost of multimedia services. Existing studies have primarily focused on optimization-based algorithms to select server locations from a small pool of candidates based on the entire topological information and thus these algorithms are not scalable due to unavailability of the small pool of candidates and low-efficiency of gathering the topological information in large-scale networks. To overcome this limitation, a novel scalable framework called NetClust is proposed in this paper. NetClust takes advantage of the latest network coordinate technique to reduce the workloads when obtaining the global network information for server placement, adopts a new Kappa -means-clustering-based algorithm to select server locations and identify the optimal matching between clients and servers. The key contribution of this paper is that the proposed framework optimizes the trade-off between the service delay performance and the deployment cost under the constraints of client location distribution and the computing/storage/bandwidth capacity of each server simultaneously. To evaluate the performance of the proposed framework, a prototype system is developed and deployed in a real-world large-scale Internet. Experimental results demonstrate that 1) NetClust achieves the lower deployment cost and lower delay compared to the traditional server selection method; and 2) NetClust offers a practical and feasible solution for multimedia service providers.
    • Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes

      Li, X.; Zhou, X.; Peng, Yonghong; Liu, B.; Zhang, R.; Hu, J.; Yu, J.; Jia, C.; Sun, C. (2014)
      Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.
    • Network coding applications to high bit-rate satellite networks

      Giambene, G.; Muhammad, M.; Luong, D.K.; Bacco, M.; Gotta, A.; Celandroni, N.; Jaff, Esua K.; Susanto, Misfa; Hu, Yim Fun; Pillai, Prashant; et al. (2015)
      Satellite networks are expected to support multimedia traffic flows, offering high capacity with QoS guarantees. However, system efficiency is often impaired by packet losses due to erasure channel effects. Reconfigurable and adaptive air interfaces are possible solutions to alleviate some of these issues. On the other hand, network coding is a promising technique to improve satellite network performance. This position paper reports on potential applications of network coding to satellite networks. Surveys and preliminary numerical results are provided on network coding applications to different exemplary satellite scenarios. Specifically, the adoption of Random Linear Network Coding (RLNC) is considered in three cases, namely, multicast transmissions, handover for multihomed aircraft mobile terminals, and multipath TCP-based applications. OSI layers on which the implementation of networking coding would potentially yield benefits are also recommended.
    • Network coding for multicast communications over satellite networks

      Jaff, Esua K.; Susanto, Misfa; Ali, Muhammad; Pillai, Prashant; Hu, Yim Fun (2015)
      Random packet errors and erasures are common in satellite communications. These types of packet losses could become significant in mobile satellite scenarios like satellite-based aeronautical communications where mobility at very high speeds is a routine. The current adaptive coding and modulation (ACM) schemes used in new satellite systems like the DVBRCS2 might offer some solutions to the problems posed by random packet errors but very little or no solution to the problems of packet erasures where packets are completely lost in transmission. The use of the current ACM schemes to combat packet losses in a high random packet errors and erasures environment like the satellite-based aeronautical communications will result in very low throughput. Network coding (NC) has proved to significantly improve throughput and thus saves bandwidth resources in such an environment. This paper focuses on establishing how in random linear network coding (RLNC) the satellite bandwidth utilization is affected by changing values of the generation size, rate of packet loss and number of receivers in a satellite-based aeronautical reliable IP multicast communication. From the simulation results, it shows that the bandwidth utilization generally increases with increasing generation size, rate of packet loss and number of receivers.
    • Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction.

      Neagu, Daniel; Avouris, N.M.; Kalapanidas, E.; Palade, V. (2002)
      In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.
    • Neural membrane mutual coupling characterisation using entropy-based iterative learning identification

      Tang, X.; Zhang, Qichun; Dai, X.; Zou, Y. (2020-11)
      This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis.
    • A neural network approach to burst detection

      Mounce, Steve R.; Day, Andrew J.; Wood, Alastair S.; Khan, Asar; Widdop, Peter D.; Machell, James (2002)
    • Neural network based correlation for estimating water permeability constant in RO desalination process under fouling

      Barello, M.; Manca, D.; Patel, Rajnikant; Mujtaba, Iqbal M. (2014-07-15)
      The water permeability constant, (Kw) is one of many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g.within the RO process model, estimation of Kw is therefore important. There are only two available literature correlations for calculating the dynamic Kw values. However, each of them are only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict Kw in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the Kw values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict Kw values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. While developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated.
    • Neural network based correlations for estimating temperature elevation for seawater in MSF desalination process

      Sowgath, Md Tanvir; Mujtaba, Iqbal M. (2006)
      Modelling played an important role in simulation, optimisation, and control of multi-stage flash (MSF) desalination processes. Top brine temperature (TBT) is one of the many important parameters that affect optimal design and operation of MSF processes. Within the MSF process model, calculation of TBT is therefore important. For a given pressure, TBT is a function of boiling point temperature (BPT) at zero salinity and temperature elevation (TE) due to salinity. In this work, we develop several neural network (NN) based correlations for predicting TE. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions by the NN based correlations were good when TE values, obtained using existing correlations from the literature are compared. Due to advancement of the microcomputer, plant automation becomes reliable means of plant maintenance. NN based correlations (models) can be updated in terms of new sets of weights and biases for the same architecture or for a new architecture reliably with new plant data.
    • Neural network modelling for shear strength of concrete members reinforced with FRP bars

      Bashir, Rizwan; Ashour, Ashraf F. (2012-12)
      This paper investigates the feasibility of using artificial neural networks (NNs) to predict the shear capacity of concrete members reinforced longitudinally with fibre reinforced polymer (FRP) bars, and without any shear reinforcement. An experimental database of 138 test specimens failed in shear is created and used to train and test NNs as well as to assess the accuracy of three existing shear design methods. The created NN predicted to a high level of accuracy the shear capacity of FRP reinforced concrete members. Garson index was employed to identify the relative importance of the influencing parameters on the shear capacity based on the trained NNs weightings. A parametric analysis was also conducted using the trained NN to establish the trend of the main influencing variables on the shear capacity. Many of the assumptions made by the shear design methods are predicted by the NN developed; however, few are inconsistent with the NN predictions.