Amjadipour, M, MacLeod, J, Lipton-Duffin, J, Tadich, A, Boeckl, JJ, Iacopi, F & Motta, N 2019, 'Electron effective attenuation length in epitaxial graphene on SiC.', Nanotechnology, vol. 30, no. 2, p. 025704.
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The inelastic mean free path (IMFP) for carbon-based materials is notoriously challenging to model, and moving from bulk materials to 2D materials may exacerbate this problem, making the accurate measurements of IMFP in 2D carbon materials critical. The overlayer-film method is a common experimental method to estimate IMFP by measuring electron effective attenuation length (EAL). This estimation relies on an assumption that elastic scattering effects are negligible. We report here an experimental measurement of electron EAL in epitaxial graphene on SiC using photoelectron spectroscopy over an electron kinetic energy range of 50-1150 eV. We find a significant effect of the interface between the 2D carbon material and the substrate, indicating that the attenuation length in the so-called 'buffer layer' is smaller than for free-standing graphene. Our results also suggest that the existing models for estimating IMFPs may not adequately capture the physics of electron interactions in 2D materials.
Argha, A, Su, SW & Celler, BG 2019, 'Static output feedback fault tolerant control using control allocation scheme', International Journal of Robust and Nonlinear Control, vol. 29, no. 1, pp. 98-116.
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© 2018 John Wiley & Sons, Ltd. This paper describes two novel schemes for fault tolerant control using robust suboptimal static output feedback design methods. These schemes can also be employed as actuator redundancy management for overactuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix (i) is not of full-rank such that it can be factorized into two matrices and (ii) it does not involve uncertainty, these schemes can be applied to systems whose control input matrix cannot be factorized and/or involve uncertainty. The so-called virtual control, in these schemes, is calculated using suboptimal H2-based static output feedback design schemes constructed to be robust against uncertainties emanating from inherent input matrix uncertainty and visibility of the control allocator to the controller. Then, using two proposed control allocation schemes (fixed and on-line), the obtained virtual control signal is redistributed among remaining (redundant or nonfaulty) set of actuators. As the proposed schemes are modular-based, they can be employed as real-time fault tolerant control schemes with no need to reconfigure the controller in the case of actuator faults or failures. The effectiveness of the proposed schemes is discussed and compared with numerical examples.
Azad, AK 2019, 'Elemental, morphological and thermal analysis of mixed microalgae species from drain water', Renewable Energy, vol. 131, pp. 617-624.
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© 2018 Elsevier Ltd In this study, Stigonematales sp. microalgae were collected from drain water and characterized for its’ morphological edifice, elemental composition, thermal condition and energy generation capacity by using scanning electron microscopy, energy dispersive X-ray, thermogravimetric analyzer and bomb calorimeter, respectively. Scanning electron micrographs revealed the top view of microalgae and ash pellet with carbon coated specimens at low voltage (5.0 kV) through the secondary electron image detector. Elemental analysis revealed all the major and minor constituents of this microalgae species and its’ ash in terms of dry weight (%) and atomic weight (%). Thermogravimetric analysis was conducted at heating rate, 10 °C/min and this experimental results determined moisture content, volatile matter, ash content and fixed carbon of the sample with 4.5%, 35%, 39.5% and 21%, respectively. Microalgae powder blended with bituminous coal by 75%, 50% and 25% measured calorific value 14.07 MJ/kg, 19.88 MJ/kg and 26.42 MJ/kg, respectively. Microalgae (75%) -coal (25%) blend showed excellent amount of energy content, 24.59 MJ/kg. Microalgae blended with coal unveiled an outstanding outcome with elevation of the volatile matter and drop of the ash content. Optimization of microalgae-coal blend in large-scale application can initiate bright future in renewable energy exploration.
Bhowmick, S, Saha, SC, Qiao, M & Xu, F 2019, 'Transition to a chaotic flow in a V-shaped triangular cavity heated from below', International Journal of Heat and Mass Transfer, vol. 128, pp. 76-86.
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© 2018 Elsevier Ltd Natural convection in a V-shaped cavity heated from below and cooled from top is investigated owing to its extensive presence in industrial systems and in nature such as in a valley. Two dimensional numerical simulation is performed for natural convection in the cavity using a Finite Volume Method. A wide range of Rayleigh numbers of Ra = 100to 108for the aspect ratio of A = 0.5 and the Prandtl number of Pr = 0.71 is considered. A set of supercritical bifurcations in a transition to a chaotic flow are described, which include a Pitchfork bifurcation from symmetric to asymmetric state and a Hopf bifurcation from steady to unsteady state. It is found that the Pitchfork bifurcation occurs between Ra = 7.5 × 103and 7.6 × 103and the Hopf bifurcation occurs between Ra = 1.5 × 107and 1.6 × 107. Additionally, a further bifurcation from periodic to chaotic state occurs between Ra = 5 × 107and 6 × 107. The power spectral density, the phase space trajectory and the largest Lyapunov exponent of unsteady flows in the transition to a chaotic state have been described. Further, heat transfer in the cavity is calculated and the corresponding dependence on the Rayleigh number is discussed and quantified.
Chen, X, Lai, C-Y, Fang, F, Zhao, H-P, Dai, X & Ni, B-J 2019, 'Model-based evaluation of selenate and nitrate reduction in hydrogen-based membrane biofilm reactor', CHEMICAL ENGINEERING SCIENCE, vol. 195, pp. 262-270.
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Dang, LC, Dang, CC & Khabbaz, H 2019, 'Modelling of columns and fibre reinforced load transfer platform supported embankments', Proceedings of the Institution of Civil Engineers - Ground Improvement.
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This study proposes a novel ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and deep cement mixing (DCM) columns supported (CS) embankment constructed over soft soils. An equivalent two-dimensional finite element model was developed to simulate the full geometry of a CS embankment reinforced without or with an FRLTP. A series of numerical analyses was firstly conducted on the proposed model for different improvement depths to assess the effectiveness of the introduction of FRLTP into the CS embankment system in terms of total and differential settlements, stress transfer mechanism and lateral displacement with depth. Subsequently, another extensive parametric study was conducted to further investigate the influence of the FRLTP key parameters including elastic deformation modulus, shear strength properties, and tensile strength on the embankment performance during construction and consolidation time. The numerical results show that the embankment with FRLTP can effectively diminish the total settlement and the lateral deformation of the embankment, meanwhile improve the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP shear strength properties appear to be the most influence factors to be considered in the design procedure of a target CS-FRLTP-embankment system.
Ding, A, Lin, D, Zhao, Y, Ngo, HH, Guo, W, Bai, L, Luo, X, Li, G, Ren, N & Liang, H 2019, 'Effect of metabolic uncoupler, 2,4‑dinitrophenol (DNP) on sludge properties and fouling potential in ultrafiltration membrane process.', The Science of the total environment, vol. 650, no. Pt 2, pp. 1882-1888.
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Energy uncoupling technology was applied to the membrane process to control the problem of bio-fouling. Different dosages of uncoupler (2,4‑dinitrophenol, DNP) were added to the activated sludge, and a short-term ultrafiltration test was systematically investigated for analyzing membrane fouling potential and underlying mechanisms. Ultrafiltration membrane was used and made of polyether-sulfone with a molecular weight cut off (MWCO) of 150 kDa. Results indicated that low DNP concentration (15-30 mg/g VSS) aggravated membrane fouling because more soluble microbial products were released and then rejected by the membrane, which significantly increased cake layer resistance compared with the control. Conversely, a high dosage of DNP (45 mg/g VSS) retarded membrane fouling owing to the high inhibition of extracellular polymeric substances (proteins and polysaccharides) of the sludge, which effectively prevented the formation of cake layer on the membrane surface. Furthermore, analyses of fouling model revealed that a high dosage of DNP delayed the fouling model from pore blocking transition to cake filtration, whereas this transition process was accelerated in the low dosage scenario.
Dong, X, Yan, Y, Tan, M, Yang, Y & Tsang, IW 2019, 'Late Fusion via Subspace Search With Consistency Preservation.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 1, pp. 518-528.
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In many real-world applications, data can be represented by multiple ways or multi-view features to describe various characteristics of data. In this sense, the prediction performance can be significantly improved by taking advantages of these features together. Late fusion, which combines the predictions of multiple features, is a commonly used approach to make the final decision for a test instance. However, it is ubiquitous that different features dispute the prediction on the same data with each other, leading to performance degeneration. In this paper, we propose an efficient and effective matrix factorization-based approach to fuse predictions from multiple sources. This approach leverages a hard constraint on the matrix rank to preserve the consistency of predictions by various features, and we thus named it as Hard-rank Constraint Matrix Factorization-based fusion (HCMF). HCMF can avoid the performance degeneration caused by the controversy of multiple features. Extensive experiments demonstrate the efficacy of HCMF for outlier detection and the performance improvement, which outperforms the state-of-the-art late fusion algorithms on many data sets.
Duan, P, Mao, G, Liang, W & Zhang, D 2019, 'A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction', IEEE Transactions on Intelligent Transportation Systems.
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IEEE This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space-time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.
Duan, W & Kirby, R 2019, 'Guided wave propagation in buried and immersed fluid-filled pipes: Application of the semi analytic finite element method', Computers and Structures, vol. 212, pp. 236-247.
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© 2018 Elsevier Ltd Developing an understanding of guided wave propagation in multi-layered systems has important applications in non-destructive evaluation. This article presents a general weighted residual formulation for guided wave propagation in fluid-filled pipes buried in an elastic solid, or immersed in a quiescent fluid. A one-dimensional semi-analytic finite element (SAFE) approach is combined with a perfectly matched layer (PML), to compute dispersion curves for different pipe applications. The speed and accuracy of this approach are compared against the scaled boundary finite element method (SBFEM) and it is shown that for a rod immersed in a liquid the two methods provide very similar computational speeds. The speed and accuracy of the model is then investigated for immersed and buried fluid-filled pipes, and it is shown that when discretising the PML no advantage in computational speed is found when using either quadratic or higher order spectral elements, provided the number of degrees of freedom in the PML is equivalent. Accordingly, it is shown that the SAFE-PML method is capable of obtaining accurately the modal characteristics of buried and immersed fluid-filled pipes, with computational speeds comparable to the SBFEM approach.
Far, H 2019, 'Dynamic Behaviour of Unbraced Steel Frames Resting on Soft Ground', Steel Construction, vol. 12, no. 1, pp. 1-6.
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Gao, J, Luo, Z, Li, H & Gao, L 2019, 'Topology optimization for multiscale design of porous composites with multi-domain microstructures', Computer Methods in Applied Mechanics and Engineering, vol. 344, pp. 451-476.
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Gao, J, Luo, Z, Li, H, Li, P & Gao, L 2019, 'Dynamic multiscale topology optimization for multi-regional micro-structured cellular composites', Composite Structures, vol. 211, pp. 401-417.
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In this paper, a new dynamic multiscale topology optimization method for cellular composites with multi-regional material microstructures is proposed to improve the structural performance. Firstly, a free-material distribution optimization method (FMDO) is developed to generate the overall configuration for the discrete element densities distributed within a multi-regional pattern. The macrostructure is divided into several sub regions, and each of them consists of a number of elements but with the same densities. Secondly, a dynamic topology optimization formulation is developed to perform the concurrent design of the macrostructure and material microstructures, subject to the multi-regional distributed element densities. A parametric level set method is employed to optimize the topologies of the macrostructure and material microstructures, with the effective macroscopic properties evaluated by the homogenization. In the numerical implementation, the quasi-static Ritz vector (QSRV) method is incorporated into the finite element analysis so as to reduce the computational cost in numerical analysis, and some kinematical connectors are introduced to make sure the connectivity between adjacent material microstructures. Finally, 2D and 3D numerical examples are tested to demonstrate the effectiveness of the proposed dynamic multiscale topology optimization method for the material-structural composites.
Gao, P, Walker, PD, Liu, H, Zhou, S & Xiang, C 2019, 'Application of an adaptive tuned vibration absorber on a dual lay-shaft dual clutch transmission powertrain for vibration reduction', Mechanical Systems and Signal Processing, vol. 121, pp. 725-744.
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© 2018 Elsevier Ltd This paper investigates the application of adaptive tuned vibration absorbers (ATVAs) to lightly damped automotive powertrains. To achieve this, a vibration characteristic analysis of a dual lay-shaft dual clutch transmission (DCT)-equipped powertrain system was performed, including a natural frequency evaluation and sensitivity studies. Transient responses due to gear changes and variations of both the gear ratio and engine speed were also evaluated. The results demonstrate that the impact of ATVAs on transient responses during gear changes is minimal, suggesting that these short-duration transients are outside the scope of application for ATVAs. However, as excitation forces originate from the engine, speed has a significant impact on excitation frequency. Furthermore, the gear ratios significantly influence the engine operating range and natural frequency in any particular gear. Therefore, variations of engine speeds and gear ratios are the main influencing factors affecting the dominant excitation frequency. Consequently, the relationships among the system natural frequencies, the dominant external excitation frequency and the inherent frequency of the ATVA are comprehensively studied. By investigating the relationships among the dominant external excitation frequency and system natural frequencies and the system sensitivity analysis results, the installation position, stiffness range and optimal frequency tuning scheme of the ATVA can be determined. The results were applied to a DCT powertrain with continuously changing engine speeds and an ATVA with corresponding variable stiffness, and a frequency tuning scheme is installed on the powertrain. By comparing the simulation results, the effectiveness and reliability of the presented method can be verified.
Guo, YJ 2019, 'Efficient angle-of-arrival estimation of lens antenna arrays for wireless information and power transfer', IEEE Journal on Selected Areas in Communications, vol. 37, no. 1, pp. 116-130.
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© 1983-2012 IEEE. Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as 'DFT beam differences (DBDs),' that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.
Han, L, Zhou, M, Jia, W, Dalil, Z & Xu, X 2019, 'Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model', Information Sciences, vol. 476, pp. 491-504.
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© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
Han, SF, Jin, W, Yang, Q, El-Fatah Abomohra, A, Zhou, X, Tu, R, Chen, C, Xie, GJ & Wang, Q 2019, 'Application of pulse electric field pretreatment for enhancing lipid extraction from Chlorella pyrenoidosa grown in wastewater', Renewable Energy, pp. 233-239.
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© 2018 Elsevier Ltd Lipid extraction is a key step of biodiesel production from microalgae, however, the application of traditional extraction methods was limited due to the difficulties of cell disruption as well as solvent toxicity. In this work, pretreatment method using pulsed electric field (PEF), was primarily applied to lipid extraction process from microalgae Chlorella pyrenoidosa grown in wastewater. After the pretreatment with PEF, the yields of fatty acid methyl esters from C.pyrenoidosa was 12.0% higher than traditional pretreatment with ultrasonic. The results indicated that PEF was an effective method for cell disruption. Fluorescence staining and scanning electron microscopy showed that the integrity of the cell membrane of microalgae was damaged under pulsed electric field, which enhanced the penetration of solvents and lipid extraction.
Hassan, M, Liu, D & Xu, D 2019, 'A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots', Journal of Intelligent and Robotic Systems.
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Hodges, J, Attia, T, Arukgoda, J, Kang, C, Cowden, M, Doan, L, Ranasinghe, R, Abdelatty, K, Dissanayake, G & Furukawa, T 2019, 'Multistage bayesian autonomy for high-precision operation in a large field', Journal of Field Robotics, vol. 36, no. 1, pp. 183-203.
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© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'A magnetically coupled multi-port, multi-operation-mode micro-grid with a predictive dynamic programming-based energy management for residential applications', International Journal of Electrical Power & Energy Systems, vol. 104, no. January 2019, pp. 784-796.
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This paper presents the development of a residential micro-grid topology based on a combination of common magnetic and electrical buses. The magnetic bus interfaces two low voltage dc buses linking a PV and a fuel cell to a high voltage dc bus connected to a grid-tied single-phase bidirectional inverter. A battery is used to store the surplus energy of the system and stabilise the dc voltage of the fuel cell bus. A synchronised bus voltage balance (SBVB) technique is used to reduce the conduction losses and increase the soft switching operation range of the converters. To improve the maximum power point tracking (MPPT) performance and system efficiency, appropriate control techniques and compensation blocks are designed. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes according to a predictive 2D dynamic programming-based energy management. A mode selection and transition strategy is developed to select the appropriate operation mode and smooth the mode transition. A detailed study of the micro-grid including steady-state operation, small signal modelling, controller design, and energy management is presented. A prototype of the system is developed, and experimental tests are conducted for an energy management scenario.
Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2019, 'A scene image classification technique for a ubiquitous visual surveillance system', Multimedia Tools and Applications, pp. 1-28.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods.
Katz, A, Shon, HK, Chekli, L & Kim, J-H 2019, 'TiO₂-Coated Optical Fibres for Groundwater Remediation.', Journal of nanoscience and nanotechnology, vol. 19, no. 2, pp. 1086-1089.
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In this study, polyethylene glycol (PEG) was tested as an alternative polymer to improve the coating of TiO₂ particles onto optical fibres. The addition of PEG helped dispersing effectively the particles in solution to control their deposition and therefore achieving better properties of the coating film. Results showed that PEG increased the effectiveness of the coating and the prepared fibres showed better performance for the removal of methylene blue (MB). This was attributed to the morphological changes induced by PEG. EDX mapping of the fibre surface showed that the addition of PEG lead to a better coverage of the fibre surface; increasing the active surface area for subsequent photocatalytic degradation. This study also showed that the light intensity, pH and initial concentration of MB have a significant influence. Finally, it was demonstrated that the coatings using PEG were better ordered and structured; showing a distinct layer-by-layer deposition.
Khan, MA, Ngo, HH, Guo, W, Liu, Y, Nghiem, LD, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Jia, H 2019, 'Optimization of hydraulic retention time and organic loading rate for volatile fatty acid production from low strength wastewater in an anaerobic membrane bioreactor.', Bioresource technology, vol. 271, pp. 100-108.
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This study aims to investigate the production of volatile fatty acids (VFAs) from low strength wastewater at various hydraulic retention time (HRT) and organic loading rate (OLR) in a continuous anaerobic membrane bioreactor (AnMBR) using glucose as carbon source. This experiment was performed without any selective inhibition of methanogens and the reactor pH was maintained at 7.0 ± 0.1. 48, 24, 18, 12, 8 and 6 h-HRTs were applied and the highest VFA concentration was recorded at 8 h with an overall VFA yield of 48.20 ± 1.21 mg VFA/100 mg CODfeed. Three different ORLs were applied (350, 550 and 715 mg CODfeed) at the optimum 8 h-HRT. The acetic and propanoic acid concentration maximums were (1.1845 ± 0.0165 and 0.5160 ± 0.0141 mili-mole/l respectively) at 550 mg CODfeed. The isobutyric acid concentration was highest (0.3580 ± 0.0407 mili-mole/l) at 715 mg CODfeed indicating butyric-type fermentation at higher organic loading rate.
Khan, SA, Islam, MR, Guo, Y & Zhu, J 2019, 'An Amorphous Alloy Magnetic-Bus-Based SiC NPC Converter with Inherent Voltage Balancing for Grid-Connected Renewable Energy Systems', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper presents an amorphous alloy magnetic-bus-based neutral point clamped (NPC) converter for grid-connected renewable generation systems. In the proposed system, the amorphous alloy high-frequency high-power density multi-winding magnetic bus generates balanced dc supplies for the five-level (5L) NPC converter for high-quality power conversion. Compared to the traditional NPC converter topologies, the proposed magnetic-bus-based architecture does not require any control circuit for voltage balancing of the series connected capacitors. The magnetic bus inherently overcomes galvanic isolation issues and may reduce the size of the boosting inductor. In this paper, a finite control set model predictive control algorithm is derived to control the grid-connected 5L-NPC inverter for multilevel voltage synthesizing, while achieving the user-defined active and reactive power values. To verify the proposed concept, a simulation model is developed and analyzed in MATLAB/Simulink environment. To validate the technology, a scale d-down prototype test platform is developed in the laboratory with silicon carbide switching devices, which achieves high blocking voltage, low power dissipation, high switching frequency, and high-Temperature operation. Based on the simulation and the experimental results, it is expected that the proposed converter will have a great potential for widespread application in renewable generation systems including superconducting generator-based wind turbines.
Kheshti, MR 2019, 'High-Gradient Magnetic Separator (HGMS) combined with adsorption for nitrate removal from aqueous solution', Separation and Purification Technology, vol. 212, pp. 650-659.
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© 2018 Elsevier B.V. This paper investigates the adsorption of nitrate anions from aqueous solutions on ammonium-functionalized magnetic mesoporous silica. The adsorbent was prepared via two-step coating process of silica on magnetic core (Fe3O4). The resultant structure was modified by 3-aminopropyl triethoxysilane (APTES), and finally acidified in HCl solution to convert the grafted amino groups to ammonium ones. Field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibration sample magnetometer (VSM), Energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR), and N2 adsorption/desorption were used to characterize the obtained samples. Experimental results showed that several factors affected the uptake behavior such as pH, contact time, and initial concentration of nitrate. The amount of sorbent loading were examined and the adsorbent shows great adsorption capacity for NO3¯ (ca.51.28 mg g−1 at 25 °C). The nitrate loaded multifunctional microsphere can be easily regenerated with NaOH solution. The separation of multifunctional magnetic microspheres from solution by novel high gradient magnetic separation (HGMS), using the collection of rods, was also investigated in details. Contrast to other methods based on filter and batch conditions, large volumes of water can be easily handled by the new designed HGMS due to the decreasing pressure drop and retention times. The effect of a set of two different experimental variables, i.e. flowrate and magnetic field strength, were investigated to identify the best working conditions for the separation of adsorbent from treated water. The most efficient backwash system was offered to reuse the magnetic particles, too. The removal efficiency of NO3¯ from solution was around 86.24% by the constructed HGMS under the optimal experimental conditions of 7.5 mL s −1 flowrate and: 3.49 mT magnitude of the magnetic field.
Kirby, R & Duan, W 2019, 'Guided wave propagation in cylindrical ducts with elastic walls enclosing a fluid moving with a uniform velocity', Wave Motion, vol. 85, pp. 1-9.
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© 2018 Elsevier B.V. Theoretical models for elastic wave propagation in fluid filled ducts normally neglects mean fluid flow. However, in many engineering applications the velocity of the fluid may influence the modal characteristics of the duct, for example in gas pipelines, turbomachinery applications and ventilation systems. Accordingly, the influence of a mean uniform fluid flow on acoustically driven duct wall vibration is analysed here for a cylindrical geometry. The semi-analytic finite element method is used to couple the elastodynamic wave equation for the duct wall to the convected wave equation for sound propagation in a uniform fluid flow. A one dimensional finite element approach is described and this is used to find the coupled eigenmodes for the duct. Under certain conditions, a uniform mean flow is seen to significantly affect the phase speed for different eigenmodes, and it is shown that this may cause energy to transfer from the fluid to the surrounding wall at frequencies much lower than those seen without mean flow. This behaviour has the potential to increase sound radiation from ducts at lower frequencies when mean flow is present.
Li, H, Wang, J, Li, R & Lu, H 2019, 'Novel analysis–forecast system based on multi-objective optimization for air quality index', Journal of Cleaner Production, vol. 208, pp. 1365-1383.
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© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring.
Liang, J, Walker, PD, Ruan, J, Yang, H, Wu, J & Zhang, N 2019, 'Gearshift and brake distribution control for regenerative braking in electric vehicles with dual clutch transmission', Mechanism and Machine Theory, vol. 133, pp. 1-22.
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© 2018 Elsevier Ltd To alleviate the problem of limited driving range per charge in electric vehicles, a dual clutch transmission based regenerative braking power-on shifting control system is proposed and investigated in this paper. Power-on shifting refers to the shift process where the power flow between the wheel and the power source is not cut off and could be maintained around a desirable value. This character is more important for regenerative braking than the normal driving conditions as the regenerative braking force from the motor accounts for a large part of the total braking force. Due to the difference between the normal driving condition and the regenerative braking process, existing normal driving shifting control strategies, which could introduce significant torque interruption, cannot be directly applied for regenerative braking. As a result, the energy recovery capability and efficiency are compromised. To solve this problem, a power-on shifting control strategy for regenerative braking is proposed as well as an energy-safety oriented braking strategy. To demonstrate the effectiveness of the proposed system, mathematical models are built and dynamic responses of the transmission system during braking both in up-shift and down-shift processes are presented. Moreover, the efficiency and recovery capability improvements made by achieving power-on shifting during regenerative braking are verified through a typical deceleration driving cycle and a specially designed daily deceleration scenario.
Lin, C, Sun, S, Walker, P & Zhang, N 2019, 'Accelerated adaptive second order super-twisting sliding mode observer', IEEE Access.
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OAPA An accelerated second order super twisting sliding mode observer with an adaptive gain is proposed for a typical nonlinear system. The key contribution of this algorithm is that the rate of convergence of observation error is accelerated remarkably by introducing "system damping". Chattering issue is attenuated with satisfactory performance compared to conventional sliding mode observer. Furthermore adaptive gain can vary with deviation between the trajectory and sliding mode switching manifold dynamically so that overshoot can be reduced. The novel observer is proven mathematically to be convergent in a finite time. Finally, an example of nonlinear system is given to verify the performance.
Lin, C, Sun, S, Yi, J, Walker, P & Zhang, N 2019, 'Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission'.
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Lin, CT, Chiu, C-Y, Singh, AK, King, J-T & Wang, YK 2019, 'A Wireless Multifunctional SSVEP-Based Brain Computer Interface Assistive System', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.
Liu, J, Cheng, T & Zhang, L 2019, 'Performance of constructed wetlands and associated mechanisms of PAHs removal with mussels', Chemical Engineering Journal, vol. 357, pp. 280-287.
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© 2018 Elsevier B.V. The role of mussels in laboratory-scale constructed wetlands (CWs) where wastewater was contaminated with five typical polycyclic aromatic hydrocarbons (PAHs) (three 3-ring PAHs and two 4-ring PAHs) was investigated in this study. The CWs performance and PAHs removal mechanisms were well studied. Results indicated that these five PAHs improved removal efficiencies of NO3-N but in the case of NH4-N accumulation occurred. Of the five added PAHs, the 4-ring PAHs were more refractory with higher concentrations in effluent than 3-ring PAHs. By monitoring the five PAHs concentration in water, mussels had excellent removal efficiency of the five PAHs (97%). According to the mass balance calculation, mussels promoted plant uptake of five PAHs, contributing 15.2% of five PAHs removal in CWs. The PAHs could also accumulate in mussels through ingested substrate. Thus, mussels presented a positive correlation with five PAHs purification at a depth of 0–10 cm in substrate, which was 34.7 μg/kg lower than the control group. Due to the purification and enhanced aerated degradation, mussels performed better in removing 3-ring PAHs in substrate, decreasing 8.3% for 3-ring PAHs compared to the control. A positive correlation between five PAHs addition and nirS, nrfA genes was observed. However, PAHs showed negative impact on nitrifying bacteria (amoA). The significant correlations between mussels and five PAHs made it possible to improve CWs for PAHs treatment with aquatic faunas.
Liu, J, Wu, C, Li, J, Fang, J, Su, Y & Shao, R 2019, 'Ceramic balls protected ultra-high performance concrete structure against projectile impact–A numerical study', International Journal of Impact Engineering, vol. 125, pp. 143-162.
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© 2018 Elsevier Ltd Ceramic materials have excellent mechanical properties such as light weight, great hardness and high compressive strength. In this paper, a numerical study is conducted to investigate the response of ceramic balls protected ultra-high performance concrete (UHPC) targets against the high-velocity rigid projectile impact using the coupled smoothed particle hydrodynamics-finite element (SPH-FE) method in LS-DYNA. Based on the validated numerical models, parametric studies are performed to explore the effect of diameter, spatial arrangement and material type of ceramic balls as well as the impact position on the dynamic performance of UHPC targets, and then perforation and ballistic limits of ceramic balls protected UHPC targets are obtained. Compared with other UHPC slabs at the striking velocities from 500 m/s to 850 m/s, UHPC slabs protected with 6-layer hex-pack arranged ceramic balls with the diameter of 20 mm is most effective in terms of reducing the depth of penetration (DOP). In addition, the utilization of ceramic balls is economical in protective structures since the damaged ceramic balls can be replaced and undamaged ceramic balls are reusable.
Liu, W, Shen, X, Du, B, Tsang, IW, Zhang, W & Lin, X 2019, 'Hyperspectral Imagery Classification via Stochastic HHSVMs.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 2, pp. 577-588.
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Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least , our algorithms find an -accurate solution using iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results.
Liu, Y, Xu, W, Zhu, J & Blaabjerg, F 2019, 'Sensorless Control of Standalone Brushless Doubly Fed Induction Generator Feeding Unbalanced Loads in a Ship Shaft Power Generation System', IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 739-749.
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© 1982-2012 IEEE. The standalone brushless doubly fed induction generator (BDFIG) has demonstrated excellent energy-saving performance in ship shaft power generation applications. As a standalone system, it exhibits unbalanced terminal voltages and poor performance under unbalanced loads. However, the existing control scheme of grid-connected BDFIGs cannot be directly applied to stabilize the amplitude and frequency of terminal voltage when the rotor speed and electrical load vary. This paper presents a new sensorless control scheme for the standalone BDFIG under unbalanced load conditions in the ship shaft power generation system. A second-order generalized integrator-based quadrature signal generator is introduced to realize the rotor speed observer for the standalone BDFIG feeding unbalanced loads. The compensation method of negative-sequence power winding voltage is proposed to eliminate the negative-sequence component of the unbalanced power winding voltage. Comprehensive experiments are carried out on a prototype BDFIG with and without the compensation of negative-sequence power winding voltage. The good performance of the proposed sensorless control scheme is verified by the experimental test results.
Lu, Z, Zhao, B, He, L, Zhang, D & Zhang, J 2019, 'Security level classification based on power system partitioning', IET Generation, Transmission and Distribution.
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Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'A study of steel wire mesh reinforced high performance geopolymer concrete slabs under blast loading', Journal of Cleaner Production, vol. 210, pp. 1150-1163.
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© 2018 Elsevier Ltd In this study, a novel green construction material, high performance alkali-activated geopolymer concrete is introduced. Both numerically and experimentally investigations were conducted on a new type of structural slabs made of steel wire mesh reinforced geopolymer concrete against close-in ground surface explosion. Steel rebar reinforced conventional concrete slabs are also studied to compare the results. The experimental investigation was conducted to study the slab damage mechanism. It is found that the steel wire mesh reinforced geopolymer concrete slab showed less damage and fragmentation under 50 kg Trinitrotoluene (TNT) blast load within 3 m, 5 m and 7 m distances as compared to the C30 concrete slab. Numerical analysis was then conducted to further investigate the slab dynamic responses. Combining the steel wire mesh reinforcement with geopolymer concrete can help increase the blast resistance capacity leading to promising and environmental friendly structural protective design.
Nguyen, AQ, Nguyen, LN, Phan, HV, Galway, B, Bustamante, H & Nghiem, LD 2019, 'Effects of operational disturbance and subsequent recovery process on microbial community during a pilot-scale anaerobic co-digestion', International Biodeterioration & Biodegradation, vol. 138, pp. 70-77.
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Nguyen, H, Khabbaz, H & Fatahi, B 2019, 'A numerical comparison of installation sequences of plain concrete rigid inclusions', Computers and Geotechnics, vol. 105, pp. 1-26.
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Soil displacement induced when installing controlled modulus columns (CMC) as ground reinforcement could affect the columns installed close by. Realising numerical analyses may provide useful insights, this paper describes a numerical approach to investigate how groups of CMC installed in different sequences could affect columns installed previously. Coupled consolidation analyses in large strain mode and incorporating soil-CMC interaction were carried out using the three-dimensional finite difference software package FLAC3D. The CMCs were modelled using advanced non-linear Hoek-Brown material with a tensile yield criterion while soils with a typical profile were characterised using the modified Cam Clay and the elastic-perfectly plastic material with a Mohr-Coulomb yield criterion. Where possible, the predicted responses of ground surrounding the CMCs were compared to a number of existing analytical methods. Predictions revealed that lateral soil movement and soil heave near existing CMCs induced by installing new CMCs towards the existing CMCs were approximately 15% and 25% greater than corresponding predictions when a reverse installation sequence was adopted. The maximum excess pore water pressures, induced near existing columns due to installing new columns towards the existing ones, were almost twice more than those caused by the reverse sequence of installation. Moreover, the predicted bending moments generated in the existing columns induced by installing new columns towards the existing CMCs were almost 22% greater than the corresponding values when the reverse installation sequence was adopted. This shows the importance of selecting an appropriate installation sequence in the CMC construction process as well as considering the initial stress field and bending moments in the surrounding soil and CMCs, respectively when designing embankments on improved soft soils.
Nguyen, LN & Oh, S 2019, 'Impacts of antiseptic cetylpyridinium chloride on microbiome and its removal efficiency in aerobic activated sludge', International Biodeterioration & Biodegradation, vol. 137, pp. 23-29.
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This study evaluated short- and long-term exposure of activated sludge (AS) microbiome to cetylpyridinium chloride (CPC), a quaternary ammonium compound widely used as biocidal additive or cationic surfactant. Toxicity assay in batch mode showed that CPC (50 μg L−1) inhibited cell growth. However, in a continuous reactor, CPC concentration in the range of 50–500 μg L−1 did not result in any observable impact on the biological activities of the AS microbiome. Similarly, 16S rRNA gene-based community profiling revealed that CPC had no observable impact on microbial diversity. At the phylogenetic structure, Rhodobacter (15 ± 7% of the total) and Asticcacaulis (9 ± 3%) were the only two phyla with increasing population in the 500 μg L−1-exposed reactors. This was also supported by an observation of no major change in the community structure. The reactors could remove >60% of CPC at initial concentrations of 50–500 μg L−1, primarily by adsorption and biodegradation. The enrichment of Rhodobacter and Asticcacaulis genus might contribute to CPC biodegradation and emerge as a potential microbial niche for the removal of CPC.
Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities', Science of the Total Environment, vol. 657, pp. 739-745.
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© 2018 Elsevier B.V. This study evaluated the removal of diclofenac (DCF) in activated sludge and its long-term exposure effects on the function and structure of the microbial community. Activated sludge could remove <50% of 50 μg/L DCF. The removal decreased significantly to below 15% when DCF concentrations increased to 500 and 5000 μg/L. Quantitative assessment of the fate of DCF showed that its main removal routes were biodegradation (21%) and adsorption (7%), with other abiotic removals being insignificant (<5%). The biodegradation occurred through cometabolic mechanisms. DCF exposure in the range of 50–5000 μg/L did not disrupt the major functions of the activated sludge ecosystem (e.g. biomass yield and heterotrophic activity) over two months of DCF exposure. Consistently, 16S rRNA gene-based community analysis revealed that the overall community diversity (e.g. species richness and diversity) and structure of activated sludge underwent no significant alterations. The analysis did uncover a significant increase in several genera, Nitratireductor, Asticcacaulis, and Pseudacidovorax, which gained competitive advantages under DCF exposure. The enrichment of Nitratireductor, Asticcacaulis, and Pseudacidovorax genus might contribute to DCF biodegradation and emerge as a potential microbial niche for the removal of DCF.
Nguyen, TT, Ngo, HH, Guo, W, Wang, XC, Ren, N, Li, G, Ding, J & Liang, H 2019, 'Implementation of a specific urban water management - Sponge City.', The Science of the total environment, vol. 652, pp. 147-162.
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Climate change, rapid urbanization and inappropriate urban planning policies in many countries have resulted in urban water-related problems, such as flooding disasters, water pollution and water shortages. To tackle these issues, the specific urban water management strategy known as Sponge City has been implemented in China since 2013. This is a complex method and one involving many challenges. This paper critically assesses the approaches associated with conventional urban water management. The Sponge City concept and its adoption are then scrutinized to comprehensively assess the limitations and opportunities. It emerges that Sponge City has four main principles, these being: urban water resourcing, ecological water management, green infrastructures, and urban permeable pavement. The uncertainties in Sponge City design and planning, and financial insufficiencies are the most serious problems that can risk the failure of the Sponge City concept. While significant barriers exist, the opportunities for implementing a Sponge City are evident. To obtain multi-ecosystem services of Sponge City, it should be implemented at the watershed scales and be flexible, depending on different decision levels or catchment characteristics. It is essential to apply an intelligent decision-making mechanism and consider the need for close cooperation between various agencies with which the central government can work. A suitable sized and harmonious Sponge City, ensuring a good balance between socio-economic development and environmental conservation, is the ideal.
Ni, BJ, Yang, Q, Li, X & Yang, F 2019, 'Calcium peroxide promotes hydrogen production from dark fermentation of waste activated sludge', Chemical Engineering Journal, vol. 355, pp. 22-32.
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© 2018 Elsevier B.V. Calcium peroxide (CaO2), one of versatile and harmless inorganic peroxy compounds, has been recently used as a highly effective oxidant to degrade pollutants in sludge and a method to achieve sludge resource utilization. However, its impact on hydrogen production has never been studied before. This investigation therefore aims to fill this knowledge gap. Results indicated that with CaO2increased from 0.05 to 0.25 g/g VSS (volatile suspended solids), the maximum hydrogen yield increased from 0.77 to 10.55 mL/g VSS. The mechanism studies revealed that CaO2accelerated the breakage and death of sludge cells. Excitation emission matrix (EEM) analyses further indicated that CaO2increased the biodegradability of the released substances, which could provide more biodegradable organics for the following reactions. Although CaO2caused inhibitions to some extents to all the tested microbes, its inhibition to hydrogen consumers was much severer than that to hydrolytic microbes and hydrogen producers. The enzyme analyses also demonstrated that the suppression of calcium peroxide to the activities of enzymes related to hydrogen consumption process was much severer than that to the activities of the activities of enzymes related to hydrogen production process. Further investigations exhibited that alkali, [rad]O2−and [rad]OH radicals, were the major intermediate products of CaO2decomposition. All of them were verified to contribute the increased hydrogen production, and their contributions were in the order of alkali > [rad]O2−> [rad]OH. This is the first work demonstrating that CaO2can enhance hydrogen production from WAS. The findings reported in our paper not only expand the application field of CaO2but also deepen the understanding of the CaO2-involved sludge fermentation process.
Pou, J, Perez, MA & Aguilera, RP 2019, 'Modular multilevel converters', IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2204-2206.
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Puthal, D, Ranjan, R, Nanda, A, Nanda, P, Jayaraman, PP & Zomaya, AY 2019, 'Secure authentication and load balancing of distributed edge datacenters', Journal of Parallel and Distributed Computing, vol. 124, pp. 60-69.
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© 2018 Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.
Sarikaya, A & Erkmen, R 2019, 'A plastic-damage model for concrete under compression', International Journal of Mechanical Sciences, vol. 150, pp. 584-593.
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A phenomenological model for plain concrete under compression is formulated within the framework of the coupled elastoplastic-damage theory. Phenomenological elastoplastic-damage models have been widely used for concrete because of their capability of representing both the permanent inelastic deformations and the degra- dation of material moduli beyond the elastic range. The essential contribution introduced in this paper is the proposed partitioning of the strain tensor within the coupled elastoplastic-damage framework which simplifies the selection of the failure surface and the potential function. Proposed partitioning permits the use of single failure criterion and single potential surface that are effective for both damage and plasticity models during inelastic deformations. Therefore, the coupled elastoplastic-damage model can be easily calibrated to fit the ob- served concrete behaviour based on well-established non-associated plasticity rules for concrete. The proposed approach also simplifies the numerical procedure by eliminating iterations that is required to equilibrate the stresses in plastic and damage components of the model. The numerical implementation is explained, and the results predicted by the model are compared with experimental data provided in the literature.
Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-The-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 2018, pp. 1-19.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'MECHANICAL PROPERTIES OF THE CEMENT-BASED MATERIALS AND EFFECT OF ELEVATED TEMPERATURE ON 3D PRINTING MORTAR SPECIMENS IN INKJET 3D PRINTING', ACI Materials Journal.
Shukla, N, Tiwari, MK & Beydoun, G 2019, 'Next generation smart manufacturing and service systems using big data analytics', Computers and Industrial Engineering.
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Siyari, P, Krunz, MM & Nguyen, D 2019, 'Distributed Power Control in Single-stream MIMO Wiretap Interference Networks with Full-duplex Jamming Receivers', IEEE Transactions on Signal Processing.
Song, Z, Zhang, X, Ngo, HH, Guo, W, Song, P, Zhang, Y, Wen, H & Guo, J 2019, 'Zeolite powder based polyurethane sponges as biocarriers in moving bed biofilm reactor for improving nitrogen removal of municipal wastewater.', The Science of the total environment, vol. 651, no. Pt 1, pp. 1078-1086.
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This study aims to enhance nitrogen removal efficiency of a moving bed biofilm reactor (MBBR) by developing a new MBBR with zeolite powder-based polyurethane sponges as biocarriers (Z-MBBR). Results indicated the total nitrogen (TN) removal efficiency and simultaneous nitrification and denitrification (SND) performance in Z-MBBR were nearly 10% higher than those in the conventional MBBR with sponges as biocarriers (S-MBBR). About 84.2 ± 4.8% of TN was removed in Z-MBBR compared to 75.1 ± 6.8% in S-MBBR. Correspondingly, the SND performance in Z-MBBR and S-MBBR was 90.7 ± 4.1% and 81.7 ± 6.5%, respectively. The amount of biofilm attached to new biocarriers (0.470 ± 0.131 g/g carrier) was 1.3 times more than that of sponge carriers (0.355 ± 0.099 g/g carrier). Based on the microelectrode measurements and microbial community analysis, more denitrifying bacteria existed in the Z-MBBR system, and this can improve the SND performance. Consequently, this new Z-MBBR can be a promising option for a hybrid treatment system to better nitrogen removal from wastewater.
Tan, X, Li, W, Zhao, M & Tam, VWY 2019, 'Numerical discrete-element method investigation on failure process of recycled aggregate concrete', Journal of Materials in Civil Engineering, vol. 31, no. 1.
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© 2018 American Society of Civil Engineers. This study numerically investigates the failure processes of recycled aggregate concrete (RAC) and natural aggregate concrete (NAC). A two-dimensional simulation based on a discrete-element method (DEM) is conducted with a universal distinct-element code (UDEC) program. RAC is modeled by a combination of rigid Voronoi blocks cemented to each other using contacts for interfaces. The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters in different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation results. The complete stress-strain curves, fracture process, and failure pattern of numerical model are verified by experimental results, proving its accuracy and validation. The initiation, propagation, and coalescence of microcracks and subsequent nonlinear deformation behaviors of cement mortar, modeled natural aggregate, and recycled aggregate concrete are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. It is found that both the new interfacial transition zone and the old interfacial transition zone are the weak links in RAC, where most microcracks initiate and propagate into the cement mortar region. The failure behaviors of MRAC revealed by both experimental and numerical results can effectively provide insights into the failure mechanism and enhancement of RAC.
Taylor, CN & Bishop, AN 2019, 'Homogeneous functionals and Bayesian data fusion with unknown correlation', Information Fusion, vol. 45, pp. 179-189.
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© 2018 Information or data fusion concerns the aggregation, or combination, of probability measures. For example, in machine learning, statistics and signal processing, one may seek to ‘combine’ posterior distributions, [e.g. 1) Bayes classifiers or 2) posteriors over target states etc], arising from distinct but not necessarily independent sources. For example, sources might include partially disjoint trainers, or spatially distinct sensors correlated via state dependent measurements, etc. Data fusion is common in risk analysis where one is broadly interested in pooling expert opinions described by probability measures, and where it is often hard to assess and account for correlation among experts. The contribution of this work is the introduction of a broad class of data fusion rules that seek the combination of two (or more) probability distributions in the presence of non-zero, but unknown, correlation. We introduce rules that are improved in the sense that they are ‘closer’ to the true Bayesian result that would be computed if one could exploit knowledge of the correlation between the input distributions. We introduce these rules under the common algorithmic constraint of avoiding the so-called ‘double-counting’ of correlated information. The general framework proposed is based on homogeneous functionals. We examine the fusion performance and computational properties when using these functionals. We also consider distributed data fusion on (possibly) time-varying and incomplete network topologies and related convergence properties.
Terechovs, AKE, Ansari, AJ, McDonald, JA, Khan, SJ, Hai, FI, Knott, NA, Zhou, J & Nghiem, LD 2019, 'Occurrence and bioconcentration of micropollutants in Silver Perch (Bidyanus bidyanus) in a reclaimed water reservoir.', The Science of the total environment, vol. 650, no. Pt 1, pp. 585-593.
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This study examined the occurrence of 49 micropollutants in reclaimed water and Silver Perch (Bidyanus bidyanus) living in a reclaimed water reservoir. The numbers of micropollutants detected in reclaimed water, Silver Perch liver, and Silver Perch flesh were 20, 23, and 19, respectively. Concentrations of all micropollutants in reclaimed water, except benzotriazole, were well below the Australian Guideline for Recycled Water (AGRW) values for potable purposes. The concentration of benzotriazole in reclaimed water was 675 ± 130 ng/L while the AGRW value for this compound was 7 ng/L. Not all micropollutants detected in the water phase were identified in the Silver Perch flesh and liver tissues. Likewise, not all micropollutants detected in the Silver Perch flesh and liver were identified in the reclaimed water. In general, micropollutant concentrations in the liver were higher than in the flesh. Perfluorooctane sulfonate (PFOS) was detected at a trace level in reclaimed water well below the AGRW guideline value for potable purposes, but showed a high and medium bioconcentration factor in Silver Perch liver and flesh, respectively. In addition, the risk quotient for PFOS was medium and high when considering its concentration in Silver Perch liver and flesh, respectively. Results reported here highlight the need to evaluate multiple parameters for a comprehensive risk assessment. The results also single out PFOS as a notable contaminant of concern for further investigation.
Thollander, P & Andersson, E 2019, 'Energy management: A practice-based assessment model', Applied Energy, vol. 235, pp. 1614-1636.
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© 2018 Elsevier Ltd Industrial energy efficiency is crucial for energy cost saving and sustainable competitiveness, but its potential is not exploited due to several barriers. Previous literature has pointed out that, among the most effective means, energy management in industrial companies could bring a valuable contribution. Therefore, it is crucial to assess and evaluate the energy management status in an organisation so to undertake the most appropriate improvement actions. So far, literature has neither described the fundamental characteristics of energy management practices, nor specifically developed an assessment model to support industrial decision-makers. Stemming from those research gaps, the present work presents and discusses an innovative energy management assessment model based on a novel characterization of energy management practices. We validated and applied the model through case studies among large Italian and Swedish manufacturing companies, both proving the model to be able to thoroughly describe the energy management status and benchmarking the adoption level of energy management practices with respect to specific baselines. The model highlights both strengths and critical areas in an industrial company's energy management, thus offering a valuable support to drive further improvement activities. The work concludes with interesting suggestions for industrial decision-makers and policy-makers, sketching also some further research avenues.
Vahedian, A, Shrestha, R & Crews, K 2019, 'Experimental and analytical investigation on CFRP strengthened glulam laminated timber beams: Full-scale experiments', Composites Part B: Engineering, vol. 164, pp. 377-389.
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Vo, HNP, Ngo, HH, Guo, W, Nguyen, TMH, Liu, Y, Liu, Y, Nguyen, DD & Chang, SW 2019, 'A critical review on designs and applications of microalgae-based photobioreactors for pollutants treatment.', The Science of the total environment, vol. 651, no. Pt 1, pp. 1549-1568.
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The development of the photobioreactors (PBs) is recently noticeable as cutting-edge technology while the correlation of PBs' engineered elements such as modellings, configurations, biomass yields, operating conditions and pollutants removal efficiency still remains complex and unclear. A systematic understanding of PBs is therefore essential. This critical review study is to: (1) describe the modelling approaches and differentiate the outcomes; (2) review and update the novel technical issues of PBs' types; (3) study microalgae growth and control determined by PBs types with comparison made; (4) progress and compare the efficiencies of contaminants removal given by PBs' types and (5) identify the future perspectives of PBs. It is found that Monod model's shortcoming in internal substrate utilization is well fixed by modified Droop model. The corroborated data also remarks an array of PBs' types consisting of flat plate, column, tubular, soft-frame and hybrid configuration in which soft-frame and hybrid are the latest versions with higher flexibility, performance and smaller foot-print. Flat plate PBs is observed with biomass yield being 5 to 20 times higher than other PBs types while soft-frame and membrane PBs can also remove pharmaceutical and personal care products (PPCPs) up to 100%. Looking at an opportunity for PBs in sustainable development, the flat plate PBs are applicable in PB-based architectures and infrastructures indicating an encouraging revenue-raising potential.
Volpin, F, Chekli, L, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2019, 'Optimisation of a forward osmosis and membrane distillation hybrid system for the treatment of source-separated urine', Separation and Purification Technology, pp. 368-375.
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© 2018 Elsevier B.V. The high concentration of nitrogen, phosphorous and potassium in human urine makes it a suitable raw material for fertiliser production. However, urine is often diluted with a significant amount of flushing water which increases the costs for the downstream nutrients recovery process. Re-using the water and the nutrients in the urine is paramount for enhancing the sustainability of our waste management system. In this work, a combination of forward osmosis (FO) and membrane distillation (MD) was used to extract distilled water from human urine. FO was chosen as MD pre-treatment to increase the overall nitrogen rejection and to prevent wetting of the MD membrane. The goal of this investigation was to tune the FO and MD operating parameters to reduce the nitrogen transport to the MD permeate. Urine pH, draw solution (DS) salt concentration and operating pressure were varied as a means to enhance the FO performances. On the other hand, feed temperature, nitrogen concentration and membrane characteristics were investigated to optimise the MD process. With 2.5 M NaCl as DS commercial FO membranes achieved a water flux between 31.5 and 28.7 L m−2 h−1 and a minimum nitrogen flux of 1.4 g L−1. An additional 33% reduction in the nitrogen transport was observed by applying minimal hydraulic pressure on the DS. However, this was also found to significantly reduce the net transmembrane water flux. Acidification of the feed was also beneficial for both FO and MD nitrogen rejection. Finally, we demonstrated that, by tuning the MD membrane porosity and thickness, higher MD permeate quality could be achieved. To conclude, the hybrid FO-MD process is expected to be an effective solution for the production of clean water and concentrated fertiliser from human urine. This double barrier separation process could be suitable for both water reclamation in space application and resource recovery in urban application.
Volpin, F, Heo, H, Hasan Johir, MA, Cho, J, Phuntsho, S & Shon, HK 2019, 'Techno-economic feasibility of recovering phosphorus, nitrogen and water from dilute human urine via forward osmosis', Water Research, vol. 150, pp. 47-55.
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© 2018 Elsevier Ltd Due to high phosphorus (P) and nitrogen (N) content, human urine has often proven to suitable raw material for fertiliser production. However, most of the urine diverting toilets or male urinals dilute the urine 2 to 10 times. This decreases the efficiency in the precipitation of P and stripping of N. In this work, a commercial fertiliser blend was used as forward osmosis (FO) draw solution (DS) to concentrate real diluted urine. During the concentration, the urea in the urine is recovered as it diffuses to the fertiliser. Additionally, the combination of concentrate PO43-, reverse Mg2+ flux from the DS and the Mg2+ presents in the flushing water, was able to recover the PO43- as struvite. With 50% concentrated urine, 93% P recovery was achieved without the addition of an external Mg2+. Concurrently, 50% of the N was recovered in the diluted fertiliser DS. An economic analysis was performed to understand the feasibility of this process. It was found that the revenue from the produced fertilisers could potentially offset the operational and capital costs of the system. Additionally, if the reduction in the downstream nutrients load is accounted for, the total revenue of the process would be over 5.3 times of the associated costs.
Wang, G, Lu, J, Choi, KS & Zhang, G 2019, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics.
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IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.
Wang, J & Zhang, W 2019, 'Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting', Renewable Energy, vol. 130, pp. 814-830.
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© 2018 Elsevier Ltd As one of the most promising sustainable energy sources, wind energy is being paid more attention by the researchers. Because of the volatility and instability of wind speed series, wind power integration faces a severe challenge; thus, an accurate wind energy forecasting plays a key role in smart grid planning and management. However, many traditional forecasting models do not consider the necessity and importance of data preprocessing and neglect the limitation of using a single forecasting model, which leads to poor forecasting accuracy. To solve these problems, a novel combined model based on two linear and four nonlinear forecasting algorithms is proposed to adapt both the linear and nonlinear characteristics of the wind energy time series. In addition, a modified Artificial Fish Swarm Algorithm and Ant Colony Optimization (AFSA-ACO) algorithm is proposed and employed to determine the optimal weight coefficients of the combined models. To verify the forecasting performance of the developed combined model, several experiments were implemented by using 10-min interval wind speed data in Shandong, China. Then, one-step (10-min), three-step (30-min) and five-step (50-min) predictions were conducted. The experimental results indicate that the developed combined model is remarkably superior to all benchmark models for the high precision and stability of wind-speed predictions.
Wang, K, Wang, P & Qiu, X 2019, 'Increasing speech intelligibility in monaural hearing by adding noise at the other ear', Applied Acoustics, vol. 146, pp. 50-55.
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© 2018 Elsevier Ltd Ambient environment noise can affect speech intelligibility in phone communication. This paper investigates the feasibility of increasing speech intelligibility in monaural hearing by adding noise at the other ear. The testing materials were generated by mixing the speech from the English Coordinate Response Measure corpus with three types of environmental noise, where 4 signals to noise ratios in the speech ear and 14 noise levels in the contralateral ear were included. The experimental results show that a proper level of contralateral noise can improve the speech intelligibility when the signal to noise ratio in the speech ear is lower than a certain level, but a large contralateral noise level has the opposite effect. A preliminary explanation for the phenomena is attempted by using a binaural loudness model and some psychoacoustic and physiological facts.
Wang, X, Liu, Y, Lu, J, Xiong, F & Zhang, G 2019, 'TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators', Future Generation Computer Systems, vol. 94, pp. 224-236.
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© 2018 Elsevier B.V. In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual's preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods.
Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2019, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 75-90.
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© 1989-2012 IEEE. Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then, we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.
Wicks, M, Millar, GJ & Altaee, A 2019, 'Process simulation of ion exchange desalination treatment of coal seam gas associated water', Journal of Water Process Engineering, vol. 27, pp. 89-98.
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© 2018 Elsevier Ltd The aim of this investigation was to develop an ion exchange process for the remediation of coal seam gas (CSG) associated water to make it suitable for beneficial reuse. The hypothesis was that computational modelling could accelerate the selection of appropriate ion exchange desalination strategies. Hence, we applied AqMB water process engineering software to predict which combination of weak acid cation (WAC), strong acid cation (SAC), weak base anion (WBA) and strong base anion (SBA) resins were most appropriate. Simulation results revealed that both SAC/WBA and SAC/SBA resin combinations were unable to meet water beneficial reuse standards for conductivity (< 950 μS/cm) due to the presence of bicarbonate species (4973 and 1918 μS/cm, respectively). Thus, a degasser unit was necessary to remove the large concentrations (ca. 1328 mg/L) of dissolved carbon dioxide formed due to decomposition of bicarbonate/carbonate species under acidic conditions in the cation resin stages. pH adjustment of effluent from the preferred SBA resin with acid not only did not meet solution conductivity guidelines but also raised the concentration of chloride or sulphate ions to levels, which may be detrimental for crop growth. Addition of a WAC resin allowed production of high quality water (either SAC/SBA/WAC or WAC/SAC/SBA combinations). To comply with sodium adsorption ratio requirements for irrigating soil it was suggested to apply micronized gypsum to the treated water. Economic evaluation suggested the treated water cost was A$1003 (WAC/SAC/SBA) to A$1276 (SAC/SBA/WAC) per ML treated which was comparable to estimated costs for a reverse osmosis desalination system.
Xu, J, Cao, Z, Wang, Y, Zhang, Y, Gao, X, Ahmed, MB, Zhang, J, Yang, Y, Zhou, JL & Lowry, GV 2019, 'Distributing sulfidized nanoscale zerovalent iron onto phosphorus-functionalized biochar for enhanced removal of antibiotic florfenicol', Chemical Engineering Journal, vol. 359, pp. 713-722.
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© 2018 Aggregation of nZVI and sulfur-modified nZVI (S-nZVI) can lower its reactivity with contaminants in water. To overcome this limitation, we synthesized biochar-supported nZVI and S-nZVI using a phosphate pretreatment of the biochar (pBC) to uniformly distribute the nZVI and S-nZVI onto the biochar support. The participation of phosphorus groups in the synthesis, and the good distribution of S-nZVI on the pBC were confirmed by FTIR, SEM, XRD, and XPS. Pretreatment of the biochar led to smaller well-dispersed S-nZVI compared to S-nZVI supported on untreated biochar. This increased the surface area of the S-nZVI and the reaction rate with the antibiotic florfenicol (FF). The removal rate of FF by pBC-S-nZVI was 4.3 times higher than that by unsupported S-nZVI. Even though FF strongly adsorbed to the pBC support, FF was fully degraded based on the mass balance results. Surface area normalized reaction rate constants (kSA) for FF removal by S-nZVI, BC-S-nZVI, and pBC-S-nZVI were similar, suggesting that the enhanced reactivity is due to the greater dispersion of S-nZVI on the treated biochar. These results provide a simple pretreatment method for dispersing nZVI or S-nZVI onto biochar supports.
Xu, R & Fatahi, B 2019, 'Novel application of geosynthetics to reduce residual drifts of mid-rise buildings after earthquakes', Soil Dynamics and Earthquake Engineering, vol. 116, pp. 331-344.
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© 2018 Elsevier Ltd Geosynthetics have been used in variety of geotechnical engineering projects, such as ground improvement, erosion control, slope stabilisation and foundation strength improvement and they have been proved to be cost and time effective in many cases. In this study, a geosynthetic reinforced composite soil (GRCS) foundation system is proposed for seismic protection of mid-rise buildings supported by a shallow foundation potentially suffering from residual structural drift or permanent foundation settlement. To evaluate the proposed GRCS, a conventional reinforced concrete moment resisting building sitting on this composite ground under the earthquake excitations of 1978 Tabas, 1994 Northridge and 1995 Kobe was numerically simulated using FLAC3D software. The effect of soil-structure interaction (SSI) was captured using direct method of analysis adopting a three-dimensional numerical model. By adopting direct calculation method, the soil deposit, the geosynthetic reinforcement, the foundation and the structure were simulated simultaneously. Inelastic behaviour of the structure was considered, while hysteretic damping algorithm was adopted representing the variation of the shear modulus and corresponding damping ratio of the soil with cyclic shear strain capturing the energy dissipation characteristics of the soil. Both material and geometry nonlinearities were taken into account at the interface between the foundation and ground surface. The results are then presented in terms of mobilised tensile force in geosynthetic layers, the response spectra at bedrock and ground surface level, the shear force developed in the superstructure, the maximum foundation rocking angle, the maximum lateral deflection, the maximum inter-storey drift, and most importantly the residual inter-storey drift and permanent foundation settlement. The results showed that the proposed GRCS could offer design engineers a rational and cost-effective alternative solution to con...
Yan, Z, Liu, J & Liu, S 2019, 'DPWeVote: differentially private weighted voting protocol for cloud-based decision-making', Enterprise Information Systems, vol. 13, no. 2, pp. 236-256.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. With the advent of Industry 4.0, cloud computing techniques have been increasingly adopted by industry practitioners to achieve better workflows. One important application is cloud-based decision-making, in which multiple enterprise partners need to arrive an agreed decision. Such cooperative decision-making problem is sometimes formed as a weighted voting game, in which enterprise partners express ‘YES/NO’ opinions. Nevertheless, existing cryptographic approaches to Cloud-Based Weighted Voting Game have restricted collusion tolerance and heavily rely on trusted servers, which are not always available. In this work, we consider the more realistic scenarios of having semi-honest cloud server/partners and assuming maximal collusion tolerance. To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase. Experiments on synthetic data have demonstrated that the proposed DPWeVote protocol managed to retain an acceptable utility for decision-making while preserving privacy in semi-honest environment.
Yang, J, Liu, X, Wang, D, Xu, Q, Yang, Q, Zeng, G, Li, X, Liu, Y, Gong, J, Ye, J & Li, H 2019, 'Mechanisms of peroxymonosulfate pretreatment enhancing production of short-chain fatty acids from waste activated sludge.', Water research, vol. 148, pp. 239-249.
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Peroxymonosulfate (PMS) has been recently used as an additive to pretreat waste activated sludge (WAS) to enhance short-chain fatty acids (SCFAs) production. However, the mechanisms of how PMS enhances SCFAs production remain largely unknown. This work therefore aims to explore the mechanisms through deeply understanding its impact on the disintegration of sludge cells, the biodegradability of organics released and the bioprocesses involved in anaerobic fermentation, and differentiating the contributions of its degradation intermediates to SCFAs production. This was demonstrated by a series of batch fermentation tests using either real sludge or model organic compounds as fermentation substrates. Experimental results showed that the maximal SCFAs yield increased from 29.69 to 311.67 mg COD/g VSS with PMS level increasing from 0 to 0.09 g/g TSS. No obvious increase in SCFAs yield was observed when PMS further increased. The mechanism explorations revealed that PMS pretreatment not only enhanced the disintegration of sludge cells but also promoted the biodegradability of organics released, thereby providing more biodegradable substrates for subsequent SCFAs production. PMS pretreatment decreased the percentages of fulvic acid-like and humic acid-like substances in the released organics. Moreover, the species and total detection frequency of other recalcitrant organics such as cyclopentasiloxane, heptasiloxane, and ethylene glycol, which were hardly degraded in ordinary anaerobic condition, also decreased remarkably. Although PMS caused harms to some extents to all the microbes in the anaerobic fermentation, its inhibitions to SCFAs consumers were much severer than that to SCFAs producers, probably due to the less tolerance of methanogens. Further analyses exhibited that 1O2, SO4•- and •OH were the major contributors to the increased SCFAs production, and their contributions were in the order of 1O2 > SO4•- > •OH. The findings obtained in this work provide insights ...
Yang, T, Ding, C & Guo, Y 2019, 'A Highly Birefringent and Nonlinear AsSe2-As2S5 Photonic Crystal Fiber with Two Zero-Dispersion Wavelengths', IEEE Photonics Journal.
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OAPA A hybrid AsSe$_{2}$-As$_{2}$S$_{5}$ photonic crystal fiber (PCF) with a solid elliptical core is proposed and studied theoretically by full-vector finite element method (FEM). The core and cladding of the PCF are made of AsSe$_{2}$ and As$_{2}$S$_{5}$ glasses, respectively. Simulation results demonstrates that, at the operating wavelength of 1.55 μm, the proposed PCF not only exhibits a very high birefringence of 0.091 but also has large nonlinear coefficients of 147.8 and 78.2 W$^{-1}$m$^{-1}$ for the X- and Y-polarized (X- and Y-pol) modes, respectively. Moreover, it is able to achieve two zero-dispersion wavelengths (ZDWs) for both the X-pol (1.52 and 2.19 μm) and Y-pol (1.43 and 2.12 μm) modes. The proposed hybrid PCF exhibits excellent polarization maintaining and nonlinearity performance, thus suitable to be used in supercontinuum spectrum generation and polarization maintaining nonlinear signal processing.
Yao, J, Wang, J, Tsang, IW, Zhang, Y, Sun, J, Zhang, C & Zhang, R 2019, 'Deep Learning from Noisy Image Labels with Quality Embedding', IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1909-1922.
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© 1992-2012 IEEE. There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among datasets severely degenerates the performance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a probabilistic model, which explicitly introduces an extra variable to represent the trustworthiness of noisy labels, termed as the quality variable. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the influence of label noise. At the same time, reliable labels are still able to be applied for training. To instantiate the model, we further propose a contrastive-additive noise network (CAN), which consists of two important layers: 1) the contrastive layer that estimates the quality variable in the embedding space to reduce the influence of noisy labels and 2) the additive layer that aggregates the prior prediction and noisy labels as the posterior to train the classifier. Moreover, to tackle the challenges in optimization, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We validate the proposed method on a range of noisy image datasets. Comprehensive results have demonstrated that CAN outperforms the state-of-the-art deep learning approaches.
Yao, M, Woo, YC, Ren, J, Tijing, LD, Choi, J-S, Kim, S-H & Shon, HK 2019, 'Volatile fatty acids and biogas recovery using thermophilic anaerobic membrane distillation bioreactor for wastewater reclamation.', Journal of environmental management, vol. 231, pp. 833-842.
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The effects of bioreactor temperatures and salinities of an anaerobic membrane distillation bioreactor (anMDBR) on the permeation performance and their potential recovery of bioresources were fully examined in this study. To the best of our knowledge, this is the first study of a lab-scale anMDBR process utilizing sub-merged hollow fiber membranes. The hybrid system utilizing both membrane distillation (MD) and anaerobic bioreactors achieved 99.99% inorganic salt rejection regardless the operation temperatures and high initial flux from (2-4 L m-2 h-1) at 45-65 °C. However, after 7-day operation, the flux dropped by 16-50% proportional to the bioreactor temperatures. It was found that the effects of bioreactor temperatures had strong impacts on both the permeation performance and fouling behavior while salinity had insignificant effect. A compact non-porous fouling layer was observed on the membrane surface from the bioreactor operated at 65 °C while only a few depositions was found on the membrane from 45 °C bioreactor. In the present study, the optimal anMDBR temperature was found to be 45 °C, showing a balanced biogas production and membrane permeation performance including less fouling formation. At this bioreactor temperature (45 °C), the biogas yield was 0.14 L/g CODremoval, while maintaining a methane recovery of 42% in the biogas, similar recovery to those at bioreactor temperatures of 55 and 65 °C. The potential recovery of volatile fatty acids made anMDBR a more economically efficient system, in addition to its lower operation cost and smaller footprint compared with most other technologies for on-site wastewater treatment.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.
Yin, S, Ji, J, Wen, G & Wu, X 2019, 'Use of degeneration to stabilize near grazing periodic motion in impact oscillators', Communications in Nonlinear Science and Numerical Simulation, vol. 66, pp. 20-30.
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© 2018 In controlling the discontinuous grazing bifurcations in impact oscillators, a discrete-in-time linear feedback control strategy in the existing literature was used to change the conditions at the grazing point based on the grazing stability criterion. Though this strategy is effective for its linear inequality constraint of the control parameter domain, a smooth and predictable bifurcating response cannot be obtained for the controlled system, but the grazing induced chaos or period-adding phenomena. To improve this control strategy and stabilize the elementary near-grazing impact periodic motion in impact oscillators, one feasible control criterion is established in this paper by performing the perturbation analysis of the eigenvalues of the Jacobian matrix. It is found that the degeneration of both eigenvalues and grazing bifurcation can stabilize the elementary near-grazing impact periodic motion and eliminate the discontinuous jump phenomenon at grazing.
Yu, Z, Hu, Y, Dzakpasu, M, Wang, XC & Ngo, HH 2019, 'Dynamic membrane bioreactor performance enhancement by powdered activated carbon addition: Evaluation of sludge morphological, aggregative and microbial properties.', Journal of environmental sciences (China), vol. 75, pp. 73-83.
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The effects of powdered activated carbon (PAC) addition on sludge morphological, aggregative and microbial properties in a dynamic membrane bioreactor (DMBR) were investigated to explore the enhancement mechanism of pollutants removal and filtration performance. Sludge properties were analyzed through various analytical measurements. The results showed that the improved sludge aggregation ability and the evolution of microbial communities affected sludge morphology in PAC-DMBR, as evidenced by the formation of large, regularly shaped and strengthened sludge flocs. The modifications of sludge characteristics promoted the formation process and filtration flux of the dynamic membrane (DM) layer. Additionally, PAC addition did not exert very significant influence on the propagation of eukaryotes (protists and metazoans) and microbial metabolic activity. High-throughput pyrosequencing results indicated that adding PAC improved the bacterial diversity in activated sludge, as PAC addition brought about additional microenvironment in the form of biological PAC (BPAC), which promoted the enrichment of Acinetobacter (13.9%), Comamonas (2.9%), Flavobacterium (0.31%) and Pseudomonas (0.62%), all contributing to sludge flocs formation and several (such as Acinetobacter) capable of biodegrading relatively complex organics. Therefore, PAC addition could favorably modify sludge properties from various aspects and thus enhance the DMBR performance.
Yuan, Z, Dong, L, Gao, Q, Huang, Z, Wang, L, Wang, G & Yu, X 2019, 'SnSb alloy nanoparticles embedded in N-doped porous carbon nanofibers as a high-capacity anode material for lithium-ion batteries', Journal of Alloys and Compounds, pp. 775-783.
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© 2018 SnSb alloy is a promising anode material for lithium-ion batteries due to its high specific capacity. However, the large volume change in the process of charge/discharge causes significant pulverizing of SnSb alloy particles, which leads to a rapid capacity fading. This paper reports the synthesis of homogenous SnSb nanoparticles that are embedded in N-doped porous carbon nanofibers through electrospinning technique with LiN3 serving as poregen agent. This distinctive structure prevents the direct contact of SnSb nanoparticles with the electrolyte and provides enough space for the volume change of SnSb alloy during the Li+ insertion/extraction process, enabling this material to deliver a high reversible capacity of 892 mA h g−1 after 100th cycle at 100 mA g−1, and a stable capacity of 487 mA h g−1 after 1000 cycles at 2000 mA g−1. These results highlight the importance of the synergistic effect of SnSb alloy nanoparticles and N-doped porous carbon nanofibers for the high performance of lithium-ion batteries.
Zhan, K, Nie, F, Wang, J & Yang, Y 2019, 'Multiview Consensus Graph Clustering.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 3, pp. 1261-1270.
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A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly connected components where is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as -means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.
Zhang, N & Zhang, H 2019, 'Comparison study on the performance of two different gas-permeable membranes used in a membrane-aerated biofilm reactor', Science of the Total Environment, vol. 658, pp. 1219-1227.
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© 2018 Elsevier B.V. This study compared the performance of two different gas-permeable membranes, PVDF and PP membrane, in a membrane-aerated biofilm reactor (MABR). The surface characteristics and membrane pore blocking of these two membrane materials were studied utilizing AFM, SEM and CLSM. The PVDF membrane surface was more rough and hydrophilic, and possessed a better microbial affinity compared to PP. Furthermore, the MABR equipped with a PVDF membrane removed more COD (97.06 ± 0.97%) and TN (85.66 ± 0.87%) compared to the MABR with PP membrane (87.13 ± 0.87% and 71.13 ± 0.71%, respectively). As well, the PP membrane exhibited severe membrane pore blocking and had a lower oxygen transfer rate than the PVDF membrane. It is concluded that the PVDF membrane has potential as an aeration membrane material for MABRs.
Zhang, Q, Shi, C, Niu, Z & Cao, L 2019, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 152-165.
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© 1989-2012 IEEE. The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
Zhang, W, Liu, T, Ye, L, Ueland, M, Forbes, SL & Su, SW 2019, 'A novel data pre-processing method for odour detection and identification system', Sensors and Actuators A: Physical.
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Zhang, Y, Liu, Q, He, Z, Zong, Z & Fang, J 2019, 'Dynamic impact response of aluminum honeycombs filled with Expanded Polypropylene foam', Composites Part B: Engineering, vol. 156, pp. 17-27.
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© 2018 The paper investigated the dynamic impact response and characteristics of aluminum honeycomb filled with EPP foam (Expanded polypropylene) experimentally and numerically. It was found that the initial peak strength and mean strength of the filled honeycomb were improved significantly attributable to the interaction effect between the aluminum honeycomb and the foam, but the specific energy absorption (SEA) decreased. For the filled specimens with the same foam density, the initial peak strength, mean strength and SEA increased with the increase in impact velocity. Compared with the characteristics in the static compression test, the initial peak strength in the dynamic impact test increased, whereas the mean strength and SEA decreased. The study showed that EPP foam filling was effective to improve the impact characteristics of the bare aluminum honeycomb. Numerical simulation for the dynamic impact of filled honeycombs was also explored. It accurately reproduced the deformation process and addressed the interaction between the wall and EPP foam. By comparison of the properties in different filling types, it showed the single-cell filling was a good choice to improve the load resistance while using the least filling material.
Zheng, J, Luo, Z, Jiang, C & Gao, J 2019, 'Robust topology optimization for concurrent design of dynamic structures under hybrid uncertainties', Mechanical Systems and Signal Processing, vol. 120, pp. 540-559.
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Zhong, Z, Zheng, L, Zheng, Z, Li, S & Yang, Y 2019, 'CamStyle: A Novel Data Augmentation Method for Person Re-Identification.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 28, no. 3, pp. 1176-1190.
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Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle .
Zhu, J 2019, 'Torque Analysis and Dynamic Performance Improvement of a PMSM for EVs by Skew Angle Optimization', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2018 IEEE. In this paper, a permanent magnet synchronous machine (PMSM) for electric vehicles (EVs) is studied. Since EVs need to face some low speed road conditions, it is necessary to drive the machine to maintain a stable torque at low speed. The stator skew slot is often adopted to reduce torque ripple; however, it declines the output torque at same time. Besides, the difference between positive rotation performance and negative rotation performance, which caused by the skew slot are often ignored. Through the finite element analysis, the cogging torque and dynamic performance of the PMSM at different skew angle are studied. Moreover, the different influence of slot skew angle on positive and negative rotation performance is studied. Then, the optimum skew angle of the PMSM is studied through comprehensive consideration. Finally, the cogging torque of the prototype is verified to be less than 2 N·m through the experiment.
Zijlema, AF, Van den hoven, E & Eggen, B 2019, 'A qualitative exploration of memory cuing by personal items in the home', Memory Studies.
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We are surrounded by personal items that can trigger memories, such as photos, souvenirs, and heirlooms. Also during holidays, we collect items to remind us of the events, but not all bring back memories to the same extent. Therefore, we explored peoples’ responses to personal items related to a holiday, using the home tour interviewing method. Sixty-three accounts of cuing responses from nine home tours were analysed using thematic analysis. This resulted in four types of cuing responses: a) ‘no-memory’ responses, b) ‘know’ responses, c) ‘memory evoked think or feel’ responses, and d) ‘remember’ responses. For each of these cuing response categories, we looked into the types of items and their characteristics. Further, we found that some items can evoke multiple memories. The majority of the memories’ content refer to events close to the moment of acquiring the item.