Abbasnejad, B, Thorby, W, Razmjou, A, Jin, D, Asadnia, M & Ebrahimi Warkiani, M 2018, 'MEMS piezoresistive flow sensors for sleep apnea therapy', Sensors and Actuators, A: Physical, vol. 279, pp. 577-585.
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© 2018 Elsevier B.V. A MEMS liquid crystal polymer (LCP), used in the membrane-based pressure sensor, has been found highly useful as a flow sensor. Here we conducted a set of elaborate experiments using an air flow generator to investigate the potential of our LCP flow sensor for sleep apnea therapy. Critical properties of the LCP flow sensor, including flow range, resolution (sensitivity), accuracy, and response time, have been systematically characterized. As a result, LCP flow sensor achieves a limit of detection of 8 LPM to measure flow rate, better than the commercial flow sensor (>10 LPM). Our LCP flow sensor shows a favourable response in a large flow range (8–160 LPM) with a sensitivity of detecting a linear voltage response of 0.004 V per 1 LPM flow rate. With minimum detectable flow, high sensitivity and resolution, we further demonstrated our LCP flow sensor for detecting human respiration. Moreover, using a two- dimensional simulation in COMSOL Multiphysics, we demonstrated the deformation of LCP membrane in response to different flow velocities which leads to resistance change in sensor's strain gauge.
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2018, 'Long-term runoff dynamics assessment measured through land use/cover (LULC) changes in a tropical complex catchment', Environment Systems and Decisions, pp. 1-18.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The estimation of excess rainfall is critically important in water resource management as it provides the basis for calculating flood peak discharge that results in surface runoff. Kelantan River basin in Malaysia is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/cover (LULC) changes making the area consistently flood prone. The current study is therefore aimed to achieve the following goals: (1) to develop a curve number (CN) and runoff maps for 1984, 2002, and 2013 LULC conditions and (2) to determine runoff dynamics due to changes in LULC as well as to assess how the extent of LULC change will affect surface runoff generation. To achieve the aforementioned goals, land use maps corresponding to 1984, 2002, and 2014 LULC conditions were analyzed and prepared for the calculation of CN values using Soil Conservation Service (SCS-CN) method. CN and runoff maps corresponding to 1984, 2002, and 2013 LULC changes were successfully developed and the performance of the method was tested. The results indicated that forest was found to be the major land use type to have changed in all the LULC conditions across the watershed leading to intense runoff dynamics in the entire watershed. Higher runoff values were observed under 2013 LULC conditions across the watershed mainly due to intense deforestation relative to those of 1984 and 2002. The results of this study indicated that runoff generation is significantly affected by deforestation instead of changes in the rainfall pattern. The findings may be useful to water resource planners in controlling water loss for future planning.
Abdulkareem, JH, Sulaiman, WNA, Pradhan, B & Jamil, NR 2018, 'Relationship between design floods and land use land cover (LULC) changes in a tropical complex catchment', Arabian Journal of Geosciences, vol. 11, no. 14.
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© 2018, Saudi Society for Geosciences. Rainfall characteristics are directly related to the climate of a basin, but this can only be noticed after a long period. Human activities, such as deforestation, tend to play a major role in transforming the land use land cover (LULC). Knowledge of the relationship between design floods and LULC is important in modeling and designing watershed management strategies. A study was conducted in the Kelantan River basin, Malaysia, to determine the impact of past and present LULC changes on peak discharge and runoff volumes. To achieve this, the basin was delineated into four catchments (Galas, Pergau, Nenggiri, and Lebir) due to its size and increased precision. Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was calibrated based on December 20–30th, 2014, flood in Kelantan. Flood hydrographs corresponding to 1984, 2002, and 2013 LULC conditions were simulated, and relative changes in peak discharge and runoff volume were determined for different return periods (2, 5, 10, 20, 50, 100 years). Results of LULC analysis showed that Galas recorded highest deforestation (54.35%). When the four catchments were compared with respect to highest contribution of outlet peak discharge, Lebir under 2013 LULC condition was the highest with 2847.70 m3/s. This was followed by Nenggiri (2196.90 m3/s), Galas (1252.7 m3/s), and Pergau (328.7 m3/s), all under the 2013 LULC condition. Results of unit response approach applied based on 50-year return period to the catchments for ranking their sub-basins revealed that the novel fa index developed in this study provides a better way of ranking sub-basins with respect to their contribution to the outlet and therefore is recommended for use. Methodologies developed in this study may be useful to land use planners from around the world which when applied can provide alternatives that will minimize the adverse effects of floods.
Abdulkareem, S.A., Augustijn, E.-.W., Mustafa, Y.T. & Filatova, T. 2018, 'Intelligent judgements over health risks in a spatial agent-based model.', International journal of health geographics, vol. 17, no. 1, p. 8.
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Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed.We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior).We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of severa...
Abdullahi, S & Pradhan, B 2018, 'Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques', Environmental Earth Sciences, vol. 77, no. 6.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes.
Abdullahi, S, Pradhan, B & Mojaddadi, H 2018, 'City Compactness: Assessing the Influence of the Growth of Residential Land Use', Journal of Urban Technology, vol. 25, no. 1, pp. 21-46.
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© 2017 The Society of Urban Technology. In the urban sprawl paradigm, residential land use exhibits a more significant growth than other categories. Consequently, large proportions of the natural environment are converted to residential areas, particularly in tropical countries. Compact urban development is one of the most sustainable urban forms with environmental perspectives, such as rural development containment and natural environment preservation. However, no proper investigation of the relationship and influence of residential growth and city compactness is available. This study evaluated and forecasted the residential development of Kajang City in Malaysia based on compact development. First, the relationship between residential land use change and city compactness was evaluated. Second, residential growth was projected by utilizing the land transformation model (LTM) and the statistical-based weight of evidence (WoE) using various spatial parameters. Both models were evaluated with respect to observed land use and compactness maps. Results indicated that most of the newly developed residential areas were in zones where the degrees of compactness increase during certain periods. In addition, LTM performed better and provided a more accurate modeling of residential growth than the WoE. However, WoE provided clearer and more informative results than LTM in terms of functional relationships between dependent and independent variables related to city compactness.
Abedin, B & Babar, A 2018, 'Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire', Information Systems Frontiers, pp. 1-12.
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© 2017 Springer Science+Business Media, LLC Social media plays a significant role in rapid propagation of information when disasters occur. Among the four phases of disaster management life cycle: prevention, preparedness, response, and recovery, this paper focuses on the use of social media during the response phase. It empirically examines the use of microblogging platforms by Emergency Response Organisations (EROs) during extreme natural events, and distinguishes the use of Twitter by EROs from digital volunteers during a fire hazard occurred in Australia state of Victoria in early February 2014. We analysed 7982 tweets on this event. While traditionally theories such as World System Theory and Institutional Theory focus on the role of powerful institutional information outlets, we found that platforms like Twitter challenge such notion by sharing the power between large institutional (e.g. EROs) and smaller non-institutional players (e.g. digital volunteers) in the dissemination of disaster information. Our results highlight that both large EROs and individual digital volunteers proactively used Twitter to disseminate and distribute fire related information. We also found that the contents of tweets were more informative than directive, and that while the total number of messages posted by top EROs was higher than the non-institutional ones, non-institutions presented a greater number of retweets.
Abedin, B. & Qahri-Saremi, H. 2018, 'Introduction to the Special Issue — Social Computing and Service Innovation: A Framework for Research', Journal of Organizational Computing and Electronic Commerce, vol. 28, no. 1, pp. 1-8.
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Adnan, R., Sabri Adlan, Z., Munir, F.A., Indra, T.M. & Masjuki, H.H. 2018, 'Effects of equivalence ratio on performance and emissions of diesel engine with hydrogen and water injection system at variable injection timing', International Journal of Mechanical and Mechatronics Engineering, vol. 18, no. 1, pp. 106-111.
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© February 2018 IJENS. This paper aims to develop a comprehensive development and research for performance and emissions of diesel engine fueled with hydrogen and water at variable injection timing. Experiments have been conducted to compare the performance and emissions between diesel alone, diesel with hydrogen and hydrogen-diesel and water injection pressure. addition of hydrogen into diesel engine resulted in higher pressure which lead to huge indicated work. Furthermore, injecting water into diesel engine with hydrogen mixture indicated a desirable outcome. Existence of water in combustion slightly decreased the amount of emissions but opposite in term of performance. The fact is water injection exist in combustion will absorb a portion of heat release which will result low in combustion process thus lead to low in performance production otherwise production of NOx emission is low. In conclusion, humidification in combustion engine is a great idea toward a high performance and low in emissions production compared to diesel alone operation which leads to a green technology production.
Afifi, W, Abdel-Rahman, MJ, Krunz, M & MacKenzie, AB 2018, 'Full-Duplex or Half-Duplex: A Bayesian Game for Wireless Networks with Heterogeneous Self-Interference Cancellation Capabilities', IEEE Transactions on Mobile Computing, vol. 17, no. 5, pp. 1076-1089.
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© 2002-2012 IEEE. Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD). Although operating in FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the coexistence problem of concurrent transmissions between multiple FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex mode. First, we consider two links and formulate the interactions between them as a Bayesian game. In this game, each link tries to maximize its throughput while minimizing the transmission power cost. We derive a closed-form expression for the Bayesian Nash equilibrium and determine the conditions under which no outage occurs at either link. Then, we study the coexistence problem between more than two links, assuming that each link is only affected by its dominant interfering link. We show that under this assumption, no more than two links will be involved in a single game. Finally, we corroborate our analytical findings via extensive simulations and numerical results.
Ahmadirouhani, R, Karimpour, MH, Rahimi, B, Malekzadeh-Shafaroudi, A, Pour, AB & Pradhan, B 2018, 'Integration of SPOT-5 and ASTER satellite data for structural tracing and hydrothermal alteration mineral mapping: implications for Cu–Au prospecting', International Journal of Image and Data Fusion, vol. 9, no. 3, pp. 237-262.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. The integration of information extracted from the Syste`m Pour l’Observation de la Terre (SPOT) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, comprehensive field and mineralogy studies demonstrates that phyllic alteration zone associated with northwestern (NW)–southeastern (SE) structural fractures is a high potential zone for Cu–Fe–Au vein-type mineralisation in the Bajestan region, the Lut block, east Iran. The fractal pattern was calculated for fractures map using the Box-Counting algorithm to the SPOT-5 data. Statistical parameters of fractures, such as density, intensity and fractures’ intersection were also determined. Band composition, specialised band ratio and Spectral Angle Mapper (SAM) classification methods were implemented to the ASTER dataset for detecting hydrothermal alteration zones, such as propylitic, phyllic, argillic and gossan. Results indicate that the maximum value of the fractal dimension, intensity, density and the intersection of the fractures are concentrated in the NW and SE parts of SPOT image maps. In the other hand, phyllic alteration zone containing sericite, alunite, kaolinite and jarosite mineral assemblages was also identified in several zones of the NW and SE parts of the ASTER image maps. Integration of the results indicates the high potential zones for the occurrence of Cu–Fe–Au mineralisation in the Bajestan region.
Ahmed, A, Abu Bakar, MS, Azad, AK, Sukri, RS & Mahlia, TMI 2018, 'Potential thermochemical conversion of bioenergy from Acacia species in Brunei Darussalam: A review', Renewable and Sustainable Energy Reviews, vol. 82, pp. 3060-3076.
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© 2017 Elsevier Ltd As the demand for energy increases and fossil fuel resources are depleted, the search for clean sources of energy has intensified worldwide. This is coupled with a strong global desire to reduce CO2emissions to curb global warming. Brunei Darussalam is committed to reduce its CO2emissions but currently utilizes fossil fuels to meet almost all of its energy requirements. This situation provides good incentives to search for renewable and sustainable resources to produce energy in the country. Acacia species are exotic species that have invaded and spread to natural habitats in Brunei Darussalam. Acacia species are a sustainable source of high quality biomass feedstock to produce bioenergy in the country. Hot tropical weather of the country is highly suitable for the rapid growth of Acacias without requiring any major agricultural input. This study reviews the thermochemical conversion of Acacia species especially; Acacia mangium and Acacia auriculiformis to produce biofuels and bio-products. The prospective of using Acacia biomass as feedstock in pyrolysis, gasification, liquefaction and combustion is also discussed. Acacia biomass is a sustainable and renewable energy resource for Brunei Darussalam to be exploited for energy requirements and can be beneficial for the economy of the country by providing new investment and employment opportunities.
Ahmed, AA, Pradhan, B, Sameen, MI & Makky, AM 2018, 'An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data', Arabian Journal of Geosciences, vol. 11, no. 11.
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© 2018, Saudi Society for Geosciences. This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.
Ahmed, J.B. & Pradhan, B. 2018, 'Termite mounds as bio-indicators of groundwater: Prospects and constraints', Pertanika Journal of Science and Technology, vol. 26, no. 2, pp. 479-498.
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© 2018 Universiti Putra Malaysia Press. Reliance on modern sophisticated equipment for making ‘discoveries’ has limited the human power of observing subtle clues in the environment that are capable of saving cost and labour that come with researching new resources and methods to improve life for all. Due to the growing scarcity of potable water, especially in African and Asian countries, newer, cheaper and reliable methods of investigating groundwater resources are becoming critical. One such potentially promising method is mapping the distribution of termite mounds in the environment. Termite mounds are conspicuous landscape features in tropical and sub-tropical regions of the world. Built from surrounding soils by several species of termite, the properties of mound soil are relatively different from the surrounding soil in most cases, indicating improved hydraulic properties. In this paper, the aim is to review the possibility of employing termite mounds as prospecting tools for groundwater search from three spatial scales of observation. From assessing the smallest to the highest scale of observation, it can be concluded that termite mounds’ prospect as surface indicators of groundwater is apparent. Review findings indicate increased surface water infiltration, presence of riparian tree vegetation and other trees with tap-root system around termite mounds, linear assemblage of termite mounds along aquiferous dykes and seep-lines as well as the dependence of termites on water but avoidance of places with risk of inundation. Whether they indicate permanent groundwater reserves in all cases or whether all species depend largely on water for their metabolism is a subject for further research.
Ahmed, M, Johir, MAH, Khourshed, C, Zhou, J, Ngo, HH, Nghiem, D, Moni, M & Sun, L 2018, 'Sorptive removal of dissolved organic matter in biologically-treated effluent by functionalized biochar and carbon nanotubes: importance of sorbent functionality', Bioresource Technology.
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The sorptive removal of dissolved organic matter (DOM) in biologically-treated effluent was studied by using multi-walled carbon nanotube (MWCNT), carboxylic functionalised MWCNT (MWCNT-COOH), hydroxyl functionalized MWCNT (MWCNT-OH) and functionalized biochar (fBC). DOM was dominated by hydrophilic fraction (79.6%) with a significantly lower hydrophobic fraction (20.4%). The sorption of hydrophobic DOM was not significantly affected by the sorbent functionality (∼10.4% variation) and sorption capacity followed the order of MWCNT > MWCNT-COOH > MWCNT-OH > fBC. In comparison, the sorption of hydrophilic fraction of DOM changed significantly (∼37.35% variation) with the change of sorbent functionality with adsorption capacity decreasing as MWCNT-OH > MWCNT-COOH > MWCNT > fBC. Furthermore, the affinity of adsorbents toward a hydrophilic compound (dinitrobenzene), a hydrophobic compound (pyrene) and humic acid was also evaluated to validate the proposed mechanisms. The results provided important insights on the type of sorbents which are most effective to remove different DOM fractions.
Ahmed, M, Zhou, J, Ngo, H, Johir, MAH & Kireesan, S 2018, 'Sorptive removal of phenolic endocrine disruptors by functionalized biochar:competitive interaction mechanism, removal efficacy and application in wastewater', Chemical Engineering Journal, vol. 335, pp. 801-811.
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Sorptive removal of six phenolic endocrine disrupting chemicals (EDCs) estrone (E1), 17β-estradiol (E2), estriol (E3), 17α-ethynylestradiol (EE2), bisphenol A (BPA) and 4-tert-butylphenol (4tBP) by functionalized biochar (fBC) through competitive interactions was investigated. EDC sorption was pH dependent with the maximum sorption at pH 3.0-3.5 due to hydrogen bonds and π-π interactions as the principal sorptive mechanism. Sorption isotherm of the EDCs was fitted to the Langmuir model. Sorption capacities and distribution coefficient values followed the order E1 > E2 ≥ EE2 > BPA > 4tBP > E3. The findings suggested that EDC sorption occurred mainly through pseudo-second order and external mass transfer diffusion processes, by forming H-bonds along with π-π electron-donor-acceptor (EDA) interactions at different pH. The complete removal of ∼500 μg L-1 of each EDC from different water decreased in the order: deionised water > membrane bioreactor (MBR) sewage effluent > synthetic wastewater. The presence of sodium lauryl sulphonate and acacia gum in synthetic wastewater significantly suppressed sorption affinity of EDCs by 38-50%, hence requiring more fBC to maintain removal efficacy.
Ahmed, MB, Zhou, J, Ngo, HH, Johir, MAH, Sun, L, Asadullah, M & Belhaj, D 2018, 'Sorption of hydrophobic organic contaminants on functionalized biochar: protagonist role of π-π electron-donor-acceptor interactions and hydrogen bonds', Journal of Hazardous Materials.
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The sorption of five potent endocrine disruptors as representative hydrophobic organic contaminants (HOCs) namely estrone (E1), 17β-estradiol (E2), estriol (E3), 17α-ethynylestradiol (EE2) and bisphenol A (BPA) on functionalized biochar (fBC) was systematically examined, with a particular focus on the importance of π-electron-donor (phenanthrene: PHEN) and π-electron-acceptors (1,3-dinitrobenzene: DNB, p-amino benzoic acid: PABA) on sorption. Experimental results suggested that hydrogen-bond formation and π-π-electron-donor-acceptor (EDA) interactions were the dominant sorption mechanisms. The sorption of HOCs decreased as E1 > E2 > EE2 > E3 > BPA based on the Freundlich and Polanyi-Mane-models. The comparison of adsorption coefficient (Kd) normalized against hexadecane-water partition coefficient (KHW) between HOCs and PHEN indicated strong π-π-EDA interactions. π-π interactions among DNB, PHEN and HOCs were verified by the observed upfield frequency (Hz) shifts using proton nuclear magnetic resonance (1H NMR) which identified the specific direction of π-π interactions. UV-vis spectra showed charge-transfer bands for π-donors (PHEN and HOCs) with the model π-acceptor (DNB) also demonstrating the role of π-π EDA interactions. The role of π-electron-donor and π-electron-acceptor domains in fBC was identified at different solution pH.
Akbari, MA, Aghaei, J, Barani, M, Niknam, T, Ghavidel, S, Farahmand, H, Korpas, M & Li, L 2018, 'Convex Models for Optimal Utility-Based Distributed Generation Allocation in Radial Distribution Systems', IEEE Systems Journal.
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IEEE This paper introduces various models for optimal and maximal utility-based distributed generation penetration in the radial distribution systems. Several problems with different probabilistic indices as objective functions constrained by power flow equations, distributed generation penetration, voltage, and thermal limits are proposed to obtain the optimal penetration of distributed generations on rural distribution networks. There are tradeoffs between interests and risks that the distribution network operators or distribution companies may be willing to take on. Thus, to have an effective method for maximal allocation of distributed generations, new indices are proposed, and the problems are formulated as a risk-constrained optimization model. The obtained problems have mixed-integer nonlinear programming and nonconvex forms because of nonlinearity and nonconvexity of the optimal power flow (OPF) equations and indices, leading to computationally nondeterministic polynomial-time-hard problems. Accordingly, in this paper, convex relaxations of OPF are introduced instead of the conventional nonlinear equations. Efficient linear equivalents of the objective function and constraints are introduced to reduce the computational burden. Test results of the proposed models on a radial distribution system are presented and discussed.
Alajlouni, D., Bliuc, D., Tran, T., Pocock, N., Nguyen, T.V., Eisman, J.A. & Center, J.R. 2018, 'Nonstandard Lumbar Region in Predicting Fracture Risk.', Journal of Clinical Densitometry, vol. 21, no. 2, pp. 220-226.
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Femoral neck (FN) bone mineral density (BMD) is the most commonly used skeletal site to estimate fracture risk. The role of lumbar spine (LS) BMD in fracture risk prediction is less clear due to osteophytes that spuriously increase LS BMD, particularly at lower levels. The aim of this study was to compare fracture predictive ability of upper L1-L2 BMD with standard L2-L4 BMD and assess whether the addition of either LS site could improve fracture prediction over FN BMD. This study comprised a prospective cohort of 3016 women and men over 60 yr from the Dubbo Osteoporosis Epidemiology Study followed up for occurrence of minimal trauma fractures from 1989 to 2014. Dual-energy X-ray absorptiometry was used to measure BMD at L1-L2, L2-L4, and FN at baseline. Fracture risks were estimated using Cox proportional hazards models separately for each site. Predictive performances were compared using receiver operating characteristic curve analyses. There were 565 women and 179 men with a minimal trauma fracture during a mean of 11 ± 7 yr. L1-L2 BMD T-score was significantly lower than L2-L4 T-score in both genders (p < 0.0001). L1-L2 and L2-L4 BMD models had a similar fracture predictive ability. LS BMD was better than FN BMD in predicting vertebral fracture risk in women [area under the curve 0.73 (95% confidence interval, 0.68-0.79) vs 0.68 (95% confidence interval, 0.62-0.74), but FN was superior for hip fractures prediction in both women and men. The addition of L1-L2 or L2-L4 to FN BMD in women increased overall and vertebral predictive power compared with FN BMD alone by 1% and 4%, respectively (p < 0.05). In an elderly population, L1-L2 is as good as but not better than L2-L4 site in predicting fracture risk. The addition of LS BMD to FN BMD provided a modest additional benefit in overall fracture risk. Further studies in individuals with spinal degenerative disease are needed.
Alam, M, Jan, MA, Shu, L, He, X & Chen, Y 2018, 'Editorial: Current and Future Trends in Wireless Communications Protocols and Technologies', Mobile Networks and Applications, vol. 23, no. 3, pp. 377-381.
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Alam, M, Rayes, A, He, X, Atiquzzaman, M, Lloret, J & Tsang, KF 2018, 'Guest Editorial Introduction to the Special Issue on Dependable Wireless Vehicular Communications for Intelligent Transportation Systems (ITS)', IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 949-952.
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Aldebei, KW, He, X, Jia, W & Weichang Yeh 2018, 'SUDMAD: Sequential and unsupervised decomposition of a multi-author document based on a hidden markov model', Journal of the Association for Information Science and Technology, vol. 69, no. 2, pp. 201-214.
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Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.
Alfaro-García, V.G., Merigó, J.M., Gil-Lafuente, A.M. & Kacprzyk, J. 2018, 'Logarithmic aggregation operators and distance measures', International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1488-1506.
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© 2018 Wiley Periodicals, Inc. The Hamming distance is a well-known measure that is designed to provide insights into the similarity between two strings of information. In this study, we use the Hamming distance, the optimal deviation model, and the generalized ordered weighted logarithmic averaging (GOWLA) operator to develop the ordered weighted logarithmic averaging distance (OWLAD) operator and the generalized ordered weighted logarithmic averaging distance (GOWLAD) operator. The main advantage of these operators is the possibility of modeling a wider range of complex representations of problems under the assumption of an ideal possibility. We study the main properties, alternative formulations, and families of the proposed operators. We analyze multiple classical measures to characterize the weighting vector and propose alternatives to deal with the logarithmic properties of the operators. Furthermore, we present generalizations of the operators, which are obtained by studying their weighting vectors and the lambda parameter. Finally, an illustrative example regarding innovation project management measurement is proposed, in which a multi-expert analysis and several of the newly introduced operators are utilized.
Alizadeh, M, Ngah, I, Hashim, M, Pradhan, B & Pour, AB 2018, 'A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) model for urban Earthquake vulnerability assessment', Remote Sensing, vol. 10, no. 6.
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© 2018 by the authors. Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to aMultilayer Perceptron (MLP) neural network for producing an Earthquake VulnerabilityMap (EVM). Finally, an EVMwas produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for deci...
Alshehri, M.D., Hussain, F. & Hussain, O. 2018, 'Clustering-Driven Intelligent Trust Management Methodologyfor the Internet of Things (CITM-IoT)', Mobile Networks and Applications, vol. 23, no. 3, pp. 419-431.
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The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.
Al-soeidat, M, Lu, DDC & Zhu, J 2018, 'An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems', IEEE Transactions on Circuits and Systems II: Express Briefs.
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IEEE In this paper, an analog, BJT-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic (PV) modules is proposed. Conventional fixed voltage reference method is the simplest method for tracking, but it does not obtain good MPPT efficiency because the maximum power point (MPP) voltage changes at different insolation levels. In reality, an approximately linear slope is formed when connecting the MPPs measured from the highest insolation level to the lowest. Utilizing this characteristic, a bipolar junction transistor (BJT), which has a similar electrical property, is used to implement a variable voltage reference that improves the accuracy of the MPP voltage when the insolation changes. The proposed circuit is simple, easy to implement and it can track the maximum power point very quickly without the need for a digital controller or PID controller. Hence, the circuit’s cost and complexity are reduced. Experimental results are given to verify the feasibility of the proposed MPPT method.
Altaee, A, Hawari, AH, Abdulaziz Al-Qahoumi, A, Amina Ltaief, A & Syed Zaidi, S 2018, 'Dilution of seawater using dewatered construction water in a hybrid forward osmosis system', Journal of Cleaner Production, vol. 195, pp. 365-373.
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Journal of Cleaner Production
Volume 195, 10 September 2018, Pages 365–373
Dilution of seawater using dewatered construction water in a hybrid forward osmosis system
Alaa H. Hawaria, , , Abdulaziz Al-Qahoumib, Amina Ltaiefa, Syed Zaidic, Ali Altaeed
a Department of Civil and Architectural Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
b MSc Environmental Engineering Master Program, Qatar University, P.O. Box 2713, Doha, Qatar
c Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
d School of Civil and Environmental Engineering, University of Technology in Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
Received 18 February 2018, Revised 24 May 2018, Accepted 25 May 2018, Available online 28 May 2018
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https://doi.org/10.1016/j.jclepro.2018.05.211
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Abstract
In this study, dewatered construction water was used for the first time as the feed solution in a combined pretreatment-forward osmosis process to dilute seawater (i.e. draw solution) for further desalination. It was found that at a feed solution and a draw solution flow rate of 2.2 L min−1 gave the optimum membrane flux with minimal fouling effects. The addition of a spacer in the membrane feed side was effective at low flow rates (0.8 and 1.5 L min−1). The feed solution was then pretreated using two methods: settling and multimedia filtration and used in the forward osmosis unit at a low flow rate of 0.8 L min−1 using a spacer at the feed side. Results revealed a significant increase in the forward osmosis membrane flux by 64.3% when multimedia filtration was carried out with a flux reduction of 7.7%. While the settling method achieved only 13.5% increase in the permeate flux and 12.5% flux reduction. The multimedia filtration process removed most of the particles that would cause fouling which resulted in an elevated and more consistent membrane flux. Results also showed that the water flux was 1.3 times higher when the membrane's act...
Altaee, A, Samaei, SM & Gato-Trinidada, S 2018, 'The application of pressure-driven ceramic membrane technology for the treatment of industrial wastewaters – A review', Separation and Purification Technology, vol. 200, pp. 198-220.
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This paper presents a review of the previous laboratory analysis and case studies on the application of the pressure-driven ceramic membrane technology for treatment of industrial wastewaters. Ceramic membranes has attracted remarkable interests in recent decades for industrial wastewater treatment because of their superior characteristic such as high fluxes , reliable working lifetime under aggressive operating conditions and ease of cleaning. The literature review revealed that the efficiency of this technology has been proven in a wide variety of wastewaters from different industries and activities including pulp and paper, textile, pharmaceutical, petrochemical, food and mining. However, there are still challenges and questions for this technology that need to be addressed in future researches such as investment cost optimisation by introducing new fabrication technologies, selectivity, permeability and packing densities improvement, fouling minimisation and proposing scale up based on experimental research results.
Altaee, A, Xu, B, Ahmed, MB, Zhou, JL, Xu, G & Wu, M 2018, 'Graphitic carbon nitride based nanocomposites for the photocatalysis of organic contaminants under visible irradiation: Progress, limitations and future directions', Science of the Total Environment, vol. 633, pp. 546-559.
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Graphitic carbon nitride (g-C3N4) has drawn great attention recently because of its visible light response, suitable energy band gap, good redox ability, and metal-free nature. g-C3N4 can absorb visible light directly, therefore has better photocatalytic ability under solar irradiation and is more energy-efficient than TiO2. However, pure g-C3N4 still has the drawbacks of insufficient light absorption, small surface area and fast recombination of photogenerated electron and hole pairs. This review summarizes the recent progress in the development of g- C3N4 nanocomposites to photodegrade organic contaminants in water. Element doping especially by potassium has been reported to be an efficient method to promote the degradation efficacy. In addition, compound doping improves photodegradation performance of g-C3N4, especially Ag3PO4-g-C3N4 which can completely degrade 10 mg L−1 of methyl orange under visible light irradiation in 5 min, with the rate constant (k) as high as 0.236 min−1. Moreover, co-doping enhances the photodegradation rate of multiple contaminants while immobilization significantly improves catalyst stability. Most of g-C3N4 composites possess high reusability enabling their practical applications in wastewater treatment. Furthermore, environmental conditions such as solution pH, reaction temperature, dissolved oxygen, and dissolved organic matter all have important effects on the photocatalytic ability of g-C3N4 photocatalyst. Future work should focus on the synthesis of innovative g-C3N4 nanocomposites for the efficient removal of organic contaminants in water and wastewater.
Altaee, A, Zhou, J, Zaragoza, G & Sharif, AO 2018, 'Impact of membrane orientation on the energy efficiency of dual stage pressure retarded osmosis', Journal of Water Process Engineering.
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The performance of Dual Stage Pressure Retarded Osmosis (DSPRO) was analyzed using a developed computer model. DSPRO process was evaluated on Pressure Retarded Osmosis (PRO) and Forward Osmosis (FO) operating modes for different sodium chloride (NaCl) draw and feed concentrations. Simulation results revealed that the total power generation in the DSPRO process operating on the PRO mode was 2.5–5 times more than that operating on the FO mode. For DSPRO operating on the PRO mode, the higher power generation was in the case of 2 M NaCl-fresh and 32% the contribution of the second stage to the total power generation in the DSPRO. To the contrast, he total power generated in the DSPRO operating on the FO mode was in the following order 5M-0.6M > 5M-0.7M > 2M-0.01 > 2M-0.6 M. Interestingly, single stage process operating on the FO mode performed better than DSPRO process due to the severe concentration polarization effects. The results also showed that power density of the DSPRO reached a maximum amount at a hydraulic pressure less than the average osmotic pressure gradient, Δπ/2, due to the variation of optimum operating pressure of each stage. Moreover, results showed that the effective specific energy in the PRO process was lower than the maximum specific energy. However, the effective specific energy of the DSPRO was larger than that of the single stage PRO due to the rejuvenation of the salinity gradient, emphasizing the high potential of the DSPRO process for power generation.
Altaee, A., Guillermo, Z., Millar, G.J., Sharif, A.O. & Alanezi, A.A. 2018, 'Limitations of osmotic gradient resource and hydraulic pressure on the efficiency of dual stage PRO process', Desalination and Water Treatment.
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A dual stage PRO process has been proposed for power generation from a salinity gradient across a semi-permeable membrane. Both closed-loop and open-loop dual stage PRO system were evaluated using 2 M NaCl and Dead Sea as draw solutions, whereas the feed solution was either fresh water or seawater. The impact of feed salinity gradient resource and feed pressure on the net power generation and water flux were evaluated. The results showed that power density in stage one reached a maximum amount at , but the maximum net power generation occurred at . This result was mainly attributed to the variation of net driving pressure in stage one and two of the PRO process. The dual stage PRO process was found to perform better at high osmotic pressure gradient across the PRO membrane, for example when Dead Sea brine or highly concentrated NaCl was the draw solution. Total power generation in the dual stage PRO process was up to 40% higher than that in the conventional PRO process. This outcome was achieved through harvesting the rest of the energy remaining in the diluted draw solution. Therefore, a dual stage PRO process has the potential of maximizing power generation from a salinity gradient resource.
Altaee, A., Jakowiecki, J., Sztyler, A., Filipek, S., Li, P., Raman, K., Barathiraja, N., Ramakrishna, S., Eswara, J.R., Sharif, A.O., Ajayan, P.M. & Renugopalakrishnan, V. 2018, 'Aquaporin graphene interface: relevance to point-of-care device for renal cell carcinoma and desalination', Interface Focus.
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Aquaporins superfamily of hydrophobic integral membrane proteins constitute water channels essential to the movement of water across the cell membrane maintaining homeostatic equilibrium. During the passage of water between the extracellular and intracellular sides of the cell, aquaporins act as ultra-sensitive filters. Due to their hydrophobic nature, aquaporins self-assemble in phospholipids and if a proper choice of lipids are made then the aquaporin biomimetic membrane can be used in the design of artificial kidney. In combination with graphene, aquaporin biomimetic membrane finds practical application in desalination and water recycling. Recently, Aquaporin 1 has emerged as an important biomarker in renal cell carcinoma and at the present time ultra-sensitive sensing of renal cell carcinoma is cumbersome and hence in this paper we are discussing epitopes from monoclonal antibody raised as a probe for point of care device for sensing renal cell carcinoma by immobilizing the antibody on the surface of a single layer graphene. A microfluidic device for sensing renal cell carcinoma is pursued in our laboratories, aquaporin interface with graphene in two not directly related situations poses challenges that require more detailed studies to understand of how graphene acts as a mediator.
Alvarez, G., Schulte, J., Stockton, G. & Wheeler, D. 2018, 'A useful demonstration of calculus in a physics high school laboratory', Physics Education, vol. 53, no. 1, pp. 015006-015006.
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The real power of calculus is revealed when it is applied to actual physical problems. In this paper, we present a calculus inspired physics experiment suitable for high school and undergraduate programs. A model for the theory of the terminal velocity of a falling body subject to a resistive force is developed and its validity tested in an experiment of a falling magnet in a column of self-induced eddy currents. The presented method combines multiple physics concepts such as 1D kinematics, classical mechanics, electromagnetism and non-trivial mathematics. It offers the opportunity for lateral as well as project-based learning.
Alvarez, G., Schulte, J., Stockton, G. & Wheeler, D. 2018, 'A useful demonstration of calculus in a physics high school laboratory', Physics Education, vol. 53, no. 1, pp. 015006-015006.
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Alzoubi, Y, Gill, AQ & Moulton, B 2018, 'A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development', Journal of Software Engineering Research and Development, vol. 6, no. 4.
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Al-Zubaydi, AYT & Hong, G 2018, 'Experimental investigation of counter flow heat exchangers for energy recovery ventilation in cooling mode', International Journal of Refrigeration, vol. 93, pp. 132-143.
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© 2018 Elsevier Ltd and IIR Ventilation heat recovery is a system that requires low power to operate and has a high capacity to reduce the energy consumption and increase the overall efficiency for air conditioning. This paper reports the experimental investigation of air-to-air heat exchangers employed for heat recovery ventilation in cooling mode. The two main objective of this research are to design, fabricate and testing two polymers heat exchangers of different plate geometries and to evaluate and compare the thermal performance two quasi-counter flow plate heat exchangers. The key aims were to evaluate the effect of the surface geometry of the plates heat exchanger on the performance parameters specified in ANSI/ASHRAE Standard 84 and ANSI/AHRI Standard 1060 and narrow the gap of the limited experimental comparison of polymers sensible heat exchanger in cooling mode. The experiments were conducted on two polymer heat exchangers, one with a flat plate and the other with a dimpled surface plate. The experimental results showed that the cooling capacity of the dimpled surface heat exchanger as ventilation heat recovery system in cooling mode was 50–60% better than that of the flat surface plate heat exchanger. In addition, the sensible efficiency of the dimpled surface heat exchanger was higher than that of the flat surface plates heat exchanger at lower air velocities and higher air initial temperatures. The highest COP was 6.6 achieved with dimpled surface heat exchanger under primary air operating temperature of 32.6 °C.
Al-Zubi, MM & Sanagavarapu, A 2018, 'Modeling a Composite Molecular Communication Channel', IEEE Transactions on Communications, vol. 66, no. 8.
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The paper deals with development of diffusive propagation model for molecular communication in composite biological microenvironments with multiple regions each with distinct diffusion properties for the understanding of the transport of information molecules. We propose a generalized analytical approach for modelling a composite, diffusive, molecular communication channel for arbitrary placement of the transmitting and receiving nano-machines. Using this approach, we derive a generalised closed-form expression for the channel impulse response of a three dimensionally (3D) diffusive medium having “N” multiple regions. In addition, a particle-based simulator for modelling a 3-D diffusive medium with multiple regions is also proposed. We use the exact analytical solution to validate the simulations. The pulse peak amplitude, pulse peak time and pulse width are derived to evaluate the system performance using both analytical and simulation approaches. It is shown that the channel impulse response and other communication metrics are significantly affected by the diffusion coefficients, region thickness, interface properties as well as the positions of transmitter and receiver nano-machines with respect to the interfaces.
Al-Zubi, MM & Sanagavarapu, A 2018, 'Modeling of Ligand-Receptor Protein Interaction in Biodegradable Spherical Bounded Biological Micro-Environments', IEEE Access, vol. PP, no. 99.
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In this paper, we propose a generalized model for the Ligand-Receptor protein interaction in 3-D spherically bounded, diffusive biological microenvironments using molecular communication paradigm. Modeling a targeted cell as a receiver nano-machine, we derive analytical expressions for the Green’s function as well as for the expected number of the activated receptors. The molecular degradation in the environment due to enzymatic effects and the changes in pH levels are included via the first-order degradation reaction mechanism. A second-order reversible reaction mechanism is employed to model the reception process that involve the reaction of ligands to activate the receptor proteins lying on the receiver surface to form ligand-receptor complexes. We also present a particle-based simulator that incorporates the reversible reaction of molecules with the receptors present on the surface of a receiver that is located inside a bounded, 3-D microfluidic environment. Our simulations also include molecular degradation and boundary absorption of the ligands due to collision. The simulation results show perfect agreement with the results obtained from the analytical, bounded, and 3-D spherical model of the medium. The proposed models can be used for accurate prediction of the drug concentration profiles and number of activated receptors at any targeted cell.
Amjadipour, M, Tadich, A, Boeckl, JJ, Lipton-Duffin, J, MacLeod, J, Iacopi, F & Motta, N 2018, 'Quasi free-standing epitaxial graphene fabrication on 3C-SiC/Si(111).', Nanotechnology, vol. 29, no. 14, p. 145601.
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Growing graphene on SiC thin films on Si is a cheaper alternative to the growth on bulk SiC, and for this reason it has been recently intensively investigated. Here we study the effect of hydrogen intercalation on epitaxial graphene obtained by high temperature annealing on 3C-SiC/Si(111) in ultra-high vacuum. By using a combination of core-level photoelectron spectroscopy, low energy electron diffraction, and near-edge x-ray absorption fine structure (NEXAFS) we find that hydrogen saturates the Si atoms at the topmost layer of the substrate, leading to free-standing graphene on 3C-SiC/Si(111). The intercalated hydrogen fully desorbs after heating the sample at 850 °C and the buffer layer appears again, similar to what has been reported for bulk SiC. However, the NEXAFS analysis sheds new light on the effect of hydrogen intercalation, showing an improvement of graphene's flatness after annealing in atomic H at 600 °C. These results provide new insight into free-standing graphene fabrication on SiC/Si thin films.
Anam, K. & Al-Jumaily, A. 2018, 'Optimized kernel extreme learning machine for myoelectric pattern recognition', International Journal of Electrical and Computer Engineering, vol. 8, no. 1, pp. 483-496.
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© 2018 Institute of Advanced Engineering and Science. All rights reserved. Myoelectric pattern recognition (MPR) is used to detect user's intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).
Andrade-Valbuena, NA & Merigo, JM 2018, 'Outlining new product development research through bibliometrics: Analyzing journals, articles and researchers', Journal of Strategy and Management, vol. 11, no. 3, pp. 328-350.
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© 2018, Emerald Publishing Limited. Purpose: New product development (NPD) is a noteworthy field that has attracted the attention of scholars for its relevance for firm success. Based on bibliometric indicators and spatial distance network analysis, the authors outline the general structure overview of NPD research through the last 40 years of scientific production; identify and categorize key articles, authors, journals, institutions, and countries related to NPD research; identify and map the research subareas that have mostly contributed to the construction of NPD intellectual structure. The paper aims to discuss these issues. Design/methodology/approach: The work uses the Web of Science Core Collection and the visualization of similarities viewer software. The analysis searches for all the documents connected to NPD available in the database. The graphical visualization maps the bibliographic data in terms of bibliographic coupling and co-citation. Findings: The general NPD citation pattern evidences a construction of knowledge and learning, as evidenced in different subjects, such as biology or physics. Relevant contributions and contributors are highlighted as journals, articles, researchers, countries and institutions in overall NPD research and in its constituent subfields. Five subareas related to the NPD field based on journals and authors network are identified: marketing; operations and production; strategy; industrial engineering and operations; and management. Originality/value: This paper contributes to the NPD literature by offering a global perspective on the field by using bibliometric data graphical networks, providing insights about the influence of individual actors and its contributions to build bridges between the different subfields of research in NPD.
Ansari, A.J., Hai, F.I., He, T., Price, W.E. & Nghiem, L.D. 2018, 'Physical cleaning techniques to control fouling during the pre-concentration of high suspended-solid content solutions for resource recovery by forward osmosis', Desalination, vol. 429, pp. 134-141.
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© 2017 The fouling propensity of digested sludge centrate, and the effectiveness of membrane flushing, air-scouring, and ultrasonication for physical cleaning were systematically evaluated. Accelerated fouling conditions were applied to simulate the long-term and intensive pre-concentration scenario that is required for phosphorus recovery from digested sludge centrate. The results suggest that membrane fouling during forward osmosis operation to pre-concentrate digested sludge centrate is mostly due to the deposition of small mineral crystals and particulate matter on the membrane surface. Both high cross-flow velocity flushing and ultrasonication were effective at preventing membrane fouling under accelerated fouling conditions. Our results also highlight the potential of intermittent membrane cleaning for achieving a higher cumulative permeate volume and lower energy consumption in comparison to continuous application to prevent membrane fouling. Among several physical cleaning regimes investigated in this study, the combination of ultrasonication and high cross-flow velocity flushing was the most effective and could maintain stable FO operation over several consecutive cleaning cycles.
Ansari, A.J., Hai, F.I., Price, W.E., Ngo, H.H., Guo, W. & Nghiem, L.D. 2018, 'Assessing the integration of forward osmosis and anaerobic digestion for simultaneous wastewater treatment and resource recovery.', Bioresource technology, vol. 260, pp. 221-226.
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This study assessed the performance and key challenges associated with the integration of forward osmosis (FO) and anaerobic digestion for wastewater treatment and resource recovery. Using a thin film composite polyamide FO membrane, maximising the pre-concentration factor (i.e. system water recovery) resulted in the enrichment of organics and salinity in wastewater. Biomethane potential evaluation indicated that methane production increased correspondingly with the FO pre-concentration factor due to the organic retention in the feed solution. At 90% water recovery, about 10% more methane was produced when using NaOAc compared with NaCl because of the contribution of biodegradable reverse NaOAc flux. No negative impact on anaerobic digestion was observed when wastewater was pre-concentrated ten-fold (90% water recovery) for both draw solutes. Interestingly, the unit cost of methane production using NaOAc was slightly lower than NaCl due to the lower reverse solute flux of NaOAc, although NaCl is a much cheaper chemical.
Anshar, M. & Williams, M.A. 2018, 'Evolving robot empathy towards humans with motor disabilities through artificial pain generation', AIMS Neuroscience, vol. 5, no. 1, pp. 56-73.
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© 2018 the Author(s). In contact assistive robots, a prolonged physical engagement between robots and humans with motor disabilities due to shoulder injuries, for instance, may at times lead humans to experience pain. In this situation, robots will require sophisticated capabilities, such as the ability to recognize human pain in advance and generate counter-responses as follow up emphatic action. Hence, it is important for robots to acquire an appropriate pain concept that allows them to develop these capabilities. This paper conceptualizes empathy generation through the realization of synthetic pain classes integrated into a robot's self-awareness framework, and the implementation of fault detection on the robot body serves as a primary source of pain activation. Projection of human shoulder motion into the robot arm motion acts as a fusion process, which is used as a medium to gather information for analyses then to generate corresponding synthetic pain and emphatic responses. An experiment is designed to mirror a human peer's shoulder motion into an observer robot. The results demonstrate that the fusion takes place accurately whenever unified internal states are achieved, allowing accurate classification of synthetic pain categories and generation of empathy responses in a timely fashion. Future works will consider a pain activation mechanism development.
Anthony Tri Tran, C. & Ha, Q. 2018, 'Introduction', Studies in Systems, Decision and Control, vol. 148, pp. 1-29.
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© 2018, Springer Nature Singapore Pte Ltd. Automatic and semi-automatic control of large-scale interconnected systems, also known as network systems (Newman, Networks: an introduction, Oxford University Press, Oxford, 2010, [101]; Barrabasi and Albert, Science, 286(5439):509–512, 1999, [10]), remains a challenging problem due to the increasing interactions between multi-variable subsystems connected in parallel, parallelised, serial and recirculation. In a broad sense, a network system can be modelled as a large set of interconnected nodes, in which a node is a fundamental unit with specific contents (Li and Chen, Phys A Stat Mech Appl, 343:263–278, 2004, [81]). The network system notion is extended over many application domains such as, but not limited to, chemical and petrochemical processes, power systems, telecommunication systems, transportation systems, supply chain systems, networked robotics and biological systems.
Ara, P, Yu, K, Cheng, S, Dutkiewicz, E & Heimlich, MC 2018, 'Human Abdomen Path-Loss Modeling and Location Estimation of Wireless Capsule Endoscope Using Round-Trip Propagation Loss', IEEE Sensors Journal, vol. 18, no. 8, pp. 3266-3277.
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© 2001-2012 IEEE. A highly accurate location estimation of a capsule endoscope in the gastrointestinal tract in the range of several millimeters is a challenging task, as radio-frequency signals encounter high power loss and a highly dynamic channel propagation environment. Therefore, an accurate path-loss model is required for the development of accurate localization algorithms. This paper presents an in-body path-loss model for the human abdomen region at 2.4 GHz. To develop the path-loss model, electromagnetic simulations using the finite-difference time-domain method were carried out on three different anatomical human models from the Virtual Family. A mathematical expression for the path-loss model was proposed based on analysis of the measured loss at different capsule locations inside the small intestine. The proposed path-loss model is a good approximation to model in-body RF propagation, since real measurements are not completely practical for the capsule-endoscope subject. Based on the proposed path-loss model, the article also presents the 2-D location estimation of a capsule endoscope inside the small intestine using the trilateration method as well as the non-linear least squares algorithm. It is shown that with 90% probability, the location estimation error is less than 4 cm.
Argha, A, Li, L & Su, S 2018, 'H2-based optimal sparse sliding mode control for networked control systems', International Journal of Robust and Nonlinear Control, vol. 28, no. 1, pp. 16-30.
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This paper is devoted to the problem of designing a sparsely distributed sliding mode control for networked systems. Indeed, this note uses a distributed sliding mode control framework by exploiting (some of) other subsystems' information to improve the performance of each local controller so that it can widen the applicability region of the given scheme. To do so, different from the traditional schemes in the literature, a novel approach is proposed to design the sliding surface, in which the level of required control effort is taken into account during the sliding surface design based on the urn:x-wiley:rnc:media:rnc3852:rnc3852-math-0004 control. We then use this novel scheme to provide an innovative less‐complex procedure that explores sparse control networks to satisfy the underlying control objective. Besides, the proposed scheme to design the sliding surface makes it possible to avoid unbounded growth of control effort during the sparsification of the control network structure. Illustrative examples are presented to show the effectiveness of the proposed approach.
Argha, A, Li, L, Su, SW & Nguyen, H 2018, 'Sparsely distributed sliding mode control for interconnected systems', Journal of the Franklin Institute, vol. 355, no. 14, pp. 6191-6214.
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© 2018 The Franklin Institute This paper proposes a framework for the design of sparsely distributed output feedback discrete-time sliding mode control (ODSMC) for interconnected systems. The major target here is to develop an observer based discrete-time sliding mode controller employing a sparsely distributed control network structure in which local controllers exploit some other sub-systems’ information as well as its own local information. As the local controllers/observers have access to some other sub-systems’ states, the control performance will be improved and the applicability region will be widened compared to the decentralised structure. As the first step, a stability condition is derived for the overall closed-loop system obtained from applying ODSMC to the underlying interconnected system, by assuming a priori known structure for the control/observer network. The developed LMI based controller design scheme provides the possibility to employ different information patterns such as fully distributed, sparsely distributed and decentralised patterns. In the second step, we propose a methodology to identify a sparse control/observer network structure with the least possible number of communication links that satisfies the stability condition given in the first step. The boundedness of the obtained overall closed-loop system is analysed and a bound is derived for the augmented system state which includes the closed-loop system state and the switching function.
Argha, A., Savkin, A., Liaw, S.-.T. & Celler, B.G. 2018, 'Effect of Seasonal Variation on Clinical Outcome in Patients with Chronic Conditions: Analysis of the Commonwealth Scientific and Industrial Research Organization (CSIRO) National Telehealth Trial.', JMIR medical informatics, vol. 6, no. 1, p. e16.
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Seasonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia.The aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS).Patients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial.Test patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variat...
Argha, A., Su, S.W., Savkin, A. & Celler, B. 2018, 'Design of optimal sliding-mode control using partial eigenstructure assignment', International Journal of Control, pp. 1-13.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group This paper describes a new framework for the design of a sliding surface for a given system while multi-channel (Formula presented.) performances of the closed-loop system are under control. In contrast to most of the current sliding surface design schemes, in this new method, the level of control effort required to maintain sliding is penalised. The proposed method for the design of optimal sliding mode control is implemented in two stages. In the first stage, a state feedback gain is derived using a linear matrix inequality (LMI)-based scheme that can assign a number of the closed-loop eigenvalues to a known value whilst satisfying performance specifications. The sliding function matrix related to the particular state feedback derived in the first stage is obtained in the second stage by using one of the two different methods developed for this goal. The proposed theory is evaluated by using numerical examples including the problem of steady-state output tracking via a state-feedback SMC for flight control.
Argha, A., Su, S.W., Savkin, A. & Celler, B.G. 2018, 'Mixed H2/H∝-based actuator selection for uncertain polytopic systems with regional pole placement', International Journal of Control, vol. 91, no. 2, pp. 1-17.
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© 2017 Informa UK Limited, trading as Taylor & Francis GroupThis paper is devoted to the problem of designing an (Formula presented.) and/or (Formula presented.) row-sparse static output feedback controller for continuous linear time-invariant systems with polytopic uncertainty. The immediate application of the proposed approach lies within the problem of the optimal selection of a subset of available actuators during the fault accommodation stage of a fault-tolerant control scheme. Incorporating an extra term for penalising the number of actuators into the optimisation objective function, we propose an explicit scheme and two iterative procedures according to the reweighted ℓ1 (REL1) and reweighted iterative support detection (RISD) algorithms for the purposes of identifying the favourable row-sparse feedback gains. Furthermore, this problem formulation allows us to incorporate additional constraints into the designing problem such as regional pole placement constraints which provide more control over the satisfactory transient behaviour and closed-loop pole locations. In this paper, we present two examples which demonstrate the remarkable performance and broad applicability of the proposed approaches.
Argha, A., Su, S.W., Savkin, A. & Celler, B.G. 2018, 'Novel frameworks for the design of fault-tolerant control using optimal sliding-mode control', International Journal of Robust and Nonlinear Control, vol. 28, no. 8, pp. 3015-3032.
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Copyright © 2018 John Wiley & Sons, Ltd. This paper describes 2 schemes for a fault-tolerant control using a novel optimal sliding-mode control, which can also be employed as actuator redundancy management for overactuated uncertain linear systems. By using the effectiveness level of the actuators in the performance indexes, 2 schemes for redistributing the control effort among the remaining (redundant or nonfaulty) set of actuators are constructed based on an H2-based optimal sliding-mode control. In contrast to the current sliding-mode fault-tolerant control design methods, in these new schemes, the level of control effort required to maintain sliding is penalised. The proposed optimal sliding-mode fault-tolerant control design schemes are implemented in 2 stages. In the first stage, a state feedback gain is derived using an LMI-based scheme that can assign a number of the closed-loop eigenvalues to a known value whilst satisfying performance specifications. The sliding function matrix related to the particular state feedback derived in the first stage is obtained in the second stage. The difference between the 2 schemes proposed for the sliding-mode fault-tolerant control is that the second one includes a separate control allocation module, which makes it easier to apply actuator constraints to the problem. Moreover, it will be shown that, with the second scheme, we can deal with actuator faults or even failures without controller reconfiguration. We further discuss the advantages and disadvantages of the 2 schemes in more details. The effectiveness of the proposed schemes are illustrated with numerical examples.
Asari, AR, Guo, Y & Zhu, J 2018, 'Core loss measurement of Somaloy 700 material under round Loci of magnetic flux density', International Journal of Engineering and Technology(UAE), vol. 7, no. 3, pp. 109-113.
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© 2018 Authors. The magnetic properties of SOMALOY 700 material are aggressively studied by some researchers in predicting the production of total core loss during the magnetization process of that particular material. Core loss is resulted due to the alternating and rotating magnetic fields in a core material. The magnetic properties of SOMALOY 700 material is studied in this paper since it offers the low core loss during the operation. 2-D measurement were conducted by controlling the fluxes to be circular with the help of LabVIEW while the core loss calculations were calculated by MathCAD. The performance of SOMALOY 700 material at different frequencies were compared. The finding indicates that the magnetization at 1000 Hz contributes higher core loss compared to the magnetization at 500 Hz and 50 Hz. The details of SOMALOY 700 material provide good information to practitioners in designing electrical machine at different variation of frequencies.
Asif, MB, Hai, FI, Dhar, BR, Ngo, HH, Guo, W, Jegatheesan, V, Price, WE, Nghiem, LD & Yamamoto, K 2018, 'Impact of simultaneous retention of micropollutants and laccase on micropollutant degradation in enzymatic membrane bioreactor', Bioresource Technology, vol. 267, pp. 473-480.
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© 2018 This study systematically compares the performance of ultrafiltration (UF) and nanofiltration (NF) based enzymatic membrane bioreactors (EMBRs) for the degradation of five micropollutants, namely atrazine, carbamazepine, sulfamethoxazole, diclofenac and oxybenzone to elucidate the impact of effective membrane retention of micropollutants on their degradation. Based on the permeate quality, NF-EMBR achieved 92–99.9% micropollutant removal (i.e., biodegradation + membrane retention), while the removal of these micropollutants by UF-EMBR varied from 20 to 85%. Mass balance analysis revealed that micropollutant degradation was improved by 15–30% in NF-EMBR as compared to UF-EMBR, which could be attributed to the prolonged contact time between laccase and micropollutants following their effective retention by the NF membrane. A small decline in permeate flux was observed during EMBR operation. However, the flux could be recovered by flushing the membrane with permeate.
Asif, MB, Hai, FI, Kang, J, van de Merwe, JP, Leusch, FDL, Price, WE & Nghiem, LD 2018, 'Biocatalytic degradation of pharmaceuticals, personal care products, industrial chemicals, steroid hormones and pesticides in a membrane distillation-enzymatic bioreactor.', Bioresource technology, vol. 247, pp. 528-536.
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Laccase-catalyzed degradation of a broad spectrum of trace organic contaminants (TrOCs) by a membrane distillation (MD)-enzymatic membrane bioreactor (EMBR) was investigated. The MD component effectively retained TrOCs (94-99%) in the EMBR, facilitating their continuous biocatalytic degradation. Notably, the extent of TrOC degradation was strongly influenced by their molecular properties. A significant degradation (above 90%) of TrOCs containing strong electron donating functional groups (e.g., hydroxyl and amine groups) was achieved, while a moderate removal was observed for TrOCs containing electron withdrawing functional groups (e.g., amide and halogen groups). Separate addition of two redox-mediators, namely syringaldehyde and violuric acid, further improved TrOC degradation by laccase. However, a mixture of both showed a reduced performance for a few pharmaceuticals such as primidone, carbamazepine and ibuprofen. Mediator addition increased the toxicity of the media in the enzymatic bioreactor, but the membrane permeate (i.e., final effluent) was non-toxic, suggesting an added advantage of coupling MD with EMBR.
Avilés-Ochoa, E, León-Castro, E, Perez-Arellano, LA & Merigó, JM 2018, 'Government transparency measurement through prioritized distance operators', Journal of Intelligent and Fuzzy Systems, vol. 34, no. 4, pp. 2783-2794.
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© 2018 - IOS Press and the authors. All rights reserved. The prioritized induced probabilistic ordered weighted average distance (PIPOWAD) has been developed. This new operator is an extension of the ordered weighted average (OWA) operator that can be used in cases where we have two sets of data that want to be compared. Some of the main characteristics of this new operator are: 1) Not all the decision makers are equally important, so the information needs to be prioritized, 2) The information has a probability to occur and 3) The decision makers can change the importance of the information based in an induced variable. Additionally, characteristics and families of the PIPOWAD operator are presented. Finally, an application of the PIPOWAD operator in order to measure government transparency in Mexico is presented.
Awadallah, M, tawadros, P, Walker, P & zhang, N 2018, 'Comparative fuel economy, cost and emissions analysis of a novel mild hybrid and conventional vehicles', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.
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Mild hybrid vehicles have been explored as a potential pathway to reduce vehicle emissions cost-effectively. The use of manual transmissions to develop novel hybrid vehicles provides an alternate route to producing low cost electrified powertrains. In this paper, a comparative analysis examining a conventional vehicle and a mild hybrid electric vehicle is presented. The analysis considers fuel economy, capital and ongoing costs and environmental emissions, and includes developmental analysis and simulation using mathematical models. Vehicle emissions (nitrogen oxides, carbon monoxide and hydrocarbons) and fuel economy are computed, analysed and compared using a number of alternative driving cycles and their weighted combination. Different driver styles are also evaluated. Studying the relationship between the fuel economy and driveability, where driveability is addressed using fuel-economical gear shift strategies. Our simulation suggests the hybrid concept presented can deliver fuel economy gains of between 5 and 10%, as compared to the conventional powertrain.
Aykin, I, Krunz, M & Xiao, Y 2018, 'Adaptive frequency-hopping schemes for CR-based multi-link satellite networks', International Journal of Satellite Communications and Networking, vol. 36, no. 4, pp. 315-331.
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Copyright © 2018 John Wiley & Sons, Ltd. In this paper, we study two dynamic frequency hopping (DFH)–based interference mitigation approaches for satellite communications. These techniques exploit the sensing capabilities of a cognitive radio to predict future interference on the upcoming frequency hops. We consider a topology where multiple low Earth orbit satellites transmit packets to a common geostationary equatorial orbit satellite. The FH sequence of each low Earth orbit–geostationary equatorial orbit link is adjusted according to the outcome of out-of-band proactive sensing scheme, performed by a cognitive radio module in the geostationary equatorial orbit satellite. On the basis of sensing results, new frequency assignments are made for the upcoming slots, taking into account the transmit powers, achievable rates, and overhead of modifying the FH sequences. In addition, we ensure that all satellite links are assigned channels such that their minimum signal-to-interference-plus-noise ratio requirements are met, if such an assignment is possible. We formulate two multi-objective optimization problems: DFH-Power and DFH-Rate. Discrete-time Markov chain analysis is used to predict future channel conditions, where the number of states are inferred using k-means clustering, and the state transition probabilities are computed using maximum likelihood estimation. Finally, simulation results are presented to evaluate the effects of different system parameters on the performance of the proposed designs.
Babar, A., Bunker, D. & Gill, A.Q. 2018, 'Investigating the Relationship between Business Analysts’ Competency and IS Requirements Elicitation: A Thematic-analysis Approach', Communications of the Association for Information Systems, vol. 42, no. 1, pp. 334-362.
Bach, QV, Le, VT, Yoon, YS, Bui, XT, Chung, W, Chang, SW, Ngo, HH, Guo, W & Nguyen, DD 2018, 'A new hybrid sewage treatment system combining a rolled pipe system and membrane bioreactor to improve the biological nitrogen removal efficiency: A pilot study', Journal of Cleaner Production, vol. 178, pp. 937-946.
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© 2018 Elsevier Ltd A new hybrid pilot plant configuration based on a modularized rolled pipe system (RPS) combined with a submerged flat sheet membrane bioreactor (MBR) was investigated to enhance the sewage treatment and membrane performance. The system was operated under actual conditions for more than four months, that is, at a constant flow rate of 30 m³/d and with two internal recycling ratios. The results indicate that the hybrid system produces an excellent effluent quality and considerably mitigated membrane fouling. The average concentrations of SS, COD, TN, NH4+-N, NO3−-N, and PO43--P remained below 2.81, 8.29, 8.77, 0.15, 8.17, and 1.49 mg/L, respectively. It was estimated that the periodic chemical cleaning of the membrane could be extended to approximately six months. The MBR and RPS can virtually complete nitrification and denitrification, respectively. The highest average denitrification rate of the RPS is 116.95 mg NO3-N/(g MLVSS d), with a hydraulic retention time of 1.05 h. Therefore, the RPS–MBR hybrid system has potential to improve the sewage treatability. The emerging RPS technique can obtain high rates of denitrification coupled with a compact design, ease of installation, and small footprint.
Bagley, D, Chung, NT-S, He, ZJ, Julian, T, Lee, Y, Mauter, M, Nghiem, L, Rodrigues, D, Wammer, K, Ward, B & Zhang, Q 2018, 'Outstanding Reviewers for Environmental Science: Water Research & Technology in 2017', ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, vol. 4, no. 6, pp. 760-760.
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Bah, A.O., Qin, P.-.Y., Ziolkowski, R.W., Cheng, Q. & Guo, Y.J. 2018, 'Realization of an Ultra-thin Metasurface to Facilitate Wide Bandwidth, Wide Angle Beam Scanning.', Scientific reports, vol. 8, no. 1, p. 4761.
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A wide bandwidth, ultra-thin, metasurface is reported that facilitates wide angle beam scanning. Each unit cell of the metasurface contains a multi-resonant, strongly-coupled unequal arm Jerusalem cross element. This element consists of two bent-arm, orthogonal, capacitively loaded strips. The wide bandwidth of the metasurface is achieved by taking advantage of the strong coupling within and between its multi-resonant elements. A prototype of the proposed metasurface has been fabricated and measured. The design concept has been validated by the measured results. The proposed metasurface is able to alleviate the well-known problem of impedance mismatch caused by mutual coupling when the main beam of an array is scanned. In order to validate the wideband and wide scanning ability of the proposed metasurface, it is integrated with a wideband antenna array as a wide angle impedance matching element. The metasurface-array combination facilitates wide angle scanning over a 6:1 impedance bandwidth without the need for bulky dielectrics or multi-layered structures.
Bai, F., Vidal-Calleja, T. & Huang, S. 2018, 'Robust Incremental SLAM Under Constrained Optimization Formulation', IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1207-1214.
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Bai, L, Wang, J, Ma, X & Lu, H 2018, 'Air Pollution Forecasts: An Overview.', International journal of environmental research and public health, vol. 15, no. 4.
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Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.
Baier-Fuentes, H, Merigó, JM, Amorós, JE & Gaviria-Marín, M 2018, 'International entrepreneurship: a bibliometric overview', International Entrepreneurship and Management Journal, pp. 1-45.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The aim of this paper is to provide an overview of the academic research on International Entrepreneurship (IE). To accomplish this, an exhaustive bibliometric analysis was carried out, involving a bibliometric performance analysis and a graphic mapping of the references in this field. Our analysis focuses on journals, papers, authors, institutions and countries. To perform the performance analysis, the work uses a series of bibliometric indicators such as h-index, productivity and citations. Furthermore, the VOS viewer to graphically map the bibliographic material is used. The graphical analysis uses co-citation, bibliographic coupling and co-occurrence of keywords. The results of both analyzes are consistent among them, and show that the USA is the most influential country in IE research as it houses the main authors and institutions in this research field. Moreover, is observed and expected the continued growth of the field globally. Our research plays an informative and complementary role as it presents most of the key aspects in International Entrepreneurship research.
Bano, M., Zowghi, D., Kearney, M., Schuck, S. & Aubusson, P. 2018, 'Mobile learning for science and mathematics school education: A systematic review of empirical evidence', Computers and Education, vol. 121, pp. 30-58.
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The ubiquity, flexibility, ease of access and diverse capabilities of mobile technologies make them valuable and a necessity in current times. However, they are under-utilized assets in mathematics and science school education. This article analyses the high quality empirical evidence on mobile learning in secondary school science and mathematics education. Our study employed a Systematic Literature Review (SLR) using well-accepted and robust guidelines. The SLR resulted in the detailed analysis of 49 studies (60 papers) published during 2003–2016. Content and thematic analyses were used to ascertain pedagogical approaches, methodological designs, foci, and intended and achieved outcomes of the studies. The apps and technologies used in these studies were further classified for domain, type and context of use. The review has highlighted gaps in existing literature on the topic and has provided insights that have implications for future research.
Bargi, A, Xu, YD & Piccardi, M 2018, 'AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-16.
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Basack, S & Nimbalkar, S 2018, 'Measured and predicted response of pile groups in soft clay subjected to cyclic lateral loading', International Journal of Geomechanics, vol. 18, no. 7.
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© 2018 American Society of Civil Engineers. Major offshore and onshore structures, including transport corridors and high-rise buildings, resting on soft compressible clays are often supported by pile foundations. Apart from the usual vertical loading from the superstructures, these piles are usually subjected to large cyclic loads arising from the actions of waves, ship impacts, or moving vehicles. Under such circumstances, vertical and lateral modes of cyclic loading are predominant and affect overall stability. Such repetitive loading on piles leads to reversal of axial stresses in the adjacent soft clay, initiating progressive degradation in soil strength and stiffness that deteriorates the pile capacity with unacceptable displacements. Although several studies have been carried out to investigate the response of a single pile, a detailed investigation on a pile group in soft soil subjected to cyclic lateral loading, which is of immense practical interest to field engineers, had yet to be conducted. In this paper, extensive laboratory model tests with steel-pipe-pile groups in soft cohesive soil were conducted followed by the development of a numerical model that was based on a two-dimensional (2D) dynamic finite-element (FE) approach. The degradation of both axial and lateral capacities of the pile group and the pattern of the degradation with variations in the cyclic-loading parameters were studied. Comparisons of the experimental data with the computed results validated the numerical analysis. The study indicates that both the axial and lateral pile capacities and displacements were significantly influenced by the cyclic-loading parameters (number of cycles, frequency, and amplitude). Relevant design recommendations are presented.
Beck, D, Thoms, JAI, Palu, C, Herold, T, Shah, A, Olivier, J, Boelen, L, Huang, Y, Chacon, D, Brown, A, Babic, M, Hahn, C, Perugini, M, Zhou, X, Huntly, BJ, Schwarzer, A, Klusmann, J-H, Berdel, WE, Wörmann, B, Büchner, T, Hiddemann, W, Bohlander, SK, To, LB, Scott, HS, Lewis, ID, D'Andrea, RJ, Wong, JWH & Pimanda, JE 2018, 'A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients.', Leukemia, vol. 32, no. 2, pp. 263-272.
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Prognostic gene expression signatures have been proposed as clinical tools to clarify therapeutic options in acute myeloid leukemia (AML). However, these signatures rely on measuring large numbers of genes and often perform poorly when applied to independent cohorts or those with older patients. Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulators of cell identity and oncogenesis, but knowledge of their utility as prognostic markers in AML is limited. Here we analyze transcriptomic data from multiple cohorts of clinically annotated AML patients and report that (i) microarrays designed for coding gene expression can be repurposed to yield robust lincRNA expression data, (ii) some lincRNA genes are located in close proximity to hematopoietic coding genes and show strong expression correlations in AML, (iii) lincRNA gene expression patterns distinguish cytogenetic and molecular subtypes of AML, (iv) lincRNA signatures composed of three or four genes are independent predictors of clinical outcome and further dichotomize survival in European Leukemia Net (ELN) risk groups and (v) an analytical tool based on logistic regression analysis of quantitative PCR measurement of four lincRNA genes (LINC4) can be used to determine risk in AML.
Belhaj, D, Athmouni, K, Ahmed, MB, Aoiadni, N, El Feki, A, Zhou, JL & Ayadi, H 2018, 'Polysaccharides from Phormidium versicolor (NCC466) protecting HepG2 human hepatocellular carcinoma cells and rat liver tissues from cadmium toxicity: Evidence from in vitro and in vivo tests.', International journal of biological macromolecules, vol. 113, pp. 813-820.
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The in vitro antioxidant, cytotoxic and cytoprotective properties and in vivo hepatoprotective activities of crude polysaccharides extracted from cyanobacteria Phormidim versicolor NCC466 (CFv-PS) were investigated. The CFv-PS, identified as heteropolysaccharides with molecular weight of 63.79kDa, exhibited relatively strong antioxidant activity, in a concentration-depended manner, in vitro assays. Additionally, CFv-PS did not induce cytotoxic effect on HepG2 human hepatocellular carcinoma cells within the range of tested concentrations (25-150μg·mL-1) while preventing them against Cd. Moreover, in rats subjected to Cd-induced hepatotoxicity, CFv-PS pretreatment significantly (P<0.05) reduced the level of ALAT, ASAT, biliburin, MDA, protein carbonyl and DNA damage, and markedly increased enzyme activities in liver tissues. These findings suggest that the cyanobacteria Phormidium versicolor is a potential source of natural products possessing antioxidant, cytoprotective and hepatoprotective properties.
Bellezoni, R.A., Sharma, D., Villela, A.A. & Pereira Junior, A.O. 2018, 'Water-energy-food nexus of sugarcane ethanol production in the state of Goiás, Brazil: An analysis with regional input-output matrix', Biomass and Bioenergy, vol. 115, pp. 108-119.
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© 2018 Elsevier Ltd Concerns about impacts of biomass growth for biofuel production emphasize the importance of planning energy crops expansion considering land, water, food and biodiversity. Brazil is the second largest ethanol producer worldwide and sugarcane is cultivated in many regions, including the Brazilian Cerrado (a Savannah-type biome). This paper analyses the impacts of first-generation sugarcane expansion in the Paranaíba basin (Goiás State), focusing on how future demand for ethanol could affect local resources availability. The study area is a sugarcane expansion frontier in Brazil, thus, the Cerrado biome should be focus of research considering competition for land and water uses. An economic-ecologic Input-Output (IO) framework was applied to develop a water-energy-food (WEF) nexus analysis. The Goiás’ IO table was expanded to assess water, energy and land uses, GHG emissions and employment levels through six different ethanol supply scenarios. Results show that if sugarcane expansion projected to 2030 considers the Goiás’ extended IO structure for the year 2008, it should cause little impact on land and water availability in the state, due to both the ample availability of suitable pasturelands for sugarcane expansion as well as water in most of the Paranaíba basin. The WEF nexus analysis is a valuable tool on guiding the sustainable management of natural resources considering water, energy, land use and GHG emissions as goals to the same policy. In particular, the hybrid extended IO-WEF nexus framework is useful to design effective biofuel policies, collectively addressing impacts on environmental, social and economic spheres, in a local or broader context.
Bengua, JA, Tuan, HD, Duong, TQ & Poor, HV 2018, 'Joint Sensor and Relay Power Control in Tracking Gaussian Mixture Targets by Wireless Sensor Networks', IEEE Transactions on Signal Processing, vol. 66, no. 2, pp. 492-506.
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IEEE This paper considers a wireless sensor network (WSN) for locating a static target or tracking a dynamic target, which is prior characterized by a Gaussian mixture (GM) distribution. An amplify-andforward relay node is acting as a wireless bridge in relaying the sensor & #x0027;s independent observations of the target to a fusion center (FC). We are concerned with the joint transmitter power allocation for the sensors and relay to optimize the Bayesian filter, which is deployed at the FC for a global estimate of the target. The mean squared error (MSE) of the Bayesian filter is already computationally intractable for fixed sensor and relay transmitter power, so power allocation to minimize its mean squared error is a very challenging problem. In this paper, the problem is addressed by an iterative procedure of very low computational complexity. Simulations are provided to support the efficiency of our proposed power allocation
Berta, M., Scholz, V.B. & Tomamichel, M. 2018, 'Rényi Divergences as Weighted Non-commutative Vector-Valued Lp-Spaces', Annales Henri Poincare, vol. 19, no. 6, pp. 1843-1867.
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© 2018, Springer International Publishing AG, part of Springer Nature. We show that Araki and Masuda’s weighted non-commutative vector-valued Lp-spaces (Araki and Masuda in Publ Res Inst Math Sci Kyoto Univ 18:339–411, 1982) correspond to an algebraic generalization of the sandwiched Rényi divergences with parameter α=p2. Using complex interpolation theory, we prove various fundamental properties of these divergences in the setup of von Neumann algebras, including a data-processing inequality and monotonicity in α. We thereby also give new proofs for the corresponding finite-dimensional properties. We discuss the limiting cases α→{12,1,∞} leading to minus the logarithm of Uhlmann’s fidelity, Umegaki’s relative entropy, and the max-relative entropy, respectively. As a contribution that might be of independent interest, we derive a Riesz–Thorin theorem for Araki–Masuda Lp-spaces and an Araki–Lieb–Thirring inequality for states on von Neumann algebras.
Best, G, Faigl, J & Fitch, R 2018, 'Online planning for multi-robot active perception with self-organising maps', Autonomous Robots, vol. 42, no. 4, pp. 715-738.
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© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons.
Best, G., Cliff, O.M., Patten, T., Mettu, R.R. & Fitch, R. 2018, 'Dec-MCTS: Decentralized planning for multi-robot active perception', International Journal of Robotics Research.
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© 2018, The Author(s) 2018. We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
Beydoun, G, Dascalu, S, Dominey-Howes, D & Sheehan, A 2018, 'Disaster Management and Information Systems: Insights to Emerging Challenges', Information Systems Frontiers, vol. 20, no. 4, pp. 649-652.
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Bhowmick, S, Xu, F, Zhang, X & Saha, SC 2018, 'Natural convection and heat transfer in a valley shaped cavity filled with initially stratified water', International Journal of Thermal Sciences, vol. 128, pp. 59-69.
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© 2018 Elsevier Masson SAS Fog is omnipresent in the natural environment, namely valleys and mountains. A strong comprehension of the fluid flow dynamics pertaining to fog formation is of crucial importance. In the present study, the two-dimensional numerical method is used to investigate the transient natural convection in a valley-shaped triangular cavity initially filled with stratified water. A wide range of Rayleigh numbers (2.26 × 105–2.26 × 109) and aspect ratios (0.1–1.0) are considered. The numerical results are verified against experimental results. The development of natural convection flows in the cavity from the start up to the steady state is classified into two stages: an early stage and a transitional stage. Transient natural convection flows in the cavity are described. Spectral analysis is performed for different governing parameters. A simple scaling analysis is performed for the thermal boundary layer and the time scale of the stratification breakup describing the disappearance of fog in the valley is obtained and validated by numerical results. Additionally, mass and heat transfer in the cavity is measured and the scaling relation between the Nusselt number and the Rayleigh number for different aspect ratios are presented.
Bickel, B, Cignoni, P, Malomo, L & Pietroni, N 2018, 'State of the Art on Stylized Fabrication', Computer Graphics Forum, vol. 37, no. 6, pp. 325-342.
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© 2018 The Authors Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as ‘stylized fabrication methods’. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion or to devise a particular interaction with the fabricated model. In this state-of-the-art report, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research.
Bin, Y, Yang, Y, Shen, F, Xie, N, Shen, HT & Li, X 2018, 'Describing Video With Attention-Based Bidirectional LSTM', IEEE Transactions on Cybernetics.
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IEEE Video captioning has been attracting broad research attention in the multimedia community. However, most existing approaches heavily rely on static visual information or partially capture the local temporal knowledge (e.g., within 16 frames), thus hardly describing motions accurately from a global view. In this paper, we propose a novel video captioning framework, which integrates bidirectional long-short term memory (BiLSTM) and a soft attention mechanism to generate better global representations for videos as well as enhance the recognition of lasting motions in videos. To generate video captions, we exploit another long-short term memory as a decoder to fully explore global contextual information. The benefits of our proposed method are two fold: 1) the BiLSTM structure comprehensively preserves global temporal and visual information and 2) the soft attention mechanism enables a language decoder to recognize and focus on principle targets from the complex content. We verify the effectiveness of our proposed video captioning framework on two widely used benchmarks, that is, microsoft video description corpus and MSR-video to text, and the experimental results demonstrate the superiority of the proposed approach compared to several state-of-the-art methods.
Binsawad, M., Sohaib, O. & Hawryszkiewycz, I.T. 2018, 'Factors Impacting Technology Business Incubator Performance', International Journal of Innovation Management, pp. 1950007-1-1950007-30.
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Bishop, AN & Del Moral, P 2018, 'On the robustness of Riccati flows to complete model misspecification', Journal of the Franklin Institute.
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© 2018 The Franklin Institute Consider the continuous-time matrix Riccati operator Ricc(Q)=AQ+QA′−QSQ+R. In this work, we consider the robustness of this operator to direct perturbations of the matrices (A, R, S) and, in particular, the flow robustness of the corresponding Riccati differential equation. For a given class of perturbation, we show that the corresponding differential equation is well defined in the sense it is bounded above and below, it has a well-defined fixed point, and it converges to this fixed point exponentially fast. Moreover, the flow of the perturbed Riccati flow is close to the nominal Riccati flow when the perturbation is small; i.e. we prove a continuity-type condition in the size of the perturbation.
Bishop, AN, Del Moral, P & Pathiraja, SD 2018, 'Perturbations and projections of Kalman–Bucy semigroups', Stochastic Processes and their Applications, vol. 128, no. 9, pp. 2857-2904.
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© 2017 Elsevier B.V. We analyse various perturbations and projections of Kalman–Bucy semigroups and Riccati equations. For example, covariance inflation-type perturbations and localisation methods (projections) are common in the ensemble Kalman filtering literature. In the limit of these ensemble methods, the regularised sample covariance tends toward a solution of a perturbed/projected Riccati equation. With this motivation, results are given characterising the error between the nominal and regularised Riccati flows and Kalman–Bucy filtering distributions. New projection-type models are also discussed; e.g. Bose–Mesner projections. These regularisation models are also of interest on their own, and in, e.g., differential games, control of stochastic/jump processes, and robust control.
Bishop, AN, Houssineau, J, Angley, D & Ristic, B 2018, 'Spatio-temporal tracking from natural language statements using outer probability theory', Information Sciences, vol. 463-464, pp. 56-74.
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© 2018 Elsevier Inc. This work considers a target tracking problem where the observed information is in the form of natural language-type statements. More specifically, the focus is on a spatio-temporal tracking problem where each uttered expression may involve both spatial, motion and temporal uncertainty, and a general modelling framework for natural language statements of a rather general semantic form is developed. This framework involves the definition of some tuple that allows one to extract the common semantics from arbitrary parsed expressions conveying some canonical information. Given this tuple, an estimation and tracking method based on the concept of outer probability measures is introduced and an estimation algorithm for handling this temporal uncertainty, along with delayed and out-of-sequence information arrival, is developed. This framework allows for modelling imprecise information in a more general and realistic sense.
Blanco-Mesa, F, Gil-Lafuente, AM & Merigo, JM 2018, 'Dynamics of stakeholder relations with multi-person aggregation', Kybernetes.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to develop a novel method to analyse dynamic interactions of stakeholders to explain how a set of agents can act by considering the power/influence positions. Design/methodology/approach: A novel mathematical application uses the importance of characteristics algorithm in combination with composition max-min to compare, group and order information according to the importance of its characteristics. The mathematical application is focused on a strategic analysis, evaluating stakeholder dynamics through power relationships. Findings: The results show a comparison of the relationships among each of the stakeholders to obtain the relative intensity and importance of relationships between them, given by the fuzzy matrix FRInM and the fuzzy matrix FRIM, respectively. This application provides a useful tool for a dynamic analysis of stakeholders in a complex environment, where the best approach to performing a strategic analysis process is sought. Research limitations/implications: The main implication of the proposed approach is taking into account the importance of information to establish the boundaries and relationships of each characteristic according to its intensity. However, limitations are due to the nature of this research, based on theoretical assumptions regarding stakeholders and the use of a hypothetical example to show the operation of algorithms. Originality/value: The primary advantage of this proposition is that it takes into account the importance of information to establish the relationships among the characteristics according to their intensity. In addition, it performs multiple comparisons among each characteristic of the information. The interests and opinions of decision makers can be parameterised. A mathematical application shows how each interest group could be classified and related according to subjective information.
Blanco-Mesa, F, Gil-Lafuente, AM & Merigó, JM 2018, 'New aggregation operators for decision-making under uncertainty: An applications in selection of entrepreneurial opportunities', Technological and Economic Development of Economy, vol. 24, no. 2, pp. 335-357.
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© 2017 Vilnius Gediminas Technical University (VGTU) Press. The main aim of this paper is to study how economic environment and logic reasoning guidance the decision-making process to start-up a new business by potential entrepreneurs. The study proposes a new method using the family of selection indices with OWA operator, which allows aggregating information according to the level of importance and their level of objectivity and subjectivity in the same formulation within the decision-making process. To develop case study, we have taken into account some industries of the sports sector and some critical environmental factors that influence the competitiveness and entrepreneurship in Colombia to start a new business. The results show in an orderly way all information aggregated, which can help potential investors and entrepreneurs to make a decision based on their preferences. Finally, the applicability of this method in real case can be given in aggregation different sources of information to help at dealing decision-making processes.
Bluff, A., Johnston, A. & Clarkson, D. 2018, 'Interaction, Narrative and Animation in Live Theatre', IEEE Computer Graphics and Applications, vol. 38, no. 2, pp. 8-14.
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© 1981-2012 IEEE. Before embarking on their most ambitious project to date, a cosmically-themed participatory theatre event with interactive 3D visuals housed inside a bespoke dome structure, the interactive artists Andrew Johnston and Andrew Bluff joined the director of Stalker Theatre, David Clarkson in a round-table discussion. They reflected on what it was like to combine physical performance with interactive graphics in a childrens theatre show and discussed how the 360° format might be used to explore the cosmos in their upcoming Big Skies production.
Boixo, S, Isakov, SV, Smelyanskiy, VN, Babbush, R, Ding, N, Jiang, Z, Bremner, MJ, Martinis, JM & Neven, H 2018, 'Characterizing Quantum Supremacy in Near-Term Devices'.
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A critical question for the field of quantum computing in the near future is
whether quantum devices without error correction can perform a well-defined
computational task beyond the capabilities of state-of-the-art classical
computers, achieving so-called quantum supremacy. We study the task of sampling
from the output distributions of (pseudo-)random quantum circuits, a natural
task for benchmarking quantum computers. Crucially, sampling this distribution
classically requires a direct numerical simulation of the circuit, with
computational cost exponential in the number of qubits. This requirement is
typical of chaotic systems. We extend previous results in computational
complexity to argue more formally that this sampling task must take exponential
time in a classical computer. We study the convergence to the chaotic regime
using extensive supercomputer simulations, modeling circuits with up to 42
qubits - the largest quantum circuits simulated to date for a computational
task that approaches quantum supremacy. We argue that while chaotic states are
extremely sensitive to errors, quantum supremacy can be achieved in the
near-term with approximately fifty superconducting qubits. We introduce cross
entropy as a useful benchmark of quantum circuits which approximates the
circuit fidelity. We show that the cross entropy can be efficiently measured
when circuit simulations are available. Beyond the classically tractable
regime, the cross entropy can be extrapolated and compared with theoretical
estimates of circuit fidelity to define a practical quantum supremacy test.
Borghei, Z, Leung, P & Guthrie, J 2018, 'Voluntary greenhouse gas emission disclosure impacts on accounting-based performance: Australian evidence', Australasian Journal of Environmental Management, vol. 25, no. 3, pp. 321-338.
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© 2018, © 2018 Environment Institute of Australia and New Zealand Inc. Using a cost-benefit framework, this research examines the impact of voluntary greenhouse gas (GHG) disclosure on accounting-based performance. Previous studies suggest that the implementation of carbon management and reporting may be associated with the improvement of accounting-based performance by the identification of cost savings opportunities or the introduction of innovation opportunities in products and services. This study is the first that applies several proxies of accounting-based performance, such as, return on assets, return on equity and return on sales, to empirically examine the association between voluntary GHG disclosure by non-GHG registered Australian companies and accounting-based performance. The level of GHG disclosure is scored through content analysis of the annual reports of sample organisations for the financial years 2009 to 2011. This research highlights that GHG disclosure is positively associated with ROA in the year following disclosure. The findings are consistent with the predictions of the cost-benefit framework. It indicates that companies bear the extra voluntary disclosure costs to gain the perceived benefits of voluntary disclosure. Our findings are useful for organisations and stakeholders who are concerned about the cost-benefit of voluntary carbon disclosure.
Borghei, Z., Leung, P. & Guthrie, J. 2018, 'Does voluntary greenhouse gas emissions disclosure reduce information asymmetry? Australian evidence', Afro-Asian Journal of Finance and Accounting, vol. 8, no. 2, pp. 123-147.
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Copyright © 2018 Inderscience Enterprises Ltd. Based on agency theory, this study investigates the consequences of carbon disclosure by non-greenhouse gas registered Australian companies on information asymmetry measures over a period after the introduction of the National Greenhouse and Energy Reporting Act 2007 and before the introduction of the Australian carbon tax. The level of carbon disclosure is scored through the content analysis of the annual reports. The findings support that carbon disclosure is negatively related to information asymmetry measures: the bid-ask spread and stock return volatility, in the year following disclosure. Overall, the research results are consistent with predictions of the agency theory and indicate that companies bear the extra voluntary reporting costs to achieve the perceived benefits of disclosure. The findings should be useful for corporate and stakeholders who are concerned about the value relevance of carbon disclosure in financial markets. For accounting standard setters, it highlights the urgency of carbon reporting guidelines.
Bowden, R. & Veitch, D. 2018, 'Finding the Right Tree: Topology Inference Despite Spatial Dependences', IEEE Transactions on Information Theory, vol. 64, no. 6, pp. 4594-4609.
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© 1963-2012 IEEE. Network tomographic techniques have almost exclusively been built on a strong assumption of mutual independence of link processes. We introduce model classes for link loss processes with non-Trivial spatial dependencies, for which the tree topology is nonetheless identifiable from leaf measurements using multicast probing. We show that these classes are large in a well-defined sense, and we provide an algorithm, SLTD, capable of returning the correct topology with certainty in the limit of infinite data.
Bown, O. & Ferguson, S. 2018, 'Creative Media + the Internet of Things = Media Multiplicities', Leonardo, vol. 51, no. 1, pp. 53-54.
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Bown, O. & Ferguson, S.J. 2018, 'Understanding media multiplicities', Entertainment Computing, vol. 25, pp. 62-70.
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Internet of Things (IoT) technologies enable new forms of media artworks. ‘Media multiplicities’ are defined here as creative media experiences made up of multiples of interacting and coordinated devices. In this paper, we review the state of the art of multiplicitous media artworks and provide a systematic analysis of the novel affordances and different forms such artworks can take, specifically that they are spatial, scalable, scatterable and sensing. We consider the analysis of media multiplicities from the point of view of both user experience and creative production. We offer three primary axes through which a categorisation of multiplicitous media forms can be framed: substrate versus object; composed versus self-organised, and homogeneous versus heterogeneous. We also analyse how the number of elements in the multiplicities (from tens to tens of thousands and beyond) affects the qualities of the experience.
Cagno, E., Neri, A. & Trianni, A. 2018, 'Broadening to sustainability the perspective of industrial decision-makers on the energy efficiency measures adoption: some empirical evidence', Energy Efficiency, vol. 11, no. 5, pp. 1193-1210.
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© 2018, Springer Science+Business Media B.V., part of Springer Nature. The industry should take further efforts towards increased energy efficiency, that is a major contributor to improve industrial sustainability performance, by implementing energy efficiency measures (EEMs). However, the rate of adoption of these measures is still quite low. Hitherto, EEMs and barriers to their adoption have been evaluated almost exclusively from the viewpoint of energy efficiency decision-makers, not accounting for the broader sustainability perspective. This work aims at understanding whether an industrial sustainability perspective can better address issues related to EEMs adoption, analyzing the question through different viewpoints and insights offered by industrial decision-makers of different industrial sustainability areas within a firm. By doing this, we aim at offering a contribution in the understanding of the low rate of adoption of EEMs. As case studies, we investigated 12 firms from Northern Italy. In comparison to previous literature, results show that an industrial sustainability perspective can better explain the real decision-making process of adopting an EEM. Indeed, people knowledgeable about different industrial sustainability areas may perceive different barriers about the same EEM. EEMs may be negatively affected by reasons related to other areas of industrial sustainability, while positive reciprocal impacts may exist among areas of industrial sustainability; thus, EEMs may have effects on areas other than energy efficiency, and these effects may be perceived only by such areas. The study concludes with some remarks for policy and industrial decision-makers and advice for further research.
Cai, C., Hu, S., Chen, X., Ni, B.J., Pu, J. & Yuan, Z. 2018, 'Effect of methane partial pressure on the performance of a membrane biofilm reactor coupling methane-dependent denitrification and anammox', Science of the Total Environment, vol. 639, pp. 278-285.
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© 2018 Complete nitrogen removal has recently been demonstrated by integrating anaerobic ammonium oxidation (anammox) and denitrifying anaerobic methane oxidation (DAMO) processes. In this work, the effect of methane partial pressure on the performance of a membrane biofilm reactor (MBfR) consisting of DAMO and anammox microorganisms was evaluated. The activities of DAMO archaea and DAMO bacteria in the biofilm increased significantly with increased methane partial pressure, from 367 ± 9 and 58 ± 22 mg-N L−1d−1to 580 ± 12 and 222 ± 22 mg-N L−1d−1, respectively, while the activity of anammox bacteria only increased slightly, when the methane partial pressure was elevated from 0.24 to 1.39 atm in the short-term batch tests. The results were supported by a long-term (seven weeks) continuous test, when the methane partial pressure was dropped from 1.39 to 0.78 atm. The methane utilization efficiency was always above 96% during both short-term and long-term tests. Taken together, nitrogen removal rate (especially the nitrate reduction rate by DAMO archaea) and methane utilization efficiency could be maintained at high levels in a broad range of methane partial pressure (0.24–1.39 atm in this study). In addition, a previously established DAMO/anammox biofilm model was used to analyze the experimental data. The observed impacts of methane partial pressure on biofilm activity were well explained by the modeling results. These results suggest that methane partial pressure can potentially be used as a manipulated variable to control reaction rates, ultimately to maintain high nitrogen removal efficiency, according to nitrogen loading rate.
Cai, X, Han, J, Li, W, Zhang, R, Pan, S & Yang, L 2018, 'A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation.', IEEE transactions on neural networks and learning systems.
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Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher's needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.
Cai, X, Han, J, Pan, S & Yang, L 2018, 'Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 591-599.
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© 2018, the Authors. Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, inter-relationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.
Canning, J 2018, 'Water photonics, non-linearity, and anomalously large electro-optic coefficients in poled silica fibers', MRS Communications, vol. 8, no. 1, pp. 29-34.
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© Copyright Materials Research Society 2018. A review of the literature offers an explanation for the large anomalous electro-optic (e.o.) effect reported by Fujiwara et al. in 1994. It is based on the large e.o. coefficient of ordered water at an interface measured in recent years >1000 pm/V. More broadly, the concept of water-based photonics, where water could be a new platform material for devices and systems, is introduced, suggesting that liquid states of matter can allow ready shaping and exploitation of many processes in ways not previously considered. This paper is a commentary on the significance of this new understanding and the broader interest of water in photonics, particularly its consideration as a new platform material.
Cao, D., Hong, G.U.A.N.G. & Wang, J.A.C.K. 2018, 'Chemical heat storage for saving the exhaust gas energy in a spark ignition engine', Journal of Clean Energy Technologies (JOCET), vol. 6, pp. 41-46.
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This study was aimed to develop a chemical heat storage system using magnesium hydroxide (Mg(OH)2) and its dehydration and hydration reactions to recover the energy wasted in internal combustion engines (IC engine). The thermal energy of exhaust gas will be stored in the dehydration of Mg(OH)2 to become MgO and H2O, and to release in the hydration of MgO. Experiments were conducted on a 6-cylinder spark ignition engine to estimate the amount of energy loss in the exhaust gas and the reactor efficiency in the dehydration process. The stored heat used to heat fresh air from the ambient temperature to more convenient temperature. Results of the preliminary investigation show that the proposed chemical heat storage system is feasible to recover approximately 5.8 % of the heat loss and the temperature of the air is from 275.5 K to 305.4 K (with the ambient temperature is from 253 K to 283 K and the water vapor pressure is 47kPa).
Cao, X., Qiu, B. & Xu, G. 2018, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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© 2018 Springer-Verlag London Ltd., part of Springer Nature We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Cao, Z & Lin, CT 2018, 'Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 1032-1035.
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© 2017 IEEE. In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.
Cao, Z, Lai, K-L, Lin, C-T, Chuang, C-H, Chou, C-C & Wang, S-J 2018, 'Exploring resting-state EEG complexity before migraine attacks.', Cephalalgia: an international journal of headache, pp. 333102417733953-333102417733953.
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Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
Castro, J., Lu, J., Zhang, G., Dong, Y. & Martinez, L. 2018, 'Opinion Dynamics-Based Group Recommender Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Recommender systems (RSs) personalize content to a user's interests to help them select the right item in information overload scenarios. Group RSs (GRSs) recommend items to a group of users. In GRSs, a recommendation is usually computed by a simple aggregation method for individual information. However, the aggregations are rigid and overlook certain group features, such as the relationships between the group members' preferences. In this paper, it is proposed a GRS based on opinion dynamics that considers these relationships using a smart weights matrix to drive the process. In some groups, opinions do not agree, hence the weights matrix is modified to reach a consensus value. The impact of ensuring agreed recommendations is evaluated through a set of experiments. Additionally, a sensitivity analysis studies its behavior. Compared to existing group recommendation models and frameworks, the proposal based on opinion dynamics would have the following advantages: 1) flexible aggregation method; 2) member relationships; and 3) agreed recommendations.
Celler, B., Argha, A., Varnfield, M. & Jayasena, R. 2018, 'Patient Adherence to Scheduled Vital Sign Measurements During Home Telemonitoring: Analysis of the Intervention Arm in a Before and After Trial.', JMIR medical informatics, vol. 6, no. 2, p. e15.
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In a home telemonitoring trial, patient adherence with scheduled vital signs measurements is an important aspect that has not been thoroughly studied and for which data in the literature are limited. Levels of adherence have been reported as varying from approximately 40% to 90%, and in most cases, the adherence rate usually dropped off steadily over time. This drop is more evident in the first few weeks or months after the start. Higher adherence rates have been reported for simple types of monitoring and for shorter periods of intervention. If patients do not follow the intended procedure, poorer results than expected may be achieved. Hence, analyzing factors that can influence patient adherence is of great importance.The goal of the research was to present findings on patient adherence with scheduled vital signs measurements in the recently completed Commonwealth Scientific and Industrial Research Organisation (CSIRO) national trial of home telemonitoring of patients (mean age 70.5 years, SD 9.3 years) with chronic conditions (chronic obstructive pulmonary disease, coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) carried out at 5 locations along the east coast of Australia. We investigated the ability of chronically ill patients to carry out a daily schedule of vital signs measurements as part of a chronic disease management care plan over periods exceeding 6 months (302 days, SD 135 days) and explored different levels of adherence for different measurements as a function of age, gender, and supervisory models.In this study, 113 patients forming the test arm of a Before and After Control Intervention (BACI) home telemonitoring trial were analyzed. Patients were required to monitor on a daily basis a range of vital signs determined by their chronic condition and comorbidities. Vital signs included noninvasive blood pressure, pulse oximetry, spirometry, electrocardiogram (ECG), blood glucose level, body temperature, ...
Cetindamar Kozanoglu, D. 2018, 'Designed by law: Purpose, accountability, andtransparency at benefit corporations', Cogent Business and Management, vol. 5, no. 1, pp. 1-14.
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The article explores the realization of major goals of the Benefit
Corporation (BC) law, which is a corporation form designed for social enterprises in the United States in 2010. BCs have a dual mission of generating both profit and social value and hence they might have the potential to transform society. This paper attempts to observe the first movers established as BCs during the period of 2010–2012. By adopting the institutional theory approach, the study examines the realization of the BC law’s three major goals: purpose, accountability, and transparency.
The paper utilizes the regulatory legitimacy concept to measure the discrepancy between design and implementation of law. The observations point out some of the challenges of establishing new innovative organizations through an institutional intervention of a law. Conclusions consist of implications of the study as well as suggestions
for further studies.
Chapman, R.J., Karim, A., Huang, Z., Flammia, S.T., Tomamichel, M. & Peruzzo, A. 2018, 'Beating the classical limits of information transmission using a quantum decoder', Physical Review A, vol. 97, no. 1.
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© 2018 American Physical Society. Encoding schemes and error-correcting codes are widely used in information technology to improve the reliability of data transmission over real-world communication channels. Quantum information protocols can further enhance the performance in data transmission by encoding a message in quantum states; however, most proposals to date have focused on the regime of a large number of uses of the noisy channel, which is unfeasible with current quantum technology. We experimentally demonstrate quantum enhanced communication over an amplitude damping noisy channel with only two uses of the channel per bit and a single entangling gate at the decoder. By simulating the channel using a photonic interferometric setup, we experimentally increase the reliability of transmitting a data bit by greater than 20% for a certain damping range over classically sending the message twice. We show how our methodology can be extended to larger systems by simulating the transmission of a single bit with up to eight uses of the channel and a two-bit message with three uses of the channel, predicting a quantum enhancement in all cases.
Chapple, A, Nguyen, LN, Hai, FI, Dosseto, A, Rashid, MHO, Oh, S, Price, WE & Nghiem, LD 2018, 'Impact of inorganic salts on degradation of bisphenol A and diclofenac by crude extracellular enzyme from Pleurotus ostreatus', Biocatalysis and Biotransformation, pp. 1-8.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group This study investigated the influence of inorganic salts on enzymatic activity and the removal of trace organic contaminants (TrOCs) by crude laccase from the white-rot fungus Pleurotus ostreatus. A systematic analysis of 15 cations and anions from common inorganic salts was presented. Laccase activity was not inhibited by monovalent cations (i.e. Na + , NH 4 + , K + ), while the presence of divalent and trivalent cations showed variable impact – from negligible to complete inhibition – of both laccase activity and its TrOC removal performance. Of interest was the observation of discrepancy between residual laccase activity and TrOC removal in the presence of some ions. Mg 2+ had negligible impact on residual laccase activity but significant impact on TrOC removal. Conversely, F − showed greater impact on residual laccase activity than on TrOC removal. This observation indicated different impacts of the interfering ions on the interaction between laccase and TrOCs as compared to that between laccase and the reagent used to measure its activity, implicating that residual laccase activity may not always be an accurate indicator of TrOC removal. The degree of impact of halides was in the order of F − > I − > Br − > Cl − . Particularly, the tolerance of the tested laccase to Cl − has important implications for a range of industrial applications.
Chekli, L, Pathak, N, Kim, Y, Phuntsho, S, Li, S, Ghaffour, N, Leiknes, T & Shon, HK 2018, 'Combining high performance fertiliser with surfactants to reduce the reverse solute flux in the fertiliser drawn forward osmosis process.', Journal of environmental management, vol. 226, pp. 217-225.
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Solutions to mitigate the reverse diffusion of solutes are critical to the successful commercialisation of the fertiliser drawn forward osmosis process. In this study, we proposed to combine a high performance fertiliser (i.e., ammonium sulfate or SOA) with surfactants as additives as an approach to reduce the reverse diffusion of ammonium ions. Results showed that combining SOA with both anionic and non-ionic surfactants can help in reducing the reverse salt diffusion by up to 67%. We hypothesised that, hydrophobic interactions between the surfactant tails and the membrane surface likely constricted membrane pores resulting in increased rejection of ions with large hydrated radii such as SO42-. By electroneutrality, the rejection of the counter ions (i.e., NH4+) also therefore subsequently improved. Anionic surfactant was found to further decrease the reverse salt diffusion due to electrostatic repulsions between the surfactant negatively-charged heads and SO42-. However, when the feed solution contains cations with small hydrated radii (e.g., Na+); it was found that NH4+ ions can be substituted in the DS to maintain its electroneutrality and thus the diffusion of NH4+ to the feed solution was increased.
Chen, C., Guo, W.S., Ngo, H.H., Chang, S.W., Nguyen, D.D., Zhang, J., Liang, S., Guo, J.B. & Zhang, X.B. 2018, 'Effects of C/N ratio on the performance of a hybrid sponge-assisted aerobic moving bed-anaerobic granular membrane bioreactor for municipal wastewater treatment.', Bioresource Technology, vol. 247, pp. 340-346.
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This study aimed to evaluate the impact of C/N ratio on the performance of a hybrid sponge-assisted aerobic moving bed-anaerobic granular membrane bioreactor (SAAMB-AnGMBR) in municipal wastewater treatment. The results showed that organic removal efficiencies were above 94% at all C/N conditions. Nutrient removal was over 91% at C/N ratio of 100/5 but was negatively affected when decreasing C/N ratio to 100/10. At lower C/N ratio (100/10), more noticeable membrane fouling was caused by aggravated cake formation and pore clogging, and accumulation of extracellular polymeric substances (EPS) in the mixed liquor and sludge cake as a result of deteriorated granular quality. Foulant analysis suggested significant difference existed in the foulant organic compositions under different C/N ratios, and humic substances were dominant when the fastest fouling rate was observed. The performance of the hybrid system was found to recover when gradually increasing C/N ratio from 100/10 to 100/5.
Chen, H, McGowan, E, Ren, N, Nassif, N, Lal, S, Qu, X & Lin, Y 2018, 'Nattokinase: A Promising Alternative in Prevention andTreatment of Cardiovascular Diseases', Biomarker Insights.
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Cardiovascular disease (CVD) is the leading cause of death in the world, however our approach to the control and management of CVD mortality is limited. Nattokinase (NK), the most active ingredient of natto, possesses a variety of favourable cardiovascular effects and the consumption of natto has been linked to a reduction in CVD mortality. Recent research has demonstrated that NK has potent fibrinolytic activity, antihypertensive, anti-atherosclerotic, lipid lowering, anti-platelet and neuroprotective effects. This review covers the major pharmacological effects of NK with a focus on its clinical relevance to CVD. It outlines the advantages of NK and the outstanding issues pertaining to NK pharmacokinetics. Available evidence suggests that NK is a unique natural compound that possesses several key cardiovascular beneficial effects for CVD patients and is therefore an ideal drug candidate for the prevention and treatment of CVD. NK is therefore a promising alternative in the management of CVD.
Chen, J, Mao, G, Li, C, Liang, W & Zhang, DG 2018, 'Capacity of Cooperative Vehicular Networks with Infrastructure Support: Multiuser Case', IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1546-1560.
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© 1967-2012 IEEE. Capacity of vehicular networks with infrastructure support is both an interesting and challenging problem as the capacity is determined by the interplay of multiple factors including vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, density and mobility of vehicles, and cooperation among vehicles and infrastructure. In this paper, we consider a typical delay-tolerant application scenario with a subset of vehicles, termed Vehicles of Interest (VoIs), having download requests. Each VoI downloads a distinct large-size file from the Internet and other vehicles without download requests assist the delivery of the files to the VoIs. A cooperative communication strategy is proposed that explores the combined use of V2I communications, V2V communications, mobility of vehicles and cooperation among vehicles and infrastructure to improve the capacity of vehicular networks. An analytical framework is developed to model the data dissemination process using this strategy, and a closed-form expression of the achievable capacity is obtained, which reveals the relationship between the capacity and its major performance-impacting parameters such as inter-infrastructure distance, radio ranges of infrastructure and vehicles, sensing range of vehicles, transmission rates of V2I and V2V communications, vehicular density, and proportion of VoIs. Numerical result shows that the proposed cooperative communication strategy significantly boosts the capacity of vehicular networks, especially when the proportion of VoIs is low. Our results provide guidance on the optimum deployment of a vehicular network infrastructure and the design of a cooperative communication strategy to improve the capacity.
Chen, Q., Zhou, Y. & Nimbalkar, S. 2018, 'Closure to "Estimation of passive earth pressure against rigid retaining wall considering arching effect in cohesive- frictional backfill under translation mode" by Yanyan Cai, Qingsheng Chen, Yitao Zhou, Sanjay Nimbalkar, and Jin Yu', International Journal of Geomechanics, vol. 18, no. 7.
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Chen, S, Wang, Z, Yuan, X & Liang, J 2018, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Journal of Visual Languages and Computing.
Chen, S, Zhang, B, Li, B & Zhang, N 2018, 'Dynamic Characteristics Analysis of Vehicle Incorporating Hydraulically Interconnected Suspension System with Dual Accumulators', Shock and Vibration, vol. 2018.
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© 2018 Shengzhao Chen et al. A novel roll-resistant hydraulically interconnected suspension with dual accumulators on each fluid circuit (DHIS) is proposed and dynamic characteristics of vehicle incorporating DHIS subsystem are studied in this paper. A 10-degrees-of-freedom (DOFs) vehicle model coupled with DHIS subsystem is established and validated. Four physical parameters of DHIS subsystems which are crucial to vehicle responses are selected with prescribed variation ranges to explore their relationships with the vehicle performance. Simulations of vehicle conducting sine-wave steering maneuvers are carried out to evaluate handling performance with roll angle and vertical tyre force for DHIS subsystem with the parameters varying, compared with the results for vehicle with the original spring-damper suspension and conventional hydraulically interconnected suspension (HIS). On the other hand, ride comfort performance indicated by total weighted root mean square accelerations at the center of gravity is studied when vehicle is excited by three different types of road pavements when the four parameters vary in the prescribed ranges. Simulation results are compared to investigate the special merits of DHIS subsystem, and the parameters that influence the handling performance and ride comfort most are identified. Overall, the DHIS subsystem can effectively enhance the vehicle handling performance compared with the original spring-damper suspension, and it can also benefit much to the ride comfort in contrast to the HIS subsystem.
Chen, S., Yu, H. & Fang, J. 2018, 'A novel multi-cell tubal structure with circular corners for crashworthiness', Thin-Walled Structures, vol. 122, pp. 329-343.
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© 2017 Elsevier Ltd Multi-cell structures have proven to own excellent energy absorbing capability and lightweight effect in the automotive and aerospace industries. The cross-sectional configuration of the multi-cell structure has a significant effect on crashworthiness. Unlike existing multi-cell tubes, a new type of five-cell profile with four circular elements at the corners (C5C) was proposed in this study. To investigate the crashworthiness of the new C5C tube, finite element (FE) models were first established by using the nonlinear finite element code LS-DYNA and validated with experimental results. Following that, the comparison of the C5C tube and other multi-cell tubes with the same mass was conducted to quantify the relative merits of the C5C tube. Then, a detailed study was performed to analyze the effect of the corner-cell size and wall thickness. Finally, the optimization design was carried out to seek the optimal structure. The results showed that the new multi-cell structure can absorb much more crash energy than other four types of tubes. Moreover, the energy absorption of this new multi-cell tube C5C was affected by the corner-cell size and wall thickness significantly. A proper corner-cell size and slightly thicker internal ribs were recommended. In addition, the multi-objective particle swarm optimization (MOPSO) algorithm and radial basis function (RBF) surrogate model can optimize the structure effectively. The outcomes of the present study will facilitate the design of multi-cell structures with better crashworthiness.
Chen, SL, Qin, PY, Lin, W & Guo, YJ 2018, 'Pattern-Reconfigurable Antenna with Five Switchable Beams in Elevation Plane', IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 3, pp. 454-457.
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© 2017 IEEE. Pattern-reconfigurable antennas with multiple switchable beams, especially with both boresight and endfire directions, are highly desired for wireless communications. In this letter, a novel pattern-reconfigurable antenna is proposed that provides an efficient solution. By reconfiguring parasitic striplines placed around a radiating dipole and reflecting metal pieces under the dipole using p-i-n diodes, the antenna main beam can be switched to five directions in the elevation plane, approximately from-90(left endfire),-45 , 0 (boresight),+45 to +90(right endfire). The proposed antenna operates at 2.45 GHz with dimensions of about 0.57λ× 0.45\lambda×, 0.28λ. An antenna prototype is fabricated and measured. For all five directional beams, the measured S-{11}| values are below 13 dB, and the measured realized gains range from 5.2 to 6.5 dBi. They agree reasonably well with the simulated ones.
Chen, W, Peng, J, Hong, H, Shahabi, H, Pradhan, B, Liu, J, Zhu, AX, Pei, X & Duan, Z 2018, 'Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China', Science of the Total Environment, vol. 626, pp. 1121-1135.
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© 2018 Elsevier B.V. The preparation of a landslide susceptibility map is considered to be the first step for landslide hazard mitigation and risk assessment. However, these maps are accepted as end products that can be used for land use planning. The main goal of this study is to assess and compare four advanced machine learning techniques, namely the Bayes’ net (BN), radical basis function (RBF) classifier, logistic model tree (LMT), and random forest (RF) models, for landslide susceptibility modelling in Chongren County, China. A total of 222 landslide locations were identified in the study area using historical reports, interpretation of aerial photographs, and extensive field surveys. The landslide inventory data was randomly split into two groups with a ratio of 70/30 for training and validation purposes. Fifteen landslide conditioning factors were prepared for landslide susceptibility modelling. The spatial correlation between landslides and conditioning factors was analyzed using the information gain (IG) method. The BN, RBF classifier, LMT, and RF models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) and statistical measures, including sensitivity, specificity, and accuracy, were employed to validate and compare the predictive capabilities of the models. Out of the tested models, the RF model had the highest sensitivity, specificity, and accuracy values of 0.787, 0.716, and 0.752, respectively, for the training dataset. Overall, the RF model produced an optimized balance for the training and validation datasets in terms of AUC values and statistical measures. The results of this study also demonstrate the benefit of selecting optimal machine learning techniques with proper conditioning selection methods for landslide susceptibility modelling.
Chen, W, Yan, X, Zhao, Z, Hong, H, Bui, DT & Pradhan, B 2018, 'Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)', Bulletin of Engineering Geology and the Environment, pp. 1-20.
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© 2018 Springer-Verlag GmbH Germany, part of Springer Nature The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results sho...
Chen, W., Simpson, J.M., March, L.M., Blyth, F.M., Bliuc, D., Tran, T., Nguyen, T.V., Eisman, J.A. & Center, J.R. 2018, 'Comorbidities Only Account for a Small Proportion of Excess Mortality After Fracture: A Record Linkage Study of Individual Fracture Types.', Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, vol. 33, no. 5, pp. 795-802.
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Nonhip, nonvertebral (NHNV) fractures constitute the majority of osteoporotic fractures, but few studies have examined the association between these fractures, comorbidity, and mortality. Our objective was to examine the relationship between individual nonhip, nonvertebral fractures, comorbidities, and mortality. The prospective population-based cohort of 267,043 subjects (45 and Up Study, Australia) had baseline questionnaires linked to hospital administrative and all-cause mortality data from 2006 to 2013. Associations between fracture and mortality were examined using multivariate, time-dependent Cox models, adjusted for age, prior fracture, body mass index, smoking, and comorbidities (cardiovascular disease, diabetes, stroke, thrombosis, and cancer), and survival function curves. Population attributable fraction was calculated for each level of risk exposure. During 1,490,651 person-years, women and men experienced 7571 and 4571 fractures and 7064 deaths and 11,078 deaths, respectively. In addition to hip and vertebral fractures, pelvis, humerus, clavicle, rib, proximal tibia/fibula, elbow and distal forearm fractures in both sexes, and ankle fractures in men were associated with increased multivariable-adjusted mortality hazard ratios ranging from 1.3 to 3.4. Comorbidity independently added to mortality such that a woman with a humeral fracture and 1 comorbidity had a similarly reduced 5-year survival as that of a woman with a hip fracture and no comorbidities. Population mortality attributable to any fracture without comorbidity was 9.2% in women and 5.3% in men. All proximal nonhip, nonvertebral fractures in women and men were associated with increased mortality risk. Coexistent comorbidities independently further increased mortality. Population attributable risk for mortality for fractures was similar to cardiovascular disease and diabetes, highlighting their importance and potential benefit for early intervention and treatment. © 2018 American Society fo...
Chen, X, Yuan, Z & Ni, BJ 2018, 'Nitrite accumulation inside sludge flocs significantly influencing nitrous oxide production by ammonium-oxidizing bacteria', Water Research, vol. 143, pp. 99-108.
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© 2018 Elsevier Ltd This work aims to clarify the role of potential nitrite (NO2−) accumulation inside sludge flocs in N2O production by ammonium-oxidizing bacteria (AOB) at different dissolved oxygen (DO) levels with focus on the conditions of no significant bulk NO2−accumulation (<0.2 mg N/L). To this end, an augmented nitrifying sludge with much higher abundance of nitrite-oxidizing bacteria (NOB) than AOB was enriched and then used for systematically designed batch tests, which targeted a range of DO levels from 0 to 3.0 mg O2/L at a fixed ammonium concentration of 10 mg N/L. A two-pathway N2O model was applied to facilitate the interpretation of batch experimental data, thus shedding light on the relationships between N2O production pathways and key process parameters (i.e., DO and NO2−accumulation inside sludge flocs). The results demonstrated (i) the biomass specific N2O production rate firstly increased and then decreased with DO, with the maximum value of 3.03 ± 0.05 mg N/h/g VSS obtained at DO level of 0.75 mg O2/L, (ii) the AOB denitrification pathway for N2O production was dominant (98.0%) at all DO levels tested even without significant bulk NO2−accumulation (<0.2 mg N/L) observed in the system, but its contribution decreased with DO, (iii) DO had a positive impact on the hydroxylamine pathway for N2O production which therefore increased with DO, and (iv) the nitrite accumulation existed inside the sludge flocs and induced significant N2O production from the AOB denitrification pathway.
Chen, Y, Dong, Y, Sun, Y & Liang, J 2018, 'A Multi-comparable visual analytic approach for complex hierarchical data', Journal of Visual Languages and Computing, vol. 47, pp. 19-30.
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© 2018 Elsevier Ltd Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.
Chen, Y, Wu, Y, Wang, D, Li, H, Wang, Q, Liu, Y, Peng, L, Yang, Q, Li, X, Zeng, G & Chen, Y 2018, 'Understanding the mechanisms of how poly aluminium chloride inhibits short-chain fatty acids production from anaerobic fermentation of waste activated sludge', Chemical Engineering Journal, vol. 334, pp. 1351-1360.
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© 2017 Elsevier B.V. Poly aluminum chloride (PAC) is accumulated in waste activated sludge at high levels. However, details of how PAC affects short-chain fatty acids (SCFA) production from anaerobic sludge fermentation has not been documented. This work therefore aims to fill this knowledge gap by analyzing the impact of PAC on the aggregate of sludge flocs, disruption of extracellular polymeric substances (EPS), and the bio-processes of hydrolysis, acidogenesis, and methanogenesis. The relationship between SCFA production and different aluminum species (i.e., Ala, Alb, and Alc) was also identified by controlling different OH/Al ratio and pH in different fermentation systems. Experimental results showed that with the increase of PAC addition from 0 to 40 mg Al per gram of total suspended solids, SCFA yield decreased from 212.2 to 138.4 mg COD/g volatile suspended solids. Mechanism exploration revealed that PAC benefited the aggregates of sludge flocs and caused more loosely- and tightly-bound extracellular polymeric substances remained in sludge cells. Besides, it was found that the hydrolysis, acidiogenesis, and methanogenesis processes were all inhibited by PAC. Although three types of Al species, i.e., Ala (Al monomers, dimer, and trimer), Alb (Al13(AlO4Al12(OH)24(H2O)7 + 12), and Alc (Al polymer molecular weight normally larger than 3000 Da), were co-existed in fermentation systems, their impacts on SCFA production were different. No correlation was found between SCFA and Ala, whereas SCFA production decreased with the contents of Alb and Alc. Compared with Alb, Alc was the major contributor to the decreased SCFA production (R2= 0.5132 vs R2= 0.98). This is the first report revealing the underlying mechanism of how PAC affects SCFA production and identifying the contribution of different Al species to SCFA inhibition.
Chen, Z, Wang, D, Sun, M, Hao Ngo, H, Guo, W, Wu, G, Jia, W, Shi, L, Wu, Q, Guo, F & Hu, H-Y 2018, 'Sustainability evaluation and implication of a large scale membrane bioreactor plant.', Bioresource technology, vol. 269, pp. 246-254.
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Membrane bioreactor (MBR) technology is receiving increasing attention in wastewater treatment and reuse. This study presents an integral sustainability evaluation of a full scale MBR plant. The plant is capable of achieving prominent technical performance in terms of high compliance rate, low variation in effluent quality and high removal efficiency during long term operation. It is also more responsive to the new local standard with rigorous limits. However, electricity consumption is found to be the dominant process resulting in elevated life cycle environmental impacts and costs, accounting for 51.6% of the costs. As such, it is suggested to optimize energy use in MBR unit and implement sludge treatment and management. The prolonged membrane life span could also contribute largely to reduced life cycle environmental concerns and expenses. This study is of great theoretical significance and applicable value in guaranteeing the performance and sustainability of large scale MBR schemes.
Chen, Z, Zhang, B, Zhang, N, Du, H & Kong, G 2018, 'Optimal preview position control for shifting actuators of automated manual transmission', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.
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© 2018, IMechE 2018. This paper is concerned with the problem of position tracking control for the motor-driven gear-shift actuating mechanism for electro-mechanical automated manual transmissions (AMT). It is well known that torque interruption is an inherent flaw of AMT. As shift duration directly affects the torque interruption interval, shift quality can be improved by minimizing the duration of the shift. To realize rapid and precise gear-shift control, an optimal discrete-time preview position control scheme is proposed. The proposed control scheme consists of state-feedback control, a discrete integrator, and preview feed-forward control. Instead of the traditional difference method, the state transformation method is utilized to construct the augmented error system such that the augmented system can be kept in a simple form. The H∞performance index is employed to reduce the effect of the shifting load disturbance during the gear-shifting process. The controller gains are obtained by solving a linear matrix inequality (LMI). Simulation and test bench results all show that the proposed preview control algorithm has better dynamic response and adaptability under different loads compared to the optimal linear quadratic regulator (LQR) controller and the standard PID controller.
Chen, Z., Yu, T., Ngo, H.H., Lu, Y., Li, G., Wu, Q., Li, K., Bai, Y., Liu, S. & Hu, H.-.Y. 2018, 'Assimilable organic carbon (AOC) variation in reclaimed water: Insight on biological stability evaluation and control for sustainable water reuse.', Bioresource technology, vol. 254, pp. 290-299.
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This review highlights the importance of conducting biological stability evaluation due to water reuse progression. Specifically, assimilable organic carbon (AOC) has been identified as a practical indicator for microbial occurrence and regrowth which ultimately influence biological stability. Newly modified AOC bioassays aimed for reclaimed water are introduced. Since elevated AOC levels are often detected after tertiary treatment, the review emphasizes that actions can be taken to either limit AOC levels prior to disinfection or conduct post-treatment (e.g. biological filtration) as a supplement to chemical oxidation based approaches (e.g. ozonation and chlorine disinfection). During subsequent distribution and storage, microbial community and possible microbial regrowth caused by complex interactions are discussed. It is suggested that microbial surveillance, AOC threshold values, real-time field applications and surrogate parameters could provide additional information. This review can be used to formulate regulatory plans and strategies, and to aid in deriving relevant control, management and operational guidance.
Chenari, R.J., Fatahi, B., Ghorbani, A. & Alamoti, M.N. 2018, 'Evaluation of strength properties of cement stabilized sand mixed with EPS beads and fly ash', GEOMECHANICS AND ENGINEERING, vol. 14, no. 6, pp. 533-544.
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Cheng, D, Ngo, HH, Guo, W, Liu, Y, Chang, SW, Nguyen, DD, Nghiem, LD, Zhou, J & Ni, B 2018, 'Anaerobic membrane bioreactors for antibiotic wastewater treatment: Performance and membrane fouling issues.', Bioresource technology, vol. 267, pp. 714-724.
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Antibiotic wastewater has become a major concern due to the toxicity and recalcitrance of antibiotics. Anaerobic membrane bioreactors (AnMBRs) are considered alternative technology for treating antibiotic wastewater because of their advantages over the conventional anaerobic processes and aerobic MBRs. However, membrane fouling remains the most challenging issue in the AnMBRs' operation and this limits their application. This review critically discusses: (i) antibiotics removal and antibiotic resistance genes (ARGs) in different types of AnMBRs and the impact of antibiotics on membrane fouling and (ii) the integrated AnMBRs systems for fouling control and removal of antibiotics. The presence of antibiotics in AnMBRs could aggravate membrane fouling by influencing fouling-related factors (i.e., sludge particle size, extracellular polymeric substances (EPS), soluble microbial products (SMP), and fouling-related microbial communities). Conclusively, integrated AnMBR systems can be a practical technology for antibiotic wastewater treatment.
Cheng, DL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD, Kumar, SM, Du, B, Wei, Q & Wei, D 2018, 'Problematic effects of antibiotics on anaerobic treatment of swine wastewater.', Bioresource technology, vol. 263, pp. 642-653.
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Swine wastewaters with high levels of organic pollutants and antibiotics have become serious environmental concerns. Anaerobic technology is a feasible option for swine wastewater treatment due to its advantage in low costs and bioenergy production. However, antibiotics in swine wastewater have problematic effects on micro-organisms, and the stability and performance of anaerobic processes. Thus, this paper critically reviews impacts of antibiotics on pH, COD removal efficiencies, biogas and methane productions as well as the accumulation of volatile fatty acids (VFAs) in the anaerobic processes. Meanwhile, impacts on the structure of bacteria and methanogens in anaerobic processes are also discussed comprehensively. Furthermore, to better understand the effect of antibiotics on anaerobic processes, detailed information about antimicrobial mechanisms of antibiotics and microbial functions in anaerobic processes is also summarized. Future research on deeper knowledge of the effect of antibiotics on anaerobic processes are suggested to reduce their adverse environmental impacts.
Cheng, DL, Ngo, HH, Guo, WS, Liu, YW, Zhou, JL, Chang, SW, Nguyen, DD, Bui, XT & Zhang, XB 2018, 'Bioprocessing for elimination antibiotics and hormones from swine wastewater.', Science of the Total Environment, vol. 621, pp. 1664-1682.
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Antibiotics and hormones in swine wastewater have become a critical concern worldwide due to the severe threats to human health and the eco-environment. Removal of most detectable antibiotics and hormones, such as sulfonamides (SAs), SMs, tetracyclines (TCs), macrolides, and estrogenic hormones from swine wastewater utilizing various biological processes were summarized and compared. In biological processes, biosorption and biodegradation are the two major removal mechanisms for antibiotics and hormones. The residuals in treated effluents and sludge of conventional activated sludge and anaerobic digestion processes can still pose risks to the surrounding environment, and the anaerobic processes' removal efficiencies were inferior to those of aerobic processes. In contrast, membrane bioreactors (MBRs), constructed wetlands (CWs) and modified processes performed better because of their higher biodegradation of toxicants. Process modification on activated sludge, anaerobic digestion and conventional MBRs could also enhance the performance (e.g. removing up to 98% SMs, 88.9% TCs, and 99.6% hormones from wastewater). The hybrid process combining MBRs with biological or physical technology also led to better removal efficiency. As such, modified conventional biological processes, advanced biological technologies and MBR hybrid systems are considered as a promising technology for removing toxicants from swine wastewater.
Cheng, H, Zhang, J, Wu, Q & Ping, A 2018, 'A Computational Model for Stereoscopic Visual Saliency Prediction', IEEE Transactions on Multimedia.
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IEEE Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: the pop-out effect, comfort zones, and background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multi-feature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets show that our proposed method outperforms several state-of-the-art saliency models.
Cheng, HC & Hsieh, MH 2018, 'Moderate deviation analysis for classical-quantum channels and quantum hypothesis testing', IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1385-1403.
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© 1963-2012 IEEE. In this paper, we study the tradeoffs between the error probabilities of classical-quantum channels and the blocklength n when the transmission rates approach the channel capacity at a rate lower than 1 {n} , a research topic known as moderate deviation analysis. We show that the optimal error probability vanishes under this rate convergence. Our main technical contributions are a tight quantum sphere-packing bound, obtained via Chaganty and Sethuraman's concentration inequality in strong large deviation theory, and asymptotic expansions of error-exponent functions. Moderate deviation analysis for quantum hypothesis testing is also established. The converse directly follows from our channel coding result, while the achievability relies on a martingale inequality.
Cheng, L, Acuna, P, Aguilera, RP, Jiang, J, Wei, S, Fletcher, J & Lu, DDC 2018, 'Model Predictive Control for DC-DC Boost Converters with Reduced-Prediction Horizon and Constant Switching Frequency', IEEE Transactions on Power Electronics.
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IEEE The implementation of multi-step direct model predictive control (MPC) for DC-DC boost converters overcomes the well-known issue of non-minimum phase behaviour. However, it can lead to a high computational burden depending on the prediction horizon length. In this work, a simple and computationally efficient MPC method for DC-DC boost converters is proposed. The key novelty of the presented control strategy lies in the way dynamic references are handled. The control strategy is capable of providing suitable references for the inductor current and the output voltage, without requiring additional control loops. Moreover, this reference design allows the predictive controller to be implemented with a single-step prediction horizon. Thus, a significant reduction in the required real-time calculations executed in the control hardware is achieved. To obtain constant switching frequency, the power switch commutation instants within a sampling period are considered as control inputs. Therefore, the predictive controller is formulated as a continuous control set MPC (CCS-MPC). Additionally, the proposed formulation is able to deal with different operation modes of the converter without changing the controller structure. Finally, an observer is used to dynamically modify the reference to provide robustness to system parameter uncertainties. Simulation and experimental results show an accurate tracking of dynamic inductor current and output voltage references, while respecting the restrictions on maximum inductor current levels of the converter.
Cheng, P, Chen, Z, Zhang, JA, Li, Y & Vucetic, B 2018, 'A Unified Precoding Scheme for Generalized Spatial Modulation', IEEE Transactions on Communications, vol. 66, no. 6, pp. 2502-2514.
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© 1972-2012 IEEE. Generalized spatial modulation (GSM) activates Nt(1 ≤ nt< Nt) available transmit antennas, and information is conveyed through ntmodulated symbols as well as the index of the ntactivated antennas. GSM strikes an attractive tradeoff between spectrum efficiency and energy efficiency. Linear precoding that exploits channel state information at the transmitter enhances the system error performance. For GSM with nt=1 (the traditional SM), the existing precoding methods suffer from high computational complexity. On the other hand, GSM precoding for nt≥ 2 is not thoroughly investigated in the open literature. In this paper, we develop a unified precoding design for GSM systems, which universally works for all ntvalues. Based on the maximum minimum Euclidean distance criterion, we find that the precoding design can be formulated as a large-scale nonconvex quadratically constrained quadratic program problem. Then, we transform this challenging problem into a sequence of unconstrained subproblems by leveraging augmented Lagrangian and dual ascent techniques. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can substantially improve the system error performance relative to the GSM without precoding and features extremely fast convergence rate with a very low computational complexity.
Cheng, T., Lu, D.D.C. & Qin, L. 2018, 'Non-Isolated Single-Inductor DC/DC Converter with Fully Reconfigurable Structure for Renewable Energy Applications', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 3, pp. 351-355.
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© 2017 IEEE. A novel non-isolated three-port converter (NITPC) is introduced in this brief. The purpose of this topology is to integrate a regenerative load such as DC bus and motor with dynamic braking, instead of the widely reported consuming load, with a photovoltaic (PV)-battery system. Conventional methods require either a separate DC-DC converter to process the reversible power flow or employing an isolated three-port converter (TPC), which allows bi-directional power flow between any two ports. However, these methods require many switches, which increases the converter size and control complexity. This brief hence presents a compact but fully functional design by combining and integrating basic converters to form a simplified single-inductor converter structure while keeping a minimum amount of switches. The resultant converter is fully reconfigurable that all possible power flow combinations among the sources and load are achieved through different switching patterns, while preserving the single power processing feature of TPC. This brief presents a design example of the proposed NITPC for a PV-battery powered DC microgrid. Detailed circuitry analysis, operation principles of both DC grid-connected and islanded modes, and experimental results of different modes in steady state and mode transitions are presented.
Cheng, XW, Jiang, ZY, Monaghan, BJ, Longbottom, RJ, Wei, DB, Hee, AC & Jiang, LZ 2018, 'Degradation of ferritic stainless steels at 1200 °C in air', Materials and Corrosion, vol. 69, no. 1, pp. 63-75.
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© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Three commercial ferritic stainless steels were investigated at 1200 °C by a thermogravimetric analyser (TGA) in air. The oxidation kinetics of the ferritic stainless steels differed significantly. The adhesion of the Cr2O3scale, the morphology of the SiO2, with or without (Cr, Mn)3O4spinel on the top can greatly influence the oxidation and the degradation behaviour of the steels. Although the SUS430 steel had less Cr among the ferritic stainless steels (16.2 wt% Cr), it did not show more degradation behaviour than the B443NT steel (21.0 wt% Cr). The spallation of the protective oxide scale on the B443NT steel was caused by vacancy condensation at the scale/substrate interface where the SiO2particles were and the developed compressive stresses within the oxide scale during oxidation. (Cr, Mn)3O4spinel on the top of the Cr2O3scale on the B445J1M steel influenced its evaporation rate. The thermodynamic aspect of the chemical composition and oxidation atmosphere of the Fe–Cr–O system was also discussed.
Chi, L., Li, B., Zhu, X., Pan, S. & Chen, L. 2018, 'Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1591-1604.
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Many applications involve processing networked streaming data in a timely manner. Graph stream classification aims to learn a classification model from a stream of graphs with only one-pass of data, requiring real-time processing in training and prediction. This is a nontrivial task, as many existing methods require multipass of the graph stream to extract subgraph structures as features for graph classification which does not simultaneously satisfy "one-pass" and "real-time" requirements. In this paper, we propose an adaptive real-time graph stream classification method to address this challenge. We partition the unbounded graph stream data into consecutive graph chunks, each consisting of a fixed number of graphs and delivering a corresponding chunk-level classifier. We employ a random hashing function to compress the original node set of graphs in each chunk for fast feature detection when training chunk-level classifiers. Furthermore, a differential hashing strategy is applied to map unlimited increasing features (i.e., cliques) into a fixed-size feature space which is then used as a feature vector for stochastic learning. Finally, the chunk-level classifiers are weighted in an ensemble learning model for graph classification. The proposed method substantially speeds up the graph feature extraction and avoids unbounded graph feature growth. Moreover, it effectively offsets concept drifts in graph stream classification. Experiments on real-world and synthetic graph streams demonstrate that our method significantly outperforms existing methods in both classification accuracy and learning efficiency.
Chikara, R.K., Chang, E.C., Lu, Y.-.C., Lin, D.-.S., Lin, C.-.T. & Ko, L.-.W. 2018, 'Monetary Reward and Punishment to Response Inhibition Modulate Activation and Synchronization Within the Inhibitory Brain Network.', Frontiers in human neuroscience, vol. 12, p. 27.
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A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.
Chitambar, E, Fortescue, B & Hsieh, MH 2018, 'The Conditional Common Information in Classical and Quantum Secret Key Distillation', IEEE Transactions on Information Theory.
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IEEE In this paper we consider two extensions of the Gács- Körner common information to three variables, the conditional common information (cCI) and the coarse-grained conditional common information (ccCI). Both quantities are shown to be useful technical tools in the study of classical and quantum resource transformations. In particular, the coarse-grained conditional common information is shown to have an operational interpretation as the optimal rate of secret key extraction from an eavesdropped classical source pXY Z when Alice (X) and Bob (Y ) are unable to communicate but share common randomness with the eavesdropper Eve (Z). Moving to the quantum setting, we consider two different ways of generating a tripartite quantum state from classical correlations pXY Z: (i) coherent encodings Σxyz √pxyz|xyz> and (ii) incoherent encodings Σxyz pxyz|xyz><xyz|. We study how well can Alice and Bob extract secret key from these quantum sources using quantum operations compared to the extraction of key from the underlying classical sources pXY Z using classical operations. While the power of quantum mechanics increases Alice and Bob’s ability to generate shared randomness, it also equips Eve with a greater arsenal of eavesdropping attacks. Therefore, it is not obvious who gains the greatest advantage for distilling secret key when replacing a classical source with a quantum one. We first demonstrate that the classical key rate of pXY Z is equivalent to the quantum key rate for an incoherent quantum encoding of the distribution. For coherent encodings, we next show that the classical and quantum rates are generally incomparable, and in fact, their difference can be arbitrarily large in either direction. Finally, we introduce a “zoo” of entangled tripartite states all characterized by the conditional common information of their encoded probability distributions. Remarkably, fo...
Chiu, SK, Saw, J, Huang, Y, Sonderegger, SE, Wong, NC, Powell, DR, Beck, D, Pimanda, JE, Tremblay, CS & Curtis, DJ 2018, 'A novel role for Lyl1 in primitive erythropoiesis.', Development (Cambridge, England).
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Stem Cell Leukemia (Scl or Tal1) and Lymphoblastic Leukemia 1 (Lyl1) are highly related members of the basic helix-loop-helix (bHLH) family of transcription factors that are co- expressed in the erythroid lineage. Previous studies suggest that Scl is essential for primitive erythropoiesis. However, analysis of single-cell RNA-sequencing data of early embryos showed that primitive erythroid cells express both Scl and Lyl1 Therefore, to determine whether Lyl1 can function in primitive erythropoiesis, we crossed conditional Scl knockout mice with mice expressing a Cre recombinase under the control of the Epo receptor, active in erythroid progenitors. Embryos with 20% expression of Scl from E9.5 survived to adulthood. However, mice with reduced expression of Scl and absence of Lyl1 (double knockout; DKO) died at E10.5 due to progressive loss of erythropoiesis. Gene expression profiling of DKO yolk sacs revealed loss of Gata1 and many of the known target genes of the SCL-GATA1 complex. ChIP-seq analyses showed that LYL1 exclusively bound a small subset of SCL targets including GATA1 Together, these data show for the first time that Lyl1 can maintain primitive erythropoiesis.
Choi, I, Milne, DN, Deady, M, Calvo, RA, Harvey, SB & Glozier, N 2018, 'Impact of Mental Health Screening on Promoting Immediate Online Help-Seeking: Randomized Trial Comparing Normative Versus Humor-Driven Feedback.', JMIR mental health, vol. 5, no. 2, p. e26.
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Given the widespread availability of mental health screening apps, providing personalized feedback may encourage people at high risk to seek help to manage their symptoms. While apps typically provide personal score feedback only, feedback types that are user-friendly and increase personal relevance may encourage further help-seeking.The aim of this study was to compare the effects of providing normative and humor-driven feedback on immediate online help-seeking, defined as clicking on a link to an external resource, and to explore demographic predictors that encourage help-seeking.An online sample of 549 adults were recruited using social media advertisements. Participants downloaded a smartphone app known as "Mindgauge" which allowed them to screen their mental wellbeing by completing standardized measures on Symptoms (Kessler 6-item Scale), Wellbeing (World Health Organization [Five] Wellbeing Index), and Resilience (Brief Resilience Scale). Participants were randomized to receive normative feedback that compared their scores to a reference group or humor-driven feedback that presented their scores in a relaxed manner. Those who scored in the moderate or poor ranges in any measure were encouraged to seek help by clicking on a link to an external online resource.A total of 318 participants scored poorly on one or more measures and were provided with an external link after being randomized to receive normative or humor-driven feedback. There was no significant difference of feedback type on clicking on the external link across all measures. A larger proportion of participants from the Wellbeing measure (170/274, 62.0%) clicked on the links than the Resilience (47/179, 26.3%) or Symptoms (26/75, 34.7%) measures (χ2=60.35, P<.001). There were no significant demographic factors associated with help-seeking for the Resilience or Wellbeing measures. Participants with a previous episode of poor mental health were less likely than those without such history to click on...
Choi, Y, Naidu, G, Jeong, S, Lee, S & Vigneswaran, S 2018, 'Effect of chemical and physical factors on the crystallization of calcium sulfate in seawater reverse osmosis brine', Desalination, vol. 426, pp. 78-87.
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© 2017 Elsevier B.V. A major challenge of seawater reverse osmosis (SWRO) desalination process corresponds to the management of concentrated brine waste because discharging the brine back into the sea influences the marine ecosystem and incurs additional costs to plants. A membrane distillation crystallizer (MDC) can further produce clean water and simultaneously recover valuable resources from the concentrated brine; this is more environmentally and economically optimal. SWRO brine contains salts, which contribute to scaling development during the MDC operation. Hence, the main goals of this study was to observe the crystallization tendency of calcium sulfate (CaSO4) under high salinity and, to examine other inorganic and organic compounds and operational conditions that affect the CaSO4crystallization. The crystallization tendency of CaSO4in SWRO brine was examined with respect to different temperatures; changes in pH values; and in the presence of co-existing ions, chemical agents, and organic matters as well as physical factors. The results showed that the size and quantity of crystals formed increased at higher temperatures. Furthermore, an increase in the pH values increased the crystal size. At higher pH, the complexion of NaCl along with CaSO4was created. Moreover, stirring enhanced CaSO4crystal formation due to the kinetic mechanism.
Choi, Y, Naidu, G, Jeong, S, Lee, S & Vigneswaran, S 2018, 'Fractional-submerged membrane distillation crystallizer (F-SMDC) for treatment of high salinity solution', Desalination, vol. 440, pp. 59-67.
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© 2018 Elsevier B.V. Membrane distillation with crystallization (MDC) is an attractive process for high saline seawater reverse osmosis (SWRO) brine treatment. MDC produces additional fresh water while simultaneously recovering valuable resources. This study developed a novel approach of fractional-submerged MDC (F-SMDC) process, in which MD and crystallizer are integrated in a feed tank with a submerged membrane. F-SMDC principle is based on the presence of temperature/concentration gradient (TG/CG) in the feed reactor. The operational conditions at the top portion of the feed reactor (higher temperature and lower feed concentration) was well suited for MD operation, while the bottom portion of the reactor (lower temperature and higher concentration) was favourable for crystal growth. F-SMDC performance with direct contact MD to treat brine and produce sodium sulfate (Na2SO4) crystals using TG/CG showed positive results. The TG/CG approach in F-SMDC enabled to achieve higher water recovery for brine treatment with a volume concentration factor (VCF) of over 3.5 compared to VCF of 2.9 with a conventional S-MDC set-up. Further, the high feed concentration and low temperature at the reactor bottom in F-SMDC enabled the formation of Na2SO4crystals with narrow crystal size distribution.
Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2018, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
Chotipant, S., Hussain, F.K. & Hussain, O.K. 2018, 'SERNOTATE: An automated approach for business service description annotation for efficient service retrieval and composition', Concurrency and Computation: Practice and Experience, vol. 30, no. 1.
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Copyright © 2017 John Wiley & Sons, Ltd. Business service advertisements are today published online to convey essential information about services to customers. However, current Web search engines are unable to search and combine online service advertisements. Semantic service annotation is important for its ability to enable machines to understand the meaning of services and support in effective service retrieval and service composition. Existing research in the area of semantic service annotation has focused on the annotation of Web services in a semi-automated approach. It cannot be applied to business service information as it is not in the form of Web Services Description Language but in free text format. Moreover, semi-automated approaches are inappropriate for annotating a large amount of online service information which changes dynamically and they are therefore not suitable for the timely dissemination of service information to customers. To solve these issues, we propose SERNOTATE, which is an automated approach for business service description annotation for efficient service retrieval and composition. We propose new semantic-based linking approaches, namely, Extended Case-based Reasoning, vector-based, and classification-based, that automatically annotate business services to relevant service concepts. Each approach assists in the single-label and multi-label annotation of service terms to concept terms to provide a better representation of services. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset.
Chou, KP, Prasad, M, Wu, D, Sharma, N, Li, DL, Lin, YF, Blumenstein, M, Lin, WC & Lin, CT 2018, 'Robust Feature-Based Automated Multi-View Human Action Recognition System', IEEE Access, vol. 6, pp. 15283-15296.
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© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.
Chu, S, Gao, L, Xiao, M, Luo, Z, Li, H & Gui, X 2018, 'A new method based on adaptive volume constraint and stress penalty for stress-constrained topology optimization', Structural and Multidisciplinary Optimization, vol. 57, no. 3, pp. 1163-1185.
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© 2017 Springer-Verlag GmbH Germany This paper focuses on the stress-constrained topology optimization of minimizing the structural volume and compliance. A new method based on adaptive volume constraint and stress penalty is proposed. According to this method, the stress-constrained volume and compliance minimization topology optimization problem is transformed into two simple and related problems: a stress-penalty-based compliance minimization problem and a volume-decision problem. In the former problem, stress penalty is conducted and used to control the local stress level of the structure. To solve this problem, the parametric level set method with the compactly supported radial basis functions is adopted. Meanwhile, an adaptive adjusting scheme of the stress penalty factor is used to improve the control of the local stress level. To solve the volume-decision problem, a combination scheme of the interval search and local search is proposed. Numerical examples are used to test the proposed method. Results show the lightweight design, which meets the stress constraint and whose compliance is simultaneously optimized, can be obtained by the proposed method.
Chua, B. 2018, 'Predicting Fork Visibility Performance On Programming Language Interoperability in Open Source Projects', 9, vol. 1, pp. 32-37.
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Despite a variety of programming languages
adopted in open source (OS
) projects, fork variation on some
languages has been minimal and slow to be adopted, and there is
little research as to why this is so. We therefore employed a K
-
nearest neighbours (KNN) technique to predict the fork visibility
performance of a productive
language from a pool of
programming languages adopted in projects. In total, 38
showcase OS projects from 2012 to 2016 were downloaded from
the GitHub website and categorized into different levels of
programming language adoption clusters. Among 33 langua
ges,
JavaScript is one of the popular languages that adopted by
community. It has been predicted the language chosen when fork
visibility is high can increase project longevity as a highly visible
language is likely to occur more often in projects with a
s
ignificant number of interoperable programming languages and
high language fork count. Conversely, a low fork language
reduces longevity in projects with an insignificant number of
interoperable programming languages and low fork count. Our
results reveal
the survival of a productive language is in response
to high language visibility (large fork number) and high
interoperability of multiple programming languages
Chuah, S, Li, W, Chen, SJ, Sanjayan, JG & Duan, WH 2018, 'Investigation on dispersion of graphene oxide in cement composite using different surfactant treatments', Construction and Building Materials, vol. 161, pp. 519-527.
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© 2017 Elsevier Ltd Graphene oxide (GO) is a novel class of two-dimensional nanoscale sheet material due to its excellent dispersibility in water, high aspect ratio and good intrinsic strengths. In order to obtain a well-distributed GO-reinforced cement composites, the dispersion of GO in water, alkali and several ionic species are investigated with the aid of UV–vis spectroscopy. High alkalinity and calcium ions are key factors inducing the agglomeration of GO in cement system. Dispersion of GO in simulated pore solution is the culmination of the alkali and salt experiments. Agglomeration of GO occurred when GO contacted with the simulated pore solution, highlighting the necessity to protect GO against such aggressive media. The test on surfactant compatibility was then carried out to ensure GO was effectively dispersed in polycarboxylate, air-entrainment and Gum Arabic admixtures within the pore solution. Polycarboxylate-based superplasticisers gave the most promising results to disperse GO in cement alkaline environment. Flexural experiments was performed to highlight the importance of fabrication protocol on the mechanical properties of GO-cement composites. The result shows that the amount of 0.03% GO by weight of cement can increase the flexural strength of GO-cement composite up to 67%.
Chuang, C-H, Cao, Z, King, J-T, Wu, B-S, Wang, Y-K & Lin, C-T 2018, 'Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving.', Frontiers in neuroscience, vol. 12, p. 181.
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Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.
Clemon, LM & Zohdi, TI 2018, 'On the tolerable limits of granulated recycled material additives to maintain structural integrity', Construction and Building Materials, vol. 167, pp. 846-852.
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© 2018 Elsevier Ltd Production and maker spaces are increasingly generating mixed plastic material waste of varying quality from 3-D printers. Industrial interest is growing in embedding granulated recycled particulate material additives into a virgin binding matrix. Examples include the introduction of granulated mixed recycled materials into 3-D printer material, concrete, and pavement. The stress load-sharing between the particulate additive and the binding matrix is an important factor in design and development of these composite materials. With mixed material additives, a designer is interested in the variation of such predicted load-sharing. However, experimental development is costly and time-consuming, thus analytical and semi-analytical estimates are desired for accelerated development. In this work, we expand on previous analytically correlated phase-averaged micro- and macrostructural loading to include variational effects present in mixed recycled material. In addition, model trade-offs are provided to aid designers in quickly selecting application specific mixtures. This framework identifies the stress contributions, and their variation, to reduce product development time and costs, which could greatly accelerate material recycling and reuse for improved infrastructure materials, low-cost 3-D printer filament, and reduced waste towards a more circular economy.
Cliff, OM, Prokopenko, M & Fitch, R 2018, 'Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems', Entropy, vol. 20, no. 2.
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Cruz, CG, Naderpour, M & Ramezani, F 2018, 'Water resource selection and optimisation for shale gas developments in Australia: A combinatorial approach', Computers & Industrial Engineering, vol. 124, pp. 1-11.
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Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2018, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Cui, Q, Gu, Y, Ni, W, Zhang, X, Tao, X, Zhang, P & Liu, RP 2018, 'Preserving Reliability of Heterogeneous Ultra-Dense Distributed Networks in Unlicensed Spectrum', IEEE Communications Magazine, vol. 56, no. 6, pp. 72-78.
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© 1979-2012 IEEE. This article investigates the prominent dilemma between capacity and reliability in heterogeneous ultra-dense distributed networks, and advocates a new measure of effective capacity to quantify the maximum sustainable data rate of a link while preserving the quality of service of the link in such networks. Recent breakthroughs are brought forth in developing the theory of the effective capacity in heterogeneous ultra-dense distributed networks. Potential applications of the effective capacity are demonstrated on the admission control, power control, and resource allocation of such networks, with substantial gains revealed over existing technologies. This new measure is of particular interest to ultra-dense deployment of the emerging 5G wireless networks in the unlicensed spectrum, leveraging the capacity gain brought by the use of the unlicensed band and the stringent reliability sustained by 5G in future heterogeneous network environments.
Cui, Z, Wang, J, Zhang, H, Ngo, HH, Jia, H, Guo, W, Gao, F, Yang, G & Kang, D 2018, 'Investigation of backwashing effectiveness in membrane bioreactor (MBR) based on different membrane fouling stages.', Bioresource technology, vol. 269, pp. 355-362.
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In this study the effect of different fouling stages of hollow fiber membranes on effective backwashing length in MBR has been investigated. Computational fluid dynamics (CFD) is imported to simulate backwashing process. A multi-physics coupling model for free porous media flow, convective mass transfer and diluted species transport was established. The laser bijection sensors (LBS) were imported to monitor the backwashing solution position inside fiber lumen. Simulation results indicated that membrane fouling degree could change the velocity of backwash solution inside fiber lumen and make a further effect on effective backwash length. The signal variations of LBS are in accordance with the simulation results. The backwashing process can only play an active role when the filtration pressure is below the critical TMP. It can be concluded that backwash duration in industrial applications need to be set based on changes in TMP.
Dai, M, Cheng, S, He, X & Wang, D 2018, 'Object tracking in the presence of shaking motions', Neural Computing and Applications, pp. 1-18.
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© 2018 The Natural Computing Applications Forum Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.
Dai, M., Cheng, S. & He, X.S. 2018, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications, vol. 29, no. 2, pp. 389-399.
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Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified particle filter with hash fingerprints for the coarse matching by the maximum a posteriori and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because hash fingerprint is merely a binary vector and the modified particle filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on eight challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms eight state-of-the-art trackers in terms of both accuracy and speed.
Dai, T, Zhu, L, Cai, X, Pan, S & Yuan, S 2018, 'Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network', Journal of Ambient Intelligence and Humanized Computing, pp. 1-19.
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© 2017 Springer-Verlag Berlin Heidelberg Citation recommendation is the task of suggesting a list of references for an author given a manuscript. This is important for academic research for it provides an efficient and easy way to find relevant literatures. In this paper, we propose a novel probabilistic topic model to automatically recommend citations for researchers. The model considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation. To fully utilize content and diversified link information in a bibliographic network, we extend LDA with matrix factorization, so that semantic topic learning and community detection are essentially reinforcing each other during parameter estimation. We also develop a flexible way to generate a family of citation link probability functions, which can substantially increase the model capacity. Experimental results on the ANN and DBLP dataset show that our model outperforms baseline algorithms for citation recommendation, and is capable of generating qualified author communities and topics.
Damanik, N, Ong, HC, Tong, CW, Mahlia, TMI & Silitonga, AS 2018, 'A review on the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends', Environmental Science and Pollution Research, vol. 25, no. 16, pp. 15307-15325.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Biodiesels have gained much popularity because they are cleaner alternative fuels and they can be used directly in diesel engines without modifications. In this paper, a brief review of the key studies pertaining to the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends, exhaust aftertreatment systems, and low-temperature combustion technology is presented. In general, most biodiesel blends result in a significant decrease in carbon monoxide and total unburned hydrocarbon emissions. There is also a decrease in carbon monoxide, nitrogen oxide, and total unburned hydrocarbon emissions while the engine performance increases for diesel engines fueled with biodiesels blended with nano-additives. The development of automotive technologies, such as exhaust gas recirculation systems and low-temperature combustion technology, also improves the thermal efficiency of diesel engines and reduces nitrogen oxide and particulate matter emissions.
Damtie, MM, Kim, B, Woo, YC & Choi, J-S 2018, 'Membrane distillation for industrial wastewater treatment: Studying the effects of membrane parameters on the wetting performance.', Chemosphere, vol. 206, pp. 793-801.
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Substantial amounts of trace hazardous elements have been detected in industrial wastewater (e.g fluoride > 900 mg/L). Feed water characteristics, operational parameters, and membrane properties are major factors affecting flux and rejection of the MD process. Membrane parameters such as membrane material type and pore size have been investigated. Fluoride ion rejection was selected to setup a methodology to remove trace elements from wastewater by adjusting the membrane parameters in DCMD. Study of the fouling thickness of the MD membrane using pH and feed water composition revealed that a PVDF membrane with a smooth surface holds a thicker fouling layer, which enhances fluoride rejection while reducing the permeate flux. On the other hand, PTFE and PP membranes showed higher mass transfer and higher wetting performance, respectively. Therefore,a PVDF membrane with low organic feed water at higher alkaline pH can be utilized to obtain high-quality permeate, while PTFE can provide the highest flux with acceptable permeate water quality. Therefore, this methodology can be applied toidentify the optimum membrane to fit the required permeate flux, rejection requirements,and operating pH to treat any kind of non-volatileinorganic pollutants from industrial wastewater.
de Koning, K., Filatova, T. & Bin, O. 2018, 'Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets', Environmental and Resource Economics, vol. 69, no. 2, pp. 247-263.
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© 2016, The Author(s). Property prices are affected by changing market conditions, incomes and preferences of people. Price trends in natural hazard zones may shift significantly and abruptly after a disaster signalling structural systemic changes in property markets. It challenges accurate market assessments of property prices and capital at risk after major disasters. A rigorous prediction of property prices in this case should ideally be done based only on the most recent sales, which are likely to form a rather small dataset. Hedonic analysis has been long used to understand how various factors contribute to the housing price formation. Yet, the robustness of its assessment is undermined when the analysis needs to be performed on relatively small samples. The purpose of this study is to suggest a model that can be widely applicable and quickly calibrated in a changing environment. We systematically study four statistical models: starting from a typical standard hedonic function and gradually changing its functional specification by reducing the hedonic analysis to some basic property characteristics and applying kriging to control for neighbourhood effects. Across different sample sizes we find that the latter performs consistently better in the out-of-sample predictions than other traditional price prediction methods. We present the specific improvements to the traditional spatial hedonic model that enhance the model’s prediction accuracy. The improved model can be used to monitor price changes in risk-prone areas, accounting for changes in flood risk and at the same time controlling for autonomous market responses to flood risk.
Deady, M, Johnston, DA, Glozier, N, Milne, D, Choi, I, Mackinnon, A, Mykletun, A, Calvo, RA, Gayed, A, Bryant, R, Christensen, H & Harvey, SB 2018, 'Smartphone application for preventing depression: Study protocol for a workplace randomised controlled trial', BMJ Open, vol. 8, no. 7.
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© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. Introduction Depression is the leading cause of life years lost due to disability. Appropriate prevention has the potential to reduce the incidence of new cases of depression, however, traditional prevention approaches face significant scalability issues. Prevention programmes delivered by via smartphone applications provide a potential solution. The workplace is an ideal setting to roll out this form of intervention, particularly among industries that are unlikely to access traditional health initiatives and whose workplace characteristics create accessibility and portability issues. The study aims to evaluate the effectiveness of a smartphone application designed to prevent depression and improve well-being. The effectiveness of the app as a universal, selective and indicated prevention tool will also be evaluated. Methods and analysis A multicentre randomised controlled trial, to determine the effectiveness of the intervention compared with an active mood monitoring control in reducing depressive symptoms (primary outcome) and the prevalence of depression at 3 months, with secondary outcomes assessing well-being and work performance. Employees from a range of industries will be invited to participate. Participants with likely current depression at baseline will be excluded. Following baseline assessment, participants, blinded to their allocation, will be randomised to receive one of two versions of the application: headgear (a 30-day mental health intervention) or a control application (mood monitoring for 30 days). Both versions of the app contain a risk calculator to provide a measure of future risk. Analyses will be conducted within an intention-To-Treat framework using mixed modelling, with additional analyses conducted to compare the moderating effect of baseline risk level and depression symptom severity on the int...
Deady, M, Johnston, DA, Glozier, N, Milne, DN, Choi, I, Mackinnon, A, Mykletun, A, Calvo, RA, Gayed, A, Bryant, R, Christensen, H & Harvey, SB 2018, 'A smartphone application for treating depressive symptoms: study protocol for a randomised controlled trial', BMC Psychiatry, vol. 18, no. 166.
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Depression is a commonly occurring disorder linked to diminished role functioning and quality of life. The development of treatments that overcome barriers to accessing treatment remains an important area of clinical research as most people delay or do not receive treatment at an appropriate time. The workplace is an ideal setting to roll-out an intervention, particularly given the substantial psychological benefits associated with remaining in the workforce. Mobile health (mhealth) interventions utilising smartphone applications (apps) offer novel solutions to disseminating evidence based programs, however few apps have undergone rigorous testing. The present study aims to evaluate the effectiveness of a smartphone app designed to treat depressive symptoms in workers.
Deng, C, Chen, Z, Liu, X, Gao, X & Tao, D 2018, 'Triplet-Based Deep Hashing Network for Cross-Modal Retrieval', IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 3893-3903.
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© 1992-2012 IEEE. Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular, cross-modal hashing has been widely and successfully used in multimedia similarity search applications. However, almost all existing methods employing cross-modal hashing cannot obtain powerful hash codes due to their ignoring the relative similarity between heterogeneous data that contains richer semantic information, leading to unsatisfactory retrieval performance. In this paper, we propose a triplet-based deep hashing (TDH) network for cross-modal retrieval. First, we utilize the triplet labels, which describe the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross-modal instances. We then establish a loss function from the inter-modal view and the intra-modal view to boost the discriminative abilities of the hash codes. Finally, graph regularization is introduced into our proposed TDH method to preserve the original semantic similarity between hash codes in Hamming space. Experimental results show that our proposed method outperforms several state-of-the-art approaches on two popular cross-modal data sets.
Deng, C, Liu, X, Li, C & Tao, D 2018, 'Active multi-kernel domain adaptation for hyperspectral image classification', Pattern Recognition.
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© 2017 Elsevier Ltd. Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness.
Deng, K, Li, Y, Zeng, J, Yuan, M, Luo, J & Yu, JX 2018, 'User Preference Analysis for Most Frequent Peer/Dominator', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Given a set of objects O (e.g., hotels), each can be represented as a point in a multi-dimensional feature space where each dimension corresponds to one attribute of the objects (such as price). Given the preference of a customer, the objects in O not dominated by any other object (i.e., beat in all dimensions) are those worthy to be further considered. Such objects are known as skyline objects in database community. Suppose we have an object o in O. If o is a skyline point, other skyline objects are called peers of o. If o is not a skyline object, it must be dominated by some skyline objects which are called dominators of o. Given a large number of user preferences, an interesting problem is to identify the most frequent peer/dominator (MFP/MFD) of o. The MFP/MFD search has unique values in competitor analysis. However, it is a challenging task because of the complexity to process a large number of user preferences. In this work, we provide robust solutions including exact and approximate methods. The test resutls demonstrate the exact algorithms outperform various baseline algorithms significantly, and the approximate algorithms make further improvement by one order of magnitude with 90%-98% accuracy.
Deng, L., Ngo, H.H., Guo, W., Wang, J. & Zhang, H. 2018, 'Evaluation of a new sponge addition-microbial fuel cell system for removing nutrient from low C/N ratio wastewater', Chemical Engineering Journal, vol. 338, pp. 166-175.
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© 2018 This study developed a new microbial fuel cell (MFC) system (Sponge-MFC), which consisted of a cathodic chamber with an added sponge and two anodic chambers, for low carbon/nitrogen (C/N) wastewater treatment. When operating in the closed-circuit state, the Sponge-MFC(C) demonstrated its superior electrochemical performance compared to the closed-circuit MFC. This superiority took the form of higher coulombic efficiencies, voltage outputs, current densities and power densities. Adding a sponge could reduce the cathode's charge transfer resistance and solution resistance, and improve its capacitance, thus increasing cathodic reaction rate and power outputs. Simultaneous nitrification denitrification (SND) and bioelectrochemical denitrification processes on the cathode coupled with the sponge's SND process were responsible for efficient removal of nitrogen from the Sponge-MFC(C). Fluorescent in situ hybridization (FISH) analysis revealed that nitrifying bacteria and highly diversified denitrifying bacteria were distributed at the cathode's outer layer and inner layer, respectively. Higher phosphorus removal efficiencies (82.06 ± 1.21%) in the Sponge-MFC(C) than that in the MFC(C) (53.97 ± 2.32%) could be ascribed to biological phosphorus removal and precipitation of phosphate salts on the cathode. These results suggested the Sponge-MFC(C) could accomplish better electrochemical behaviors and nutrient removal due to sponge addition when treating wastewater with low C/N ratio.
Deng, WY, Lendasse, A, Ong, YS, Tsang, IWH, Chen, L & Zheng, QH 2018, 'Domain Adaption via Feature Selection on Explicit Feature Map', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rather than using all features. However, invariant features across domains may lie in a higher order space, instead of in the original feature space. Moreover, the discriminative ability of some invariant features such as shared background information is weak, and needs to be further filtered. Therefore, in this paper, we propose a novel domain adaption algorithm based on an explicit feature map and feature selection. The data are first represented by a kernel-induced explicit feature map, such that high-order invariant features can be revealed. Then, by minimizing the marginal distribution difference, conditional distribution difference, and the model error, the invariant discriminative features are effectively selected. This problem is NP-hard to be solved, and we propose to relax it and solve it by a cutting plane algorithm. Experimental results on six real-world benchmarks have demonstrated the effectiveness and efficiency of the proposed algorithm, which outperforms many state-of-the-art domain adaption approaches.
Deng, Z., Chen, J., Zhang, T., Cao, L. & Wang, S. 2018, 'Generalized Hidden-Mapping Minimax Probability Machine for the training and reliability learning of several classical intelligent models', Information Sciences, vol. 436-437, pp. 302-319.
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© 2018 Elsevier Inc. Minimax Probability Machine (MPM) is a binary classifier that optimizes the upper bound of the misclassification probability. This upper bound of the misclassification probability can be used as an explicit indicator to characterize the reliability of the classification model and thus makes the classification model more transparent. However, the existing related work is constrained to linear models or the corresponding nonlinear models by applying the kernel trick. To relax such constraints, we propose the Generalized Hidden-Mapping Minimax Probability Machine (GHM-MPM). GHM-MPM is a generalized MPM. It is capable of training many classical intelligent models, such as feedforward neural networks, fuzzy logic systems, and linear and kernelized linear models for classification tasks, and realizing the reliability learning of these models simultaneously. Since the GHM-MPM, similarly to the classical MPM, was originally developed only for binary classification, it is further extended to multi-class classification by using the obtained reliability indices of the binary classifiers of two arbitrary classes. The experimental results show that GHM-MPM makes the trained models more transparent and reliable than those trained by classical methods.
Ding, C & Tao, D 2018, 'Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 1002-1014.
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© 1979-2012 IEEE. Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low-and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.
Ding, M, Lopez-Perez, D, Mao, G, Lin, Z & Das, SK 2018, 'DNA-GA: A Tractable Approach for Performance Analysis of Uplink Cellular Networks', IEEE Transactions on Communications, vol. 66, no. 1, pp. 355-369.
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© 1972-2012 IEEE. In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution.
Ding, T, Ding, M, Mao, G, Lin, Z, Zomaya, A & Lopez Perez, D 2018, 'Performance Analysis of Dense Small Cell Networks with Dynamic TDD', IEEE Transactions on Vehicular Technology.
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IEEE Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it serves as the predecessor of the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. In this paper, we study the performance of the synchronous dynamic TDD from a media access control (MAC) layer viewpoint, which has been widely adopted in the existing 4th-generation (4G) systems. Furthermore, we analyse the coverage probability and the area spectral efficiency (ASE) in the downlink (DL) and uplink (UL) of dense small cell networks (SCNs) considering the synchronous dynamic TDD transmissions, and the performance impact of dynamic TDD transmissions on the ASE in the DL and UL of dense SCNs is discussed. Moreover, the performance impact of interference cancellation (IC) is also explored. Our analytical results shed new light on the performance of dynamic TDD in future synchronous 5G networks.
Ding, W, Lin, C-T & Cao, Z 2018, 'Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes.', IEEE transactions on cybernetics.
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Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.
Ding, W, Lin, C-T & Pedrycz, W 2018, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE transactions on cybernetics.
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Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.
Ding, W, Lin, CT & Prasad, M 2018, 'Hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection and its application in neonatal cerebral cortex MRI', Expert Systems with Applications, vol. 101, pp. 243-257.
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© 2018 Elsevier Ltd A wide variety of feature selection methods have been developed as promising solutions to find the classification pattern inside increasing applications. But the exploring efficient, flexible and robust feature selection method to handle the rising big data is still an exciting challenge. This paper presents a novel hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection algorithm (CTFGES). It aims to select out the high-quality feature subsets, which can enrich the research of feature selection and classification in the heterogeneous big data. Firstly, we construct a flexible hierarchical co-evolutionary clustering tree model to speed up the process of feature selection, which can effectively extract the features from the parent and children branches of four-layer co-evolutionary clustering tree. Secondly, we design a mixed co-evolutionary game equilibrium scheme with adaptive dynamics to guide parent and children branch subtrees to approach the optimal equilibrium regions, and enable their feature sets to converge stably to the Nash equilibrium. So both noisy heterogeneous features and non-identified redundant ones can be further eliminated. Finally, the extensive experiments on various big datasets are conducted to demonstrate the more excellent performance of CTFGES, in terms of accuracy, efficiency and robustness, compared with the representative feature selection algorithms. In addition, the proposed CTFGES algorithm has been successfully applied into the feature segmentation of large-scale neonatal cerebral cortex MRI with varying noise ratios and intensity non-uniformity levels. The results indicate that it can be adaptive to derive from the cortical folding surfaces and achieves the satisfying consistency with medical experts, which will be potential significance for successfully assessing the impact of aberrant brain growth on the neurodevelopment of neonatal cerebrum.
Ding, W., Lin, C.T., Chen, S., Zhang, X. & Hu, B. 2018, 'Multiagent-consensus-MapReduce-based attribute reduction using co-evolutionary quantum PSO for big data applications', Neurocomputing, vol. 272, pp. 136-153.
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© 2017 Elsevier B.V. The attribute reduction for big data applications has become an urgent challenge in pattern recognition, machine learning and data mining. In this paper, we introduce the multi-agent consensus MapReduce optimization model and co-evolutionary quantum PSO with self-adaptive memeplexes for designing the attribute reduction method, and propose a multiagent-consensus-MapReduce-based attribute reduction algorithm (MCMAR). Firstly, the co-evolutionary quantum PSO with self-adaptive memeplexes is designed for grouping particles into different memeplexes, which aims to explore the search space and locate the global best region during the attribute reduction of big datasets. Secondly, the four layers neighborhood radius framework with compensatory scheme is constructed to partition big attribute sets by exploiting the interdependency among multiple-relevant-attribute sets. Thirdly, a novel multi-agent consensus MapReduce optimization model is adopted to perform the multiple-relevance-attribute reduction, in which five kinds of agents are used to conduct the ensemble co-evolutionary optimization. So the uniform reduction framework of different agents' co-evolutionary game under the bounded rationality is further refined. Fourthly, the approximation MapReduce parallelism mechanism is permitted to formalize to the multi-agent co-evolutionary consensus structure, interaction and adaptation, which enhances different agents to share their solutions. Finally, extensive experimental studies substantiate the effectiveness and accuracy of MCMAR on some well-known benchmark datasets. Moreover, successful applications in big medical datasets are expected to dramatically scaling up MCMAR for complex infant brain MRI in terms of efficiency and feasibility.
Ding, W., Lin, C.T., Prasad, M., Cao, Z. & Wang, J.D. 2018, 'A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue', IEEE Transactions on Fuzzy Systems.
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IEEE The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles & #x0027; states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered co-evolutionary model with multi-agent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependency among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise, compared with representative algorithms. Moreover, LCQ-ABR* can be successfully applied in the consistent segmentation for neonatal brain 3D-MRI, and the consistent segmentation results further demonstrate its stronger applicability.
Dinh, H., Niyato, D., Nguyen, D., Dutkiewicz, E., Wang, P. & Zhu, H. 2018, 'A Dynamic Edge Caching Framework for Mobile 5G Networks', IEEE Wireless Communications.
Do, MH, Ngo, HH, Guo, WS, Liu, Y, Chang, SW, Nguyen, DD, Nghiem, LD & Ni, BJ 2018, 'Challenges in the application of microbial fuel cells to wastewater treatment and energy production: A mini review.', The Science of the total environment, vol. 639, pp. 910-920.
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Wastewater is now considered to be a vital reusable source of water reuse and saving energy. However, current wastewater has multiple limitations such as high energy costs, large quantities of residuals being generated and lacking in potential resources. Recently, great attention has been paid to microbial fuel cells (MFCs) due to their mild operating conditions where a variety of biodegradable substrates can serve as fuel. MFCs can be used in wastewater treatment facilities to break down organic matter, and they have also been analysed for application as a biosensor such as a sensor for biological oxygen which demands monitoring. MFCs represent an innovation technology solution that is simple and rapid. Despite the advantages of this technology, there are still practical barriers to consider including low electricity production, current instability, high internal resistance and costly materials used. Thus, many problems must be overcome and doing this requires a more detailed analysis of energy production, consumption, and application. Currently, real-world applications of MFCs are limited due to their low power density level of only several thousand mW/m2. Efforts are being made to improve the performance and reduce the construction and operating costs of MFCs. This paper explores several aspects of MFCs such as anode, cathode and membrane, and in an effort to overcome the practical challenges of this system.
Dong, F, Lu, J, Zhang, G & Li, K 2018, 'Active fuzzy weighting ensemble for dealing with concept drift', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 438-450.
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© 2018, the Authors. The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles.
Dong, F., Zhang, G., Lu, J. & Li, K. 2018, 'Fuzzy competence model drift detection for data-driven decision support systems', Knowledge-Based Systems, vol. 143, pp. 284-294.
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© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.
Dong, X, Zheng, L, Ma, F, Yang, Y & Meng, D 2018, 'Few-Example Object Detection with Model Communication', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
Dong, X., Dong, J., Zhou, H., Sun, J. & Tao, D. 2018, 'Automatic Chinese Postal Address Block Location Using Proximity Descriptors and Cooperative Profit Random Forests', IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4401-4412.
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© 1982-2012 IEEE. Locating the destination address block is key to automated sorting of mails. Due to the characteristics of Chinese envelopes used in mainland China, we here exploit proximity cues in order to describe the investigated regions on envelopes. We propose two proximity descriptors encoding spatial distributions of the connected components obtained from the binary envelope images. To locate the destination address block, these descriptors are used together with cooperative profit random forests (CPRFs). Experimental results show that the proposed proximity descriptors are superior to two component descriptors, which only exploit the shape characteristics of the individual components, and the CPRF classifier produces higher recall values than seven state-of-the-art classifiers. These promising results are due to the fact that the proposed descriptors encode the proximity characteristics of the binary envelope images, and the CPRF classifier uses an effective tree node split approach.
Dong, Y, Fatahi, B, Khabbaz, H & Zhang, H 2018, 'Influence of particle contact models on soil response of poorly graded sand during cavity expansion in discrete element simulation', Journal of Rock Mechanics and Geotechnical Engineering.
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The discrete element method (DEM) has been extensively adopted to investigate many complex geotechnical related problems due to its capability to incorporate the discontinuous nature of granular materials. In particular, when simulating large deformations or distortion of soil (e.g. cavity expansion), DEM can be very effective as other numerical solutions may experience convergence problems. Cavity expansion theory has widespread applications in geotechnical engineering, particularly in problems concerning in situ testing, pile installation and so forth, explaining why cavity expansion simulation using DEM has been adopted in this study. In addition, the behaviour of geomaterials in a macro-level is utterly determined by microscopic properties, highlighting the importance of contact models. Despite the fact that there are numerous contact models proposed to mimic the realistic behaviour of granular materials, there are lack of studies on the effects of these contact models on the soil response. Hence, in this study, a series of three-dimensional numerical simulations with different contact constitutive models was conducted to simulate the response of sandy soils during cylindrical cavity expansion. In this numerical investigation, three contact models, i.e. linear contact model, rolling resistance contact model, and Hertz contact model, are considered. It should be noted that the former two models are linear based models, providing linearly elastic and frictional plasticity behaviours, whereas the latter one consists of nonlinear formulation based on an approximation of the theory of Mindlin and Deresiewicz. To examine the effects of these contact models, several cylindrical cavities were created and expanded gradually from an initial radius of 0.055 m to a final radius of 0.1 m. The numerical predictions confirm that the calibrated contact models produced similar results regarding the variations of cavity pressure, radial stress, deviatoric stress, volumetric st...
Dorji, P, Choi, J, Kim, DI, Phuntsho, S, Hong, S & Shon, HK 2018, 'Membrane capacitive deionisation as an alternative to the 2nd pass for seawater reverse osmosis desalination plant for bromide removal', Desalination, vol. 433, pp. 113-119.
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© 2018 Elsevier B.V. Most Australian surface and ground waters have relatively high concentration of bromide between 400 and 8000 μg/L and even higher concentration in seawater between 60,000–78,000 μg/L. Although bromide is not regulated, even at low concentrations of 50–100 μg/L, it can lead to the formation of several types of harmful disinfection by-products (DBPs) during the disinfection process. One of the major concerns with brominated DBPs is the formation of bromate (BrO3−), a serious carcinogen that is formed when water containing a high concentration of bromide is disinfected. As a result, bromate is highly regulated in Australian water standards with the maximum concentration of 20 μg/L in the drinking water. Since seawater reverse osmosis (SWRO) desalination plays an important role in augmenting fresh water supplies in Australia, SWRO plants in Australia usually adopt 2nd pass brackish water reverse osmosis (BWRO) for effective bromide removal, which is not only energy-intensive to operate but also has higher capital cost. In this study, we evaluated the feasibility of membrane capacitive deionisation (MCDI) as one of the alternatives to the 2nd pass BWRO for effective bromide removal in a more energy efficient way.
Du, B, Wang, S, Xu, C, Wang, N, Zhang, L & Tao, D 2018, 'Multi-Task Learning for Blind Source Separation', IEEE Transactions on Image Processing, vol. 27, no. 9, pp. 4219-4231.
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© 1992-2012 IEEE. Blind source separation (BSS) aims to discover the underlying source signals from a set of linear mixture signals without any prior information of the mixing system, which is a fundamental problem in signal and image processing field. Most of the state-of-the-art algorithms have independently handled the decompositions of mixture signals. In this paper, we propose a new algorithm named multi-task sparse model to solve the BSS problem. Source signals are characterized via sparse techniques. Meanwhile, we regard the decomposition of each mixture signal as a task and employ the idea of multi-task learning to discover connections between tasks for the accuracy improvement of the source signal separation. Theoretical analyses on the optimization convergence and sample complexity of the proposed algorithm are provided. Experimental results based on extensive synthetic and real-world data demonstrate the necessity of exploiting connections between mixture signals and the effectiveness of the proposed algorithm.
Du, B., Tang, X., Wang, Z., Zhang, L. & Tao, D. 2018, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion', IEEE Transactions on Cybernetics.
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IEEE Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
Du, X, Yin, H, Huang, Z, Yang, Y & Zhou, X 2018, 'Exploiting detected visual objects for frame-level video filtering', World Wide Web, pp. 1-26.
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© 2017 Springer Science+Business Media, LLC Videos are generated at an unprecedented speed on the web. To improve the efficiency of access, developing new ways to filter the videos becomes a popular research topic. One on-going direction is using visual objects to perform frame-level video filtering. Under this direction, existing works create the unique object table and the occurrence table to maintain the connections between videos and objects. However, the creation process is not scalable and dynamic because it heavily depends on human labeling. To improve this, we propose to use detected visual objects to create these two tables for frame-level video filtering. Our study begins with investigating the existing object detection techniques. After that, we find object detection lacks the identification and connection abilities to accomplish the creation process alone. To supply these abilities, we further investigate three candidates, namely, recognizing-based, matching-based and tracking-based methods, to work with the object detection. Through analyzing the mechanism and evaluating the accuracy, we find that they are imperfect for identifying or connecting the visual objects. Accordingly, we propose a novel hybrid method that combines the matching-based and tracking-based methods to overcome the limitations. Our experiments show that the proposed method achieves higher accuracy and efficiency than the candidate methods. The subsequent analysis shows that the proposed method can efficiently support the frame-level video filtering using visual objects.
Duan, N, Xu, W, Feng, H, Wang, S, Guo, Z, Li, Y, Wang, S & Zhu, J 2018, 'A Scalar Hysteresis Model of Ferromagnetic Materials Based on the Elemental Operators', IEEE Transactions on Magnetics.
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IEEE This paper introduces an elemental operator with biaxial anisotropy to simulate the scalar hysteresis phenomenon of the ferromagnetic materials. The equilibrium position of the magnetization for the elemental operator can be determined by energy minimization. To directly describe the magnetic properties of each operator, an improved analytical expression is deduced by the partial approximate substitutions. Moreover, this approach utilizes the concept of distribution function density in order to consider the interaction field and the coercive force of the elemental operators. To verify the presented model, the magnetic hysteresis of two different magnetic materials under alternating excitations is measured by the magnetic property measurement system and calculated by this elemental operator method, respectively. The comparisons suggest that this elemental operator is effective and can be a useful tool to simulate the scalar magnetic properties of ferromagnetic materials.
Duan, N., Xu, W., Wang, S. & Zhu, J. 2018, 'Current Distribution Calculation of Superconducting Layer in HTS Cable Considering Magnetic Hysteresis by Using XFEM', IEEE Transactions on Magnetics, vol. 54, no. 3.
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© 2017 IEEE. This paper presents a coupled field-circuit analysis method for the high-temperature superconducting (HTS) cable considering magnetic hysteresis by using the improved extended finite-element method (XFEM). The quasi-3-D cylindrical coordinate HTS cable model is first proposed based on the shell element theory. The quasi-3-D meshing elements are used instead of the traditional 3-D meshing elements to overcome the difficulties in meshing. A new Preisach type hysteresis model of HTS tapes is first combined with the improved XFEM to determine the magnetic hysteresis inductance. A magnetic field-circuit coupled program for current analysis of superconducting layers is coded. The numerical simulation results of this field-circuit coupled method are reported compared with the experimental test results for the case of an HTS cable with two layers.
Duan, P., Zhang, C., Mao, G. & Zhang, B. 2018, 'Applying Distributed Constraint Optimization Approach to the User Association Problem in Heterogeneous Networks', IEEE Transactions on Cybernetics, vol. 48, no. 6, pp. 1696-1707.
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© 2013 IEEE. User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter η which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when η =3. Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.
Duong, HC, Álvarez, IRC, Nguyen, TV & Nghiem, LD 2018, 'Membrane distillation to regenerate different liquid desiccant solutions for air conditioning', Desalination, vol. 443, pp. 137-142.
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© 2018 The capacity of membrane distillation (MD) to regenerate three commonly used liquid desiccant solutions (i.e. CaCl2, LiCl, and a mixture of CaCl2/LiCl) for liquid desiccant air-conditioners (LDAC) was evaluated. The results demonstrate considerable impact of the concentration polarisation effect on the process water flux during MD regeneration of these three desiccant solutions. For each of these liquid desiccant solutions, the experimentally measured water flux of the MD process was about half of the calculated value using the process mass transfer coefficient (Km) obtained during the process characterisation without taking into account the concentration polarisation effect. The observed deviation between the experimentally measured and calculated process water flux indicates the need to include the concentration polarisation effect in the model for calculating water flux. Although Ca2+concentration in the CaCl2and CaCl2/LiCl liquid desiccant solutions exceeded the solubility limit for CaCO3, membrane scaling was not observed. Nevertheless, there was evidence that membrane fouling might occur during extended MD regeneration of liquid desiccant solutions containing CaCl2.
Duong, HC, Chuai, D, Woo, YC, Shon, HK, Nghiem, LD & Sencadas, V 2018, 'A novel electrospun, hydrophobic, and elastomeric styrene-butadiene-styrene membrane for membrane distillation applications', Journal of Membrane Science, vol. 549, pp. 420-427.
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© 2017 In this study, a novel hydrophobic, microporous membrane was fabricated from styrene-butadiene-styrene (SBS) polymer using electrospinning and evaluated for membrane distillation applications. Compared to a commercially available polytetrafluoroethylene (PTFE) membrane, the SBS membrane had larger membrane pore size and fiber diameter and comparable membrane porosity. The fabricated SBS showed slightly lower water flux than the PTFE membrane because it was two times thicker. However, the SBS membrane had better salt rejection and most importantly could be fabricated via a simple process. The SBS membrane was also more hydrophobic than the reference PTFE membrane. In particular, as temperature of the reference water liquid increased to 60 °C, the SBS membrane remained hydrophobic with a contact angle of 100° whereas the PTFE became hydrophilic with a contact angle of less than 90°. The hydrophobic membrane surface prevented the intrusion of liquid into the membrane pores, thus improving the salt rejection of the SBS membrane. In addition, the SBS membrane had superior mechanical strength over the PTFE membrane. Using the SBS membrane, stable water flux was achieved throughout an extended MD operation period of 120 h to produce excellent quality distillate (over 99.7% salt rejection) from seawater.
Durán Santomil, P., Otero González, L., Martorell Cunill, O. & Merigó Lindahl, J.M. 2018, 'Backtesting an equity risk model under Solvency II', Journal of Business Research, vol. 89, pp. 216-222.
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© 2018 Elsevier Inc. Backtesting is a technique for validating internal models under Solvency II, which allows for evaluating the discrepancies between the results provided by a model and real observations. This paper aims to establish various backtesting tests and to show their applications to equity risk in Solvency II. Normal and empirical models with a rolling window are used to determine VaR at the 99.5% confidence level over a one-year time horizon. The proposed methodology performs the backtesting of annualized returns arising from the accumulation of daily returns. The results show that even if a model is conservative when tested out of a sample, it may be inadequate when evaluated in a sample, thereby highlighting the problems inherent in the out-of-sample backtesting proposed by the regulator.
D'Urso, G, Smith, SL, Mettu, R, Oksanen, T & Fitch, R 2018, 'Multi-vehicle refill scheduling with queueing', Computers and Electronics in Agriculture, vol. 144, pp. 44-57.
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© 2017 We consider the problem of refill scheduling for a team of vehicles or robots that must contend for access to a single physical location for refilling. The objective is to minimise time spent in travelling to/from the refill station, and also time lost to queuing (waiting for access). In this paper, we present principled results for this problem in the context of agricultural operations. We first establish that the problem is NP-hard and prove that the maximum number of vehicles that can usefully work together is bounded. We then focus on the design of practical algorithms and present two solutions. The first is an exact algorithm based on dynamic programming that is suitable for small problem instances. The second is an approximate anytime algorithm based on the branch and bound approach that is suitable for large problem instances with many robots. We present simulated results of our algorithms for three classes of agricultural work that cover a range of operations: spot spraying, broadcast spraying and slurry application. We show that the algorithm is reasonably robust to inaccurate prediction of resource utilisation rate, which is difficult to estimate in cases such as spot application of herbicide for weed control, and validate its performance in simulation using realistic scenarios with up to 30 robots.
Eager, D 2018, 'Playground equipment falls', Journal of Paediatrics and Child Health, vol. 54, no. 2018, pp. 705-705.
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Eager, D. & Hayati, H. 2018, 'Additional injury prevention criteria for impact attenuation surfacing within children’s playgrounds', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering.
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Eckert, P.R., Flores Filho, A.F., Perondi, E.A. & Dorrell, D.G. 2018, 'Dual quasi-halbach linear tubular actuator with coreless moving-coil for semiactive and active suspension', IEEE Transactions on Industrial Electronics, vol. 65, no. 12, pp. 9873-9883.
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© 1982-2012 IEEE. This paper proposes the application of a specific topology of coreless linear tubular actuator for semiactive and active suspension systems. The actuator consists of two quasi-Halbach arrays of permanent magnets, inner and outer, and a moving-coil armature. Some of the advantages of this actuator for the proposed applications include low moving mass, low ripple of force, reduced magnetic losses, and high force density. The paper also introduces a new method for determining actuator requirements to operate with an active and/or semiactive suspension system and presents the application of the proposed approach to a harmonically base excited single degree-of-freedom suspension system. Additionally, the electromagnetic model for magnetic field distribution, open-circuit induced voltage, and axial force and force ripple was developed and validated by means of finite-element analysis and with experimental results obtained from a prototype.
Eeshwarasinghe, D, Loganathan, P, Kalaruban, M, Sounthararajah, DP, Kandasamy, J & Vigneswaran, S 2018, 'Removing polycyclic aromatic hydrocarbons from water using granular activated carbon: kinetic and equilibrium adsorption studies', Environmental Science and Pollution Research, vol. 25, no. 14, pp. 13511-13524.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Polycyclic aromatic hydrocarbons (PAHs) constitute a group of highly persistent, toxic and widespread environmental micropollutants that are increasingly found in water. A study was conducted in removing five PAHs, specifically naphthalene, acenaphthylene, acenaphthene, fluorene and phenanthrene, from water by adsorption onto granular activated carbon (GAC). The pseudo-first-order (PFO) model satisfactorily described the kinetics of adsorption of the PAHs. The Weber and Morris diffusion model’s fit to the data showed that there were faster and slower rates of intra-particle diffusion probably into the mesopores and micropores of the GAC, respectively. These rates were negatively related to the molar volumes of the PAHs. Batch equilibrium adsorption data fitted well to the Langmuir, Freundlich and Dubinin–Radushkevich models, of which the Freundlich model exhibited the best fit. The adsorption affinities were related to the hydrophobicity of the PAHs as determined by the log Kowvalues. Free energies of adsorption calculated from the Dubinin–Radushkevich model and the satisfactory kinetic data fitting to the PFO model suggested physical adsorption of the PAHs. Adsorption of naphthalene, acenaphthylene and acenaphthene in fixed-bed columns containing a mixture of GAC (0.5 g) + sand (24.5 g) was satisfactorily simulated by the Thomas model.
El-Sayed, H, Sankar, S, Daraghmi, YA, Tiwari, P, Rattagan, E, Mohanty, M, Puthal, D & Prasad, M 2018, 'Accurate traffic flow prediction in heterogeneous vehicular networks in an intelligent transport system using a supervised non-parametric classifier', Sensors (Switzerland), vol. 18, no. 6.
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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.
El-Sayed, H, Sankar, S, Prasad, M, Puthal, D, Gupta, A, Mohanty, M & Lin, CT 2018, 'Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment', IEEE Access, vol. 6, pp. 1706-1717.
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© 2013 IEEE. A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network de vices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.
Erfani, S. & Abedin, B. 2018, 'Impacts of the use of social network sites on users' psychological well‐being: A systematic review', Journal of the Association for Information Science and Technology.
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Erkmen, R.E. & Gottgens, B. 2018, 'A Shell Element for Buckling Analysis of Thin-Walled Composite-Laminated Members', International Journal of Structural Stability and Dynamics, vol. 18, no. 2.
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© 2018 World Scientific Publishing Company. This paper introduces a new shell element formulation and investigates the buckling behavior of thin-walled beams composed of fiber-reinforced polymer composite-laminates, which is a primary design concern for thin-walled beams composed of fiber-reinforced polymer composite-laminates due to their slenderness. Although global buckling behavior can be captured using beam-column type two-node simple finite element formulations, shell-type more sophisticated elements are needed in order to be able to capture the effects due to cross-sectional deformations. Pursuit of an efficient shell element formulation continues to date and in this study, a new flat rectangular shell element formulation is developed for the buckling analysis of thin-walled composite-laminated members. The plate component of the shell is locking-free and based on the twist-Kirchhoff theory. For the membrane component of the shell element, variational formulation employing drilling degrees of freedom is adopted. Convergence studies were presented to illustrate the numerical performances of the element. A broad class of problems including distortional as well as global buckling cases were solved and compared with solutions from the literature to validate the use of the developed shell element for the buckling analysis of thin-walled composite-laminated members.
Eskandari, M, Li, L & Moradi, MH 2018, 'Decentralized Optimal Servo Control System for Implementing Instantaneous Reactive Power Sharing in Microgrids', IEEE Transactions on Sustainable Energy, pp. 1-1.
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Esmaili, N., Norman, B. & Rajgopal, J. 2018, 'Shelf-space optimization models in decentralized automated dispensing cabinets', Operations Research for Health Care.
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Fahmideh, M & Beydoun, G 2018, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering.
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© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected ...
Fahmideh, M & Beydoun, G 2018, 'Reusing empirical knowledge during cloud computing adoption', Journal of Systems and Software, vol. 138, pp. 124-157.
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© 2017 Elsevier Inc. Moving existing legacy systems to cloud platforms is an ever popular option. But, such endeavour may not be hazard-free and demands a proper understanding of requirements and risks involved prior to taking any action. The time is indeed ripe to undertake a realistic view of what migrating legacy systems to cloud may offer, an understanding of exceptional situations causing system quality goal failure in such a transition, and insights on countermeasures. The cloud migration body of knowledge, although is useful, is dispersed over the current literature. It is hard for busy practitioners to digest, synthesize, and harness this body of knowledge into practice when integrating legacy systems with cloud services. We address this issue by creating an innovative synergy between the approaches evidence-based software engineering and goal-oriented modelling. We develop an evidential repository of commonly occurred obstacles and platform agnostic resolution tactics related to cloud enablement of legacy systems. The repository is further utilized during systematic goal-obstacle elaboration of given cloud migration scenarios. The applicability of our proposed framework is also demonstrated.
Fan, M, Zhang, X, Du, L, Chen, L & Tao, D 2018, 'Semi-Supervised Learning Through Label Propagation on Geodesics', IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1486-1499.
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© 2013 IEEE. Graph-based semi-supervised learning (SSL) has attracted great attention over the past decade. However, there are still several open problems in this paper, including: 1) how to construct an effective graph over data with complex distribution and 2) how to define and effectively use pair-wise similarity for robust label propagation. In this paper, we utilize a simple and effective graph construction method to construct the graph over data lying on multiple data manifolds. The method can guarantee the c onnectiveness between pair-wise data points. Then, the global pair-wise data similarity is naturally characterized by geodesic distance-based joint probability, where the geodesic distance is approximated by the graph distance. The new data similarity is much more effective than previous Euclidean distance-based similarities. To apply data structure for robust label propagation, Kullback-Leibler divergence is utilized to measure the inconsistency between the input pair-wise similarity and the output similarity. In order to further consider intraclass and interclass variances, a novel regularization term on sample-wise margins is introduced to the objective function. This enables the proposed method fully utilizes the input data structure and the label information for classification. An efficient optimization method and the convergence analysis have been proposed for our problem. Besides, out-of-sample extension is discussed and addressed. Comparisons with the state-of-the-art SSL methods on image classification tasks have been presented to show the effectiveness of the proposed method.
Fan, X, He, X, Xiang, C, Puthal, D, Gong, L, Nanda, P & Fang, G 2018, 'Towards System Implementation and Data Analysis for Crowdsensing BasedOutdoor RSS Maps', IEEE Access.
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With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., WiFi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed,
this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS)
paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users
could cause inherent noises and blank spots in collected dataset. In this paper, we study (1) how to tame the sensing noises from heterogenous mobile devices, and (2) how to construct accurate and complete RSS
maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called iMap to collect RSS measurements with heterogeneous mobile devices. Second, through observing
experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate
system and polar coordinate system, respectively.
Fan, X., Walker, P.D. & Wang, Q. 2018, 'Modeling and simulation of longitudinal dynamics coupled with clutch engagement dynamics for ground vehicles', Multibody System Dynamics, vol. 43, no. 2, pp. 153-174.
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© 2017, The Author(s). During the engagement of the dry clutch in automotive transmissions, clutch judder may occur. Vehicle suspension and engine mounts couple the torsional and longitudinal models, leading to oscillations of the vehicle body that are perceived by the driver as poor driving quality. This paper presents an effective formulation for the modeling and simulation of longitudinal dynamics and powertrain torsional dynamics of the vehicle based on non-smooth dynamics of multibody systems. In doing so friction forces between wheels and the road surface are modeled along with friction torque in the clutch using Coulomb’s friction law. First, bilateral constraint equations of the system are derived in Cartesian coordinates and the dynamical equations of the system are developed using the Lagrange multiplier technique. Complementary formulations are proposed to determine the state transitions from stick to slip between wheels and road surface and from the clutch. An event-driven scheme is used to represent state transition problem, which is solved as a linear complementarity problem (LCP), with Baumgarte’s stabilization method applied to reduce constraint drift. Finally, the numerical results demonstrate that the modeling technique is effective in simulating the vehicle dynamics. Using this method stick-slip transitions between driving wheel and the road surface and from the clutch, as a form of clutch judder, are demonstrated to occur periodically for certain values of the parameters of input torque from engine, and static and dynamic friction characteristics of tire/ground contact patch and clutch discs.
Fang, J., Sun, G., Qiu, N., Pang, T., Li, S. & Li, Q. 2018, 'On hierarchical honeycombs under out-of-plane crushing', International Journal of Solids and Structures, vol. 135, pp. 1-13.
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© 2017 Hierarchy has been introduced to honeycomb structures in pursuing ultralight materials with outstanding mechanical properties. Nevertheless, the hierarchical honeycombs under the out-of-plane loads have not been well studied experimentally and analytically for energy absorption to date. This study aimed to apply a special structural hierarchy to the honeycomb by replacing the sides of hexagons with smaller hexagons. The quasi-static test of the hierarchical honeycomb specimen was first conducted experimentally to investigate the crushing behaviours; and then the corresponding finite element (FE) analyses were performed. Finally, the analytical solutions to the mean crushing force and plateau stress were derived based on the simplified super folding element (SSFE) method. It was shown that the experimental data and numerical results agreed well in terms of crushing force versus displacement relation and energy absorption characteristics; and the analytical results were validated by the experimental test. Importantly, the hierarchy could improve the energy absorption; and the increase in the order and number of replacement hexagons could excavate the advantage even further. Specifically, the second order honeycomb characterized by five smaller replacement hexagons at each order can yield a plateau stress 2.63 and 4.16 times higher than the regular honeycomb and the aluminium foam, respectively. While it might lead to global bending, structural hierarchy provides new architectural configurations for developing novel ultralight materials with exceptional energy absorption capacity under out-of-plane loads.
Fanos, A.M., Pradhan, B., Mansor, S., Yusoff, Z.M. & Abdullah, A.F.B. 2018, 'A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data', Landslides, pp. 1-18.
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© 2018 Springer-Verlag GmbH Germany, part of Springer Nature The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.
Far, C. & Far, H. 2018, 'Improving Energy Efficiency of Existing Residential Buildings Using Effective Thermal Retrofit of Building Envelope', Indoor and Built Environment.
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Far, H. 2018, 'Advanced Computation Methods for Soil Structure Interaction Analysis of Structures Resting on Soft Soils', International Journal of Geotechnical Engineering.
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Adopting the most accurate and realistic modelling technique and computation method for treatment of dynamic soil-structure interaction effects in seismic analysis and design of structures resting on soft soil deposits is one of the most discussed and challenging issues in the field of seismic design and re-qualification of different structures. In this study, a comprehensive critical review has been carried out on available and well-known modelling techniques and computation methods for dynamic soil-structure interaction analysis. Discussing and comparing the advantages and disadvantages of employing each method, in this study, the most precise and reliable modelling technique as well as computation method have been identified and proposed to be employed in studying dynamic soil-structure interaction analysis of structures resting on soft soil deposits.
Farrok, O, Islam, MR, Guo, Y, Zhu, J & Xu, W 2018, 'A Novel Design Procedure for Designing Linear Generators', IEEE Transactions on Industrial Electronics.
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IEEE A number of research works have been carried out based on the optimization of linear generators for harvesting oceanic wave energy, but no significant method of shape optimization for determination of the optimal shape of linear generator has been found. Moreover, inclusion of some parameters, such as shape of linear generator & #x0027;s translator and stator, has been considered out of the scope of the conventional, adaptive or knowledge based genetic algorithm. This paper proposes a novel method through which any type of linear generator & #x0027;s shape can be optimized graphically. A mathematical model of the proposed method including human intervened genetic algorithm is presented. The proposed method has been applied to a direct-drive system based linear generator where the maximization of electrical power output and minimization of steel core volume have been considered as the optimization objectives. The optimization parameters have been further optimized graphically within functional volumetric and electromagnetic constraints to achieve improved design solutions. The proposed method has included comprehensive geometric dimensions, magnetic and electrical parameters. Finally, the shape of steel cores of the translator and special m-shaped stator of the linear generator is determined and simulated using the copper wire. This optimized shape of the linear generator is capable of satisfying the multi-objectives of maximal electrical power generation and reduction of its size. The ANSYS/Ansoft software has been used to create the platform for analyses of the whole system.
Farrok, O, Islam, MR, Sheikh, MRI, Guo, Y & Zhu, J 2018, 'A Split Translator Secondary Stator Permanent Magnet Linear Generator for Oceanic Wave Energy Conversion', IEEE Transactions on Industrial Electronics, vol. 65, no. 9, pp. 7600-7608.
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IEEE Almost all flux switching permanent magnet linear generators (FSPMLGs) and Vernier hybrid machines contain a heavy solid translator due to their design limitations for electricity generation from the oceanic waves. This paper presents the new design of a FSPMLG in which the translator weight is reduced and an additional static steel core is inserted inside the translator cavity to improve the magnetic flux linkage of the main stator. The generated voltage, current, power, efficiency, core loss, force ripples and cogging force minimization of the proposed FSPMLG are presented. From the dynamic model of the oceanic wave, it is shown that the translator with lower mass could generate electricity more effectively. The special stator and translator sets have been optimized by using the genetic algorithm before they are used in the proposed FSPMLG. To analyze the performance and verify the feasibility of the new design of FSPMLG, the finite element analysis is performed by using the commercial software package ANSYS/Ansoft.
Farrok, O, Islam, MR, Sheikh, MRI, Guo, Y, Zhu, J & Lei, G 2018, 'Oceanic Wave Energy Conversion by a Novel Permanent Magnet Linear Generator Capable of Preventing Demagnetization', IEEE Transactions on Industry Applications.
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IEEE In the conventional permanent magnet linear generators (PMLGs) used for oceanic wave energy conversion system, demagnetization could cause everlasting degradation in electrical power generation. This paper presents a new design that can be applied to various PMLGs to avoid demagnetization. To check the effectiveness of the proposed technique, a PMLG is considered which allows both the fixed and variable length of air gaps for analysis. The finite element analysis is used by using the software package ANSYS/Ansoft to simulate the testing PMLG for two conditions: with and without using the proposed technique. Different parameters and characteristics of the PMLG under both conditions are presented in detail. Both the simulation and test results show that the proposed design is able to avoid the demagnetization problem successfully.
Fatahi, B., Van Nguyen, Q., Xu, R. & Sun, W.J. 2018, 'Three-dimensional response of neighboring buildings sitting on pile foundations to seismic pounding', International Journal of Geomechanics, vol. 18, no. 4.
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© 2018 American Society of Civil Engineers. Seismic pounding occurs when the separation gap between buildings and structures is not wide enough, particularly during major earthquake events; this can cause them to collide, causing local damage or, in extreme cases, collapse. This study investigated the impact that this separation gap has on the seismic response of midrise buildings supported on piles while considering seismic soil-pile-structure interaction (SSPSI). To achieve this aim, three 15-story reinforced concrete buildings sitting on pile foundations and with five different separation gaps under excitations from the 1994 Northridge and 1995 Kobe earthquakes were numerically simulated. This study used three-dimensional numerical modeling to simultaneously capture the effects of seismic pounding and SSPSI. Because the considered structure, pile foundation, and soil deposit are three-dimensional in nature, the adopted three-dimensional numerical modeling can provide a more realistic simulation to capture the seismic behavior of the system. The nonlinear behavior of structural elements was included, and the dynamic soil properties were obtained from field data and backbone curves. A contact pair interface with small-sliding surface-to-surface formulation between buildings was used to capture possible seismic pounding, and contact interfaces with a finite-sliding formulation were used to simulate the interaction between the piles and the soil. The results, including lateral building deflections, interstory drifts, structural shear forces, foundation rocking, lateral pile deflections, and the distributions of bending moments and shear forces of the piles, are presented and discussed. The findings of this study will give engineers a better insight into the possible effects of seismic pounding on the seismic performance of buildings, and the response of endbearing piles in soft soils.
Feng, X, Chang, L, Lin, X, Qin, L, Zhang, W & Yuan, L 2018, 'Distributed computing connected components with linear communication cost', Distributed and Parallel Databases, vol. 36, no. 3, pp. 555-592.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The paper studies three fundamental problems in graph analytics, computing connected components (CCs), biconnected components (BCCs), and 2-edge-connected components (ECCs) of a graph. With the recent advent of big data, developing efficient distributed algorithms for computing CCs, BCCs and ECCs of a big graph has received increasing interests. As with the existing research efforts, we focus on the Pregel programming model, while the techniques may be extended to other programming models including MapReduce and Spark. The state-of-the-art techniques for computing CCs and BCCs in Pregel incur O(m× # supersteps) total costs for both data communication and computation, where m is the number of edges in a graph and #supersteps is the number of supersteps. Since the network communication speed is usually much slower than the computation speed, communication costs are the dominant costs of the total running time in the existing techniques. In this paper, we propose a new paradigm based on graph decomposition to compute CCs and BCCs with O(m) total communication cost. The total computation costs of our techniques are also smaller than that of the existing techniques in practice, though theoretically almost the same. Moreover, we also study distributed computing ECCs. We are the first to study this problem and an approach with O(m) total communication cost is proposed. Comprehensive empirical studies demonstrate that our approaches can outperform the existing techniques by one order of magnitude regarding the total running time.
Feng, X, Wan, W, Xu, RYD, Perry, S, Li, P & Zhu, S 2018, 'A novel spatial pooling method for 3D mesh quality assessment based on percentile weighting strategy', Computers & Graphics, vol. 74, pp. 12-22.
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Feng, X.I.A.N.G., Wan, W., Richard Yi Da Xu, Chen, H., Li, P. & Sánchez, J.A. 2018, 'A perceptual quality metric for 3D triangle meshes based on spatial pooling', Frontiers of Computer Science.
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Fitch, R, Isler, V, Tokekar, P & Scaramuzza, D 2018, 'Guest editorial: Special issue on active perception', Autonomous Robots, vol. 42, no. 2, pp. 175-176.
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Forouzesh, M, Shen, Y, Yari, K, Siwakoti, YP & Blaabjerg, F 2018, 'High-Efficiency High Step-Up DC-DC Converter with Dual Coupled Inductors for Grid-Connected Photovoltaic Systems', IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 5967-5982.
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© 1986-2012 IEEE. This paper introduces a non-isolated high step-up DC-DC converter with dual coupled inductors suitable for distributed generation applications. By implementing an input parallel connection, the proposed DC-DC structure inherits shared input current with low ripple, which also requires small capacitive filter at its input. Moreover, this topology can reach high voltage gain by using dual coupled inductors in series connection at the output stage. The proposed converter uses active clamp circuits with a shared clamp capacitor for the main switches. In addition to the active clamp circuit, the leakage energy is recycled to the output by using an integrated regenerative snubber. Indeed, these circuits allow soft-switching conditions, i.e., zero voltage switching and zero current switching for active and passive switching devices, respectively. The mentioned features along with a common ground connection of the input and output make the proposed topology a proper candidate for transformer-less grid-connected photovoltaic systems. The operating performance, analysis and mathematical derivations of the proposed DC-DC converter have been demonstrated in the paper. Moreover, the main features of the proposed converter have been verified through experimental results of a 1-kW laboratory prototype.
Fu, H, Gong, M, Wang, C & Tao, D 2018, 'MoE-SPNet: A mixture-of-experts scene parsing network', Pattern Recognition, vol. 84, pp. 226-236.
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© 2018 Elsevier Ltd Scene parsing is an indispensable component in understanding the semantics within a scene. Traditional methods rely on handcrafted local features and probabilistic graphical models to incorporate local and global cues. Recently, methods based on fully convolutional neural networks have achieved new records on scene parsing. An important strategy common to these methods is the aggregation of hierarchical features yielded by a deep convolutional neural network. However, typical algorithms usually aggregate hierarchical convolutional features via concatenation or linear combination, which cannot sufficiently exploit the diversities of contextual information in multi-scale features and the spatial inhomogeneity of a scene. In this paper, we propose a mixture-of-experts scene parsing network (MoE-SPNet) that incorporates a convolutional mixture-of-experts layer to assess the importance of features from different levels and at different spatial locations. In addition, we propose a variant of mixture-of-experts called the adaptive hierarchical feature aggregation (AHFA) mechanism which can be incorporated into existing scene parsing networks that use skip-connections to fuse features layer-wisely. In the proposed networks, different levels of features at each spatial location are adaptively re-weighted according to the local structure and surrounding contextual information before aggregation. We demonstrate the effectiveness of the proposed methods on two scene parsing datasets including PASCAL VOC 2012 and SceneParse150 based on two kinds of baseline models FCN-8s and DeepLab-ASPP.
Fu, Q., Luo, Y., Wen, Y., Tao, D., Li, Y. & Duan, L.Y. 2018, 'Toward Intelligent Product Retrieval for TV-to-Online (T2O) Application: A Transfer Metric Learning Approach', IEEE Transactions on Multimedia, vol. 20, no. 8, pp. 2114-2125.
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© 1999-2012 IEEE. It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high-precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This TML approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains.
Fu, Y., Li, C., Luan, T.H., Zhang, Y. & Mao, G. 2018, 'Infrastructure-cooperative algorithm for effective intersection collision avoidance', Transportation Research Part C: Emerging Technologies, vol. 89, pp. 188-204.
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© 2018 Elsevier Ltd To guarantee the road safety by avoiding collisions at the intersections is one of the major tasks of intelligent transportation systems (ITSs), which contributes to the minimal fatalities and property loss in crashes. This paper proposes an effective algorithm for infrastructure-cooperative intersection accident pre-warning system with the aid of vehicular communications. The proposed algorithm realizes accurate and efficient collision avoidances through five steps, i.e., defining variable, reasoning the vehicles evolution state, verifying safe driving behavior, assessing risk, and making decision. The critical factors are theoretically analyzed, and a vehicle state evolution model based on the Dynamic Bayesian Networks (DBNs) is established. The efficient risk assessment method based on identifying the dangerous driving behavior at intersection and different collision avoidance strategies are proposed according to the actual situation. Finally, extensive simulations are carried out to verify the performance of the proposal, and simulation results show that the proposed algorithm can effectively detect risk and accurately migrate the collision.
Fujioka, T, Hoang, AT, Okuda, T, Takeuchi, H, Tanaka, H & Nghiem, LD 2018, 'Water reclamation using a ceramic nanofiltration membrane and surface flushing with ozonated water', International Journal of Environmental Research and Public Health, vol. 15, no. 4.
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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. A new membrane fouling control technique using ozonated water flushing was evaluated for direct nanofiltration (NF) of secondary wastewater effluent using a ceramic NF membrane. Experiments were conducted at a permeate flux of 44 L/m2h to evaluate the ozonated water flushing technique for fouling mitigation. Surface flushing with clean water did not effectively remove foulants from the NF membrane. In contrast, surface flushing with ozonated water (4 mg/L dissolved ozone) could effectively remove most foulants to restore the membrane permeability. This surface flushing technique using ozonated water was able to limit the progression of fouling to 35% in transmembrane pressure increase over five filtration cycles. Results from this study also heighten the need for further development of ceramic NF membrane to ensure adequate removal of pharmaceuticals and personal care products (PPCPs) for water recycling applications. The ceramic NF membrane used in this study showed approximately 40% TOC rejection, and the rejection of PPCPs was generally low and highly variable. It is expected that the fouling mitigation technique developed here is even more important for ceramic NF membranes with smaller pore size and thus better PPCP rejection.
Fujioka, T, O'Rourke, BE, Michishio, K, Kobayashi, Y, Oshima, N, Kodamatani, H, Shintani, T & Nghiem, LD 2018, 'Transport of small and neutral solutes through reverse osmosis membranes: Role of skin layer conformation of the polyamide film', Journal of Membrane Science, vol. 554, pp. 301-308.
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© 2018 Elsevier B.V. The polyamide skin layer of reverse osmosis (RO) membranes was characterised using advanced and complementary analytical techniques to investigate the mechanisms underlying the permeation of contaminants of emerging concern in potable water reuse – N-nitrosodimethylamine (NDMA) and N-nitrosomethylethylamine (NMEA). This study used five RO membrane samples with similar membrane properties. The five RO membrane samples spanned over a large range of water permeance (0.9–5.8 L/m2h bar) as well as permeation of NDMA (9–66%) and NMEA (3–29%). Despite these differences among the five RO membranes, characterisations of the skin layer using positron annihilation lifetime spectroscopy, atomic force microscopy and field emission scanning electron microscopy revealed almost no variation in their free-volume hole-radius (0.270–0.275 nm), effective surface area (198–212%) and thickness (30–35 nm) of the skin layer. The results suggest that there could be other RO skin layer properties, such as the interconnectivity of the protuberances within the polyamide skin layer additional to the free-volume hole-size and thickness of the skin layer, which can also govern water and solute permeation.
Galvao, J.R., Biffe Di Renzo, A., Esber Schaphauser, P., Dutra, G., Dreyer, U.J., Kalinowski, A., Canning, J., Zamarreno, C.R., Cardozo Da Silva, J.C. & Martelli, C. 2018, 'Optical fiber bragg grating instrumentation applied to horse gait detection', IEEE Sensors Journal, vol. 18, no. 14, pp. 5778-5785.
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© 2001-2012 IEEE. This paper presents two in vivo instrumentation techniques to study the different types of gait of horses performing athletics using fiber Bragg gratings (FBG). These techniques can be used as an auxiliary tool in the early diagnosis of injuries related to the horse's locomotor system, mainly in the distal portion of the digit, one of the most common causes of retirement when they are athletes. Therefore, the first technique presented consists of the fixation of FBGs without encapsulation, directly on the dorsal wall of the hoof in each of the limbs. In the second technique presented, the FBG sensor is encapsulated in a prototype developed using a composite material reinforced with carbon fiber in a horseshoe shape. The second technique is associated with digital image processing (DIP) for better visualization of the deformation and compression forces that act upon the limbs. The first method showed sensitivity to detection of the digit compression against the ground, being able to identify walking patterns. The second technique, with the encapsulated sensor elements, also allows the capture of characteristic signals of gait, such as step walk, trot, and gallop under training conditions. Both, FBG sensor interrogation and DIP, analysis techniques have proven good performance and promising results for the clinical and biomechanical study and medical evaluations of horses even during dynamic training and competitions.
Ganesan, B, Luximon, A, Al-Jumaily, AA, Yip, J, Gibbons, PJ & Chivers, A 2018, 'Developing a Three-Dimensional (3D) Assessment Method for Clubfoot-A Study Protocol.', Frontiers in Physiology, vol. 8, pp. 1098-1098.
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Background: Congenital talipes equinovarus (CTEV) or clubfoot is a common pediatric congenital foot deformity that occurs 1 in 1,000 live births. Clubfoot is characterized by four types of foot deformities: hindfoot equinus; midfoot cavus; forefoot adductus; and hindfoot varus. A structured assessment method for clubfoot is essential for quantifying the initial severity of clubfoot deformity and recording the progress of clubfoot intervention. Aim: This study aims to develop a three-dimensional (3D) assessment method to evaluate the initial severity of the clubfoot and monitor the structural changes of the clubfoot after each casting intervention. In addition, this study explores the relationship between the thermophysiological changes in the clubfoot at each stage of the casting intervention and in the normal foot. Methods: In this study, a total of 10 clubfoot children who are <2 years old will be recruited. Also, the data of the unaffected feet of a total of 10 children with unilateral clubfoot will be obtained as a reference for normal feet. A Kinect 3D scanner will be used to collect the 3D images of the clubfoot and normal foot, and an Infrared thermography camera (IRT camera) will be used to collect the thermal images of the clubfoot. Three-dimensional scanning and IR imaging will be performed on the foot once a week before casting. In total, 6-8 scanning sessions will be performed for each child participant. The following parameters will be calculated as outcome measures to predict, monitor, and quantify the severity of the clubfoot: Angles cross section parameters, such as length, width, and the radial distance; distance between selected anatomical landmarks, and skin temperature of the clubfoot and normal foot. The skin temperature will be collected on selected areas (forefoot, mid foot, and hindfoot) to find out the relationship between the thermophysiological changes in the clubfoot at each stage of the casting treatment and in the normal foot. Ethics...
Gao, H, Li, Y, Wang, S, Zhu, J, Yang, Q, Zhang, C & Li, J 2018, 'Losses analysis of soft magnetic ring core under sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) excitations', AIP Advances, vol. 8, no. 5.
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© 2018 Author(s). Practical core losses in electrical machines differ significantly from those experimental results using the standardized measurement method, i.e. Epstein Frame method. In order to obtain a better approximation of the losses in an electrical machine, a simulation method considering sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) waveforms is proposed. The influence of the pulse width modulation (PWM) parameters on the harmonic components in SPWM and SVPWM is discussed by fast Fourier transform (FFT). Three-level SPWM and SVPWM are analyzed and compared both by simulation and experiment. The core losses of several ring samples magnetized by SPWM, SVPWM and sinusoidal alternating current (AC) are obtained. In addition, the temperature rise of the samples under SPWM, sinusoidal excitation are analyzed and compared.
Gao, L, Chen, J, Liu, Y, Yamauchi, Y, Huang, Z & Kong, X 2018, 'Revealing the chemistry of an anode-passivating electrolyte salt for high rate and stable sodium metal batteries', Journal of Materials Chemistry A, vol. 6, no. 25, pp. 12012-12017.
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© The Royal Society of Chemistry 2018. Stabilizing the reactive metal anode is a critical challenge for the development of next-generation alkali metal batteries. This work demonstrates that sodium-difluoro(oxalato)borate (NaDFOB)-based carbonate-ester electrolytes have excellent compatibility with the anode in sodium metal batteries, enabling high rate performance and long cycle life. The NaDFOB-based electrolytes possess favourable electrochemical stability and effectively passivate the Na metal anode by forming a compact, robust and conductive solid-electrolyte interphase (SEI) layer. Quantitative nuclear magnetic resonance (NMR) measurements of electrolyte solvents indicated that the SEI layer forms mainly in the initial cycles, and it prevents further solvent degradation. Solid-state NMR and X-ray photoelectron spectroscopy studies revealed the chemical composition of the NaDFOB-derived SEI film, which includes a mixed phase consisting primarily of sodium diborate, tetrafluoroborate and carbonate. The formation of such a composite SEI rich in borate and tetrafluoroborate provides robust structural and chemical stability, and facilitates fast ion transport for uniform Na stripping/plating.
Gärdenfors, P. 2018, 'Levels of communication and lexical semantics', Synthese, pp. 1-21.
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The meanings of words are not permanent but change over time. Some changes of meaning are quick, such as when a pronoun changes its reference; some are slower, as when two speakers find out that they are using the same word in different senses; and some are very slow, such as when the meaning of a word changes over historical time. A theory of semantics should account for these different time scales. In order to describe these different types of meaning changes, I present an analysis of three levels of communication: instruction, coordination of common ground and coordination of meaning. My first aim is to show that these levels must be considered when discussing lexical semantics. A second aim is to use the levels to identify the communicative roles of some of the main word classes, in particular nouns, adjectives, verbs, indexicals and quantifiers. I argue that the existence of word classes can, to a large extent, be explained by the communicative needs that arise on the different levels. © 2014 Springer Science+Business Media Dordrecht.
Gardner, A.P. & Willey, K. 2018, 'Academic identity reconstruction: the transition of engineering academics to engineering education researchers', Studies in Higher Education, vol. 43, no. 2, pp. 234-250.
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The field of research (FoR) that an academic participates in is both a manifestation of, and a contributor to the development of their identity. When an academic changes that FoR the question then arises as to how they reconcile this change with their identity. This paper uses the identity-trajectory framework to analyse the discourse of 19 engineering academics in relation to their educational research. The findings reveal insights into the identity changes experienced in the transition from typical engineering academic to engineering education researcher. Participants’ responses illustrate how various aspects of their research activities contribute to the development of the networking and intellectual strands of their academic identity as engineering education researchers, and the effect of their university environment on this development. Conference participation was found to be an important contributor to progression of the intellectual and networking strands of identity-trajectory for researchers at all stages of development, although for different reasons.
Gardner, AP & Willey, K 2018, 'The role of peer review in identity development for engineering education researchers', European Journal of Engineering Education.
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Gatsios, E., García-Rincón, J., Rayner, J.L., McLaughlan, R.G. & Davis, G.B. 2018, 'LNAPL transmissivity as a remediation metric in complex sites under water table fluctuations.', Journal of environmental management, vol. 215, pp. 40-48.
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Water table fluctuations affect the recoverability of light non-aqueous phase liquid (LNAPL) petroleum hydrocarbons. LNAPL transmissivity (Tn) is being applied as an improved metric for LNAPL recoverability. In this paper, the applicability of Tn as a lagging and leading metric in unconsolidated aquifers under variable water table conditions was investigated. Tn values obtained through baildown testing and recovery data-based methods (skimming) were compared in three areas of a heterogeneous gasoline contaminated site in Western Australia. High-resolution characterisation methods were applied to account for differences in the stratigraphic profile and LNAPL distribution. The results showed a range of Tn from 0 m2/day to 2.13 m2/day, exhibiting a strong spatial and temporal variability. Additionally, observations indicated that Tn reductions may be more affected by the potentiometric surface elevation (Zaw) than by the application of mass recovery technologies. These observations reflected limitations of Tn as a lagging metric and a Remedial Endpoint. On the other hand, the consistency and accuracy of Tn as a leading metric was affected by the subsurface conditions. For instance, the area with a larger vertical LNAPL distribution and higher LNAPL saturations found Tn to be less sensitive to changes in Zaw than the other two areas during the skimming trials. Tn values from baildown and skimming tests were generally in a close agreement (less than a factor of 2 difference), although higher discrepancies (by a factor up to 7.3) were found, probably linked to a preferential migration pathway and Zaw. Under stable Zaw, Tn was found to be a relatively reliable metric. However, variable water table conditions affected Tn and caution should be exercised in such scenarios. Consequently, remediation practitioners, researchers and regulators should account for the nexus between Tn, LNAPL distribution, geological setting and temporal effects for a more efficient and sustainab...
Gaviria-Marin, M., Merigo, J.M. & Popa, S. 2018, 'Twenty years of the Journal of Knowledge Management: a bibliometric analysis', Journal of Knowledge Management.
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© 2018, Emerald Publishing Limited. Purpose: In 2017, the Journal of Knowledge Management (JKM) celebrates its 20th anniversary. This study aims to show an updated analysis of their publications to provide a general overview of the journal, focusing on a bibliometric analysis of its publications between 1997 and 2016. Design/methodology/approach: The methodology involves two procedures: a performance analysis and a science mapping analysis of JKM. The performance analysis uses a series of bibliometric indicators such as h-index, productivity and citations. This analysis considers different dimensions, including papers, authors, universities and countries. VOSviewer software is used to carry out the mapping of science of JKM, which, based on the concurrence of key words and co-citation points of view, seeks to graphically analyze the structure of the references of this journal. Findings: There is a positive evolution in the number of publications (although with certain oscillations), which shows a growing interest in publishing in JKM. The USA and the UK lead the publications in this journal, although at a regional level, Europe is the most productive. The low participation of emerging economies in JKM is also observed. Practical implications: The paper will identify the leading trends in the journal in terms of papers, authors, institutions, countries, journals and keywords. This study is useful for obtaining a quick snapshot of what is happening in the journal. Originality/value: From the historical record of JKM publications, this study presents an exclusive bibliometric analysis of its publications until 2016 and identifies its main trends.
Ge, X, Tian, X, Qiu, Y, Mao, G & Han, T 2018, 'Small Cell Networks with Fractal Coverage Characteristics', IEEE Transactions on Communications.
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IEEE To meet massive wireless traffic demand in the future fifth generation (5G) cellular networks, small cell networks are emerging as an attractive solution for 5G network deployments. The cellular coverage characteristic is a key issue for the deployment of small cell networks. Considering the anisotropic path loss in wireless channels of real cellular scenarios, in this paper the fractal coverage characteristic is first used to evaluate the performance of small cell networks. Moreover, the coverage probability, average achievable rate and the area spectral efficiency are derived for fractal small cell networks. Compared with the average achievable rate and area spectral efficiency with isotropic path loss models, the average achievable rate and area spectral efficiency with anisotropic path loss models has been underestimated in fractal small cell networks. Considering the impact of the anisotropic path loss on wireless channels, most of performances of wireless cellular networks need to be re-evaluated. This paper provides a tractable method to investigate the performance of small cell networks with fractal coverage characteristics.
Ghabrial, A, Franklin, D & Zaidi, H 2018, 'A Monte Carlo simulation study of the impact of novel scintillation crystals on performance characteristics of PET scanners.', Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), vol. 50, pp. 37-45.
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The purpose of this study is to validate a Monte Carlo simulation model for the clinical Siemens Biograph mCT PET scanner using the GATE simulation toolkit, and to evaluate the performance of six different scintillation materials in this model using the National Electrical Manufactures Association (NEMA) NU 2-2007 protocol.A model of the Biograph mCT PET detection system and its geometry was developed. NEMA NU 2-2007 phantoms were also modelled. The accuracy of the developed scanner model was validated through a comparison of the simulation results from GATE, SimSET and PeneloPET toolkits, and experimental data obtained using the NEMA NU 2-2007 protocols. The evaluated performance metrics included count rate performance, spatial resolution, sensitivity, and scatter fraction (SF). Thereafter, the mCT PET scanner was simulated with six different candidate high-performance scintillation materials, including LSO, LaBr3, CeBr3, LuAP, GLuGAG and LFS-3, and its performance evaluated according to the NEMA NU 2-2007 specifications.The Monte Carlo simulation model demonstrates good agreement with the experimental data and results from other simulation packages. For instance, the scatter fraction calculated using GATE simulation is 34.35% while the experimentally measured value is 33.2%, 38.48% for the SimSET, and 34.8% for the PeneloPET toolkit. The best-performing scintillation materials were found to be LuAP, LSO and LFS-3, while GLuGAG offers acceptable performance if cost is the dominant concern.The main performance characteristics of the Biograph mCT PET scanner can be simulated accurately using GATE with a good agreement with other Monte Carlo simulation packages and experimental measurements. Newly developed scintillators show promise and offer alternative options for the design of novel generation PET scanners.
Ghaffari Jadidi, M., Valls Miro, J. & Dissanayake, G. 2018, 'Gaussian processes autonomous mapping and exploration for range-sensing mobile robots', Autonomous Robots, vol. 42, no. 2, pp. 273-290.
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© 2017, Springer Science+Business Media, LLC. Most of the existing robotic exploration schemes use occupancy grid representations and geometric targets known as frontiers. The occupancy grid representation relies on the assumption of independence between grid cells and ignores structural correlations present in the environment. We develop a Gaussian processes (GPs) occupancy mapping technique that is computationally tractable for online map building due to its incremental formulation and provides a continuous model of uncertainty over the map spatial coordinates. The standard way to represent geometric frontiers extracted from occupancy maps is to assign binary values to each grid cell. We extend this notion to novel probabilistic frontier maps computed efficiently using the gradient of the GP occupancy map. We also propose a mutual information-based greedy exploration technique built on that representation that takes into account all possible future observations. A major advantage of high-dimensional map inference is the fact that such techniques require fewer observations, leading to a faster map entropy reduction during exploration for map building scenarios. Evaluations using the publicly available datasets show the effectiveness of the proposed framework for robotic mapping and exploration tasks.
Ghasemi, K, Pradhan, B & Jena, R 2018, 'Spatial Identification of Key Alteration Minerals Using ASTER and Landsat 8 Data in a Heavily Vegetated Tropical Area', Journal of the Indian Society of Remote Sensing, vol. 46, no. 7, pp. 1061-1073.
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© 2018, Indian Society of Remote Sensing. The Central Gold Belt (CGB) of Malaysia is a major host to gold deposits. Penjom, Raub, Selising and Buffalo reef are major gold mines in CGB. The study area, Selinsing gold mine, is located at the northwest of Pahang province on the lineament known as the Raub Bentong Suture. Presence of dense vegetation and cloud cover in tropical regions are main obstacles in alteration mapping using satellite imageries. In this study, Landsat 8 and ASTER level 1B images were used to map clay minerals and quartz rich zones at Selinsing gold mine and surrounding areas. Direct principal component analysis (DPCA), matched filtering (MF) and band ratio were the effective methods used in this study. High concentration of clay minerals was detected using band ratio 6/7, DPC2 and MF and ratio 14/12 was carried out to highlight quartz rich zones. The results of image processing methods were verified by in situ inspection and X-ray diffraction analyses. The results show that, in spite of limited bedrock exposure, the known gold prospects and potential areas of mineralization can be recognized by the methods employed in this study.
Ghasemi, M, Ghavidel, S, Aghaei, J, Akbari, E & Li, L 2018, 'CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems', International Transactions on Electrical Energy Systems, vol. 28, no. 5.
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Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems.
Ghavidel Jirsaraie, S, Azizivahed, A & Li, L 2018, 'A hybrid Jaya Algorithm for Reliability–Redundancy Allocation Problems', Engineering Optimization, vol. 50, pp. 698-715.
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This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.
Gheisari, S, Catchpoole, DR, Charlton, A, Melegh, Z, Gradhand, E & Kennedy, PJ 2018, 'Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding.', Diagnostics (Basel, Switzerland), vol. 8, no. 3.
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Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.
Ghorbani, S., Eyni, H., Tiraihi, T., Salari Asl, L., Soleimani, M., Atashi, A., Pour Beiranvand, S. & Ebrahimi Warkiani, M. 2018, 'Combined effects of 3D bone marrow stem cell-seeded wet-electrospun poly lactic acid scaffolds on full-thickness skin wound healing', International Journal of Polymeric Materials and Polymeric Biomaterials, pp. 1-8.
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© 2017 Taylor & Francis Tissue engineering has emerged as an alternative treatment to traditional grafts for skin wound healing. Three-dimensional nanofibers have been used extensively for this purpose due to their excellent biomedical-related properties. In this study, high porous 3D poly lactic acid nanofibrous scaffolds (PLA-S) were prepared by wet-electrospinning technique and seeded with rat bone-marrow stem cells (BMSCs) to characterize the biocompatibility and therapeutic efficacy of these fibers on the treating full-thickness dermal wounds. The results of in vitro andin vivo studies indicate that the 3D fibrous PLA-S can be a potential wound dressing for wound repair, particularly when seeded with BMSCs.
Gill, A.Q., Henderson-Sellers, B. & Niazi, M. 2018, 'Scaling for agility: A reference model for hybrid traditional-agile software development methodologies', Information Systems Frontiers: a journal of research and innovation, vol. 20, no. 2, pp. 315-341.
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The adoption of agility at a large scale often requires the integration of agile and non-agile development elements for architecting a hybrid adaptive methodology. The challenge is ”which elements or components (agile or non-agile) are relevant to develop the context-aware hybrid adaptive methodology reference architecture?” This paper addresses this important challenge and develops a hybrid adaptive methodology reference architecture model using a qualitative constructive empirical research approach. In this way, we have uncovered the agility, abstraction, business value, business policy, rules, legal, context and facility elements or components that have not been explicitly modelled or discussed in International Standards (IS) such as the ISO/IEC 24744 metamodel. It is anticipated that a context-aware hybrid adaptive methodology can be architected by using the proposed context-aware hybrid adaptive methodology reference architecture elements for a particular situation when using a situational method engineering approach.
Glynn, PD, Voinov, AA, Shapiro, CD & White, PA 2018, 'Response to Comment by Walker et al. on “From Data to Decisions: Processing Information, Biases, and Beliefs for Improved Management of Natural Resources and Environments”', Earth's Future, vol. 6, no. 5, pp. 762-769.
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©2018. The Authors. Our different kinds of minds and types of thinking affect the ways we decide, take action, and cooperate (or not). The comment by Walker et al. (2018, https://doi.org/10.1002/2017EF000750) illustrates several points made by Glynn et al. (2017, https://doi.org/10.1002/2016EF000487) and many other articles. Namely, biases and beliefs often drive scientific reasoning, and scientists, just like other humans, are intimately attached to their values and heuristics. Scientists, just like many other people, also tend to read and interpret text in ways that best match their individual perceptions of a problem or issue: in many cases paraphrasing and changing the meaning of what they read to better match their initial ideas. Walker et al. are doing interesting and important research on uncertainty. Nonetheless, they misinterpret the work, assumptions, and conclusions brought forth by Glynn et al. (2017, https://doi.org/10.1002/2016EF000487).
Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2018, 'A review of neural networks in plant disease detection using hyperspectral data', Information Processing in Agriculture, vol. 5, no. 3, pp. 354-371.
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© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data.
Golkarian, A, Naghibi, SA, Kalantar, B & Pradhan, B 2018, 'Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.', Environmental monitoring and assessment, vol. 190, no. 3, p. 149.
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Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
Gong, C, Liu, T, Tang, Y, Yang, J, Yang, J & Tao, D 2018, 'A Regularization Approach for Instance-Based Superset Label Learning.', IEEE Transactions on Cybernetics, vol. 48, no. 3, pp. 967-978.
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Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.
Gong, C, Tao, D, Chang, X & Yang, J 2018, 'Ensemble Teaching for Hybrid Label Propagation', IEEE Transactions on Cybernetics.
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IEEE Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot consistently yield satisfactory performance due to two reasons: one is the instability of single propagation method in dealing with various practical data, and the other one is the improper propagation sequence ignoring the labeling difficulties of different examples. To remedy above defects, this paper proposes a novel propagation algorithm called hybrid diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates multiple propagation methods as base ''learners'' to fully exploit their individual wisdom, which helps HyDEnT to be stable and obtain consistent encouraging results. More importantly, HyDEnT conducts propagation under the guidance of an ensemble of ''teachers''. That is to say, in every propagation round the simplest curriculum examples are wisely designated by a teaching algorithm, so that their labels can be reliably and accurately decided by the learners. To optimally choose these simplest examples, every teacher in the ensemble should comprehensively consider the examples' difficulties from its own viewpoint, as well as the common knowledge shared by all the teachers. This is accomplished by a designed optimization problem, which can be efficiently solved via the block coordinate descent method. Thanks to the efforts of the teachers, all the unlabeled examples are logically propagated from simple to difficult, leading to better propagation quality of HyDEnT than the existing methods. Experiments on six popular datasets reveal that HyDEnT achieves the highest classification accuracy when compared with six state-of-the-art propagation methodologies such as harmonic functions, Fick's law assisted propagation, linear neighborhood propagation, semisupervised ensemble learning, bipartite graph-based consensus maximization, and teaching-to-learn and learnin...
Govindan, K, Cheng, TCE, Mishra, N & Shukla, N 2018, 'Big data analytics and application for logistics and supply chain management', Transportation Research Part E: Logistics and Transportation Review, vol. 114, pp. 343-349.
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Gracia, L, Solanes, JE, Muñoz-Benavent, P, Esparza, A, Valls Miro, J & Tornero, J 2018, 'Cooperative transport tasks with robots using adaptive non-conventional sliding mode control', Control Engineering Practice, vol. 78, pp. 35-55.
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© 2018 Elsevier Ltd This work presents a hybrid position/force control of robots aimed at handling applications using multi-task and sliding mode ideas. The proposed robot control is based on a novel adaptive non-conventional sliding mode control used to robustly satisfy a set of inequality constraints defined to accomplish the cooperative transport task. In particular, these constraints are used to guarantee the reference parameters imposed by the task (e.g., keeping the load at a desired orientation) and to guide the robot using the human operator's forces detected by a force sensor located at the robot tool. Another feature of the proposal is the multi-layered nature of the strategy, where a set of four tasks are defined with different priorities. The effectiveness of the proposed adaptive non-conventional sliding mode control is illustrated by simulation results. Furthermore, the applicability and feasibility of the proposed robot control for transport tasks are substantiated by experimental results using a redundant 7R manipulator.
Gracia, L., Solanes, J.E., Muñoz-Benavent, P., Valls Miro, J., Perez-Vidal, C. & Tornero, J. 2018, 'Adaptive Sliding Mode Control for Robotic Surface Treatment Using Force Feedback', Mechatronics, vol. 52, pp. 102-118.
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© 2018 Elsevier Ltd This work presents a hybrid position-force control of robots in order to apply surface treatments such as polishing, grinding, finishing, deburring, etc. The robot force control is designed using sliding mode concepts to benefit from robustness. In particular, the sliding mode force task is defined using equality constraints to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. In order to deal with sudden changes in material stiffness, which are ultimately transferred to the polishing tool and can produce instability and compromise polishing performance, several adaptive switching gain laws are considered and compared. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. Hence, deviations from the reference trajectory are allowed if such deviations are required to satisfy the constraints mentioned above. Finally, a third-level task is also considered for the case of redundant robots in order to use the remaining degrees of freedom to keep the manipulator close to the home configuration with safety in mind. The main advantages of the method are increased robustness and low computational cost. The applicability and effectiveness of the proposed approach are substantiated by experimental results using a redundant 7R manipulator: the Rethink Robotics Sawyer collaborative robot.
Graham, C., Smith, W., Moncur, W. & van den Hoven, E. 2018, 'Introduction: Mortality in design', Design Issues, vol. 34, no. 1, pp. 3-14.
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Gray, S, Voinov, A, Paolisso, M, Jordan, R, Bendor, T, Bommel, P, Glynn, P, Hedelin, B, Hubacek, K, Introne, J, Kolagani, N, Laursen, B, Prell, C, Schmitt Olabisi, L, Singer, A, Sterling, E & Zellner, M 2018, 'Purpose, processes, partnerships, and products: Four Ps to advance participatory socio-environmental modeling: Four', Ecological Applications, vol. 28, no. 1, pp. 46-61.
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© 2017 by the Ecological Society of America. Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.
Gu, K, Tao, D, Qiao, JF & Lin, W 2018, 'Learning a No-Reference Quality Assessment Model of Enhanced Images with Big Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1301-1313.
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© 2012 IEEE. In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.
Gu, Y, Gu, M, Long, Y, Xu, G, Yang, Z, Zhou, J & Qu, W 2018, 'An enhanced short text categorization model with deep abundant representation', World Wide Web, pp. 1-15.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature Short text categorization is a crucial issue to many applications, e.g., Information Retrieval, Question-Answering System, MRI Database Construction and so forth. Many researches focus on data sparsity and ambiguity issues in short text categorization. To tackle these issues, we propose a novel short text categorization strategy based on abundant representation, which utilizes Bi-directional Recurrent Neural Network(Bi-RNN) with Long Short-Term Memory(LSTM) and topic model to catch more contextual and semantic information. Bi-RNN enriches contextual information, and topic model discovers more latent semantic information for abundant text representation of short text. Experimental results demonstrate that the proposed model is comparable to state-of-the-art neural network models and method proposed is effective.
Guan, J, Feng, Y & Ying, M 2018, 'Decomposition of quantum Markov chains and its applications', Journal of Computer and System Sciences, vol. 95, pp. 55-68.
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© 2018 Elsevier Inc. Markov chains have been widely employed as a fundamental model in the studies of probabilistic and stochastic communicating and concurrent systems. It is well-understood that decomposition techniques play a key role in reachability analysis and model-checking of Markov chains. (Discrete-time) quantum Markov chains have been introduced as a model of quantum communicating systems [1] and also a semantic model of quantum programs [2]. The BSCC (Bottom Strongly Connected Component) and stationary coherence decompositions of quantum Markov chains were introduced in [3–5]. This paper presents a new decomposition technique, namely periodic decomposition, for quantum Markov chains. We further establish a limit theorem for them. As an application, an algorithm to find a maximum dimensional noiseless subsystem of a quantum communicating system is given using decomposition techniques of quantum Markov chains.
Guan, N, Liu, T, Zhang, Y, Tao, D & Davis, LS 2018, 'Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated Cauchy loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order < formula > < tex > $O(\sqrt{\ln{n}/n})$ < /tex > < /formula > , where < formula > < tex > $n$ < /tex > < /formula > is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.
Gui, J, Liu, T, Sun, Z, Tao, D & Tan, T 2018, 'Fast Supervised Discrete Hashing', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 2, pp. 490-496.
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© 2017 IEEE. Learning-based hashing algorithms are 'hot topics' because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called 'fast supervised discrete hashing' (FSDH) based on 'supervised discrete hashing' (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.
Guo, F., Wang, W., Shen, J., Shao, L., Yang, J., Tao, D. & Tang, Y.Y. 2018, 'Video Saliency Detection Using Object Proposals', IEEE Transactions on Cybernetics.
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IEEE In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms.
Guo, J, Yang, T, Yuan, J & Zhang, A 2018, 'A Novel Linear Physical-layer Network Coding Scheme for Y-channel without Transmitter CSI', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Guo, J., Yuan, J. & Zhang, J. 2018, 'An Achievable Throughput Scaling Law of Wireless Device-to-Device Caching Networks with Distributed MIMO and Hierarchical Cooperations', IEEE Transactions on Wireless Communications, vol. 17, no. 1, pp. 492-505.
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© 2002-2012 IEEE. In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of n nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations, respectively. The distributed MIMO is applied to the first stage between source nodes and neighbors of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbors. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with n, with a vanishing outage probability. Furthermore, we derive an explicit expression of the optimal throughput as a function of system parameters, such as pathloss factor under a target outage probability. Analytical and numerical results demonstrate that our proposed scheme outperforms existing ones when the local cache size is limited.
Guo, K, Liu, L, Xu, X, Xu, D & Tao, D 2018, 'GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy', IEEE Transactions on Neural Networks and Learning Systems.
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GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deliver a solution to the maximum correntropy criterion (MCC)-based low-rank decomposition problem. Experimental results show that GoDec+ is efficient and robust to different corruptions including Gaussian noise, Laplacian noise, salt & pepper noise, and occlusion on both synthetic and real vision data. We further apply GoDec+ to more general applications including classification and subspace clustering. For classification, we construct an ensemble subspace from the low-rank GoDec+ matrix and introduce an MCC-based classifier. For subspace clustering, we utilize GoDec+ values low-rank matrix for MCC-based self-expression and combine it with spectral clustering. Face recognition, motion segmentation, and face clustering experiments show that the proposed methods are effective and robust. In particular, we achieve the state-of-the-art performance on the Hopkins 155 data set and the first 10 subjects of extended Yale B for subspace clustering.
Guo, Q, Zhang, Y, Celler, BG & Su, SW 2018, 'State-Constrained Control of Single-Rod Electrohydraulic Actuator With Parametric Uncertainty and Load Disturbance', IEEE Transactions on Control Systems Technology.
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IEEE The suppression of disturbances under parametric uncertainties is one of the most common control problems in electrohydraulic systems, as both disturbances and uncertainties often significantly degrade the tracking performance and bias the load pressure of the electrohydraulic actuator (EHA). This brief presents a state-constrained control of single-rod EHA to restrict the position tracking error to a prescribed accuracy and guarantee the load pressure in the maximal power boundary. Furthermore, a dynamic surface is designed to avoid the explosion of complexity due to the repeatedly calculated differentiations of the virtual control variables in the backstepping iteration. Integrating with a disturbance observer and the parametric estimation law, this state-constrained controller guarantees the asymptotic convergence of system state error under parametric uncertainties and large load disturbances. The effectiveness of the proposed controller has been demonstrated by a comparative experiment on the motion control of the two-degree-of-freedom robotic arm.
Guo, T., Pan, S., Zhu, X. & Zhang, C. 2018, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Guo, X, Yu, JX, Li, RH & Miao, X 2018, 'Direction-based multiple views on data', World Wide Web, pp. 1-35.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature In this paper, we propose the direction heat maps to visualize the spreads of points w.r.t. different observers in the Euclidean space. It is a new way to look points. Traditionally, the spread of points is unique because it depends on the absolute coordinates of points w.r.t. the zero point. However, considering the relative directions of the points, the spread varies if the observers are different. To visualize the multiple views on the same set of points, we design a pie-shaped heat map. On a direction range the more points are, on the heat map the darker the corresponding sector is. Supporting by the heat map, we can visually know whether the points spread over evenly around or the points scatter only in small direction ranges. The heat map can be widely used in many decision-making applications where the tasks are direction sensitive. Typically, in bicycle sharing applications, the heat map can tell a user the general spread of the available bikes around, and the user may choose to walk on the deep coloured direction to meet more bikes. To generate the heat map, we propose the direction profile to store the direction intervals associated with the number of points in the intervals. We design an algorithm to build the direction profile incrementally. To measure a spread, we define the direction-based centrality which is a statistic value to reflect the uniform degree of the spread. The lower the centrality is, the more evenly the points spread. The observer with the minimum centrality is a direction-based center, while the observer with the maximum centrality is a direction-based edge. We design an algorithm to calculate the centralities. To find the center faster, we also design an algorithm to approximate the centralities. The experimental results show that the heat map can visualize different spreads effectively and the direction-based centralities can be calculated efficiently.
Guo, YJ, Qin, PY, Chen, SL, Lin, W & Ziolkowski, RW 2018, 'Advances in Reconfigurable Antenna Systems Facilitated by Innovative Technologies', IEEE Access, vol. 6, pp. 5780-5794.
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© 2013 IEEE. Future fifth generation (5G) wireless platforms will require reconfigurable antenna systems to meet their performance requirements in compact, light-weight, and cost-effective packages. Recent advances in reconfigurable radiating and receiving structures have been enabled by a variety of innovative technology solutions. Examples of reconfigurable partially reflective surface antennas, reconfigurable filtennas, reconfigurable Huygens dipole antennas, and reconfigurable feeding network-enabled antennas are presented and discussed. They represent novel classes of frequency, pattern, polarization, and beam-direction reconfigurable systems realized by the innovative combinations of radiating structures and circuit components.
Guo, YJ, Xu, KD, Liu, Y & Tang, X 2018, 'Novel Surface Plasmon Polariton Waveguides with Enhanced Field Confinement for Microwave-Frequency Ultra-Wideband Bandpass Filters', IEEE Access, vol. 6, pp. 10249-10256.
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© 2013 IEEE. In this paper, a novel planar waveguide based on spoof surface plasmon polaritons (SSPPs) using fish-bone corrugated slot structure is first proposed in the microwave region. Low-dispersion band can be realized by such structure with tight field confinement of SSPPs, resulting in size miniaturization of the proposed waveguide. The high frequency stopband of the proposed ultra-wideband bandpass filter (BPF) is created by using this proposed waveguide, while the low frequency stopband is properly designed through introducing the microstrip-to-slotline transition. The 2-D E-fields distribution, surface current flow, and energy flow patterns are all calculated and illustrated to demonstrate the electromagnetic (EM) characteristics of the proposed ultra-wideband BPF. The BPF tuning characteristics is explored to provide a guideline for facilitating the design process. To validate the predicted performance, the proposed filter is finally designed, fabricated, and measured. Measured results illustrate high performance of the filter, in which the reflection coefficient is better than -10 dB from 2.1 to 8 GHz with the smallest insertion loss of 0.37 dB at 4.9 GHz, showing good agreement with numerical simulations. The proposed surface plasmon polariton waveguides are believed to be significantly promising for further developing plasmonic functional devices and integrated 2-D circuits with enhanced confinement of SSPPs in microwave and even terahertz bands.
Han, B., Pan, Y. & Tsang, I.W. 2018, 'Robust Plackett–Luce model for k-ary crowdsourced preferences', Machine Learning, vol. 107, no. 4, pp. 675-702.
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© 2017, The Author(s). The aggregation of k-ary preferences is an emerging ranking problem, which plays an important role in several aspects of our daily life, such as ordinal peer grading and online product recommendation. At the same time, crowdsourcing has become a trendy way to provide a plethora of k-ary preferences for this ranking problem, due to convenient platforms and low costs. However, k-ary preferences from crowdsourced workers are often noisy, which inevitably degenerates the performance of traditional aggregation models. To address this challenge, in this paper, we present a RObust PlAckett–Luce (ROPAL) model. Specifically, to ensure the robustness, ROPAL integrates the Plackett–Luce model with a denoising vector. Based on the Kendall-tau distance, this vector corrects k-ary crowdsourced preferences with a certain probability. In addition, we propose an online Bayesian inference to make ROPAL scalable to large-scale preferences. We conduct comprehensive experiments on simulated and real-world datasets. Empirical results on “massive synthetic” and “real-world” datasets show that ROPAL with online Bayesian inference achieves substantial improvements in robustness and noisy worker detection over current approaches.
Han, B., Tsang, I.W., Chen, L., Yu, C.P. & Fung, S.F. 2018, 'Progressive Stochastic Learning for Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from ``easy'' tasks to ``complex'' tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from ``reliable'' labels to ``noisy'' labels. To realize POSTAL mechanism, we design a cluster of ``screening losses,'' which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.
Han, F, Wei, D, Ngo, HH, Guo, W, Xu, W, Du, B & Wei, Q 2018, 'Performance, microbial community and fluorescent characteristic of microbial products in a solid-phase denitrification biofilm reactor for WWTP effluent treatment', Journal of Environmental Management, vol. 227, pp. 375-385.
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© 2018 Microbial products, i.e. extracellular polymeric substance (EPS) and soluble microbial product (SMP), have a significant correlation with microbial activity of biologically based systems. In present study, the spectral characteristics of two kinds of microbial products were comprehensively evaluated in a solid-phase denitrification biofilm reactor for WWTP effluent treatment by using poly (butylene succinate) (PBS) as carbon source. After the achievement of PBS-biofilm, nitrate and total nitrogen removal efficiencies were high of 97.39 ± 1.24% and 96.38 ± 1.1%, respectively. The contents of protein and polysaccharide were changed different degrees in both LB-EPS and TB-EPS. Excitation-emission matrix (EEM) implied that protein-like substances played a significant role in the formation of PBS-biofilm. High-throughput sequencing result implied that the proportion of denitrifying bacteria, including Simplicispira, Dechloromonas, Diaphorobacter, Desulfovibrio, increased to 9.2%, 7.4%, 4.8% and 3.6% in PBS-biofilm system, respectively. According to EEM-PARAFAC, two components were identified from SMP samples, including protein-like substances for component 1 and humic-like and fulvic acid-like substances for component 2, respectively. Moreover, the fluorescent scores of two components expressed significant different trends to reaction time. Gas chromatography-mass spectrometer (GC-MS) implied that some new organic matters were produced in the effluent of SP-DBR due to biopolymer degradation and denitrification processes. The results could provide a new insight about the formation and stability of solid-phase denitrification PBS-biofilm via the point of microbial products.
Han, L, Zhou, M, Jia, W, Dalil, Z & Xu, X 2018, 'Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model', Information Sciences.
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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.
Hannan, M.A., Faisal, M., Ker, P.J., Mun, L.H., Parvin, K., Mahlia, T.M.I. & Blaabjerg, F. 2018, 'A review of internet of energy based building energy management systems: Issues and recommendations', IEEE Access, vol. 6, pp. 38997-39014.
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© 2013 IEEE. A building energy management system (BEMS) is a sophisticated method used for monitoring and controlling a building's energy requirements. A number of potential studies were conducted in nearly or net zero energy buildings (nZEBs) for the optimization of building energy consumption through efficient and sustainable ways. Moreover, policy makers are approving measures to improve building energy efficiency in order to foster sustainable energy usages. However, the intelligence of existing BEMSs or nZEBs is inadequate, because of the static set points for heating, cooling, and lighting, the complexity of large amounts of BEMS data, data loss, and network problems. To solve these issues, a BEMS or nZEB solution based on the Internet of energy (IoE) provides disruptive opportunities for revolutionizing sustainable building energy management. This paper presents a critical review of the potential of an IoE-based BEMS for enhancing the performance of future generation building energy utilization. The detailed studies of the IoE architecture, typical nZEB configuration, different generations of nZEB, and smart building energy systems for future BEMS are investigated. The operations, advantages, and limitations of the existing BEMSs or nZEBs are illustrated. A comprehensive review of the different types of IoE-based BEMS technologies, such as energy routers, storage systems and materials, renewable sources, and plug-and-play interfaces, is then presented. The rigorous review indicates that existing BEMSs require advanced controllers integrated with IoE-based technologies for sustainable building energy usage. The main objective of this review is to highlight several issues and challenges of the conventional controllers and IoE applications of BEMSs or nZEBs. Accordingly, the review provides several suggestions for the research and development of the advanced optimized controller and IoE of future BEMSs. All the highlighted insights and recommendations of this...
Hao, P., Zhang, G., Martinez, L. & Lu, J. 2018, 'Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation', IEEE Transactions on Cybernetics.
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IEEE Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.
Hao, S., Shi, C., Niu, Z. & Cao, L. 2018, 'Concept coupling learning for improving concept lattice-based document retrieval', Engineering Applications of Artificial Intelligence, vol. 69, pp. 65-75.
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© 2017 Elsevier Ltd The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections.
Hasbi, A, Perreault, ML, Shen, MYF, Fan, T, Nguyen, T, Alijaniaram, M, Banasikowski, TJ, Grace, AA, O'Dowd, BF, Fletcher, PJ & George, SR 2018, 'Activation of dopamine D1-D2 receptor complex attenuates cocaine reward and reinstatement of cocaine-seeking through inhibition of DARPP-32, ERK, and ΔFosB', Frontiers in Pharmacology, vol. 8, no. JAN.
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© 2018 Hasbi, Perreault, Shen, Fan, Nguyen, Alijaniaram, Banasikowski, Grace, O'Dowd, Fletcher and George. A significant subpopulation of neurons in rat nucleus accumbens (NAc) coexpress dopamine D1 and D2 receptors, which can form a D1-D2 receptor complex, but their relevance in addiction is not known. The existence of the D1-D2 heteromer in the striatum of rat and monkey was established using in situ PLA, in situ FRET and co-immunoprecipitation. In rat, D1-D2 receptor heteromer activation led to place aversion and abolished cocaine CPP and locomotor sensitization, cocaine intravenous self-administration and reinstatement of cocaine seeking, as well as inhibited sucrose preference and abolished the motivation to seek palatable food. Selective disruption of this heteromer by a specific interfering peptide induced reward-like effects and enhanced the above cocaine-induced effects, including at a subthreshold dose of cocaine. The D1-D2 heteromer activated Cdk5/Thr75-DARPP-32 and attenuated cocaine-induced pERK and ΔFosB accumulation, together with inhibition of cocaine-enhanced local field potentials in NAc, blocking thus the signaling pathway activated by cocaine: D1R/cAMP/PKA/Thr34-DARPP-32/pERK with ΔFosB accumulation. In conclusion, our results show that the D1-D2 heteromer exerted tonic inhibitory control of basal natural and cocaine reward, and therefore initiates a fundamental physiologic function that limits the liability to develop cocaine addiction.
hassan, M, liu, D & Paul, G 2018, 'Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements', Journal of Intelligent and Robotic Systems.
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Multiple autonomous industrial robots can be of great use in manufacturing applications, particularly if the environment is unstructured and custom manufacturing is required. Autonomous robots that are equipped with manipulators can collaborate to carry out manufacturing tasks such as surface preparation by means of grit-blasting, surface coating or spray painting, all of which require complete surface coverage. However, as part of the collaboration process, appropriate base placements relative to the environment and the target object need to be determined by the robots. The problem of finding appropriate base placements is further complicated when the object under consideration is large and has a complex geometric shape, and thus the robots need to operate from a number of base placements in order to obtain complete coverage of the entire object. To address this problem, an approach for Optimization of Multiple Base Placements (OMBP) for each robot is proposed in this paper. The approach aims to optimize base placements for multi-robot collaboration by taking into account task-specific objectives such as makespan, fair workload division amongst the robots, and coverage percentage; and manipulator-related objectives such as torque and manipulability measure. In addition, the constraint of robots maintaining an appropriate distance between each other and relative to the environment is taken into account. Simulated and real-world experiments are carried out to demonstrate the effectiveness of the approach and to verify that the simulated results are accurate and reliable.
Hassan, M, Liu, D & Xu, D 2018, '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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Hassanzadeh-Barforoushi, A, Law, AMK, Hejri, A, Asadnia, M, Ormandy, CJ, Gallego-Ortega, D & Ebrahimi Warkiani, M 2018, 'Static droplet array for culturing single live adherent cells in an isolated chemical microenvironment.', Lab on a chip, vol. 18, no. 15, pp. 2156-2166.
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We present here a new method to easily and reliably generate an array of hundreds of dispersed nanoliter-volume semi-droplets for single-cells culture and analysis. The liquid segmentation step occurs directly in indexed traps by a tweezer-like mechanism and is stabilized by spatial confinement. Unlike common droplet-based techniques, the semi-droplet wets its surrounding trap walls thus supporting the culturing of both adherent and non-adherent cells. To eliminate cross-droplet cell migration and chemical cross-talk each semi-droplet is separated from a nearby trap by an ∼80 pL air plug. The overall setup and injection procedure takes less than 10 minutes, is insensitive to fabrication defects and supports cell recovery for downstream analysis. The method offers a new approach to easily capture, image and culture single cells in a chemically isolated microenvironment as a preliminary step towards high-throughput single-cell assays.
Haydar, H., Far, H. & Saleh, A. 2018, 'Portal Steel Trusses vs. Portal Steel Frames for Long- span Industrial Buildings', Steel Construction, vol. 11, no. 3, pp. 205-217.
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Portal frames and portal truss structures are two of the most cost effective and sustainable structural commodities for utilisation in the design and construction of long span industrial buildings. Although the application of both structure types as steel cladded structures is widely accepted, due to frame complexity and variation of frame types for use in single story buildings, that exceed spans greater than 30 meters, literature providing a comprehensive investigation on the concepts of portal trusses and portal frames is scarce. This study compares the behaviour of portal truss configuration with pitched portal frames for use in long span industrial buildings that exceed 30 meters with focus on weight, costs and time for construction. Furthermore, this study entails a numerical investigation that utilises SAP2000 computer program to model and structurally optimise the member properties for both portal frame and portal truss configurations. Based on the results obtained from the investigation, it has become apparent that the portal truss configurations are lighter and cheaper to fabricate and construct due to the smaller sections used in comparison to the pitched portal frame that require a shorter time for construction.
He, L., Lu, Z., Zhang, J., Geng, L., Zhao, H. & Li, X. 2018, 'Low-carbon economic dispatch for electricity and natural gas systems considering carbon capture systems and power-to-gas', Applied Energy, vol. 224, pp. 357-370.
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© 2018 Elsevier Ltd To mitigate the global warming threat, CO2emission reduction is an irreversible trend for the sustainable development of power systems. Among various low-carbon technologies, gas-fired power plants and power-to-gas facilities play an important role to reduce emissions, and they are increasing the interdependency between electricity and natural gas systems. Considering also the increasing penetration of wind power generation, this paper proposes a low-carbon economic dispatch model under both constraints of the electricity and natural gas systems. To reduce CO2emission and improve the wind power utilization, mathematical formulations of the post-combustion carbon capture system and power-to-gas facility are presented in the proposed model. Additionally, a flexible operation mode of post-combustion carbon capture system and power-to-gas facility is further analyzed. The objective function of the presented model is to minimize the total cost, which consists of the operation cost, the CO2processing cost and the penalty cost of wind power curtailment. Then the optimization model is converted into a mixed integer linear programming problem for efficient computation purpose. Numerical case studies are carried out to validate the effectiveness of the proposed model and the flexible operation mode.
He, Q, Wang, J & Lu, H 2018, 'A hybrid system for short-term wind speed forecasting', Applied Energy, vol. 226, pp. 756-771.
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© 2018 Elsevier Ltd Wind speed forecasting is important for high-efficiency utilization of wind energy. Correspondingly, numerous researchers have always focused on the development of reliable forecasting models of wind speed, which is often noisy, unstable and irregular. Current approaches could adapt to various wind speed data. However, many of these usually ignore the importance of the selection of the modeling sample, which often results in poor forecasting performance. In this study, a hybrid forecasting system is proposed that contains three modules: data preprocessing, data clustering, and forecasting modules. In this system, the decomposing technique is applied to reduce the influence of noise within the raw data series to obtain a more stable sequence that is conducive to extract traits from the original data. To extract the characteristic of similarity within wind speed data, a kernel-based fuzzy c-means clustering algorithm is used in data clustering module. In the forecasting module, a sample with a highly similar fluctuation pattern is selected as training dataset, and which could reduce the training requirement of model to improve the forecasting accuracy. The experimental results indicate that the developed system outperforms the discussed traditional forecasting models with respect to forecasting accuracy.
He, T, Cai, L, Meng, T, Chen, L, Deng, Z & Cao, Z 2018, 'Parallel Community Detection Based on Distance Dynamics for Large-Scale Network', IEEE Access, vol. 6, pp. 42775-42789.
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© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.
He, T, Lu, DDC, Li, L, Zhang, J, Zheng, L & Zhu, J 2018, 'Model Predictive Sliding Mode Control for Three-Phase AC/DC Converters', IEEE Transactions on Power Electronics.
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IEEE This paper presents a model predictive sliding mode control (MPSMC) strategy for a three-phase AC/DC converter to achieve better stability and dynamic performances. In the conventional model predictive control (MPC) method, a proportional integral (PI) controller is used to generate the active power reference to regulate the dc voltage. This traditional model predictive PI control (MPPIC) scheme produces a large overshoot/undershoot, a long settling time and a large steady-state error under disturbances. To overcome these deficiencies, a sliding mode controller is employed to replace PI controller in MPC. Since the sliding surface and the control law are designed based on the system model, the proposed MPSMC scheme can reduce the effects of the unknown disturbances. Compared with MPPIC, the simulation results obtained from MPSMC show that the dc voltage settling time can be minimized by 80% and the overshoot can be eliminated from 6.2% during the steady state progress. The active and reactive powers from MPSMC can be controlled to the desired values, respectively, with a smaller overshoot/undershoot and a faster response speed. Similar dynamic improvements can be achieved with MPSMC when the load voltage demand varies. Simulation results are validated through an experimental setup and experimental results are reported.
He, Y, Jayawickrama, BA, Dutkiewicz, E, Srikanteswara, S & Mueck, M 2018, 'Priority Access and General Authorized Access Interference Mitigation in the Spectrum Access System', IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 4969-4983.
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© 1967-2012 IEEE. To meet the capacity needs of next generation wireless communications, the U.S. Federal Communications Commission has recently introduced the spectrum access system. Spectrum is shared between three tiers - incumbents, priority access licensees (PAL), and general authorized access (GAA) licensees. When the incumbents are absent, PAL and GAA share the spectrum under the constraint that GAA ensure the interference to PAL is no more than $-$40 dBm with at least 99% confidence. We consider the scenario where locations are not shared between PAL and GAA. We propose a PAL-GAA cochannel interference mitigation technique that does not expose base station locations. Our approach relies on GAA sharing the distribution and maximum number of transmitters in a finite area. We show how PAL can derive the distribution of the aggregate interference using the probability density function and characteristic function, and notify GAA about the exclusion zones in space that will guarantee that the interference requirement is met. We also propose a numerical approximation using inverse fast Fourier and discrete Fourier transforms. Analytically calculated distribution aligns well with the numerical results. Additionally, we formulate an optimization problem for the optimal exclusion zone size. We analytically prove convexity of the problem. Our approach reduces the exclusion zone size by over 42%, which gives significantly more spectral opportunities to GAA in the spatial domain.
Herr, D., Paler, A., Devitt, S.J. & Nori, F. 2018, 'A local and scalable lattice renormalization method for ballistic quantum computation', npj Quantum Information, vol. 4, no. 1.
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Herr, D., Paler, A., Devitt, S.J. & Nori, F. 2018, 'Lattice surgery on the Raussendorf lattice', Quantum Science and Technology, vol. 3, no. 3.
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© 2018 IOP Publishing Ltd. Lattice surgery is a method to perform quantum computation fault-tolerantly by using operations on boundary qubits between different patches of the planar code. This technique allows for universal planar code computation without eliminating the intrinsic two-dimensional nearest-neighbor properties of the surface code that eases physical hardware implementations. Lattice surgery approaches to algorithmic compilation and optimization have been demonstrated to be more resource efficient for resource-intensive components of a fault-tolerant algorithm, and consequently may be preferable over braid-based logic. Lattice surgery can be extended to the Raussendorf lattice, providing a measurement-based approach to the surface code. In this paper we describe how lattice surgery can be performed on the Raussendorf lattice and therefore give a viable alternative to computation using braiding in measurement-based implementations of topological codes.
Hill, M. & Tran, N. 2018, 'MicroRNAs Regulating MicroRNAs in Cancer.', Trends in cancer, vol. 4, no. 7, pp. 465-468.
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MicroRNAs (miRNA) are capable of self-regulation, termed miRNA to miRNA interaction. Very little is known about these interactions and their impact on the cellular milieu. We discuss known miRNA to miRNA interactions, potential mechanisms, and their role in cancer.
Ho, L & Fatahi, B 2018, 'Analytical solution to axisymmetric consolidation of unsaturated soil stratum under equal strain condition incorporating smear effects', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 42, no. 15, pp. 1890-1913.
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© 2018 John Wiley & Sons, Ltd. This paper proposes closed-form analytical solutions to the axisymmetric consolidation of an unsaturated soil stratum using the equal strain hypothesis. Following the 1-dimensional (1D) consolidation theory for unsaturated soil mechanics, polar governing equations describing the air and water flows are first presented on the basis of Fick's law and Darcy's law, respectively. The current study takes into account the peripheral smear caused by an installation of vertical drain. Separation of variables and Laplace transformation are mainly adopted in the analytical derivation to obtain final solutions. Then, the hydraulic conductivity ratio, the radius of influence zone and smear parameters influencing time-dependent excess pore pressures, and the average degree of consolidation are graphically interpreted. In this study, a comparison made between the proposed equal strain results and the existing free strain results suggests that both hypotheses would deliver similar predictions. Moreover, it is found that the smear zone resulting from vertical drain installations would hinder the consolidation rate considerably.
Ho, L., Fatahi, B. & Khabbaz, H. 2018, 'Analytical solution to one-dimensional consolidation in unsaturated soil deposit incorporating time-dependent diurnal temperature variation', International Journal of Geomechanics, vol. 18, no. 5.
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© 2018 American Society of Civil Engineers. Several experimental studies have demonstrated that temperature changes may significantly influence the deformation of unsaturated soils. Thus, there is an essential need to develop a predictive framework for unsaturated consolidation capturing the nonisothermal effect. This paper presents an analytical solution to the one-dimensional (1D) consolidation of unsaturated soil deposit in response to temperature variation. A set of governing equations of flow incorporating the nonisothermal condition were first obtained. Then, Fourier sine series and the Laplace transformation were used to derive solutions based on these governing equations. This study highlighted the effect of diurnal temperature variation on pore pressures and soil deformation at different depths while considering two conditions of interest: (1) no external applied load, and (2) application of step loading to the ground surface. In addition, the thermal diffusivity characterizing the consolidation behavior of unsaturated soils was also investigated and is discussed in this paper. It is predicted that a decrease in thermal diffusivity would attenuate the effects of diurnal temperature on the unsaturated consolidation.
Ho, N., Peng, H., Mayoh, C., Liu, P.Y., Atmadibrata, B., Marshall, G.M., Li, J. & Liu, T. 2018, 'Delineation of the frequency and boundary of chromosomal copy number variations in paediatric neuroblastoma.', Cell cycle (Georgetown, Tex.), vol. 17, no. 6, pp. 749-758.
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Neuroblastoma, the most common solid tumour in early childhood, is characterized by very frequent chromosomal copy number variations (CNVs). While chromosome 2p amplification, 17q gain, 1p and 11q deletion in human neuroblastoma tissues are well-known, the exact frequencies and boundaries of the chromosomal CNVs have not been delineated. We analysed the publicly available single nucleotide polymorphism (SNP) array data which were originally generated by the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative, defined the frequencies and boundaries of chromosomes 2p11.2 - 2p25.3 amplification, 17q11.1-17q25.3 gain, 1p13.3-1p36.33 deletion and 11q13.3-11q25 deletion in neuroblastoma tissues, and identified chromosome 7q14.1 (Chr7:38254795-38346971) and chromosome 14q11.2 (Chr14:21637401-22024617) deletion in blood and bone marrow samples from neuroblastoma patients, but not in tumour tissues. Kaplan Meier analysis showed that double deletion of Chr7q14.1 and Chr14q11.2 correlated with poor prognosis in MYCN gene amplified neuroblastoma patients. In conclusion, the oncogenes amplified or gained and tumour suppressor genes deleted within the boundaries of chromosomal CNVs in tumour tissues should be studied for their roles in tumourigenesis and as therapeutic targets. Focal deletions of Chr7q14.1 and Chr14q11.2 together in blood and bone marrow samples from neuroblastoma patients can be used as a marker for poorer prognosis and more aggressive therapies.
Hoang, TM, Ngo, HQ, Duong, TQ, Tuan, HD & Marshall, A 2018, 'Cell-free Massive MIMO Networks: Optimal Power Control against Active Eavesdropping', IEEE Transactions on Communications.
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OAPA This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. Firstly, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equippower allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency.
Ho-Le, TP, Pham, HM, Center, JR, Eisman, JA, Nguyen, HT & Nguyen, TV 2018, 'Prediction of changes in bone mineral density in the elderly: contribution of "osteogenomic profile".', Archives of osteoporosis, vol. 13, no. 1, p. 68.
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The contribution of genetic variants to longitudinal bone loss has not been well documented. We constructed an "osteogenomic profile" based on 62 BMD-associated genetic variants and showed that the profile was significantly associated with bone loss, independently from baseline BMD and age. The osteogenomic profile can help predict bone loss in an individual.The rate of longitudinal bone loss (ΔBMD) is a risk factor for fracture. The variation in ΔBMD is partly determined by genetic factors. This study sought to define the association between an osteogenomic profile and ΔBMD.The osteogenomic profile was created from 62 BMD-associated SNPs from genome-wide association studies (GWAS) that were genotyped in 1384 elderly men and women aged 60+ years. Weighted genetic risk scores (GRS) were constructed for each individual by summing the products of the number of risk alleles and the sex-specific regression coefficients [associated with BMD from GWAS]. ΔBMD, expressed as annual percent change-in-BMD, was determined by linear regression analysis for each individual who had had at least two femoral neck BMD measurements.The mean ΔBMD was - 0.65% (SD 1.64%) for women and - 0.57% (SD 1.40%) for men, and this difference was not statistically significant (P = 0.32). In women, each unit increase in GRS was associated with 0.21% (SE 0.10) higher ΔBMD at the femoral neck (P = 0.036), and this association was independent of baseline BMD and age. In logistic regression analysis, each unit increase of GRS was associated with 41% odds (95%CI: 1.07-1.87) of rapid bone loss (ΔBMD ≤ - 1.2%/year; mean of rapid loss group = - 2.2%/year). There was no statistically significant association between ΔBMD and GRS in men.We conclude that the osteogenomic profile constructed from BMD-associated genetic variants is modestly associated with long-term changes in femoral neck BMD in women, but not in men.
Hong, H., Liu, J., Bui, D.T., Pradhan, B., Acharya, T.D., Pham, B.T., Zhu, A.X., Chen, W. & Ahmad, B.B. 2018, 'Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)', Catena, vol. 163, pp. 399-413.
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© 2018 Elsevier B.V. Landslides are a manifestation of slope instability causing different kinds of damage affecting life and property. Therefore, high-performance-based landslide prediction models are useful to government institutions for developing strategies for landslide hazard prevention and mitigation. Development of data mining based algorithms shows that high-performance models can be obtained using ensemble frameworks. The primary objective of this study is to investigate and compare the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT). The Guangchang district (Jiangxi province, China) was selected as the case study. Firstly, a landslide inventory map with 237 landslide locations was constructed; the landslide locations were then randomly divided into a ratio of 70/30 for the training and validating models. Secondly, fifteen landslide conditioning factors were prepared, such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, profile curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Relief-F with the 10-fold cross-validation method was applied to quantify the predictive ability of the conditioning factors and for feature selection. Using the JDT and its three ensemble techniques, a total of four landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using area under the receiver operating characteristic (ROC) curve (AUC) and statistical indexes. The result showed that all landslide models have high performance (AUC > 0.8). However, the JDT with the Rotation Forest model presents the highest prediction capability (AUC = 0.855), followed by the JDT with the...
Hong, H., Pradhan, B., Sameen, M.I., Kalantar, B., Zhu, A. & Chen, W. 2018, 'Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach', Landslides, vol. 15, no. 4, pp. 753-772.
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© 2017, Springer-Verlag GmbH Germany. Landslide is a natural disaster that threatens human lives and properties worldwide. Numerous have been conducted on landslide susceptibility mapping (LSM), in which each has attempted to improve the accuracy of final outputs. This study presents a novel region-partitioning approach for LSM to understand the effects of partitioning a focused region into smaller areas on the prediction accuracy of common regression models. Results showed that the partitioning of the study area into two regions using the proposed method improved the prediction rate from 0.77 to 0.85 when support vector machine was used, and from 0.87 to 0.88 when logistic regression model was utilized. The spatial agreements of the models were also improved after partitioning the area into two regions based on Shannon entropy equations. Our comparative study indicated that the proposed method outperformed the geographically weighted regression model that considered the spatial variations in landslide samples. Overall, the main advantages of the proposed method are improved accuracy and the reduction of the effects of spatial variations exhibited in landslide-conditioning factors.
Ho-Pham, L.T., Ho-Le, T.P., Mai, L.D., Do, T.M., Doan, M.C. & Nguyen, T.V. 2018, 'Sex-difference in bone architecture and bone fragility in Vietnamese.', Scientific reports, vol. 8, no. 1, p. 7707.
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This study sought to define the sex-difference in trabecular and cortical bone parameters in Vietnamese individuals. The study involved 1404 women and 864 men aged between 20 and 86 years who were recruited from Ho Chi Minh City, Vietnam. Trabecular and cortical volumetric BMD were measured at the proximal tibia and proximal radius at 4%, 38%, and 66% points, using a peripheral quantitative computed tomography XCT2000 (Stratec, Germany). Polar strength strain index was estimated from cortical bone parameters. Changes in bone parameters were assessed by the multiple linear regression model. Among individuals aged 20-39 years, women had significantly lower peak trabecular BMD at both the radius (40%) and tibia (16%) than men, but the age-related reduction in trabecular BMD were similar between two sexes. For cortical BMD, peak values in women and men were comparable, but the age-related diminution was greater in women than men. At any age, polar strength strain index in women was lower than men, and the difference was mainly attributable to cortical bone area and total bone mass. We conclude that in the elderly, sex-related difference in trabecular BMD is originated during growth, but sex-related difference in cortical BMD is determined by differential age-related bone loss.
Ho-Pham, LT, Chau, PMN, Do, AT, Nguyen, HC & Nguyen, TV 2018, 'Type 2 diabetes is associated with higher trabecular bone density but lower cortical bone density: the Vietnam Osteoporosis Study.', Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, vol. 29, no. 9, pp. 2059-2067.
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It is not clear why type 2 diabetes (T2D) has an increased risk of fracture despite higher areal bone mineral density. This study showed that compared with controls, T2D patients had higher trabecular bone density but lower cortical bone density, resulting in a lower bone strength.To define the association between type 2 diabetes and bone architecture and measures of bone strength.The study was part of the Vietnam Osteoporosis Study, in which 1115 women and 614 men aged ≥ 30 were randomly recruited from Ho Chi Minh City. HbA1c levels were measured with analyzers ADAMS™ A1c HA-8160 (Arkray, Kyoto, Japan). The diagnosis of T2D was made if HbA1c was ≥ 6.5%. Trabecular and cortical volumetric bone density (vBMD) was measured in the forearm and leg by a pQCT XCT2000 (Stratec, Germany). Polar stress strain index (pSSI) was derived from the pQCT measurements. Difference in bone parameters between T2D and non-diabetic individuals was assessed by the number of standard deviations (effect size [ES]) by the propensity score analysis.The prevalence of T2D was ~ 8%. The results of propensity score matching for age, sex, and body mass index in 137 pairs of diabetic and non-diabetic individuals showed that T2D patients had significantly higher distal radius trabecular vBMD (ES 0.26; 95% CI, 0.02 to 0.50), but lower cortical vBMD (ES - 0.22; - 0.46 to 0.00) and reduced pSSI (ES - 0.23; - 0.47 to - 0.02) compared with non-diabetic individuals. Multiple linear regression analysis based on the entire sample confirmed the results of the propensity score analysis.Compared with non-diabetic individuals, patients with T2D have greater trabecular but lower cortical vBMD which leads to lower bone strength.
Hou, Z., Wang, Y., Sui, Y., Gu, J., Zhao, T. & Zhou, X. 2018, 'Managing high-performance computing applications as an on-demand service on federated clouds', Computers and Electrical Engineering, vol. 67, pp. 579-595.
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© 2018 Elsevier Ltd There are several challenges (e.g., imbalance between supply and demand of hardware resources and software licenses, and usability) under modern High-Performance Computing (HPC) environment. As a means of providing an on-demand service for end users, we propose a Software-as-a-Service (SaaS) approach for managing commercial HPC applications as a Web-based service deployed on top of federated clouds. Some inter-trusted private or public clouds are federated to create a unified service platform with a large amount of hardware resources. In addition, an on-demand, pay-per-use model for Web-service-enabled HPC applications is proposed. Further, we provide an economic analysis of the proposed approach from the perspective of end users, cloud service providers, and Independent Software Vendors (ISVs). We conduct a simulation using two HPC application services on three federated clouds. A combined Quality of Service (QoS) and economic evaluation demonstrates a better effect of the proposed approach comparing with existing HPC platforms.
Hou, ZJ, Yang, Y, Chiu, L, Zhu, X & Xue, Q 2018, 'Wideband Millimeter-Wave On-Chip Quadrature Coupler with Improved In-Band Flatness in 0.13-μm SiGe Technology', IEEE Electron Device Letters, vol. 39, no. 5, pp. 652-655.
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© 1980-2012 IEEE. This letter proposes a compact and broadband quadrature coupler with a center frequency of 55 GHz, which consists of a 90° broadside coupled-line to support the differential signal propagation and two T-type L-C networks to support the common signal propagation. To analyze the proposed coupler, an equivalent circuit model is provided for estimation of the distributed and lumped component values. The measured results of the proposed on-chip quadrature coupler show that the return loss and isolation are greater than 20 dB with a bandwidth of 105%, while the insertion loss is about -0.85 dB. The magnitude imbalances are less than 1 dB within the bandwidth of 56% and the phase differences are with ±1° errors within the bandwidth of 96.9%. The chip size, excluding the test pads, is only 0.31 × 0.22 mm2.
Hou, ZJ, Yang, Y, Chiu, L, Zhu, X, Dutkiewicz, E, Vardaxoglou, JC & Xue, Q 2018, 'A W-Band Balanced Power Amplifier Using Broadside Coupled Strip-Line Coupler in SiGe BiCMOS 0.13-μm Technology', IEEE Transactions on Circuits and Systems I: Regular Papers.
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IEEE Load-variation insensitivity, for impedance matching between power amplifiers (PAs) and transmitting antennas, contributes to challenging the design of millimeter-wave wireless systems. In this paper, a W-band two-way balanced PA based on a compact quadrature coupler with a broadside coupled strip-line (BCSL) as the core is presented to enhance the load-variation insensitivity and stability. The proposed coupler is truly broadband with low amplitude and phase imbalance. The proposed $W$-band balanced PA achieves higher power-added efficiency (PAE) and unsaturated output power Psat over wide frequency bandwidth. The W-band balanced PA is implemented in a 0.13- & #x03BC;m SiGe BiCMOS process and achieves a measured Psat of 16.3 dBm and a peak PAE of 14.1 & #x0025; at 100 GHz (with 1.6-V power supply). The measured Psat with 1-dB bandwidth is from 91 to 102 GHz. The measured results present the feasibility of the compact quadrature coupler. The total chip surface area (with pads) is 0.64 mm & #x00B2;, where the size of the proposed quadrature coupler area is only 0.04 mm & #x00B2;.
Hou, ZJ, Yang, Y, Zhu, X, Li, YC, Dutkiewicz, E & Xue, Q 2018, 'A Compact and Low-Loss Bandpass Filter Using Self-Coupled Folded-Line Resonator With Capacitive Feeding Technique', IEEE Electron Device Letters.
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IEEE This letter proposes a compact and low-loss on-chip bandpass filter (BPF) design in (Bi)-CMOS technology. The proposed BPF consists of a self-coupled folded-line resonator (SCFLR) and a pair of metal-insulator-metal (MIM) capacitors. The proposed resonator has a property of flexible self-resonant-frequency (SRF) to form a transmission zero (TZ), which is analyzed in details by a simplified LC equivalent circuit. Moreover, the parametric studies of the feeding capacitance for the proposed BPF design have been performed to demonstrate the tenability of the resonant frequency. For verification, the proposed BPF is fabricated in a standard 0.13-μm (Bi)-CMOS technology. The measured results show that the proposed BPF has a notch with 25.4 dB suppression at 65 GHz and an insertion loss of 1.66 dB in the passband. The chip size of the device, excluding the test pads, is only 0.009mm2 (0.11 × 0.086mm2).
Houssineau, J & Bishop, AN 2018, 'Smoothing and filtering with a class of outer measures', SIAM-ASA Journal on Uncertainty Quantification, vol. 6, no. 2, pp. 845-866.
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© 2018 Society for Industrial and Applied Mathematics and American Statistical Association. Filtering and smoothing with a generalized representation of uncertainty is considered. Here, uncertainty is represented using a class of outer measures. It is shown how this representation of uncertainty can be propagated using outer-measure-type versions of Markov kernels and generalized Bayesian-like update equations. This leads to a system of generalized smoothing and filtering equations where integrals are replaced by supremums and probability density functions are replaced by positive functions with supremum equal to one. Interestingly, these equations retain most of the structure found in the classical Bayesian filtering framework. It is additionally shown that the Kalman filter recursion can be recovered from weaker assumptions on the available information on the corresponding hidden Markov model.
Hu, C, Lu, J, Liu, X & Zhang, G 2018, 'Robust vehicle routing problem with hard time windows under demand and travel time uncertainty', Computers and Operations Research, vol. 94, pp. 139-153.
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© 2018 Elsevier Ltd Due to an increase in customer-oriented service strategies designed to meet more complex and exacting customer requirements, meeting a scheduled time window has become an important part of designing vehicle routes for logistics activities. However, practically, the uncertainty in travel times and customer demand often means vehicles miss these time windows, increasing service costs and decreasing customer satisfaction. In an effort to find a solution that meets the needs of real-world logistics, we examine the vehicle routing problem with hard time windows under demand and travel time uncertainty. To address the problem, we build a robust optimization model based on novel route-dependent uncertainty sets. However, due to the complex nature of the problem, the robust model is only able to tackle small-sized instances using standard solvers. Therefore, to tackle large instances, we design a two-stage algorithm based on a modified adaptive variable neighborhood search heuristic. The first stage of the algorithm minimizes the total number of vehicle routes, while the second stage minimizes the total travel distance. Extensive computational experiments are conducted with modified versions of Solomon's benchmark instances. The numerical results show that the proposed two-stage algorithm is able to find optimal solutions for small-sized instances and good-quality robust solutions for large-sized instances with little increase to the total travel distance and/or the number of vehicles used. A detailed analysis of the results also reveals several managerial insights for decision-makers in the logistics industry.
Hu, D, Xu, W, Dian, R, Liu, Y & Zhu, J 2018, 'Loss Minimization Control of Linear Induction Motor Drive for Linear Metros', IEEE Transactions on Industrial Electronics, vol. 65, no. 9, pp. 6870-6880.
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© 1982-2012 IEEE. The linear induction motor (LIM) drive in linear metros suffers heavily from low efficiency due to its large air-gap length and the partial load conditions, where high loss appears in both the LIM and the inverter when a constant excitation current is generally required. Worse still, the end-effects, including both the transversal edge-effect and longitudinal end-effect, would lead to the decrease of magnetizing inductance and the increase of secondary resistance, resulting in extra loss and further deterioration of drive efficiency. To reduce the loss of the LIM drive, this paper proposes a novel loss model based loss minimization control (LMC) scheme for LIM drives. First, in the equivalent circuit and the mathematical model of LIM, four coefficients are introduced to evaluate the influence of the end-effects. Based on a thorough analysis of the LIM copper and core losses, together with the inverter conduction and switching losses, a novel integrated loss model of LIM drive is then developed, and an improved LMC scheme to obtain the optimal solution online is proposed to minimize the loss of the LIM drive. The proposed control method is successfully implemented in a 3-kW LIM drive prototype. The effectiveness of the proposed method is validated by the experimental results.
Hu, H., Liu, X., Zhao, J. & Guo, Y. 2018, 'Analysis and minimization of detent end force in linear permanent magnet synchronous machines', IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2475-2486.
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© 1982-2012 IEEE. In this paper, the end forces caused by the longitude end effects in linear permanent magnet synchronous machines (LPMSMs) are analyzed and minimized. First, the left/right-end forces are calculated based on an analytical model and the Maxwell stress tensor, in which the optimal integration surfaces are investigated. Then, based on the spectrum analysis of the left/right-end forces, two different methods are adopted to minimize the fundamental and high-order harmonics, respectively. The optimal length of the primary iron is obtained from the phase difference of the fundamental and a two-step iteration instead of the trial-and-error with the finite element method. Furthermore, step-skewed auxiliary irons are added to the primary end to eliminate the high-order harmonics. Third, to reduce the secondary end effect when the primary moves to the secondary end, a compensation method of adding mirror permanent magnet is proposed and good results are obtained. Finally, an LPMSM prototype is manufactured and experiments are conducted. The experimental results verify the theoretical study.
Hu, J, Liang, B & Qiu, XJ 2018, 'Transparent and Ultra-lightweight Design for Ultra-Broadband Asymmetric Transmission of Airborne Sound', Chinese Physics Letters, vol. 35, no. 2.
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© 2018 Chinese Physical Society and IOP Publishing Ltd. Acoustic one-way manipulations have recently attracted significant attention due to the deep implications in many diverse fields such as biomedical imaging and treatment. However, the previous mechanisms of asymmetric manipulation of airborne sound need to use elaborate heavyweight structures and only work in certain frequency ranges. We propose a mechanism for designing an ultra-lightweight and optically transparent structure with asymmetric transmission property for normally incident plane waves. Instead of fabricating solids into complicated artificial structures with limited bandwidth and heavy weight, we simply use xenon to fill a spatial region of asymmetric shape which allows the incident plane wave to pass along one direction while reflecting the reversed wave regardless of frequency. We demonstrate both analytically and numerically its effectiveness of producing highly-asymmetric transmission within an ultra-broad band. Our design offers new possibility for the design of one-way devices and may have far-reaching impact on various scenarios such as noise control.
Hu, J., Zhang, Q., Lee, D.J. & Ngo, H.H. 2018, 'Feasible use of microbial fuel cells for pollution treatment', Renewable Energy.
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© 2017 Elsevier Ltd. The microbial fuel cells (MFC) can directly transform chemical energy in feed substance to electricity by anodic aspiration pathways. This mini review provides an order-of-magnitude argument that MFC has much lower catalyst density at electrode surface and much higher diffusional resistance for substrates than the chemical fuel cell, the former should not be used as an energy generation unit; rather, it should be applied in low power density level applications such as biofilm wastewater treatment. The literature studies using MFC for pollution treatment are discussed.
Hu, KZ, Tang, MC, Li, M & Ziolkowski, RW 2018, 'Compact, Low-Profile, Bandwidth-Enhanced Substrate Integrated Waveguide Filtenna', IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 8, pp. 1552-1556.
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© 2011 IEEE. In this letter, a compact, low-profile, bandwidth-enhanced, dual-cavity substrate integrated waveguide (SIW) filtenna is demonstrated. Two SIW cavities are stacked vertically on top of each other. A complementary split-ring resonator slot is etched in the top surface of the uppermost cavity, causing the top surface to act as a patch antenna. The operational impedance bandwidth is significantly enhanced by merging the three resonances that arise from this configuration. One is introduced by the patch, and the other two are inherently generated by the two cavities. A metallized coupling post is introduced from the ground plane through both cavities to the upper surface to excite the fundamental resonant mode of the patch, as well as to electromagnetically couple the two cavities. The optimized filtenna was fabricated by a standard printed circuit board technology and tested. It has a low profile λ 0 and a compact size 0.62λ 0×0.62λ0 at its center frequency, f0=2.95GHz. The measured results agree well with their simulated values. They demonstrate a 6.3% fractional bandwidth, a maximum realized gain of 6.73 dBi, a flat gain profile within its passband, and an excellent out-of-band selectivity.
Hu, S, Zou, L, Yu, JX, Wang, H & Zhao, D 2018, 'Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 824-837.
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© 1989-2012 IEEE. RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
Hu, Y, Wang, XC, Ngo, HH, Sun, Q & Yang, Y 2018, 'Anaerobic dynamic membrane bioreactor (AnDMBR) for wastewater treatment: A review', Bioresource Technology, vol. 247, pp. 1107-1118.
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© 2017 Elsevier Ltd Recently, an increasing level of attention has focused on the emerging technology of anaerobic dynamic membrane bioreactors (AnDMBRs), owing to its merits such as low membrane module cost, easy control of membrane fouling, low energy consumption and sludge production, as well as biogas production. As research on AnDMBRs is still in the nascent stage, an introduction of bioreactor configurations, dynamic membrane (DM) module, and DM layer formation and cleaning is firstly presented. The process performance of the AnDMBR for wastewater treatment is then reviewed with regard to pollutant removal, DM filterability, biogas production, and potential advantages over the conventional anaerobic membrane bioreactor (AnMBR). In addition, the important parameters affecting process performance are briefly discussed. Lastly, the challenges encountered and perspectives regarding the future development of the AnDMBR process to promote its practical applications are presented.
Hu, Y, Zheng, L, Yang, Y & Huang, Y 2018, 'Twitter100k: A Real-World Dataset for Weakly Supervised Cross-Media Retrieval', IEEE Transactions on Multimedia, vol. 20, no. 4, pp. 927-938.
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© 2017 IEEE. This paper contributes a new large-scale dataset for weakly supervised cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia, NUS Wide, and Flickr30k, have two major limitations. First, these datasets are lacking in content diversity, i.e., only some predefined classes are covered. Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications. To overcome these drawbacks, the proposed Twitter100k dataset is characterized by two aspects: it has 100 000 image-text pairs randomly crawled from Twitter, and thus, has no constraint in the image categories; and text in Twitter100k is written in informal language by the users. Since strongly supervised methods leverage the class labels that may be missing in practice, this paper focuses on weakly supervised learning for cross-media retrieval, in which only text-image pairs are exploited during training. We extensively benchmark the performance of four subspace learning methods and three variants of the correspondence AutoEncoder, along with various text features on Wikipedia, Flickr30k, and Twitter100k. As a minor contribution, we also design a deep neural network to learn cross-modal embeddings for Twitter100k. Inspired by the characteristic of Twitter100k, we propose a method to integrate optical character recognition into cross-media retrieval. The experiment results show that the proposed method improves the baseline performance.
Hu, Y., Lu, J. & Qiu, X. 2018, 'Direction of arrival estimation of multiple acoustic sources using a maximum likelihood method in the spherical harmonic domain', Applied Acoustics, vol. 135, pp. 85-90.
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© 2018 Elsevier Ltd Direction of arrival estimation (DOA) of multiple acoustic sources has been used for a wide range of applications, including room geometry inference, source separation and speech enhancement. The beamformer-based and subspace-based methods are most commonly used for spherical microphone arrays; however, the former suffers from spatial resolution limitations, while the later suffers from performance degradation in noisy environment. This letter proposes a multiple source DOA estimation approach based on the maximum likelihood method in the spherical harmonic domain and implements an efficient sequential iterative search of maxima on the cost function in the spherical harmonic domain. The proposed method avoids the division of the spherical Bessel function, which makes it suitable for both rigid-sphere and open-sphere configurations. Simulation results show that the proposed method has a significant superiority over the commonly used frequency smoothing multiple signal classification method. Experiments in a normal listening room and a reverberation room validate the effectiveness of the proposed method.
Hua, J., Huang, M. & Wang, G. 2018, 'Graph Layout Performance Comparisons of Force-directed Algorithms', International Journal of Performability Engineering, vol. 14, no. 1, pp. 67-76.
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Due to force-directed algorithms’ capabilities of producing aesthetically pleasing graph layouts, which follow metrics for graph drawing aesthetics, these layouts have become the most common methods in the practical data visualization area. However, evaluating the performance of relevant algorithms remains a challenge, since graph layout quality is largely relying on aspects such as human intuition, personal judgment and methods’ pre-setting parameters. In addition, most aesthetics criteria of graph drawing conflict with each other. This study evaluated the performance measurements of four force-directed algorithms in terms of seven commonly applied aesthetic criteria based on practical raw data collected, and demonstrated the experimental framework. The early outcomes compared twenty final graph layouts and gave empirical evidences; the study may assist with future detailed force-directed algorithms selection based on users’ specific requirements.
Hua, J., Huang, M., Zreika, M. & Wang, G. 2018, 'Applying Data Visualization Techniques for Stock Relationship Analysis', Filomat, vol. 32, no. 4.
Huang, HL, Wang, XL, Rohde, PP, Luo, YH, Zhao, YW, Liu, C, Li, L, Liu, NL, Lu, CY & Pan, JW 2018, 'Demonstration of topological data analysis on a quantum processor', Optica, vol. 5, no. 2, pp. 193-198.
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© 2018 Optical Society of America. Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points—topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.
Huang, K., Kong, X. & Liu, L. 2018, 'Analytical model of information system development productivity in adaptive and perfective maintenance phases', International Journal of Industrial and Systems Engineering, vol. Vol. 29, no. No. 3, 2018, pp. 338-351.
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This paper presents an analytical model for information system (IS)
maintenance productivity in adaptive and perfective phases. The modelling approach is from economic view point. The productivity model considers the economic value of the maintenance phase, pre-committed fixed cost and variable cost consumed in adaptive/perfective maintenance. Influence factors to the productivity are analysed using simulation. The simulation provides a tool for IS project managers to tune the project parameters to obtain the optimal productivity in adaptive/perfective maintenance phases.
Huang, L, Li, M, Ngo, HH, Guo, W, Xu, W, Du, B, Wei, Q & Wei, D 2018, 'Spectroscopic characteristics of dissolved organic matter from aquaculture wastewater and its interaction mechanism to chlorinated phenol compound', Journal of Molecular Liquids, vol. 263, pp. 422-427.
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© 2017 In present study, the characteristics of dissolved organic matter (DOM) from aquaculture wastewater and its interaction to 4-chlorophenol (4-CP) was evaluated via a spectroscopic approach. According to EEM-PARAFAC analysis, two components were derived from the interaction samples between DOM and 4-CP, including humic-like and fulvic-like substances for component 1 and protein-like substances for component 2, respectively. The fluorescence intensity scores of two PARAFAC-derived components decreased with increasing 4-CP concentration. Synchronous fluorescence coupled to two-dimensional correlation spectroscopy (2D-COS) implied that DOM fractions quenched different degrees and occurred in the order of fulvic-like and humic-like fractions > protein-like fraction. Moreover, the quenching mechanisms were mainly caused by static quenching process. It was also found from Fourier transform infrared spectroscopy that the main functional groups for interaction between 4-CP and DOM were O–H stretching and C[dbnd]O stretching vibration. The obtained results provided a spectroscopic approach for characterizing the interaction between organic pollutant and DOM from aquaculture wastewater.
Huang, L, Li, M, Si, G, Wei, J, Ngo, HH, Guo, W, Xu, W, Du, B, Wei, Q & Wei, D 2018, 'Assessment of microbial products in the biosorption process of Cu(II) onto aerobic granular sludge: Extracellular polymeric substances contribution and soluble microbial products release', Journal of Colloid and Interface Science, vol. 527, pp. 87-94.
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© 2018 In the present study, the responses of microbial products in the biosorption process of Cu(II) onto aerobic granular sludge were evaluated by using batch and spectroscopic approaches. Batch experimental data showed that extracellular polymeric substances (EPSs) contributed to Cu(II) removal from an aqueous solution, especially when treating low metal concentrations, whereas soluble microbial products (SMPs) were released under the metal stress during biosorption process. A three-dimensional excitation-emission matrix (3D-EEM) identified four main fluorescence peaks in the EPS, i.e., tryptophan protein-like, aromatic protein-like, humic-like and fulvic acid-like substances, and their fluorescence intensities decreased gradually in the presence of Cu(II) during the sorption process. Particularly, tryptophan protein-like substances quenched the Cu(II) binding to a much higher extent through a static quenching process with less than one class of binding sites. According to the synchronous fluorescence spectra, the whole fluorescence intensity of released SMP samples expressed an increased trend with different degrees along with contact time. Two-dimensional correlation spectroscopy (2D-COS) suggested that the fulvic-like fluorescence fraction might be more susceptible to metal exposure than other fractions. The result of molecular weight distribution demonstrated that the SMPs released from the biosorption process differed significantly according to contact time. The result obtained could provide new insights into the responses of microbial products from aerobic granular sludge with heavy metal treatment.
Huang, W, Yu, JX & Shang, Z 2018, 'Handling query skew in large indexes: a view based approach', Frontiers of Computer Science, vol. 12, no. 1, pp. 146-162.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany. Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.
Huang, W, Zhang, Y, Shang, Z & Yu, JX 2018, 'To Meet or Not to Meet: Finding the Shortest Paths in Road Networks', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 4, pp. 772-785.
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© 1989-2012 IEEE. Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has his/her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. We model it as minimum path pair ( MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, we devise algorithms by enumerating node pairs. We adopt a location-based pruning strategy to reduce the number of node pairs for enumeration. An efficient algorithm based on point-To-point shortest path calculation is proposed to further improve query efficiency. We also give two fast approximate algorithms with approximation bounds. Extensive experiments are conducted to show the effectiveness and efficiency of our methods.
Huang, Y, Cao, L, Zhang, J, Pan, L & Liu, Y 2018, 'Exploring feature coupling and model coupling for image source identification', IEEE Transactions on Information Forensics and Security, vol. 13, no. 12, pp. 3108-3121.
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© 2005-2012 IEEE. Recently, there has been great interest in feature-based image source identification. Previous statistical learning-based methods usually regarded the identification process as a classification problem. They assumed the dependence of features and the dependence of models. However, the two assumptions are usually problematic because of the genuine coupling of features and models. To address the issues, in this paper, we propose a novel image source identification scheme. For the feature coupling, a coupled feature representation is adopted to analyze the coupled interaction among features. The coupling relations among features and their powers are measured with Pearson's correlations and integrated in a Taylor-like expansion manner. Regarding model coupling, a new coupled probability representation is developed. The model coupling relationships are characterized with conditional probabilities induced by the confusion matrix and then combined with the law of total probability. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed scheme. Via mining the feature coupling and model coupling, the identification accuracy can be significantly improved.
Huang, Y, Ng, ECY, Zhou, JL, Surawski, NC, Chan, EFC & Hong, G 2018, 'Eco-driving technology for sustainable road transport: A review', Renewable and Sustainable Energy Reviews, vol. 93, pp. 596-609.
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© 2018 Elsevier Ltd Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated.
Huang, Y, Organ, B, Zhou, JL, Surawski, NC, Hong, G, Chan, EFC & Yam, YS 2018, 'Emission measurement of diesel vehicles in Hong Kong through on-road remote sensing: Performance review and identification of high-emitters.', Environmental pollution (Barking, Essex : 1987), vol. 237, pp. 133-142.
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A two-year remote sensing measurement program was carried out in Hong Kong to obtain a large dataset of on-road diesel vehicle emissions. Analysis was performed to evaluate the effect of vehicle manufacture year (1949-2015) and engine size (0.4-20 L) on the emission rates and high-emitters. The results showed that CO emission rates of larger engine size vehicles were higher than those of small vehicles during the study period, while HC and NO were higher before manufacture year 2006 and then became similar levels between manufacture years 2006 and 2015. CO, HC and NO of all vehicles showed an unexpectedly increasing trend during 1998-2004, in particular ≥6001 cc vehicles. However, they all decreased steadily in the last decade (2005-2015), except for NO of ≥6001 cc vehicles during 2013-2015. The distributions of CO and HC emission rates were highly skewed as the dirtiest 10% vehicles emitted much higher emissions than all the other vehicles. Moreover, this skewness became more significant for larger engine size or newer vehicles. The results indicated that remote sensing technology would be very effective to screen the CO and HC high-emitters and thus control the on-road vehicle emissions, but less effective for controlling NO emissions. No clear correlation was observed between the manufacture year and percentage of high-emitters for ≤3000 cc vehicles. However, the percentage of high-emitters decreased with newer manufacture year for larger vehicles. In addition, high-emitters of different pollutants were relatively independent, in particular NO emissions, indicating that high-emitter screening criteria should be defined on a CO-or-HC-or-NO basis, rather than a CO-and-HC-and-NO basis.
Huang, Y, Organ, B, Zhou, JL, Surawski, NC, Hong, G, Chan, EFC & Yam, YS 2018, 'Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong', Atmospheric Environment, vol. 182, pp. 58-74.
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© 2018 Elsevier Ltd Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOxemissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.
Husby, T., de Groot, H.L.F., Hofkes, M.W. & Filatova, T. 2018, 'Flood protection and endogenous sorting of households: the role of credit constraints', Mitigation and Adaptation Strategies for Global Change, vol. 23, no. 2, pp. 147-168.
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© 2015, The Author(s). Human migration is increasingly seen as a promising climate change adaptation and flood risk reduction strategy. The purpose of this paper is to investigate how spatial differences in flood risk, due to differences in flood protection, reduce the mobility of vulnerable households through a credit constraint mechanism. Using an equilibrium model with two households types and endogenous sorting, we show how spatial differences in flood protection lead to clustering of vulnerable households in a risky region, in a real-world setting of common United States (US) flood zones. We find clustering effects of some size for flood zones with return periods of less than 30 years.
Hussain, W & Hussain, F 2018, 'Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs', Future Generation Computer Systems.
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In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider’s perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.
Hussain, W, Hussain, FK, Hussain, O, Bagia, R & Chang, E 2018, 'Risk-based framework for SLA violation abatement from the cloud service provider’s perspective', The Computer Journal.
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The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.
Idrees, MO & Pradhan, B 2018, 'Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud', Journal of Rock Mechanics and Geotechnical Engineering, vol. 10, no. 3, pp. 534-544.
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© 2018 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences The use of terrestrial laser scanning (TLS) in the caves has been growing drastically over the last decade. However, TLS application to cave stability assessment has not received much attention of researchers. This study attempted to utilize rock surface orientations obtained from TLS point cloud collected along cave passages to (1) investigate the influence of rock geostructure on cave passage development, and (2) assess cave stability by determining areas susceptible to different failure types. The TLS point cloud was divided into six parts (Entry hall, Chamber, Main hall, Shaft 1, Shaft 2 and Shaft 3), each representing different segments of the cave passages. Furthermore, the surface orientation information was extracted and grouped into surface discontinuity joint sets. The computed global mean and best–fit planes of the entire cave show that the outcrop dips 290° with a major north-south strike. But at individual level, the passages with dip angle between 26° and 80° are featured with dip direction of 75°–322°. Kinematic tests reveal the potential for various failure modes of rock slope. Our findings show that toppling is the dominant failure type accounting for high-risk rockfall in the cave, with probabilities of 75.26%, 43.07% and 24.82% in the Entry hall, Main hall and Shaft 2, respectively. Unlike Shaft 2 characterized by high risk of the three failure types (32.49%, 24.82% and 50%), the chamber and Shaft 3 passages are not suffering from slope failure. The results also show that the characteristics of rock geostructure considerably influence the development of the cave passages, and four sections of the cave are susceptible to different slope failure types, at varying degrees of risk.
Imani, MH, Ghadi, MJ, Ghavidel, S & Li, L 2018, 'Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs', Renewable and Sustainable Energy Reviews, vol. 94, pp. 486-499.
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© 2018 Elsevier Ltd During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers. In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions. Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs on an 11-bus MG over a 24-h period and also a 14-bus MG over a period of 336 h (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required.
Inan, D.I., Beydoun, G. & Opper, S. 2018, 'Agent-Based Knowledge Analysis Framework in Disaster Management', Information Systems Frontiers, pp. 1-20.
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© 2017 Springer Science+Business Media, LLC Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge-intensive and time sensitive. Timely sharing of the required knowledge is critical for DM. For recurring disasters (e.g. floods), developed countries tend to have dedicated document repositories of Disaster Management Plans (DISPLANs) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner, and sharing activities between plans, often requires significant domain knowledge and intimate understanding of the plans in the first place. This paper introduces an Agent-Based (AB) knowledge analysis method to convert DISPLANs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching of knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood DISPLANs plans used by SES (State Emergency Service), an authoritative DM agency in New South Wales (NSW) State of Australia (hereinafter referred to as SES NSW) to illustrate and give a preliminary validation of the approach. It is illustrated by using displans along the flood-prone Murrumbidgee river in central NSW.
Inan, D.I., Beydoun, G. & Opper, S. 2018, 'Agent-Based Knowledge Analysis Framework in Disaster Management.', Information Systems Frontiers, vol. 20, pp. 783-802.
Inan, DI, Beydoun, G & Pradhan, B 2018, 'Developing a decision support system for Disaster Management: Case study of an Indonesia volcano eruption', International Journal of Disaster Risk Reduction, vol. 31, pp. 711-721.
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© 2018 Elsevier Ltd Disaster Management activities often focus on specific tasks (e.g. evacuation, logistic or coordination) and are confined to one specific DM phase (e.g. Preparedness or Response). New awareness about an external change, be it environmental or organisational, typically act as a trigger for such focussed activities. A variety of views or stakeholders are also involved in those activities, and their various concerns get often intertwined. This work advocates the use of a Decision Support System (DSS) that can be deployed as a single access point. Such a system requires a sufficient amount of representative knowledge, and facilities to avail the knowledge to the appropriate stakeholders in an appropriate form. With the multitude of stakeholders and their varying knowledge requirements, the system will need to present the knowledge differently according to the stakeholders needs in their decision making process. Such processes can vary, e.g. whether for policy making or for operational real time responses. This paper presents a hybrid of knowledge elicitation and retrieval mechanisms, some are top down and others are bottom up. The mechanisms make use of the Meta Object Facility (MOF) to structure and present the knowledge appropriately according to different interests and roles. A case study of the recent Mt. Agung volcano eruption in Bali Indonesia is successfully used to demonstrate the efficacy of the mechanisms proposed and the resultant DSS.
Ingkiriwang, Y. & Far, H. 2018, 'Numerical Investigation of the Design of Single-span Steel Portal Frames Using the Effective Length and Direct Analysis Methods', Steel Construction, vol. 11, no. 3, pp. 184-191.
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The era of globalisation enables the design, fabrication and erection of steel structures to take place in different locations far away from each other. Therefore, a widely acceptable steel design standard is required, and designers should be familiar with alternative specifications that may be considerably different from one another. This study deals with single span unbraced steel portal frames and makes a comparison between the design methodologies adopted by the Australian and American design provisions, in particular, the effective length method (ELM) and direct analysis method (DAM). A brief discussion on the main features of both standards is also presented. Furthermore, the results of the parametric study are portrayed, highlighting the differences between these two design standards regarding stress interaction.
Finally, of the two aforementioned methods, the most applicable optimisation method for the design and development of cost-effective industrial portal frame buildings is proposed with respect to the structure geometry.
Irga, PJ, Barker, K & Torpy, FR 2018, 'Conservation mycology in Australia and the potential role of citizen science.', Conservation biology : the journal of the Society for Conservation Biology.
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Fungi are undoubtedly important for ecosystem functioning; however, they have been omitted or given scant attention in most biodiversity policy documents, management plans, and formal conservation schedules throughout the world. This oversight may be due to a general lack of awareness in the scientific community and compounded by a scarcity of mycology-associated curricula at the tertiary level and a lack of mycologists in research institutions. Although molecular techniques advance the systematic cataloging of fungi and facilitate insights into fungal communities, the scarcity of professional mycologists in the environmental sciences hampers conservation efforts. Conversely, citizen science initiatives are making significant contributions to the mycology discipline by increasing awareness and extending the scope of fungal surveys. Future research by professional and amateur mycologists into the distribution of fungi and their function in ecosystems will help identify wider and more effective conservation goals.
Irga, PJ, Pettit, TJ & Torpy, FR 2018, 'The phytoremediation of indoor air pollution: a review on the technology development from the potted plant through to functional green wall biofilters', Reviews in Environmental Science and Bio/Technology.
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Poor indoor air quality is a health problem of escalating magnitude, as communities become increasingly urbanised and people’s behaviours change, lending to lives spent almost exclusively in indoor environments. The accumulation of, and continued exposure to, indoor air pollution has been shown to result in detrimental health outcomes. Particulate matter penetrating into the building, volatile organic compounds (VOCs) outgassing from synthetic materials and carbon dioxide from human respiration are the main contributors to these indoor air quality concerns. Whilst a range of physiochemical methods have been developed to remove contaminants from indoor air, all methods have high maintenance costs. Despite many years of study and substantial market demand, a well evidenced procedure for indoor air bioremediation for all applications is yet to be developed. This review presents the main aspects of using horticultural biotechnological tools for improving indoor air quality, and explores the history of the technology, from the humble potted plant through to active botanical biofiltration. Regarding the procedure of air purification by potted plants, many researchers and decades of work have confirmed that the plants remove CO2 through photosynthesis, degrade VOCs through the metabolic action of rhizospheric microbes, and can sequester particulate matter through a range of physical mechanisms. These benefits notwithstanding, there are practical barriers reducing the value of potted plants as standalone air cleaning devices. Recent technological advancements have led to the development of active botanical biofilters, or functional green walls, which are becoming increasingly efficient and have the potential for the functional mitigation of indoor air pollutant concentrations.
Islam, MR, Muttaqi, KM, Sutanto, D & Zhu, J 2018, 'Design and Implementation of Amorphous Magnetic Material Common Magnetic Bus for the Replacement of Common DC Bus', IEEE Transactions on Magnetics.
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IEEE In recent years, microgrid technologies have gained considerable interest, where a common dc-bus is generally used to interconnect multiple power sources. The existing technology of common dc bus-based direct integration of multiple sources has some critical problems, such as isolation and the requirement of additional control circuit for voltage imbalance and common mode. A common high-frequency magnetic bus (instead of the common dc bus) is proposed in this paper to overcome the limitations and complications of the existing technology of dc bus-based direct integration of multiple sources. In this paper, an extensive investigation on electromagnetic performance of advanced magnetic materials (amorphous and nanocrystalline alloys) is presented. The design process of an efficient and compact high-frequency common magnetic bus for integration of multiple power sources is also discussed. The new technology will be a fundamental component for the future grids to interconnect various sources magnetically, providing an inherent solution to voltage imbalance and isolation problems.
Ismaiel, B, Abolhasan, M, Ni, W, Smith, D, Franklin, D & Jamalipour, A 2018, 'Analysis of Effective Capacity and Throughput of Polling Based Device-To-Device Networks', IEEE Transactions on Vehicular Technology.
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IEEE Next-generation wireless networks will give rise to heterogeneous networks by integrating multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. Device-to-Device (D2D) communication has proven to be a promising technology that can increase the capacity and coverage of wireless networks. D2D communication was first introduced in Long Term evolution advanced (LTE-A) and has gained immense popularity for offloading traffic using licensed and unlicensed band. Challenges arise from resource allocation, provision of quality-of-service (QoS), and the quantification of capacity in an unlicensed band due to the distributed nature of Wi-Fi. In this paper, we propose an analytical performance model for Scalable MAC Protocol (SCMP) in which resource allocation mechanism is based on IEEE 802.11 Point Coordinated Function (PCF) to access the Wi-Fi channel for voice and video/multimedia traffic. In SC-MP, D2D communication is applied to further offload the video/multimedia traffic. In particular, the paper establishes a three-state semi-Markovian model to derive a closed-form expression of effective capacity in terms of transmission rate and quality-of-service (QoS). Further, SC-MP is analytically modeled using four-state traditional Markov model to derive the saturation throughput. The analytical results are validated through simulations, hence proving the appropriateness of the model.
Jafari, M, Malekjamshidi, Z & Zhu, J 2018, '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.
Jafari, M, Malekjamshidi, Z & Zhu, J 2018, 'Copper Loss Analysis of a Multi-winding High-frequency Transformer for a Magnetically-coupled Residential Micro-grid', IEEE Transactions on Industry Applications.
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Improvements in characteristics of magnetic materials and switching devices have provided the feasibility of replacing the electrical buses with high-frequency magnetic links in small-scale micro-grids. This can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid, and isolate the sources and loads. To optimally design the magnetic link, an accurate evaluation of copper loss of the windings considering both the current waveforms and parasitic effects are required. This paper studies the copper loss analysis of a three-winding high-frequency magnetic link for residential micro-grid applications. Due to the non-sinusoidal nature of the voltages and currents, the loss analysis is carried out on a harmonic basis taking into account the variations of phase shift, duty ratio and amplitude of the waveforms. The high-frequency parasitic phenomena including the skin and proximity effects are taken into account. The maximum and minimum copper loss operating conditions of the magnetic link and their dependency on the phase shift angle and the duty ratio of the connected waveforms are studied. A prototype of the micro-grid including the magnetic link is developed to validate the theoretical analysis, evaluate the micro-grid efficiency and perform the loss breakdown.
Jafari, M, Malekjamshidi, Z, Lu, DDC & Zhu, J 2018, 'Development of a Fuzzy-Logic-Based Energy Management System for a Multi-Port Multi-Operation Mode Residential Smart Micro-grid', IEEE Transactions on Power Electronics.
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IEEE In this paper a grid-tied residential smart micro-grid topology is proposed which integrates energies of a PV, a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. In contrast to multiple-converter based micro-grids with a common electric bus, using a multi-port converter with a common magnetic bus can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid and isolates the conversion ports. The resultant topology utilizes a centralized system level control which leads to the faster and more flexible energy management. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes. A fuzzy controlled energy management unit is designed to select the appropriate operation mode considering both real-time and long-term-predicted data of the system. A mode transition process is designed to smooth the mode variation by using a state transition diagram and bridging modes. To improve the micro-grid operation performance, appropriate control techniques such as synchronized bus-voltage balance are used. A prototype of the proposed micro-grid is developed and experimentally tested for three different energy management scenarios. Energy distribution and energy cost analysis are performed to validate the proposed control method.
Jafarzadeh, H, Akbari, P & Abedin, B 2018, 'A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA', Expert Systems with Applications.
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Jamil, S, Vigneswaran, S & Jeong, S 2018, 'Application of forward osmosis membrane in nanofiltration mode to treat reverse osmosis concentrate from wastewater reclamation plants', Water Science and Technology, vol. 77, no. 8, pp. 1990-1997.
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© IWA Publishing 2018. Reverse osmosis concentrate (ROC) from wastewater reclamation plants have high concentrations of organic and inorganic compounds, which have to be removed before its disposal. Forward osmosis (FO) and nanofiltration (NF) membranes were tested to treat the ROC for possible water reuse. This research investigated the combined and individual influence of organic and inorganic matter on the fouling of NF and FO membranes. The results revealed that the NF membrane removed most of the organic compounds and some inorganics. The study further highlighted that the FO membrane at NF mode removed the majority of the inorganic compounds and some organics from the ROC. A pretreatment of granulated activated carbon (GAC) adsorption removed 90% of the organic compounds from ROC. In addition, GAC adsorption and acid pretreatment of ROC improved the net water permeate flux by 17% when an FO membrane was used in the NF system. Acid treatment (by bringing the pH down to 5) helped to remove inorganic ions. Therefore, the resultant permeate can be recycled back to the RO water reclamation plant to improve its efficiency.
Jan, M.A., Usman, M., He, X. & Rehman, A.U. 2018, 'SAMS: A Seamless and Authorized Multimedia Streaming framework for WMSN-based IoMT', IEEE Internet of Things Journal.
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IEEE An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for long-term usage. These nodes are capable of capturing both multimedia and non-multimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, the underlying routing protocols need to provide an acceptable level of Quality-of-Service (QoS) support for the multimedia traffic. In this paper, we propose a seamless and authorized multimedia streaming framework (SAMS) for a cluster-based hierarchical WMSN. SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their cluster heads. Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its cluster head. This waiting may result in an excessive packet loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for inter-cluster communication. In case of buffer overflow, a member node in one cluster switches to a neighboring cluster head provided that the latter has an available channel for allocation. The experimental results show that SAMS provides an acceptable level of QoS and enhances the security of the underlying network.
Jan, MA, Nanda, He, XS & Liu, RP 2018, 'A Sybil Attack Detection Scheme for a Forest Wildfire Monitoring Application', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, vol. 80, pp. 613-626.
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Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in human-inaccessible terrains to monitor and collect time-critical and delay-sensitive events. There have been several studies on the use of WSN in different applications. All such studies have mainly focused on Quality of Service (QoS) parameters such as delay, loss, jitter, etc. of the sensed data. Security provisioning is also an important and challenging task lacking in all previous studies. In this paper, we propose a Sybil attack detection scheme for a cluster-based hierarchical network mainly deployed to monitor forest wildfire. We propose a two-tier detection scheme. Initially, Sybil nodes and their forged identities are detected by high-energy nodes. However, if one or more identities of a Sybil node sneak through the detection process, they are ultimately detected by the two base stations. After Sybil attack detection, an optimal percentage of cluster heads are elected and each one is informed using nomination packets. Each nomination packet contains the identity of an elected cluster head and an end user’s specific query for data collection within a cluster. These queries are user-centric, on-demand and adaptive to an end user requirement. The undetected identities of Sybil nodes reside in one or more clusters. Their goal is to transmit high false-negative alerts to an end user for diverting attention to those geographical regions which are less vulnerable to a wildfire. Our proposed approach has better network lifetime due to efficient sleep–awake scheduling, higher detection rate and low false-negative rate.
Jan, MA, Tan, Z, He, X & Ni, W 2018, 'Moving towards highly reliable and effective sensor networks', Ad-Hoc and Sensor Wireless Networks, vol. 40, no. 3-4, pp. 163-168.
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Jay, B 2018, 'Self-Quotation in a Typed, Intensional Lambda-Calculus', Electronic Notes in Theoretical Computer Science, vol. 336, pp. 207-222.
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© 2018 Intensional lambda-calculus adds intensional combinators to lambda-calculus to facilitate analysis. In particular, they are used to factorise data structures and programs into their components, which can be used to convert abstractions into combinators. This paper shows how to type an intensional lambda-calculus using no more than the types of System F. Even the quotation function used to access program syntax can be defined and typed, as can its inverse: the calculus supports typed self-quotation. Thus, one may freely alternate between program analysis and program execution. Proofs of all results have been verified in Coq.
Jayawickrama, B.A., He, Y., Dutkiewicz, E. & Mueck, M.D. 2018, 'Scalable Spectrum Access System for Massive Machine Type Communication', IEEE Network, vol. 32, no. 3, pp. 154-160.
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© 1986-2012 IEEE. Future 5G networks aspire to enable new services with vastly different data rate, latency and scalability requirements. The consensus is that these new services will fall into three categories: eMBB, URLLC, and mMTC. Due to unique characteristics of these services and the limited availability of finite spectrum resources, 5G will need to carefully map appropriate bands and spectrum usage models for each service. The SAS is an emerging spectrum sharing model that is gaining momentum in the U.S. SAS presents an opportunity for operators to access the 3.5 GHz military radar band for commercial use. This article discusses the feasibility of the current SAS model in the context of mMTC. We propose a scalable SAS framework that can manage the mMTC uplink interference to the incumbent with less overhead. The simulation setup models the interference levels in New York City and its surrounding counties. The results show that mMTC uplink transmission can be enabled using our framework even on the coast of New York, where mMTC density is high, without causing a harmful level of interference to the incumbent.
Ji, K, Chen, Z, Sun, R, Ma, K, Yuan, Z & Xu, G 2018, 'GIST: A generative model with individual and subgroup-based topics for group recommendation', Expert Systems with Applications, vol. 94, pp. 81-93.
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© 2017 Elsevier Ltd In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.
Ji, LY, Qin, PY & Guo, YJ 2018, 'Wideband Fabry-Perot Cavity Antenna with a Shaped Ground Plane', IEEE Access, vol. 6, pp. 2291-2297.
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© 2013 IEEE. This paper proposes a novel approach to broaden the 3-dB gain bandwidth of Fabry-Perot cavity (FPC) antennas by utilizing a shaped ground plane. The shaped ground plane is flat in the middle to accommodate the source antenna, and then angled up in the shape of trapezoids. Compared with an FPC antenna with a traditional flat ground plane, the 3-dB gain bandwidth of the one with a shaped ground plane is improved from 11% to 20.2% with the maximum realized gain and the 10-dB impedance bandwidth almost unchanged. To validate the feasibility of the proposed approach, an FPC antenna prototype has been designed, fabricated, and measured. It consists of a U-slot rectangular microstrip patch antenna as the source, a Rogers RT6006 superstrate as the partially reflective surface, and the proposed shaped ground plane. Measured results on input reflection coefficients and radiation patterns agree well with simulated ones. Therefore, this new approach can be an effective way to enhance the gain bandwidth without increasing the cavity profile or using multi-layer superstrate structures.
Jia, H, Liu, W, Wang, J, Ngo, H-H, Guo, W & Zhang, H 2018, 'Optimization of sensing performance in an integrated dual sensors system combining microbial fuel cells and upflow anaerobic sludge bed reactor.', Chemosphere, vol. 210, pp. 931-940.
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Two bio-cathode microbial fuel cells (MFCs) with Upflow anaerobic sludge blanket (UASB) are integrated to construct a UASB-MFC dual sensors system, developed to solve the problems of low accuracy and less information about the single sensor system. The bio-cathode MFC is developed as the biosensor for UASB operation performance and online monitoring. Owing to the biosorption of anaerobic microbes, the MFC in the suspended layer is more suitable for effluent chemical oxygen demand (COD) real-time monitoring, and the MFC in the sludge layer is more suitable for total volatile fatty acid (TVFA) real-time monitoring. Electrochemical analysis discovers that the lower electron transfer resistance determines the higher sensitivity of MFC in the suspended layer. The difference in species abundance indicates that TVFA has a stronger inhibitory effect on MFC in the sludge layer. The novel UASB-MFC dual sensors system shows promising potential for COD and TVFA simultaneous online monitoring, which can enhance the reliability of anaerobic reaction.
Jian, S, Cao, L, Lu, K & Gao, H 2018, 'Unsupervised Coupled Metric Similarity for Non-IID Categorical Data', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 9, pp. 1810-1823.
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© 1989-2012 IEEE. Appropriate similarity measures always play a critical role in data analytics, learning, and processing. Measuring the intrinsic similarity of categorical data for unsupervised learning has not been substantially addressed, and even less effort has been made for the similarity analysis of categorical data that is not independent and identically distributed (non-IID). In this work, a Coupled Metric Similarity (CMS) is defined for unsupervised learning which flexibly captures the value-to-attribute-to-object heterogeneous coupling relationships. CMS learns the similarities in terms of intrinsic heterogeneous intra-and inter-attribute couplings and attribute-to-object couplings in categorical data. The CMS validity is guaranteed by satisfying metric properties and conditions, and CMS can flexibly adapt to IID to non-IID data. CMS is incorporated into spectral clustering and k-modes clustering and compared with relevant state-of-the-art similarity measures that are not necessarily metrics. The experimental results and theoretical analysis show the CMS effectiveness of capturing independent and coupled data characteristics, which significantly outperforms other similarity measures on most datasets.
Jiang, Q., Ngo, H.H., Nghiem, L.D., Hai, F.I., Price, W.E., Zhang, J., Liang, S., Deng, L. & Guo, W. 2018, 'Effect of hydraulic retention time on the performance of a hybrid moving bed biofilm reactor-membrane bioreactor system for micropollutants removal from municipal wastewater.', Bioresource Technology, vol. 247, pp. 1228-1232.
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This study evaluated micropollutants removal and membrane fouling behaviour of a hybrid moving bed biofilm reactor-membrane bioreactor (MBBR-MBR) system at four different hydraulic retention times (HRTs) (24, 18, 12 and 6h). The results revealed that HRT of 18h was the optimal condition regarding the removal of most selected micropollutants. As the primary removal mechanism in the hybrid system was biodegradation, the attached growth pattern was desirable for enriching slow growing bacteria and developing a diversity of biocoenosis. Thus, the efficient removal of micropollutants was obtained. In terms of membrane fouling propensity analysis, a longer HRT (e.g. HRTs of 24 and 18h) could significantly mitigate membrane fouling when compared with the shortest HRT of 6h. Hence, enhanced system performance could be achieved when the MBBR-MBR system was operated at HRT of 18h.
Jiang, W, Qi, J, Yu, JX, Huang, J & Zhang, R 2018, 'HyperX: A Scalable Hypergraph Framework', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Hypergraphs are generalizations of graphs where the hyperedges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion create excessive replicas and result in large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm "Pregel", which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.
Jordan, R, Gray, S, Zellner, M, Glynn, PD, Voinov, A, Hedelin, B, Sterling, EJ, Leong, K, Olabisi, LS, Hubacek, K, Bommel, P, BenDor, TK, Jetter, AJ, Laursen, B, Singer, A, Giabbanelli, PJ, Kolagani, N, Carrera, LB, Jenni, K & Prell, C 2018, 'Twelve Questions for the Participatory Modeling Community', Earth's Future, vol. 6, no. 8, pp. 1046-1057.
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©2018. The Authors. Participatory modeling engages the implicit and explicit knowledge of stakeholders to create formalized and shared representations of reality and has evolved into a field of study as well as a practice. Participatory modeling researchers and practitioners who focus specifically on environmental resources met at the National Socio-Environmental Synthesis Center (SESYNC) in Annapolis, Maryland, over the course of 2 years to discuss the state of the field and future directions for participatory modeling. What follows is a description of 12 overarching groups of questions that could guide future inquiry.
Ju, M, Ding, C, Zhang, D & Guo, YJ 2018, 'Gamma-correction-based visibility restoration for single hazy images', IEEE Signal Processing Letters, vol. 25, no. 7, pp. 1084-1088.
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© 1994-2012 IEEE. In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance.
Kabir, MI, Subhani, M, Shrestha, R & Samali, B 2018, 'Experimental and theoretical analysis of severely damaged concrete beams strengthened with CFRP', Construction and Building Materials, vol. 178, pp. 161-174.
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© 2018 Elsevier Ltd In recent years, carbon fibre reinforced polymer (CFRP) has gained its popularity for repairing reinforced concrete structures. At the same time, numerous research have been conducted on the use of different admixtures in the concrete to enhance its various physical properties. This study investigates the repairing techniques of three concrete beams which contain fly ash, waste rubber and polypropylene fibre in the concrete mix. The control beams were loaded up to its ultimate strength (severely damaged) and then repaired using CFRP, externally bonded to the beam soffit and anchored with complete CFRP wraps. The repaired beams were tested under four-point bending set-up to investigate its failure modes and improvement in strength, stiffness and ductility. In terms of strength and stiffness, two of the repaired beams (with fly ash and waste rubber) exceeded the capacity of the control beams, whereas the beam with polypropylene fibre gained around half of the strength and stiffness compared to its control counterpart. While the ductility of the repaired beams was found to be less than the control ones, the repaired beams exhibit pseudo ductile behaviour. In addition, an analytical study is conducted considering the effect of transverse CFRP anchorage wraps on the flexural capacity of repaired beams using shear friction model which can predict the strength of repaired beam to great accuracy.
Kaiwartya, O., Abdullah, A.H., Cao, Y., Lloret, J., Kumar, S., Shah, R.R., Prasad, M. & Prakash, S. 2018, 'Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things', IEEE Internet of Things Journal, vol. 5, no. 2, pp. 571-580.
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IEEE Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering fault tolerance and communication delay as two conflicting objectives. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, fault tolerance and delay computation, crossover and mutation, and non-dominance based sorting. Analytical and simulation based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes fault tolerance for virtualization in WSNs.
Kalantar, B, Pradhan, B, Naghibi, SA, Motevalli, A & Mansor, S 2018, 'Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)', Geomatics, Natural Hazards and Risk, vol. 9, no. 1, pp. 49-69.
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© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively.
Kalaruban, M., Loganathan, P., Kandasamy, J. & Vigneswaran, S. 2018, 'Submerged membrane adsorption hybrid system using four adsorbents to remove nitrate from water.', Environmental Science and Pollution Research.
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Nitrate contamination of ground and surface waters causes environmental pollution and human health problems in many parts of the world. This study tests the nitrate removal efficiencies of two ion exchange resins (Dowex 21K XLT and iron-modified Dowex 21K XLT (Dowex-Fe)) and two chemically modified bio-adsorbents (amine-grafted corn cob (AG corn cob) and amine-grafted coconut copra (AG coconut copra)) using a dynamic adsorption treatment system. A submerged membrane (microfiltration) adsorption hybrid system (SMAHS) was used for the continuous removal of nitrate with a minimal amount of adsorbents. The efficiency of membrane filtration flux and replacement rate of adsorbent were studied to determine suitable operating conditions to maintain the effluent nitrate concentration below the WHO drinking standard limit of 11.3 mg N/L. The volume of water treated and the amount of nitrate adsorbed per gramme of adsorbent for all four flux tested were in the order Dowex-Fe > Dowex > AG coconut copra > AG corn cob. The volumes of water treated (L/g adsorbent) were 0.91 and 1.85, and the amount of nitrate removed (mg N/g adsorbent) were 9.8 and 22.2 for AG corn cob and Dowex-Fe, respectively, at a flux of 15 L/(m2/h).
Kalaruban, M., Loganathan, P., Shim, W.G., Kandasamy, J. & Vigneswaran, S. 2018, 'Mathematical modelling of nitrate removal from water using a submerged membrane adsorption hybrid system with four adsorbents', Applied Sciences (Switzerland), vol. 8, no. 2.
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© 2018 by the authors. Excessive concentrations of nitrate in ground water are known to cause human health hazards. A submerged membrane adsorption hybrid system that includes a microfilter membrane and four different adsorbents (Dowex 21K XLT ion exchange resin (Dowex), Fe-coated Dowex, amine-grafted (AG) corn cob and AG coconut copra) operated at four different fluxes was used to continuously remove nitrate. The experimental data obtained in this study was simulated mathematically with a homogeneous surface diffusion model that incorporated membrane packing density and membrane correlation coefficient, and applied the concept of continuous flow stirred tank reactor. The model fit with experimental data was good. The surface diffusion coefficient was constant for all adsorbents and for all fluxes. The mass transfer coefficient increased with flux for all adsorbents and generally increased with the adsorption capacity of the adsorbents.
Kalhori, H., Makki Alamdari, M., Zhu, X. & Samali, B. 2018, 'Nothing-on-Road Axle Detection Strategies in Bridge-Weigh-in-Motion for a Cable-Stayed Bridge: Case Study', Journal of Bridge Engineering, vol. 23, no. 8.
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© 2018 American Society of Civil Engineers. This case-study article aims to share the field-test observations of a real-world cable-stayed bridge with the research community of bridge-weigh-in-motion to address the challenges of axle identification. Various structural members of the bridge, including cables, girders, and the deck, were instrumented with strain gauges at different locations to measure the axial, bending, or shear strain responses. Numerous field tests were conducted by running light and heavy vehicles traveling at different speeds, in different traffic directions, and in different lateral locations on the bridge. Because the identification of closely spaced axles is important to ensuring true classification of the vehicles, vehicles with tandem- and tridem-axle configurations were adopted in the field test. The study aimed to identify the sensor arrangement through which the closely spaced axles can be reliably detected regardless of the speed, traveling direction, and lateral location of the vehicle on the bridge. It was found that the axial strains on the cables and bending strains in the girders provided the global response of the structure; hence, they were unable to identify the closely spaced axles. In contrast, it was observed that the longitudinal strains under the deck were able to identify the closely spaced axles, provided they were positioned as closely as possible to the wheel path. Finally, the shear responses at the end of the span were able to identify the closely spaced axles irrespective of the traveling direction and lateral location of the vehicle. In this study, due to the testing limitations, including the short span of the bridge and the presence of a roundabout at one end of the bridge, it was not feasible to maintain a constant speed; therefore, identification of axle weight and axle spacing, which requires a constant-speed assumption, is not discussed.
Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2018, '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.
Kamyabniya, A., Lotfi, M.M., Naderpour, M. & Yih, Y. 2018, 'Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach', Information Systems Frontiers, vol. 20, no. 4, pp. 759-782.
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© 2017, Springer Science+Business Media, LLC. Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.
Kang, G., Li, J. & Tao, D. 2018, 'Shakeout: A New Approach to Regularized Deep Neural Network Training', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1245-1258.
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© 1979-2012 IEEE. Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines L-{0} , L-{1} and L-{2} regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.
Kang, Y., Zhang, J., Li, B., Zhang, Y., Sun, H., Hao Ngo, H., Guo, W., Xie, H., Hu, Z. & Zhao, C. 2018, 'Improvement of bioavailable carbon source and microbial structure toward enhanced nitrate removal by Tubifex tubifex', Chemical Engineering Journal, vol. 353, pp. 699-707.
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© 2018 Elsevier B.V. The research provides new insights into how T. tubifex affects microbe-available organic carbon transformation, microbial community, and related inorganic nitrogen transformation. With different T. tubifex densities under enriched NO3-N concentration with different C/N ratio, the groups with low and high T. tubifex abundance had 17.1% and 27.2% higher TOC concentrations compared to control group under C/N ratio of 3.0 and 2.0, respectively. According to the OC characteristics analyzed by GC–MS and EEM, the group with T. tubifex contained higher dissolved organic carbon (DOC) content and short-chain compounds. As the NO3-N concentration increased and C/N ratio decreased, the proportions of carboxylic acid derivatives (methyl acetate), sulfur-containing compounds, humic-like products and the emission of CO2were enhanced with T. tubifex, which could be utilized by denitrifiers directly. The OC degradation was highly correlated with the effect of T. tubifex on microbial community and nutrient removal. The T. tubifex can increase OC transformed into sediment and change the microbial community similarity to that in their digestive system. According to the principal component analysis (PCA), the improved proportions of OC that available for microbes and altered microbial community with T. tubifex could enhance denitrification, which experienced a 21% higher denitrifiers and threefold increased NO3-N removal efficiency than that in control group.
Kani, K., Malgras, V., Jiang, B., Hossain, M.S.A., Alshehri, S.M., Ahamad, T., Salunkhe, R.R., Huang, Z. & Yamauchi, Y. 2018, 'Periodically Arranged Arrays of Dendritic Pt Nanospheres Using Cage-Type Mesoporous Silica as a Hard Template', Chemistry - An Asian Journal, vol. 13, no. 1, pp. 106-110.
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Karamanakos, P., Geyer, T. & Aguilera, R.P.P. 2018, 'Long-Horizon Direct Model Predictive Control: Modified Sphere Decoding for Transient Operation', IEEE Transactions on Industry Applications.
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IEEE In this paper we present modifications to the sphere decoder initially introduced in [2] and modified in [3] that significantly reduce the computation times during transients. The relative position of the unconstrained solution of the integer quadratic program underlying model predictive control (MPC) with respect to the convex hull of the (truncated) lattice of integer points is examined. If it is found that the unconstrained solution does not lie within the convex hull—a phenomenon that is observed mostly during transients—then a projection is performed onto the convex hull. By doing so, a new sphere that guarantees feasibility and includes a significant smaller number of candidate solutions is computed. This reduces the computation time by up to three orders of magnitude when solving the optimization problem at hand. Nonetheless, the reduction of the computational burden comes at a cost of (mild) suboptimal results. The effectiveness of the proposed algorithm is tested with a variable speed drive system consisting of a three-level neutral point clamped (NPC) voltage source inverter and a medium-voltage induction machine. Based on the presented results, the sphere decoding algorithm with the proposed refinements maintains the very fast transient responses inherent to direct MPC. Moreover, it is observed that the occasional implementation of suboptimal solutions does not lead to a deterioration of the system performance.
Karmokar, DK, Guo, YJ, Qin, PY, Chen, SL & Bird, TS 2018, 'Substrate integrated waveguide-based periodic backward-to-forward scanning leaky-wave antenna with low cross-polarization', IEEE Transactions on Antennas and Propagation, vol. 66, no. 8, pp. 3846-3856.
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© 1963-2012 IEEE. For many leaky-wave antennas (LWAs), it is challenging to realize beam scanning through broadside. A problem is the presence of an open stopband (OSB), which restricts radiation in the broadside direction. In this paper, a novel substrate integrated waveguide (SIW)-based LWA is described to overcome the OSB problem and provide beam scanning continuously from the backward to the forward direction from a conventional periodic LWA. It is demonstrated that the n =-1 spatial harmonic can be excited efficiently from an SIW LWA and enables broadside radiation. However, it was found in our initial design that when the beam scans through the broadside, the cross-polarization level increases significantly compared to the beam close to the backfire direction. A technique is developed to reduce the cross-polarization level. As a result, a new antenna configuration is created. The antenna design has been realized and measured to validate the concept. The measured beam scanning range of the prototype is from -74° to +45° (119° of beam scanning) when the frequency sweeps from 7.625 to 11 GHz, and the measured cross-polarization level is 20.8 dB low at the main beam direction for the broadside beam.
Kendall, S, Redshaw, S, Ward, S, Wayland, S & Sullivan, E 2018, 'Systematic review of qualitative evaluations of reentry programs addressing problematic drug use and mental health disorders amongst people transitioning from prison to communities.', Health & justice, vol. 6, no. 1, p. 4.
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The paper presents a systematic review and metasynthesis of findings from qualitative evaluations of community reentry programs. The programs sought to engage recently released adult prison inmates with either problematic drug use or a mental health disorder.Seven biomedical and social science databases, Cinahl, Pubmed, Scopus, Proquest, Medline, Sociological abstracts and Web of Science and publisher database Taylor and Francis were searched in 2016 resulting in 2373 potential papers. Abstract reviews left 140 papers of which 8 were included after detailed review. Major themes and subthemes were identified through grounded theory inductive analysis of results from the eight papers. Of the final eight papers the majority (6) were from the United States. In total, the papers covered 405 interviews and included 121 (30%) females and 284 (70%) males.Findings suggest that the interpersonal skills of case workers; access to social support and housing; and continuity of case worker relationships throughout the pre-release and post-release period are key social and structural factors in program success.Evaluation of community reentry programs requires qualitative data to contextualize statistical findings and identify social and structural factors that impact on reducing incarceration and improving participant health. These aspects of program efficacy have implications for reentry program development and staff training and broader social and health policy and services.
Kenning, G. 2018, 'LAUGH: co-design for people living with advanced dementia', Australian Journal Of Dementia Care.
Kenning, G. 2018, 'Reciprocal design: inclusive design approaches for people with late stage dementia', Design for Health, pp. 1-21.
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Kenning, G. & Treadaway, C. 2018, 'Designing for dementia: iterative grief and transitional objects', Mortality and Design in Design Issues, vol. 34, no. 1.
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Keshavarzi, A., Shrestha, C.K., Melville, B., Khabbaz, H., Ranjbar-Zahedani, M. & Ball, J.E. 2018, 'Estimation of maximum scour depths at upstream of front and rear piers for two in-line circular columns', Evironental Flud Mcancs.
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Khalid, M., Aguilera, R.P., Savkin, A.V. & Agelidis, V.G. 2018, 'A market-oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage', Wind Energy, vol. 21, no. 4, pp. 242-254.
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Copyright © 2018 John Wiley & Sons, Ltd. In this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid-tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time-varying market-price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market-price data, depict the proficiency of the proposed methodology over standard linear programming methods.
Khalid, M., Aguilera, R.P., Savkin, A.V. & Agelidis, V.G. 2018, 'On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting', Applied Energy, vol. 211, pp. 764-773.
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© 2017 Elsevier Ltd This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity. The obtained results depict the effectiveness of the strategy to help power system operators in ensuring economically optimal energy dispatch. Moreover, the results can aid power system planners in the selection of optimal BESS capacity for given power ratings in order to maximize their operational profits.
Khan, AA, Abolhasan, M & Ni, W 2018, 'An evolutionary game theoretic approach for stable and optimized clustering in vanets', IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4501-4513.
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© 1967-2012 IEEE. Discovering and maintaining efficient routes for data dissemination in vehicular ad hoc networks (VANETs) has proven to be a very challenging problem. Clustering is one of the control protocols used to provide efficient and stable routes for data dissemination. However, the rapid changes in network topology in VANETs creates frequent cluster reformation, which can seriously affect route stability. We propose a novel evolutionary game theoretic (EGT) framework to automate the clustering of nodes and nominations of cluster heads, to achieve cluster stability in VANETs. The equilibrium point is proven analytically and the stability is also tested using Lyapunov function. The performance of the proposed evolutionary game is empirically investigated with different cost functions using static and mobile scenarios. The simulation results demonstrate the effectiveness and robustness of our proposed EGT approach for different populations and speeds, thus reducing the overhead of frequent cluster reformation in VANETs.
Khan, F., ur Rehman, A., Usman, M., Tan, Z. & Puthal, D. 2018, 'Performance of Cognitive Radio Sensor Networks Using Hybrid Automatic Repeat ReQuest: Stop-and-Wait', Mobile Networks and Applications, vol. 23, no. 3, pp. 479-488.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model.
Khan, H, Razmjou, A, Ebrahimi Warkiani, M, Kottapalli, A & Asadnia, M 2018, 'Sensitive and Flexible Polymeric Strain Sensor for Accurate Human Motion Monitoring.', Sensors (Basel, Switzerland), vol. 18, no. 2.
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Flexible electronic devices offer the capability to integrate and adapt with human body. These devices are mountable on surfaces with various shapes, which allow us to attach them to clothes or directly onto the body. This paper suggests a facile fabrication strategy via electrospinning to develop a stretchable, and sensitive poly (vinylidene fluoride) nanofibrous strain sensor for human motion monitoring. A complete characterization on the single PVDF nano fiber has been performed. The charge generated by PVDF electrospun strain sensor changes was employed as a parameter to control the finger motion of the robotic arm. As a proof of concept, we developed a smart glove with five sensors integrated into it to detect the fingers motion and transfer it to a robotic hand. Our results shows that the proposed strain sensors are able to detect tiny motion of fingers and successfully run the robotic hand.
Khan, MA, Ngo, HH, Guo, W, Liu, Y, Zhang, X, Guo, J, Chang, SW, Nguyen, DD & Wang, J 2018, 'Biohydrogen production from anaerobic digestion and its potential as renewable energy', Renewable Energy.
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© 2017. The current demand-supply scenario for fossil fuels requires an alternative energy source with cleaner combustion products whilst production of hydrogen from anaerobic digestion involves the utilization of waste materials and zero emission of greenhouse gasses. However, large scale industrial application has yet not been implemented due to numerous challenges in its production, storage, and transportation. This review study demonstrates that production of hydrogen from anaerobic digestion is potentially a worthy alternative regarding energy density, environmental impact, and cost. Moreover, dependence on fossil fuel systems in the future could be minimized when biohydrogen production is feasible from renewable energy sources.
Kim, DI, Gwak, G, Dorji, P, He, D, Phuntsho, S, Hong, S & Shon, H 2018, 'Palladium Recovery through Membrane Capacitive Deionization from Metal Plating Wastewater', ACS Sustainable Chemistry and Engineering, vol. 6, no. 2, pp. 1692-1701.
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© 2017 American Chemical Society. The potential application of membrane capacitive deionization (MCDI) for recovery of palladium (Pd) ions from catalyst solution wastewater generated from the plating industry was investigated in this study. Several major issues were explored in this work to verify the suitability of MCDI for Pd recovery from a practical perspective: adsorption and desorption efficiencies, desorption mechanisms into high concentration of Pd concentrate, and its sustainability in long-term operation. The lab-scale MCDI operation achieved satisfactory and highly competitive Pd removal (99.07-99.94% removal with 1.42-1.52 of Pd selectivity over ammonium ions) showing that Pd can be effectively collected from plating industry wastewater. A high concentration of Pd concentrate (64.77 and 919.44 mg/L of Pd from the 10 and 100 mg/L Pd containing catalyst solution, respectively) was obtained through successive five operation cycles of adsorption/desorption phases. However, it is significant to note that the desorption efficiency was inversely proportional to the concentration of Pd concentrate which is likely due to the Pd ions discharged from carbon electrode toward Pd solution against the enhanced concentration gradient. The long-term operation results suggest that scaling could reduce the MCDI efficiency during Pd recovery (0.17% decrease in Pd removal for every cycle on average) and hence may require an adequate electrode cleaning regime.
Kim, J.E., Phuntsho, S., Chekli, L., Choi, J.Y. & Shon, H.K. 2018, 'Environmental and economic assessment of hybrid FO-RO/NF system with selected inorganic draw solutes for the treatment of mine impaired water', Desalination, vol. 429, pp. 96-104.
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© 2017 Elsevier B.V. A hybrid forward osmosis (FO) and reverse osmosis (RO)/nanofiltration (NF) system in a closed-loop operation with selected draw solutes was evaluated to treat coal mine impaired water. This study provides an insight of selecting the most suitable draw solution (DS) by conducting environmental and economic life cycle assessment (LCA). Baseline environmental LCA showed that the dominant components to energy use and global warming are the DS recovery processes (i.e. RO or NF processes) and FO membrane materials, respectively. When considering the DS replenishment in FO, the contribution of chemical use to the overall global warming impact was significant for all hybrid systems. Furthermore, from an environmental perspective, the FO-NF hybrid system with Na2SO4shows the lowest energy consumption and global warming with additional considerations of final product water quality and FO brine disposal. From an economic perspective, the FO-NF with Na2SO4showed the lowest total operating cost due to its lower DS loss and relatively low solute cost. In a closed-loop system, FO-NF with NaCl and Na2SO4had the lowest total water cost at optimum NF recovery rates of 90 and 95%, respectively. FO-NF with Na2SO4had the lowest environmental and economic impacts. Overall, draw solute performances and cost in FO and recovery rate in RO/NF play a crucial role in determining the total water cost and environmental impact of FO hybrid systems in a closed-loop operation.
Kim, JE, Phuntsho, S, Ali, SM, Choi, JY & Shon, HK 2018, 'Forward osmosis membrane modular configurations for osmotic dilution of seawater by forward osmosis and reverse osmosis hybrid system.', Water research, vol. 128, pp. 183-192.
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This study evaluates various options for full-scale modular configuration of forward osmosis (FO) process for osmotic dilution of seawater using wastewater for simultaneous desalination and water reuse through FO-reverse osmosis (RO) hybrid system. Empirical relationship obtained from one FO membrane element operation was used to simulate the operational performances of different FO module configurations. The main limiting criteria for module operation is to always maintain the feed pressure higher than the draw pressure throughout the housing module for safe operation without affecting membrane integrity. Experimental studies under the conditions tested in this study show that a single membrane housing cannot accommodate more than four elements as the draw pressure exceeds the feed pressure. This then indicates that a single stage housing with eight elements is not likely to be practical for safe FO operation. Hence, six different FO modular configurations were proposed and simulated. A two-stage FO configuration with multiple housings (in parallel) in the second stage using same or larger spacer thickness reduces draw pressure build-up as the draw flow rates are reduced to half in the second stage thereby allowing more than four elements in the second stage housing. The loss of feed pressure (pressure drop) and osmotic driving force in the second stage are compensated by operating under the pressure assisted osmosis (PAO) mode, which helps enhance permeate flux and maintains positive pressure differences between the feed and draw chamber. The PAO energy penalty is compensated by enhanced permeate throughput, reduced membrane area, and plant footprint. The contribution of FO/PAO to total energy consumption was not significant compared to post RO desalination (90%) indicating that the proposed two-stage FO modular configuration is one way of making the FO full-scale operation practical for FO-RO hybrid system.
Kim, S., Piao, G., Han, D.S., Shon, H.K. & Park, H. 2018, 'Solar desalination coupled with water remediation and molecular hydrogen production: A novel solar water-energy nexus', Energy and Environmental Science, vol. 11, no. 2, pp. 344-353.
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© 2018 The Royal Society of Chemistry. A novel sunlight-water-energy nexus technology is presented that combines the photoelectrocatalytic (PEC) desalination of saline water and desalination-driven wastewater remediation coupled with the production of molecular hydrogen (H2) from water. To accomplish this, morphologically tailored TiO2nanorod (TNR) and hydrogen-treated TNR (H-TNR) array photoanodes are placed in an anode cell and Pt foils are located in a cathode cell, while a middle cell containing saline water (0.17 M NaCl) faces these cells through anion and cation exchange membranes, respectively. Upon irradiation by simulated sunlight (AM 1.5G, 100 mW cm-2), the photogeneration of charge carriers initiates the transport of chloride and sodium in the middle cell to the anode and cathode cells, respectively, leading to the desalination of saline water. The chloride in the anode cell is converted to reactive chlorine species (RCS), which effectively decompose urea to N2as a primary product (>80%), while the sodium in the cathode cell accelerates the H2production from water with a Faradaic efficiency of ∼80%. The PEC performance of the H-TNR photoanodes is superior to that of the TNR in the anodic and cathodic processes because of the reduced charge transfer resistance and sub-nanosecond charge transfer kinetics (∼0.19 ns), leading to a specific energy consumption of 4.4 kW h m-3for 50% desalination, with an energy recovery of ∼0.8 kW h m-3. The hybrid system is found to operate for a period of ∼60 h with natural seawater, and virtually all the photoanodes are shown to be capable of driving the hybrid process. Although tested as a proof-of-concept, the present technology opens up a novel field involving a sunlight-water-energy nexus, promising high efficiency desalination and the desalination-driven remediation of water with simultaneous H2production.
King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2018, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.
Kong, F, Sun, X, Guo, YJ, Leung, VCM, Zhu, Q & Zhu, H 2018, 'Queue-aware Power Consumption Minimization in Two-tier Heterogeneous Networks', IEEE Transactions on Vehicular Technology.
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IEEE In this paper, we study the network average power consumption minimization problem in a 2-tier heterogeneous network (HetNet) by optimally tuning the activation ratio of micro base stations (BSs) under the quality of service (QoS) constraints of the network mean queueing delay and the network signal-to-interference ratio (SIR) coverage. With the consideration of dynamic packets arrivals, each BS can either be busy or be idle depending on its queueing status. The network performance is thus critically determined by the traffic intensity of each BS. With the assumption of universal frequency reuse, the average traffic intensity of each tier is characterized by a set of fixed-point equations, which can be solved by a proposed iterative method. By using the approximation that BSs of the same tier have the same SIR coverage, the cumulative distribution function (CDF) of the traffic intensity of each tier is further obtained. On that basis, the network average power consumption per area, the network mean queueing delay and the network SIR coverage are characterized. Numerical results demonstrate that if the idle power coefficient is below a certain threshold, then the optimal activation ratio equals the one to minimize the network average power consumption per area; otherwise, the optimal activation ratio can be obtained according to the QoS constraints. It is further shown that universal frequency reuse outperforms spectrum partitioning in terms of both the network average power consumption and the network SIR coverage in the considered scenario.
Kong, S, Lee, JH & Li, S 2018, 'A new distributed algorithm for efficient generalized arc-consistency propagation', Autonomous Agents and Multi-Agent Systems, vol. 32, no. 5, pp. 569-601.
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© 2018, The Author(s). Generalized arc-consistency propagation is predominantly used in constraint solvers to efficiently prune the search space when solving constraint satisfaction problems. Although many practical applications can be modelled as distributed constraint satisfaction problems, no distributed arc-consistency algorithms so far have considered the privacy of individual agents. In this paper, we propose a new distributed arc-consistency algorithm, called DisAC3.1, which leaks less private information of agents than existing distributed arc-consistency algorithms. In particular, DisAC3.1 uses a novel termination determination mechanism, which allows the agents to share domains, constraints and communication addresses only with relevant agents. We further extend DisAC3.1 to DisGAC3.1, which is the first distributed algorithm that enforces generalized arc-consistency on k-ary (k≥ 2) constraint satisfaction problems. Theoretical analyses show that our algorithms are efficient in both time and space. Experiments also demonstrate that DisAC3.1 outperforms the state-of-the-art distributed arc-consistency algorithm and that DisGAC3.1 ’s performance scales linearly in the number of agents.
Kulasinghe, A, Schmidt, H, Perry, C, Whitfield, B, Kenny, L, Nelson, C, Warkiani, ME & Punyadeera, C 2018, 'A Collective Route to Head and Neck Cancer Metastasis', Scientific Reports, vol. 8, no. 1.
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© 2018 The Author(s). Distant metastasis (DM) from head and neck cancers (HNC) portends a poor patient prognosis. Despite its important biological role, little is known about the cells which seed these DM. Circulating tumour cells (CTCs) represent a transient cancer cell population, which circulate in HNC patients' peripheral blood and seed at distant sites. Capture and analysis of CTCs offers insights into tumour metastasis and can facilitate treatment strategies. Whilst the data on singular CTCs have shown clinical significance, the role of CTC clusters in metastasis remains limited. In this pilot study, we assessed 60 treatment naïve HNC patients for CTCs with disease ranging from early to advanced stages, for CTC clusters utilizing spiral CTC enrichment technology. Single CTCs were isolated in 18/60-30% (Ranging from Stage I-IV), CTC clusters in 15/60-25% (exclusively Stage IV) with 3/15-20% of CTC clusters also containing leukocytes. The presence of CTC clusters associated with the development of distant metastatic disease(P = 0.0313). This study demonstrates that CTC clusters are found in locally advanced patients, and this may be an important prognostic marker. In vivo and in vitro studies are warranted to determine the role of these CTC clusters, in particular, whether leukocyte involvement in CTC clusters has clinical relevance.
Kuruneru, STW, Sauret, E, Saha, SC & Gu, YT 2018, 'Coupled CFD-DEM simulation of oscillatory particle-laden fluid flow through a porous metal foam heat exchanger: Mitigation of particulate fouling', Chemical Engineering Science, vol. 179, pp. 32-52.
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© 2018 Elsevier Ltd A coupled finite volume-discrete element (FVM-DEM) numerical method is developed to investigate oscillating multiphase foulant-laden air (solid-gas) flow and particulate fouling in a porous heat exchanger channel comprising an array of circular cylinders. Oscillatory fluids serve as a steppingstone to advance existing conventional anti-fouling techniques and to yield optimum energy consumption. A performance evaluation criteria based on various amplitudes of pulsation and foulant injection rates is established. The fouling characteristics and pressure drop is compared against a non-oscillating steady flow. For the solid foulant particles considered in this study, the results show that the time-averaged deposition fraction is sensibly invariant with increasing foulant injection rate; however, the same observation is not realized for steady-flow cases. The 1 Hz case showed no significant disparity in terms of deposition fraction at high injection rates compared with the non-oscillating steady flow case. In addition, the 1 Hz case showed miniscule reduction of low-density foulants. The higher-frequency spectrum has superior fouling mitigation capabilities although comparatively higher pressure drop is generated. The 6-pore configuration outperforms the 3-pore under certain oscillatory flow conditions. An optimum frequency of 5 Hz exhibits superior fouling mitigation performance while achieving a low time-averaged pressure drop.
Kusakunniran, W, Wu, Q, Ritthipravat, P & Zhang, J 2018, 'Hard exudates segmentation based on learned initial seeds and iterative graph cut.', Computer methods and programs in biomedicine, vol. 158, pp. 173-183.
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(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the exi...
La, H.M., Dinh, T.H., Pham, N.H., Ha, Q.P. & Pham, A.Q. 2018, 'Automated robotic monitoring and inspection of steel structures and bridges', Robotica, pp. 1-21.
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Copyright © Cambridge University Press 2018 This paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.
Laengle, S, Modak, NM, Merigo, JM & Zurita, G 2018, 'Twenty-Five Years of Group Decision and Negotiation: A Bibliometric Overview', Group Decision and Negotiation, vol. 27, no. 4, pp. 505-542.
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© 2018, Springer Science+Business Media B.V., part of Springer Nature. Twenty-five years ago, in 1992, a journal named Group Decision and Negotiation was established in association with the Institute for Operations Research and the Management Sciences with the vision of promoting theoretical and empirical research, real-world applications and case studies on group decision and negotiation processes. To celebrate its 25 years of continuous and outstanding contributions, this study aims to develop a bibliometric analysis of the publications of the journal between 1992 and 2016. The Web of Science Core Collection database is used to identify the leading trends of the journal in terms of impacts, topics, authors, universities and countries. Moreover, it utilizes the visualization of similarities viewer software to analyze the bibliographic couplings, co-citations, citations, co-authorships and co-occurrences of keywords.
Lai, W, Ni, W, Wang, H & Liu, RP 2018, 'Analysis of Average Packet Loss Rate in Multi-Hop Broadcast for VANETs', IEEE Communications Letters, vol. 22, no. 1, pp. 157-160.
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© 2017 IEEE. Multi-hop relay can effectively improve the average packet loss rate (PLR) of vehicular ad hoc networks within a particular zone of interest. Challenges arise from analyzing the average PLR affected by distributed relay selections, which depend on the mobility of vehicles, the wireless channel conditions, and media access control (MAC). In this letter, we propose an average PLR analysis model taking into account the above three factors. However, the closed-form expression for the average PLR is intractable mainly due to the multiple integral of the joint distance distribution integrating with the channel conditions and vehicle mobility. An explicit expression for the upper bound of the average PLR is obtained by using Taylor series expansion, Holder's inequality, and the relay probability relaxation, which can facilitate the selection of the parameters at the physical and MAC layers for a better PLR. Simulation results validate our analyses.
Lamqadem, AA, Pradhan, B, Saber, H & Rahimi, A 2018, 'Desertification Sensitivity Analysis Using MEDALUS Model and GIS: A Case Study of the Oases of Middle Draa Valley, Morocco.', Sensors (Basel, Switzerland), vol. 18, no. 7.
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Oases can play a significant role in the sustainable economic development of arid and Saharan regions. The aim of this study was to map the desertification-sensitive areas in the Middle Draa Valley (MDV), which is in the southeast of Morocco. A total of 13 indices that affect desertification processes were identified and analyzed using a geographic information system. The Mediterranean desertification and land use approach; which has been widely used in the Mediterranean regions due to its simplicity; flexibility and rapid implementation strategy; was applied. All the indices were grouped into four main quality indices; i.e., soil quality; climate quality; vegetation quality and management quality indices. Each quality index was constructed by the combination of several sub-indicators. In turn; the geometric mean of the four quality index maps was used to construct a map of desertification-sensitive areas; which were classified into four classes (i.e., low; moderate; high and very high sensitivity). Results indicated that only 16.63% of the sites in the study were classified as least sensitive to desertification; and 50.34% were classified as highly and very highly sensitive areas. Findings also showed that climate and human pressure factors are the most important indicators affecting desertification sensitivity in the MDV. The framework used in this research provides suitable results and can be easily implemented in similar oasis arid areas.
Lan, L, Wang, X, Zhang, S, Tao, D, Gao, W & Huang, TS 2018, 'Interacting Tracklets for Multi-Object Tracking', IEEE Transactions on Image Processing, vol. 27, no. 9, pp. 4585-4597.
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© 1992-2012 IEEE. In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets, and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient quadratic pseudo-Boolean optimization. It yields promising tracking performance on the challenging PETS09 and MOT16 dataset.
Lanese, I. & Devitt, S. 2018, 'Preface for the special issue of the 8th Conference on Reversible Computation (RC 2016)', Science of Computer Programming, vol. 151, p. 1.
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Lapko, Y., Trianni, A., Nuur, C. & Masi, D. 2018, 'In Pursuit of Closed-Loop Supply Chains for Critical Materials: An Exploratory Study in the Green Energy Sector', Journal of Industrial Ecology.
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© 2018, Yale University. A closed-loop supply chain (CLSC) is considered not only an important solution for ensuring sustainable exploitation of materials, but also a promising strategy for securing long-term availability of materials. The latter is especially highlighted in the materials criticality discourse. Critical raw materials (CRMs), being exposed to supply disruptions, create an uncertain operational environment for many industries, particularly for green energy technologies that employ multiple CRMs. However, recycling rates of CRMs are very low and engagement of companies in CLSC for CRM is limited. This study examines factors influencing CLSC for CRM development in photovoltaic panels and wind turbine technologies. The aim is to analyze how the factors manifest themselves in different companies along the supply chain and to identify enabling and bottleneck conditions for implementation of CLSC for CRM. The novelty of the study is twofold: the focus on material rather than product flows, and examination of factors from a multiactor perspective. The evidence obtained suggests that the manufacturing companies and reverse supply-chain operators engaged in the study take different perspectives (product vs. material) regarding development of CLSC for CRM and thus emphasize different factors. The findings underline the need for interactions between supply-chain actors, a sound competitive environment for recycling processes, and investment in technologies and infrastructure development if CLSC for CRM is to be developed. The paper provides implications for practitioners and policy makers for implementation of CLSC for CRM, and suggests prospects for further research.
Le, X., Chen, S., Yan, Z. & Xi, J. 2018, 'A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints', IEEE Transactions on Cybernetics.
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IEEE In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.
Lee, D., Van Dorp Schuitman, J., Qiu, X. & Burnett, I. 2018, 'Development of a clarity parameter using a time-varying loudness model', Journal of the Acoustical Society of America, vol. 143, no. 6, pp. 3455-3459.
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© 2018 Acoustical Society of America. The perceived sound clarity is often estimated with the clarity index, which is calculated on the basis of physical acoustic measures that can correlate weakly to the way humans perceive sound for certain test conditions. Therefore, this study proposes a clarity parameter based on a binaural room impulse response processed with a time-varying loudness model. The proposed parameter is validated by calculating the correlation coefficient with subject responses collected from previous listening experiments. Results show that the parameter outperforms the clarity index in most of the tested conditions, but its performance is less robust than parameter for clarity (PCLA).
Lee, J.H., Sameen, M.I., Pradhan, B. & Park, H.J. 2018, 'Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods', Geomorphology, vol. 303, pp. 284-298.
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© 2017 Elsevier B.V. This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.
Lee, S, Kim, Y, Park, J, Shon, HK & Hong, S 2018, 'Treatment of medical radioactive liquid waste using Forward Osmosis (FO) membrane process', Journal of Membrane Science, vol. 556, pp. 238-247.
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© 2018 Elsevier B.V. The use of forward osmosis (FO) for concentrating radioactive liquid waste from radiation therapy rooms in hospitals was systematically investigated in this study. The removal of natural and radioactive iodine using FO was first investigated with varying pHs and draw solutions (DSs) to identify the optimal conditions for FO concentration. Results showed that FO had a successful rejection rate for both natural and radioactive iodine (125I) of up to 99.3%. This high rejection rate was achieved at a high pH, mainly due to electric repulsion between iodine and membrane. Higher iodine removal by FO was also attained with a DS that exhibits a reverse salt flux (RSF) adequate to hinder iodine transport. Following this, actual radioactive medical liquid waste was collected and concentrated using FO under these optimal conditions. The radionuclides in the medical waste (131I) were removed effectively, but the water recovery rate was limited due to severe membrane fouling. To enhance the recovery rate, hydraulic washing was applied, but this had only limited success due to combined organic-inorganic fouling of the FO membrane. Finally, the effect of FO concentration on the reduction of septic tank volume was simulated as a function of recovery rate. To our knowledge, this study is the first attempt to explore the potential of FO technology for treating radioactive waste, and thus could be expanded to the dewatering of the radioactive liquid wastes from a variety of sources, such as nuclear power plants.
Lee, S., Choi, J., Park, Y.G., Shon, H., Ahn, C.H. & Kim, S.H. 2018, 'Hybrid desalination processes for beneficial use of reverse osmosis brine: Current status and future prospects', Desalination.
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© 2018 Elsevier B.V. As water shortage has increasingly become a serious global problem, desalination using seawater reverse osmosis (SWRO) is considered as a sustainable source of potable water sources. However, a major issue on the SWRO desalination plant is the generation of brine that has potential adverse impact due to its high salt concentration. Accordingly, it is necessary to develop technologies that allow environmentally friendly and economically viable management of SWRO brines.This paper gives an overview of recent research works and technologies to treat SWRO brines for its beneficial use. The treatment processes have been classified into two different groups according to their final purpose: 1) technologies for producing fresh water and 2) technologies for recovering energy. Topics in this paper includes membrane distillation (MD), forward osmosis (FO), pressure-retarded osmosis (PRO), reverse electrodialysis (RED) as emerging tools for beneficial use of SWRO brine. In addition, a new approach to simultaneously recover water and energy from SWRO brine is introduced as a case study to provide insight into improving the sustainability of seawater desalination.
Lees, T, Fatima-Shad, K, Simpson, A, Nassif, N, Lin, Y & Lal, S 2018, 'Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality', Biomarker Insights, vol. 13, pp. 1-13.
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Background: Heart rate variability (HRV) is a non-invasive measure of the function of the autonomic nervous system, and its dynamic nature may provide a means through which stroke and its associated complications may be predicted, monitored, and managed.
Objective: The objective of this review is to identify and provide a critique on the most recent uses of HRV in stroke diagnosis/management and highlight areas that warrant further research.
Methods: The MEDLINE, CINAHL, and OVID MEDLINE databases were canvassed using a systematic search strategy, for articles investigating the use of HRV in stroke diagnosis and management. Initial paper selections were based on title alone, and final paper inclusion was informed by a full-text critical appraisal.
Results: The systematic search returned 98 records, of which 51 were unique. Following screening, 22 records were included in the final systematic review. The included papers provided some information regarding predicting incident stroke, which largely seems to be best predicted by time- and frequency-domain HRV parameters. Furthermore, post-stroke complications and functionality are similarly predicted by time- and frequency-domain parameters, as well as non-linear parameters in some instances.
Conclusions: Current research provides good evidence that HRV parameters may have utility as a biomarker for stroke and for post-stroke complications and/or functionality. Future research would benefit from the integration of non-linear, and novel parameters, the hybridisation of HRV parameters, and the expansion of the utilisation of predictive regression and hazard modelling.
Lei, B, Li, W, Li, Z, Wang, G & Sun, Z 2018, 'Effect of cyclic loading deterioration on concrete durability: Water absorption, freeze-thaw, and carbonation', Journal of Materials in Civil Engineering, vol. 30, no. 9.
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© 2018 American Society of Civil Engineers. The effect of cyclic loading deterioration on freeze-thaw and carbonation resistances of concrete were experimentally investigated in this study. A novel loading method was designed, which simultaneously considers both mechanical loading and environmental actions for concrete. It shows that with the increase of cyclic compressive loading, the porosity and water absorption of concrete initially decrease but then increase when the stress is above a threshold level because of the cracking initiation caused by cyclic compression. With the increase of concrete porosity, both dynamic elastic modulus loss and carbonation depth obviously exhibit an increasing trend. On the other hand, under the same stress level, the freeze-thaw and carbonation resistances of high-strength concrete are relatively superior to those of low-strength concrete. Compared with the unloaded concrete, the carbonation depth and dynamic elastic modulus loss after mechanical loading below the stress level threshold are lower. This is probably due to the denser microstructure compacted by the compression. However, if the loading level becomes above the threshold level, both the carbonation depth and dynamic elastic modulus loss dramatically increase, which is due to the cracks initiation and propagation after cyclic loading deterioration. Therefore, the combination of mechanical and environmental actions is more severe than a single environmental action without considering the mechanical loading.
Lei, B, Li, W, Tang, Z, Tam, VWY & Sun, Z 2018, 'Durability of recycled aggregate concrete under coupling mechanical loading and freeze-thaw cycle in salt-solution', Construction and Building Materials, vol. 163, pp. 840-849.
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© 2017 Elsevier Ltd In this study, a novel coupling testing protocol with separated repetitive loading and freezing-thaw cycles in salt-solution is designed to simulate coupling mechanical loading and complex environmental effects on durability and deterioration of recycled aggregate concrete (RAC). The Micromechanical properties and porosity of RAC were also characterized by scanning electron microscopy (SEM) and microhardness. The results show that the number and width of cracks of RAC and NAC under freeze-thaw cycles obviously increased with the increase of alternating times of repetitive load and the compressive stress level. The compressive strength losses for both RAC and NAC increase with the increase of compressive stress level and alternative times of repetitive load. However, the compressive strength of natural aggregate concrete (NAC) became lower than that of RAC after freeze-thaw cycles. It was found that the freeze-thaw resistance of RAC seems even better than that of NAC under the same freeze-thaw attacks and cyclic mechanical loading. It indicates that after freeze-thaw cycles in salt-solution, the durability of RAC is better than that of NAC. On the other hand, the microhardness and SEM characterization results indicate that the interface transition zone (ITZ) was a weak part in both RAC and NAC, and the ITZ in NAC obviously deteriorated faster than that of RAC.
Lenka, R.K., Rath, A.K., Tan, Z., Sharma, S., Puthal, D., Simha, N.V.R., Prasad, M., Raja, R. & Tripathi, S.S. 2018, 'Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors', IEEE Access, vol. 6, pp. 30162-30173.
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© 2013 IEEE. Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.
León-Castro, E, Avilés-Ochoa, E, Merigó, JM & Gil-Lafuente, AM 2018, 'Heavy Moving Averages and Their Application in Econometric Forecasting', Cybernetics and Systems, vol. 49, no. 1, pp. 26-43.
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© 2017 Taylor & Francis Group, LLC. This paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented.
Li, F, Yang, Y, Chi, Z, Zhao, L, Yang, Y & Luo, J 2018, 'Trinity: Enabling Self-Sustaining WSNs Indoors with Energy-Free Sensing and Networking', ACM Transactions on Embedded Computing Systems, vol. 17, no. 2.
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Li, H, Luo, Z, Gao, L & Qin, Q 2018, 'Topology optimization for concurrent design of structures with multi-patch microstructures by level sets', Computer Methods in Applied Mechanics and Engineering, vol. 331, pp. 536-561.
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© 2017 Elsevier B.V. This paper focuses on the novel concurrent design for cellular structures consisting of multiple patches of material microstructures using a level set-based topological shape optimization method. The macro structure is featured with the configuration of a cluster of non-uniformly distributed patches, while each patch hosts a number of identical material microstructures. At macro scale, a discrete element density based approach is presented to generate an overall structural layout involving different groups of discrete element densities. At micro scale, each macro element is regarded as an individual microstructure with a discrete intermediate density. Hence, all the macro elements with the same discrete densities (volume fractions) are represented by a unique microstructure. The representative microstructures corresponding to different density groups are topologically optimized by incorporating the numerical homogenization approach into a parametric level set method. The multiscale concurrent designs are integrated into a uniform optimization procedure, so as to optimize both topologies for the macrostructure and its microstructures, as well as locations of the microstructures in the design space. Numerical examples demonstrate that the proposed method can substantially improve the structural performance with an affordable computation and manufacturing cost.
Li, H, Luo, Z, Gao, L & Walker, P 2018, 'Topology optimization for functionally graded cellular composites with metamaterials by level sets', Computer Methods in Applied Mechanics and Engineering, vol. 328, pp. 340-364.
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The application of auxetic composites in practice often relies on a compromise between properties as auxetics are mostly too porous (not dense enough or not stiff enough) to bear structural loads. Hence, the focus of this paper is topological design optimization of new functionally graded cellular composites with auxetics using a level set method. Firstly, a new hierarchical multi-scale formulation is developed to account for both the auxetic behavior of the microstructure and the stiffness of the macrostructure. The composite, comprising multiple layers of periodic microstructures, is tailored to have functionally graded properties for stiffness and auxetic behaviors, subject to volumetric gradient constraints. Secondly, the microstructures underpinning composite layers are topologically designed under the consideration of boundary and loading conditions of the macrostructure. Finally, a level set method is applied to evolve the shape and topology of the microstructure for each layer, with the numerical homogenization method to evaluate the effective properties of the microstructures. Several numerical examples are used to demonstrate the effectiveness of the proposed method. It can be seen that such composites systematically gear together the features of the functionally graded materials, cellular composites, and metamaterials towards a new kind of man-made composites.
Li, H, Wang, J, Lu, H & Guo, Z 2018, 'Research and application of a combined model based on variable weight for short term wind speed forecasting', Renewable Energy, vol. 116, pp. 669-684.
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© 2017 Elsevier Ltd Wind speed forecasting plays a prominent part in the operation of wind power plants and power systems. However, it is often difficult to obtain satisfactory prediction results because wind speed data comprise random nonlinear series. Current some statistical models are not proficient in predicting nonlinear time series, whereas artificial intelligence models often fall into local optima. For these reasons, a novel combined forecasting model, which combines hybrid models based on decomposed methods and optimization algorithms, is successfully developed with variable weighting combination theory for multi-step wind speed forecasting. In this model, three different hybrid models are proposed and to further improve the forecasting performance, a modified support vector regression is used to integrate all the results obtained by each hybrid model and obtain the final forecasting results. To verify the forecasting effectiveness of the proposed forecasting model, 10-min wind speed series from Penglai, China, are used as case studies. The experimental results indicate that the developed combined model not only outperforms other benchmark models but also can be satisfactorily used for planning for smart grids.
Li, H., Öchsner, A., Yarlagadda, P.K.D.V., Xiao, Y., Furushima, T., Wei, D., Jiang, Z. & Manabe, K.I. 2018, 'A new constitutive analysis of hexagonal close-packed metal in equal channel angular pressing by crystal plasticity finite element method', Continuum Mechanics and Thermodynamics, vol. 30, no. 1, pp. 69-82.
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© 2017, Springer-Verlag GmbH Germany. Most of hexagonal close-packed (HCP) metals are lightweight metals. With the increasing application of light metal products, the production of light metal is increasingly attracting the attentions of researchers worldwide. To obtain a better understanding of the deformation mechanism of HCP metals (especially for Mg and its alloys), a new constitutive analysis was carried out based on previous research. In this study, combining the theories of strain gradient and continuum mechanics, the equal channel angular pressing process is analyzed and a HCP crystal plasticity constitutive model is developed especially for Mg and its alloys. The influence of elevated temperature on the deformation mechanism of the Mg alloy (slip and twin) is novelly introduced into a crystal plasticity constitutive model. The solution for the new developed constitutive model is established on the basis of the Lagrangian iterations and Newton Raphson simplification.
Li, J & Wu, C 2018, 'Damage evaluation of ultra-high performance concrete columns after blast loads', International Journal of Protective Structures, vol. 9, no. 1, pp. 44-64.
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© 2017, © The Author(s) 2017. As emerging advanced construction material, ultra-high performance concretes have seen increasing field applications over the past two decades to take advantages of their ultra-high mechanical strength and durability; yet the systematic study on its dynamic behaviour under impact and blast loads is not commonly seen. This article presents an experimental and numerical study on the static and dynamic behaviour of an existing ultra-high performance concrete material. Experimental study on its flexural behaviour under static loads is conducted and an inverse study is carried out to derive its uniaxial tensile constitutive law. The derived relationship is used in the material model in hydro-code LS-DYNA together with dynamic material properties to study ultra-high performance concrete columns under blast loads. The residual loading capacity of the column is studied and pressure–impulse diagrams for assessing the ultra-high performance concrete column damage under blast loads are proposed. Parametric study on effects of ultra-high performance concrete strength, column height, cross-section size and reinforcement ratio is performed and analytical equations are proposed for generating pressure–impulse diagrams for generic ultra-high performance concrete columns.
Li, J, Wei, J, Ngo, HH, Guo, W, Liu, H, Du, B, Wei, Q & Wei, D 2018, 'Characterization of soluble microbial products in a partial nitrification sequencing batch biofilm reactor treating high ammonia nitrogen wastewater.', Bioresource technology, vol. 249, pp. 241-246.
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In present study, the characterization of soluble microbial products (SMP) was evaluated in a partial nitrification sequencing batch biofilm reactor (SBBR). During the stable operation of SBBR, the NH4+-N removal efficiency and nitrite accumulation ratio were 96.70±0.41% and 93.77±1.04%, respectively. According to excitation-emission matrix (EEM), the intensities of protein-like substances were reduced under anoxic and aerobic phases, whereas humic-like substances had little change during the whole cycle. Parallel factor analysis (PARAFAC) further indentified two components and their fluorescence intensity scores were both reduced. Synchronous fluorescence spectra revealed that the fluorescence intensity of protein-like fraction decreased with reaction time. Two-dimensional correlation spectroscopy (2D-COS) further demonstrated that protein-like fraction might decrease earlier than the other fractions. The information obtained in present study is of fundamental significance for understanding the key components in SMP and their changes in partial nitrification system by using a spectral approach.
Li, J, Wu, C, Hao, H, Liu, Z & Yang, Y 2018, 'Basalt scale-reinforced aluminium foam under static and dynamic loads', Composite Structures, vol. 203, pp. 599-613.
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© 2018 Elsevier Ltd In this paper, mechanical performance and deformation behaviour of basalt scale-reinforced closed-cell aluminium foams are investigated. Quasi-static uniaxial compressive tests on the constitutive alloy material reveal that after basalt scale reinforcement, the alloy elasticity modulus and yield strength show noticeable enhancement. Quasi-static compression tests on the foam material show that while basalt scale-reinforced aluminium foam has higher plastic crush stress and plateau stress, the densification strain is lower than non-reinforced foam. A method based on energy absorption efficiency is adopted to accurately measure the densification strain for both foam materials. In the subsequent split-Hopkinson pressure bar tests, dynamic compressive behaviour of basalt scale-reinforced aluminium foams with relative densities ranged from 14% to 33% is studied experimentally under strain rate ranging from 480/s to 1720/s. Clear material rate sensitivity is noted from the dynamic tests. The results indicate that the plateau stress of aluminium foam increases with relative density and strain rate. In addition, with the increase in strain rates, an increase in the energy absorption capacity is observed and this characteristic is beneficial when the foam material is used to absorb impact energy. A mesoscopic model based on the X-ray CT for the aluminium foam material is developed. The simulations and the test data agreed well for the quasi-static loading case. However, it is noted that the mesoscale model without consideration of the base material rate sensitivity and the entrapped gas underestimated the strength enhancement under dynamic loading scenario.
Li, J., Wu, C. & Liu, Z.X. 2018, 'Comparative evaluation of steel wire mesh, steel fibre and high performance polyethylene fibre reinforced concrete slabs in blast tests', Thin-Walled Structures.
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© 2017 Elsevier Ltd. Concrete is the most widely used construction material in the modern construction practice. Due to its relatively low tensile resistance, concrete tends to experience tensile failure and cracking under external loads. To enhance the tensile performance and ductility of concrete material, possible solutions including fibre reinforcement and steel mesh reinforcement are investigated in the present study. Steel fibre, ultra-high molecular weight polyethylene (UHMWPE) fibre and steel wire meshes were mixed with varying volume fraction in the concrete matrix. Static material tests including uniaxial compression and flexural bending tests showed that the steel fibre addition yielded better strength enhancement while UHMWPE fibre provided better material ductility. Concrete samples with hybrid steel fibre-steel mesh reinforcement showed high strength and ductility. Field blast tests are designed to study the behaviour of reinforced concrete slabs under close-in detonations. Different damage profiles are observed from the blast tests. The advantages and disadvantages of using different reinforcing materials are discussed. From the results, the advantages of replacing steel fibre with UHMWPE fibre or steel wire mesh were demonstrated.
Li, J., Xie, H., Ma, R. & Low, K.H. 2018, 'Output Feedback Image-Based Visual Servoing of Rotorcrafts', Journal of Intelligent and Robotic Systems.
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Li, K, Ni, W, Duan, L, Abolhasan, M & Niu, J 2018, 'Wireless Power Transfer and Data Collection in Wireless Sensor Networks', IEEE Transactions on Vehicular Technology, vol. 67, no. 3, pp. 2686-2697.
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© 1967-2012 IEEE. In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using wireless power transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, whereas the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aimin g to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semidecentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.
Li, L, Liu, Z & Zhang, J 2018, 'Unsupervised image co-segmentation via guidance of simple images', Neurocomputing, vol. 275, pp. 1650-1661.
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© 2017 Elsevier B.V. This paper proposes a novel image co-segmentation method, which aims to segment the common objects in a group of images. The proposed method takes advantages of the reliability of simple images and successfully improves the performance. The images are first ranked by the complexities based on their saliency maps. Then, the simple images, in which objects are common and easy to be segmented, are selected and processed to obtain their segmentation results, these segmentation results are taken as the samples of the targeted objects. Finally, the remaining complicated images are segmented with the guidance of the samples. The experiments on the iCoseg dataset demonstrate the outperformance and robustness of the proposed method.
Li, L, Nimbalkar, S & Zhong, R 2018, 'Finite element model of ballasted railway with infinite boundaries considering effects of moving train loads and Rayleigh waves', Soil Dynamics and Earthquake Engineering, vol. 114, pp. 147-153.
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© 2018 Elsevier Ltd This paper proposes a three-dimensional model incorporating finite element (FE) meshes with infinite element (IE) boundaries for ballasted railways. Moving train loads are simulated with sliding motions of moving elements which have hard contact feature at the interface with supporting rails. Dynamic responses of ballasted railway under different train speeds are investigated in time domain and frequency domain to identify the predominant frequency and critical speed. Rayleigh wave (R-Wave) propagation is simulated using the combined FE-IE model to determine the velocity of R-Wave in the layered embankment model and its relationship with the critical speed of the ballasted railway. The proposed model is successfully validated against the results of Euler-Bernoulli Elastic Beam (E-BEB) model.
Li, M, Liu, Y & Guo, YJ 2018, 'Shaped Power Pattern Synthesis of a Linear Dipole Array by Element Rotation and Phase Optimization Using Dynamic Differential Evolution', IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 4, pp. 697-701.
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© 2002-2011 IEEE. A novel strategy for synthesizing shaped power pattern by using element rotation and phase optimization for a linear dipole array is presented. Element rotation can be considered as an array amplitude weighting technique, and it provides additional degrees of freedom in the pattern synthesis, but potentially increases the cross-polarization level (XPL). In order to obtain a desired copolarized shaped pattern with both the sidelobe level and XPL constrained, we simultaneously optimize all the rotation angles and excitation phases of all the dipole elements by applying the dynamic differential evolution algorithm. Two experiments for synthesizing flat-top and cosecant-squared patterns are conducted to validate the effectiveness and advantages of the proposed strategy.
Li, W, Liu, BM, Liu, D, Liu, RP, Wang, P, Luo, S & Ni, W 2018, 'Unified Fine-grained Access Control for Personal Health Records in Cloud Computing', IEEE Journal of Biomedical and Health Informatics.
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IEEE Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy which is based on different patients' access policies. Then the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, We prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.
Li, W, Long, R, Chen, H, Yang, T, Geng, J & Yang, M 2018, 'Effects of personal carbon trading on the decision to adopt battery electric vehicles: Analysis based on a choice experiment in Jiangsu, China', Applied Energy, vol. 209, pp. 478-488.
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© 2017 Elsevier Ltd. The implementation of personal carbon trading (PCT) to influence transport choices has recently been suggested as a method to reduce private carbon emissions. In this study, we conducted a choice experiment in Jiangsu, China, to evaluate if PCT influences individual decisions to adopt battery electric vehicles (BEVs). The results showed that PCT can effectively change the decision to adopt and encourage the adoption of BEVs. PCT was shown to be more effective than free parking as well as eliminating road tolls, vehicle and vessel tax, and purchase tax, but less effective than government subsidies. In addition, we found that improving some BEV performance attributes was preferred to policy incentives, including PCT. These results improve our understanding of the effectiveness of PCT and the individual decision to adopt BEVs. Our findings could facilitate the practical implementation of PCT and provide suitable guidelines for developing BEV promotion strategies.
Li, W, Luo, Z, Long, C, Huang, Z, Huang, L, Yu, Q & Sun, Z 2018, 'Mechanical strengths and microstructures of recycled aggregate concrete incorporating nanoparticles', Advances in Civil Engineering Materials, vol. 7, no. 1, pp. 188-205.
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Copyright © 2018 by ASTM International The influences of nanoparticles on the mechanical properties and microstructures of recycled aggregate concrete (RAC) were investigated in this study. The compressive and flexural strengths of RAC with different dosages of nanoSiO2(NS) and nanoCaCO3(NC) were tested. Both the scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) techniques were applied to analyze the microstructures and porosity of interfacial transition zones (ITZs) in RAC. Based on the comparison on compressive strength and flexural strength, the NS is more effective than the NC for improving the mechanical properties of RAC. Dispersion of NC particles by a superplasticizer can somewhat improve the early-age strength of RAC but is unlikely to enhance the 28-day mechanical strengths. This may be attributed to the better dispersion and pozzolanic activity of NS compared to NC. The results show that a denser microstructure and a reduction of porosity within ITZs were observed by incorporating NS, which occurred along with the improvement of the mechanical strengths of RAC. Moreover, the NS-modified RAC exhibits less total porosity than the NC-modified RAC. The NS-modified RAC mainly contains small size pores, but the NC-modified RAC has more large pores than the NS-modified RAC, which has negative effects on the mechanical properties of RAC.
Li, W, Luo, Z, Sun, Z, Hu, Y & Duan, WH 2018, 'Numerical modelling of plastic-damage response and crack propagation in RAC under uniaxial loading', Magazine of Concrete Research, vol. 70, no. 9, pp. 459-472.
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© 2017 ICE Publishing: All rights reserved. In order to better understand the failure mechanism of recycled aggregate concrete (RAC), a numerical study on modelled recycled aggregate concrete (MRAC) was conducted to investigate the plastic-damage response and crack propagation under uniaxial loading. In the numerical model, the nanoscale mechanical properties and the thickness of the interfacial transition zones (ITZs) were obtained based on advanced nanoindentation. The constitutive relationships of new and old cement mortars and corresponding ITZs were developed using plastic-damage constitutive relationships. The effects of the relative mechanical properties between new and old cement mortars on the failure pattern and stress-strain response of MRAC were investigated. After calibration and verification with the uniaxial compression test, the numerical model was found to be able to reveal the failure pattern and stress-strain curves of MRAC under uniaxial tension. The results showed that microcracks usually first appear around the weak new and old ITZs, and then propagate into the new and old cement mortars. With an increase in the relative strength between new and old cement mortars, the microcrack initiation locations gradually shifted from the new ITZs to the old ITZs. Therefore, the numerical results can provide insight into the modification of RAC using mix design optimisation and ITZ enhancement.
Li, W, Luo, Z, Wu, C & Duan, WH 2018, 'Impact performances of steel tube-confined recycled aggregate concrete (STCRAC) after exposure to elevated temperatures', Cement and Concrete Composites, vol. 86, pp. 87-97.
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© 2017 Elsevier Ltd The impact behaviours of steel tube-confined recycled aggregate concrete (STCRAC) following exposure to elevated temperatures of 20 °C, 200 °C, 500 °C and 700 °C were experimentally investigated using a 100 mm-diameter split Hopkinson pressure bar (SHPB). The recycled coarse aggregate (RCA) replacement ratios were set as 0, 50% and 100%. The effect of RCA replacement ratio and exposure temperature on the impact properties of STCRAC were analysed in terms of failure modes, stress-strain time history curve and dynamic increase factor (DIF). The results show that the fire-damaged STCRAC can maintain its integrity during impact load. However, there were evident degradations in the dynamic behaviour of STCRAC after exposure to high temperatures of 500 °C and 700 °C. The ultimate impact strength, impact secant modulus and residual impact strength of STCRAC obviously decreased because of the damage due to high temperature exposure. But the degradations of both the ultimate impact strength and impact secant modulus of STCRAC under impact loading were less severe than those under quasi-static loading. The remaining strength factor and the DIF tended to increase with the raise of the elevated temperatures. Overall, during the impact loading, the fire-deteriorated STCRAC exhibited excellent impact behaviour.
Li, W., Xie, Y., Zhou, H., Han, Y. & Zhan, K. 2018, 'Structure-aware image fusion', Optik, vol. 172, pp. 1-11.
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© 2018 Elsevier GmbH Most existing multi-modal image fusion methods require multi-scale transforms. However, this requirement does not necessarily lead to the fusion result containing the original intensity of source images, and multi-scale transforms need a high computational complexity. In this paper, we tackle the problem of multi-modal image fusion in the spatial domain with a low computational complexity. A salient structure extraction method and a structure-preserving filter are developed to fuse medical images. The developed structure-preserving filter has a property that it recovers small-scale details of the guidance image in the neighborhood of large-scale structures of the input image. Based on the property of the structure-preserving filter, the fusion result is constructed by combining the output of the structure-preserving filter and the source images. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art approaches in terms of three performance metrics.
Li, W., Yang, M. & Sandu, S. 2018, 'Electric vehicles in China: A review of current policies', Energy and Environment.
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© 2018, The Author(s) 2018. Prompted by the urgency of reducing greenhouse gas emissions in the transport sector, the Chinese government has set ambitious targets for the uptake of electric vehicles. To achieve these targets is however a challenging task, due to various barriers in the uptake of electric vehicles, at both micro- and macro-levels. A range of monetary and non-monetary incentives has been implemented, or being considered for implementation, to overcome these barriers. This paper reviews these incentives with a view to assess the extent to which they are likely to remove the barriers in the uptake of electric vehicles. The review suggests that the primary focus of these incentives is to remove the micro-level barriers, such as high upfront costs, poor technical performance, and insufficient charging infrastructure. Limited attention has been paid to the macro-level barriers (for example, fragmented authority and local protectionism), despite ample evidence suggesting that these barriers could significantly impede the uptake of electric vehicles. Further, these incentives have tended to rely on regulation-based measures to remove the barriers. Only in the recent years, there appears to be a gradual shift towards market-based measures. This shift could improve the effectiveness of electric vehicle policies. The effectiveness of these policies could be enhanced if one recognizes the underlying macro-level barriers that are likely to protract or distort the implementation of market-based measures. This paper also provides some recommendations to remove these macro-level barriers.
Li, X, Liu, K, Dong, Y & Tao, D 2018, 'Patch Alignment Manifold Matting', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results using subspace reconstructing error. Furthermore, we utilize Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting. The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.
Li, X, Lu, Q, Dong, Y & Tao, D 2018, 'SCE: A Manifold Regularized Set-Covering Method for Data Partitioning', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1760-1773.
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Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the structure information of the raw data in performing the cluster ensemble. In this paper, we propose a Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem. In particular, we construct a Laplacian regularized objective function to capture the structure information among clusters. Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function. Finally, we verify the performance of the SCE algorithm on both synthetic and real data sets. The experimental results show the effectiveness of our proposed method SCE algorithm.
Li, X, Shankaran, R, Orgun, MA, Fang, G & Xu, Y 2018, 'Resource Allocation for Underlay D2D Communication with Proportional Fairness', IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6244-6258.
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© 1967-2012 IEEE. As an emerging paradigm, device-to-device (D2D) communication has the capability to complement and enhance the conventional cellular network by offering high spectral and energy efficiency. However, the problem of cochannel interference makes the resource allocation very complex and challenging in underlay D2D communication networks. This paper proposes a novel joint power control and resource scheduling scheme to enhance both the network throughput and the users' fairness of the underlay D2D communication networks. Unlike other previous work in this area, our scheme aims at maximizing the sum of all users' proportional fairness functions, while simultaneously taking into account factors such as fairness, signal-to-interference-plus-noise ratio requirements, and severe interference. The proposed scheme offers a practical solution because it works for lengthy time slots, a realistic scenario for the underlay D2D communication system. We also take into consideration the time-varying feature of user's channel condition in our proposed solution. Numerical results confirm that our proposed scheme not only dramatically improves the system throughput, but also boosts the system fairness while guaranteeing the Quality-of-Service levels of all D2D users and cellular users.
Li, X, Wang, L, Liu, Y, Li, W, Dong, B & Duan, WH 2018, 'Dispersion of graphene oxide agglomerates in cement paste and its effects on electrical resistivity and flexural strength', Cement and Concrete Composites, vol. 92, pp. 145-154.
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© 2018 Elsevier Ltd Actual dispersion of graphene oxide (GO) in cement paste was investigated by using both X-ray computed tomography and X-ray photoelectron spectroscopy. It was found that GO nanosheets are mainly agglomerated, as an individual phase, with platelet-like morphology and little GO being absorbed onto surfaces of cement particles and hydration products. By performing an electrical resistivity test, GO agglomerates are found to be more electrically insulative than cement paste. Therefore, it is not possible to develop self-sensing cement composites by incorporating GO directly without resolving its dispersion issue. However, GO agglomerates enhance the flexural strength of cement paste because of their special morphology and intrinsic strength. Results showed that the flexural strength of cement paste was increased by 83% with incorporation of 0.04% GO by weight of cement.
Li, X, Zhao, L, Ji, W, Wu, Y, Wu, F, Yang, MH, Tao, D & Reid, I 2018, 'Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
Li, X., Liu, Y.M., Li, W.G., Li, C.Y., Sanjayan, J.G., Duan, W.H. & Li, Z. 2018, 'Corrigendum to “Effects of graphene oxide agglomerates on workability, hydration, microstructure and compressive strength of cement paste” [Constr. Build. Mater. 145 (2017) 402–410] (S0950061817307110) (10.1016/j.conbuildmat.2017.04.058))', Construction and Building Materials, vol. 179, pp. 537-538.
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© 2018 The authors regret that the SEM image of GO (Fig. 1) should be replaced by a more representative one, which is provided by the material supplier, as given below. The workability measurement (Fig. 2) was plotted incorrect and should be revised by the following. Consequently, the sentence in Abstract should read as follows: The mini-slump diameter was reduced by 27% with the incorporation of 0.03% by weight GO in cement paste. The sentence in Section 4.1 should read as follows: However, when 0.03% by weight GO was added, the mini-slump diameter showed a sudden decrease to a level that was about 27% lower than that of the plain cement paste C. The Acknowledgement should read: The authors are grateful for the financial support of the Australian Research Council in conducting this study. Thanks also go to Mr. Samuel Chuah and Mr. Hongsen He for their great support in materials testing during their stay at the Monash University. The authors would like to apologise for any inconvenience caused.
Li, Y, Li, W, Deng, D, Wang, K & Duan, WH 2018, 'Reinforcement effects of polyvinyl alcohol and polypropylene fibers on flexural behaviors of sulfoaluminate cement matrices', Cement and Concrete Composites, vol. 88, pp. 139-149.
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© 2018 Elsevier Ltd The fracture behavior of unoiled/uncoated polyvinyl alcohol (PVA) fiber reinforced sulphoaluminate cement (SAC) matrices was experimentally investigated and compared with those of polypropylene (PP) fiber reinforced SAC and PVA fiber reinforced Portland cement (PC) matrices in this study. In the experimental investigation, three-point bending tests were carried out for notched fiber reinforced cement beams. Special attentions were paid on their deflection-hardening and multiple crack patterns. The different flexural behaviors between the plain SAC and PC matrices were evaluated using the double-K fracture model. The results indicate that the PVA fiber reinforced SAC matrices exhibited better flexural behaviors when compared with the PVA fiber reinforced PC matrix having comparable matrix strength. The bond strength between SAC matrix and PVA fiber are relatively better than that between the counterpart PC matrix and PVA fiber, while the bond strength between SAC matrix and PVA fiber is obviously stronger than that between the SAC and PP fibers.
Li, Y, Qiao, Y, Wang, X & Duan, R 2018, 'Tripartite-to-Bipartite Entanglement Transformation by Stochastic Local Operations and Classical Communication and the Structure of Matrix Spaces', Communications in Mathematical Physics, vol. 358, no. 2, pp. 791-814.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. We study the problem of transforming a tripartite pure state to a bipartite one using stochastic local operations and classical communication (SLOCC). It is known that the tripartite-to-bipartite SLOCC convertibility is characterized by the maximal Schmidt rank of the given tripartite state, i.e. the largest Schmidt rank over those bipartite states lying in the support of the reduced density operator. In this paper, we further study this problem and exhibit novel results in both multi-copy and asymptotic settings, utilizing powerful results from the structure of matrix spaces. In the multi-copy regime, we observe that the maximal Schmidt rank is strictly super-multiplicative, i.e. the maximal Schmidt rank of the tensor product of two tripartite pure states can be strictly larger than the product of their maximal Schmidt ranks. We then provide a full characterization of those tripartite states whose maximal Schmidt rank is strictly super-multiplicative when taking tensor product with itself. Notice that such tripartite states admit strict advantages in tripartite-to-bipartite SLOCC transformation when multiple copies are provided. In the asymptotic setting, we focus on determining the tripartite-to-bipartite SLOCC entanglement transformation rate. Computing this rate turns out to be equivalent to computing the asymptotic maximal Schmidt rank of the tripartite state, defined as the regularization of its maximal Schmidt rank. Despite the difficulty caused by the super-multiplicative property, we provide explicit formulas for evaluating the asymptotic maximal Schmidt ranks of two important families of tripartite pure states by resorting to certain results of the structure of matrix spaces, including the study of matrix semi-invariants. These formulas turn out to be powerful enough to give a sufficient and necessary condition to determine whether a given tripartite pure state can be transformed to the bipa...
Li, Y, Tian, X, Liu, T & Tao, D 2018, 'On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1975-1985.
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Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing across different tasks. However, these two types of relatedness are mainly learned independently, leading to a loss of information. In this paper, we propose a new strategy to measure the relatedness that jointly learns shared parameters and shared feature representations. The objective of our proposed method is to transform the features of different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized. We give a detailed introduction to our proposed MTL method. Additionally, an alternating algorithm is introduced to optimize the nonconvex objection. A theoretical bound is given to demonstrate that the relatedness between tasks can be better measured by our proposed MTL algorithm. We conduct various experiments to verify the superiority of the proposed joint model and feature MTL method.
Li, Y, Zhu, L & Zhu, J 2018, 'Core Loss Calculation Based on Finite-Element Method with Jiles-Atherton Dynamic Hysteresis Model', IEEE Transactions on Magnetics, vol. 54, no. 3.
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© 2018 IEEE. For accurate computation of core losses, the Jiles-Atherton ( J - A ) dynamic hysteresis model accounting for hysteresis, eddy current and excess losses is incorporated into the finite-element method (FEM). The J - A dynamic hysteresis model is constructed by combining the traditional J - A hysteresis model with the models of instantaneous eddy current and excess losses. The J - A model parameters and dynamic loss coefficients are determined by fitting the models to the measurement data of a single sheet tester (SST 500) and Epstein frame tester. To find the robust best fit, the particle swarm optimization algorithm is employed. By using the proposed J - A dynamic hysteresis model and FEM, the magnetic characteristics of a magnetic core is simulated and the core loss distribution within the core obtained. The calculated and measured results are compared to show the accuracy and effectiveness of the proposed model.
Li, Z, Nie, F, Chang, X, Nie, L, Zhang, H & Yang, Y 2018, 'Rank-Constrained Spectral Clustering With Flexible Embedding', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Spectral clustering (SC) has been proven to be effective in various applications. However, the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed graph structure, which usually requires a rounding procedure to further partition the data. Also, the obtained cluster number cannot reflect the ground truth number of connected components in the graph. To alleviate these drawbacks, we propose a rank-constrained SC with flexible embedding framework. Specifically, an adaptive probabilistic neighborhood learning process is employed to recover the block-diagonal affinity matrix of an ideal graph. Meanwhile, a flexible embedding scheme is learned to unravel the intrinsic cluster structure in low-dimensional subspace, where the irrelevant information and noise in high-dimensional data have been effectively suppressed. The proposed method is superior to previous SC methods in that: 1) the block-diagonal affinity matrix learned simultaneously with the adaptive graph construction process, more explicitly induces the cluster membership without further discretization; 2) the number of clusters is guaranteed to converge to the ground truth via a rank constraint on the Laplacian matrix; and 3) the mismatch between the embedded feature and the projected feature allows more freedom for finding the proper cluster structure in the low-dimensional subspace as well as learning the corresponding projection matrix. Experimental results on both synthetic and real-world data sets demonstrate the promising performance of the proposed algorithm.
Lian, D., Zheng, K., Ge, Y., Cao, L., Chen, E. & Xie, X. 2018, 'GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization', ACM Transactions on Information Systems, vol. 36, no. 3.
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© 2018 ACM. Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data; Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collectivematrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space.
Liang, J, Yang, H, Wu, J, Zhang, N & Walker, PD 2018, 'Power-on shifting in dual input clutchless power-shifting transmission for electric vehicles', Mechanism and Machine Theory, vol. 121, pp. 487-501.
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© 2017 Elsevier Ltd The purpose of this paper is to study the practical application of a two motor electric vehicle powertrain utilizing a combination of fixed and multiple speed gear ratios. To realize power-on shifting without torque hole and maximize overall efficiency, a high-speed motor is adopted as the primary motor connecting to the multiple speed clutchless automated manual transmission and a low-speed high torque electric motor is employed as the assisting motor connected to the final shaft with fixed gear ratio. Motor torque and speed are controlled using improved model predictive flux control method in conjunction with synchronizer mechanism actuation to best fill the torque hole to improve both drivability and driving comfort. To evaluate the proposed system, a complete mathematical model is built and compared with a conventional single motor transmission system. The detailed transient dynamic results in terms of final shaft torque, acceleration and vehicle jerk demonstrate the effectiveness of the proposed powertrain.
Liang, J., Yang, H., Wu, J., Zhang, N. & Walker, P.D. 2018, 'Shifting and power sharing control of a novel dual input clutchless transmission for electric vehicles', Mechanical Systems and Signal Processing, vol. 104, pp. 725-743.
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© 2017 Elsevier Ltd To improve the overall efficiency of electric vehicles and guarantee the driving comfort and vehicle drivability under the concept of simplifying mechanism complexity and minimizing manufacturing cost, this paper proposes a novel clutchless power-shifting transmission system with shifting control strategy and power sharing control strategy. The proposed shifting strategy takes advantage of the transmission architecture to achieve power-on shifting, which greatly improves the driving comfort compared with conventional automated manual transmission, with a bump function based shifting control method. To maximize the overall efficiency, a real-time power sharing control strategy is designed to solve the power distribution problem between the two motors. Detailed mathematical model is built to verify the effectiveness of the proposed methods. The results demonstrate the proposed strategies considerably improve the overall efficiency while achieve non-interrupted power-on shifting and maintain the vehicle jerk during shifting under an acceptable threshold.
Liang, T., Chen, L., Wu, J., Xu, G. & Wu, Z. 2018, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Liang, X & Wu, C 2018, 'Meso-scale modelling of steel fibre reinforced concrete with high strength', Construction and Building Materials, vol. 165, pp. 187-198.
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© 2018 Based on Delaunay triangulation, a 3D meso-scale model is successfully developed and verified. This approach modelling fibre and concrete separately and linking them with slide line contact has the capability to truly reflect the interfacial behaviour of fibre and mortar, and thus achieve high fidelity of numerical simulations. However, meso-scale modelling usually means tremendous complexity and long computational time. This paper proposes a model to achieve relatively high computation efficiency, as well as accuracy. Besides, the model has the potential to deal with small specimens cut from steel fibre reinforced concrete (SFRC) blocks.
Liang, X, Wu, C, Su, Y, Chen, Z & Li, Z 2018, 'Development of ultra-high performance concrete with high fire resistance', Construction and Building Materials, vol. 179, pp. 400-412.
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© 2018 Elsevier Ltd Fire or high temperature is a big challenge to ultra-high performance concrete (UHPC). Strength loss of UHPCs can reach up to 80% after exposure to 800 °C. In this study, a total of six UHPC mixtures were designed and tested after subjected to elevated temperatures up to 1000 °C. The effects of aggregate type, fibre type and heating rate were investigated. Residual compressive strengths and stress-strain relationships were studied. Besides, attention was paid to explosive spalling since UHPCs are usually of compact structure and thus more vulnerable to explosive spalling than other concretes. Scanning electron microscope (SEM) analysis was conducted to help understand the mechanism of variation of internal structure under different temperatures. It was found the mixture containing steel slag and hybrid fibre had excellent fire resistance. After being subjected to 1000 °C, this mixture retained a residual compressive strength of 112.8 MPa or a relative value of 69%.
Lim, S, Park, MJ, Phuntsho, S, Mai-Prochnow, A, Murphy, AB, Seo, D & Shon, H 2018, 'Dual-layered nanocomposite membrane incorporating graphene oxide and halloysite nanotube for high osmotic power density and fouling resistance', Journal of Membrane Science, vol. 564, pp. 382-393.
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© 2018 Elsevier B.V. This study introduces a thin-film composite (TFC) membrane with a dual-layered nanocomposite substrate synthesized using a dual-blade casting approach for application in osmotic power generation by the pressure-retarded osmosis (PRO) process. The approach incorporates halloysite nanotubes (HNTs) into the bottom polymer substrate layer and graphene oxide (GO) on the top layer substrate, on which a thin polyamide active layer is formed. The fabricated membrane substrate showed highly desirable membrane substrate properties such as a high porosity, opened-bottom surface, suitable top-skin surface morphology for subsequent active layer formation and high mechanical strength, which are essential for high-performance PRO processes. At a GO loading of 0.25 wt% and HNT loading of 4 wt%, the power density (PD) of the nanocomposite membrane was 16.7 W/m2and the specific reverse solute flux (SRSF) was 2.4 g/L operated at 21 bar applied pressure using 1 M NaCl as draw solution and deionized water as feed, which is significantly higher than the those for a single-layered or commercial PRO membrane. This membrane performance was observed to be stable in the pressure cycle test and under long-term operation. The membrane substrate with HNTs incorporated exhibited high fouling resistance to sodium alginate and colloidal silica foulants, with the PD decreasing by 17% after 3 h of operation, compared to a membrane substrate without HNTs and commercial PRO membranes, which decreased by 26% and 57%, respectively. A fluorescence microscope study of the membranes subjected to feed water containing Escherichia coli confirmed the good antibacterial properties of the dual-layered TFC membrane. The study provides an attractive alternative approach for developing PRO membranes with high PD and fouling resistance.
Lim, S. & Shon, H. 2018, 'Characterization of Membranes for Membrane-Based Salinity-Gradient Processes', Membrane-Based Salinity Gradient Processes for Water Treatment and Power Generation, pp. 125-154.
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Pressure retarded osmosis (PRO) is one of the alternative applications for harvesting renewable energy by a natural phenomenon. As a particular free energy, the salinity gradient energy (SGE) from saline solutions with different concentrations has recently been revitalized as a renewable energy source. In recent studies for development of PRO membranes, various attempts and novel applications have been conducted for enhancing energy production as well as an ease of the membrane modulation. At this stage, newly developed PRO membranes should be clearly characterized and evaluated to find the reason why novel approaches are attractive for its performance improvement compared to commercial and existing PRO membranes. In this chapter, therefore we would like to suggest the guideline for PRO membrane characterization in which most of contents are made of various attempts and investigations in literature. This chapter aims to understand various characterizations for PRO membranes to confirm the proof of the performance improvement for its purpose.
Lin, C., Ge, Y., Bird, T.S. & Liu, K. 2018, 'Circularly Polarized Horns Based on Standard Horns and a Metasurface Polarizer', IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 3, pp. 480-484.
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© 2017 IEEE. A method for designing circularly polarized (CP) horn antenna is proposed, which is based on a standard pyramidal horn with a linear-to-circular polarization transformer. A compact transparent metasurface transformer is first designed to perform linear-to-circular polarization transformation, and then this is inserted in a linearly polarized standard pyramidal horn to create a CP horn. To validate the CP horn design, both simulations and experiments were conducted and good agreement was obtained. The CP performance achieved a 3 dB axial-ratio bandwidth of 7.4%, a maximum gain reduction of 1.1 dB without altering the horn's original size for a light increase in weight and at a small cost increase.
Lin, CT, Chiu, CY, Singh, AK, King, JT & Wang, YK 2018, '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, C-T, Chiu, T-C, Wang, Y-K, Chuang, C-H & Gramann, K 2018, 'Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.', Brain Research, vol. 1679, pp. 91-100.
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Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.
Lin, CT, Hsieh, TY, Liu, YT, Lin, YY, Fang, CN, Wang, YK, Yen, G, Pal, NR & Chuang, CH 2018, 'Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 950-962.
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© 1989-2012 I EEE. The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
Lin, CT, King, JT, Fan, JW, Appaji, A & Prasad, M 2018, 'The Influence of Acute Stress on Brain Dynamics during Task Switching Activities', IEEE Access, vol. 6, pp. 3249-3255.
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© 2013 IEEE. Task switching is a common method to investigate executive functions such as working memory and attention. This paper investigates the effect of acute stress on brain activity using task switching. Surprisingly few studies have been conducted in this area. There is behavioral and physiological evidence to indicate that acute stress makes the participants more tense which results in a better performance. In this current study, under stressful conditions, the participants gave quick responses with high accuracy. However, unexpected results were found in relation to salivary cortisol. Furthermore, the electroencephalogram results showed that acute stress was pronounced at the frontal and parietal midline cortex, especially on the theta, alpha, and gamma bands. One possible explanation for these results may be that the participants changed their strategy in relation to executive functions during stressful conditions by paying more attention which resulted in a higher working memory capacity which enhanced performance during the task switching.
Lin, CT, King, JT, Singh, AK, Prasad, M, Ma, Z, Lin, JW, Machado, AMC, Appaji, A & Gupta, A 2018, 'Voice Navigation Effects on Real-World Lane Change Driving Analysis using an Electroencephalogram', IEEE Access.
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OAPA Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this study, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.
Lin, CT, Nascimben, M, King, JT & Wang, YK 2018, 'Task-related EEG and HRV entropy factors under different real-world fatigue scenarios', Neurocomputing, vol. 311, pp. 24-31.
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© 2018 Elsevier B.V. We classified the alertness levels of 17 subjects in different experimental sessions in a six-month longitudinal study based on a daily sampling system and related alertness to performance on a psychomotor vigilance task (PVT). As to our best knowledge, this is the first EEG-based longitudinal study for real-world fatigue. Alertness and PVT performance showed a monotonically increasing relationship. Moreover, we identified two measures in the entropy domain from electroencephalography (EEG) and heart rate variability (HRV) signals that were able to identify the extreme classes of PVT performers. Wiener entropy on selected leads from the frontal-parietal axis was able to discriminate the group of best performers. Sample entropy from the HRV signal was able to identify the worst performers. This joint EEG-HRV quantification provides complementary indexes to indicate more reliable human performance.
Lin, JY, Wong, SW, Zhu, L, Yang, Y, Zhu, X, Xie, ZM & He, Y 2018, 'A Dual-Functional Triple-Mode Cavity Resonator with the Integration of Filters and Antennas', IEEE Transactions on Antennas and Propagation, vol. 66, no. 5, pp. 2589-2593.
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© 1963-2012 IEEE. A novel concept for the two-in-one design with the integration of filters and antennas, using a single cavity, is presented in this communication. Basically, a triple-mode resonator (TMR) is utilized as a common feeder to achieve filtering and radiating functions at three different frequency bands. Each of the three bands adopts one of the three fundamental modes, namely, TE011, TE101, and TM110, in a single TMR to realize the relevant functions. Owing to the modal orthogonality of these fundamental modes, high isolation can be effectively realized among these three channels. Based on the proposed TMR, three different prototypes are designed for different applications. For the first prototype, the structure of a dual-band filter plus an antenna is presented using a second-order TMR. Based on the combination of a duplexer plus an antenna, the second prototype is attained. To further explore the function of the proposed TMR, the structure of a filter plus a dual-polarization antenna is depicted as the third prototype. For verification, the third prototype is fabricated and tested. Good agreement between the simulated and the measured results is achieved, which proves the feasibility of the proposed design methodology.
Lin, M., Shan, S., Liu, P., Ma, L., Huang, L., Yang, M., Lawson, T., Wang, Z., Huang, Z., Shi, B., Yan, L. & Liu, Y. 2018, 'Hydroxyl-Functional Groups on Graphene Trigger the Targeted Delivery of Antitumor Drugs', Journal of Biomedical Nanotechnology, vol. 14, no. 8, pp. 1420-1429.
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Lin, S, Yu, J, Ni, W & Liu, RP 2018, 'Decoupling 5G network control: Centralised coordination and distributed adaptation', International Journal of Computers, Communications and Control, vol. 13, no. 2, pp. 192-204.
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© 2018 by CC BY-NC. Fifth generation mobile networks (5G) will be featured by miniaturised cells and massive dense deployment. Traditional centralised network control cannot adapt to high signalling delay, and is therefore not scalable for future 5G networks.To address this issue, we adopt the software-defined networking (SDN) approach of decoupled network control and data transmission. In particular, delay-sensitive interference suppression for data transmission is decoupled from delay-tolerant topology control and base station coordination. This substantially alleviates the requirement of network control on delay and complexity, hence simplifying 5G control plane design, reducing signalling overhead, and enhancing network scalability. Case studies show that our decoupled network control is effective for timely interference mitigation and reliable topology management. The stability and scalability of our approach are also demonstrated.
Lin, W & Ziolkowski, RW 2018, 'Electrically Small, Low-Profile, Huygens Circularly Polarized Antenna', IEEE Transactions on Antennas and Propagation, vol. 66, no. 2, pp. 636-643.
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© 1963-2012 IEEE. The design, simulation studies, and experimental verification of an electrically small, low-profile, broadside-radiating Huygens circularly polarized (HCP) antenna are reported. To realize its unique circular polarization cardioid-shaped radiation characteristics in a compact structure, two pairs of the metamaterial-inspired near-field resonant parasitic elements, the Egyptian axe dipole (EAD) and the capacitively loaded loop (CLL), are integrated into a crossed-dipole configuration. The EAD (CLL) elements act as the orthogonal electric dipole (magnetic dipole) radiators. Balanced broadside-radiated electric and magnetic field amplitudes with the requisite 90° phase difference between them are realized by exciting these two pairs of electric and magnetic dipoles with a specially designed, unbalanced crossed-dipole structure. The electrically small (ka = 0.73) design operates at 1575 MHz. It is low profile 0.04λ0, and its entire volume is only 0.0018λ03. A prototype of this optimized HCP antenna system was fabricated, assembled, and tested. The measured results are in good agreement with their simulated values. They demonstrate that the prototype HCP antenna resonates at 1584 MHz with a 0.6 dB axial ratio, and produces the predicted Huygens cardioid-shaped radiation patterns. The measured peak realized LHCP gain was 2.7 dBic, and the associated front-to-back ratio was 17.7 dB.
Lin, W, Chen, SL, Ziolkowski, RW & Guo, YJ 2018, 'Reconfigurable, Wideband, Low-Profile, Circularly Polarized Antenna and Array Enabled by an Artificial Magnetic Conductor Ground', IEEE Transactions on Antennas and Propagation, vol. 66, no. 3, pp. 1564-1569.
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© 1963-2012 IEEE. A reconfigurable, wideband, and low-profile circular polarization (CP) antenna is presented. Its wideband CP reconfigurability is realized by incorporating RF switches into a cross-bowtie radiator. A compact, wide bandwidth, and polarization-independent artificial magnetic conductor ground plane is developed to minimize the overall profile of the antenna while maintaining its wide bandwidth. The simplicity of this single-element design facilitates the realization of a reconfigurable, wide bandwidth CP array that achieves higher directivity without changing its overall profile. Prototypes of the single element and of a 1 × 4 array of these elements were fabricated and tested. The measured results for both prototypes are in good agreement with their simulated values, validating their design principles. They are low profile with a height ∼ 0.05 λ0. The array exhibits a wide fractional operational bandwidth: 1.65 GHz (21.7%), and a high realized gain: 13 dBic. Since they would enhance their channel capacity and avoid polarization mismatch issues, these reconfigurable CP antenna systems are very suitable for modern wireless systems.
Lin, W, Wong, H & Ziolkowski, RW 2018, 'Circularly Polarized Antenna with Reconfigurable Broadside and Conical Beams Facilitated by a Mode Switchable Feed Network', IEEE Transactions on Antennas and Propagation, vol. 66, no. 2, pp. 996-1001.
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© 1963-2012 IEEE. A pattern-reconfigurable slot-augmented patch antenna is reported that radiates circularly polarized (CP) fields and is switchable between a broadside and a conical radiating mode. Two novel design strategies were adopted to realize these advantageous performance characteristics. First, a mode switchable feed network which includes a set of eight L-probes was developed that facilitates the proper excitation of either the patch's TM11or TM21mode and yields symmetric radiation patterns. Second, their operating bands were made to overlap by augmenting this patch with an annular slot. Consequently, the radiation pattern is easily switched between the broadside and the conical modes within the overlapping operational bandwidths. A prototype was fabricated and tested. The measured results are in good agreement with their simulated values, verifying the design concepts. The measured operating bandwidth, 7.8% from 2.45 to 2.65 GHz, was determined from the overlap of the impedance and axial ratio bandwidths for both modes. The measured realized gain values are stable for both radiating states within this operational bandwidth, the broadside (conical) peak value being 8.5 dBic (5.8 dBic). The CP pattern reconfigurability of this antenna with its favorable performance characteristics makes it an ideal candidate for many RFID and satellite communication applications.
Liu, A, Lu, J, Liu, F & Zhang, G 2018, 'Accumulating regional density dissimilarity for concept drift detection in data streams', Pattern Recognition, vol. 76, pp. 256-272.
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© 2017 Elsevier Ltd In a non-stationary environment, newly received data may have different knowledge patterns from the data used to train learning models. As time passes, a learning model's performance may become increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years. However, very few existing methods pay attention to small regional drifts, and their accuracy may vary due to differing statistical significance tests. This paper presents a novel concept drift detection method, based on regional-density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first is a k-nearest neighbor-based space-partitioning schema (NNPS), which transforms unmeasurable discrete da ta instances into a set of shared subspaces for density estimation. The second is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall differences. The third component is a tailored statistical significance test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift and has been proven to follow a normal distribution. As a result, the NN-DVI's accuracy and false-alarm rate are statistically guaranteed. Additionally, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing problems related to concept drift-detection.
Liu, C, Li, X, Lei, G, Ma, B, Chen, L, Wang, Y & Zhu, J 2018, 'Performance Evaluation of an Axial Flux Claw Pole Machine With Soft Magnetic Composite Cores', IEEE Transactions on Applied Superconductivity, vol. 28, no. 3, pp. 1-5.
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© 2017 IEEE. By using the powder metallurgy technology and the magnetic isotropy characteristic of the soft magnet composite (SMC) material, various kinds of perm anent magnet machines can be designed and fabricated easily. However, the torque density of permanent magnet machines with SMC cores are generally lower than those with silicon steels, with the traditional machine topology, since the permeability of the SMC is much lower than that of silicon steels. To develop the potential of SMC in high-performance drive applications, we propose a novel axial flux claw pole machine (AFCPM) with SMC cores. To eliminate the unwanted axial force in the AFCPM, the two-phase configuration concept is adopted, both the configuration of the stator-rotor-stator and the rotor-stator-rotor AFCPM are designed, analyzed, and compared. Based on the 3-D finite-element method, it can be found that the AFCPM with the configuration of the stator-rotor-stator can have very low cogging torque and torque ripple; moreover, since the rotor is in between of the two stator cores, the unbalanced axial force has been decreased greatly. On the other hand, the two-phases AFCPM with the rotor-stator-rotor can have the highest torque density and efficiency.
Liu, C, Lu, J, Wang, Y, Lei, G, Zhu, J & Guo, Y 2018, 'Design Issues for claw pole machines with soft magnetic composite cores', Energies, vol. 11, no. 8.
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© 2018 by the authors. By using global ring winding, the torque coefficient of the transverse flux machine (TFM) is proportional to its number of pole pairs, and thus the TFM possesses high torque density ability when compared with other electrical machines. As a special kind of TFM, the claw pole machine (CPM) can have more torque due to its special claw pole teeth. The manufacturing of CPM or TFM with silicon steels was very difficult in the past, and is a handicap for the progress of this kind of machine. Thanks to the advent of soft magnetic composite (SMC) materials, the manufacturing process of CPM has become more and more simple. More attention has been paid to this kind of technology, and some mass production CPMs with SMC cores have appeared. However, there are few works that discuss the key design issues for this kind of machine. In this paper, a small CPM with SMC is used as as a research benchmark. Various design methods that can be adopted to improve its performance have been studied, including unequal stator claw pole teeth, a skewing magnet design, consequent pole design, and etc. The 3D finite element method (FEM) is used for the machine analysis, and it is verified by the experimental results of a CPM with SMC cores.
Liu, C., Zhao, Q., Yan, B., Elsayed, S., Ray, T. & Sarker, R. 2018, 'Adaptive Sorting-based Evolutionary Algorithm for Many-Objective Optimization', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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Liu, F, Gong, C, Huang, X, Zhou, T, Yang, J & Tao, D 2018, 'Robust Visual Tracking Revisited: From Correlation Filter to Template Matching', IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2777-2790.
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© 2018 IEEE. In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.
Liu, F., Lu, J. & Zhang, G. 2018, 'Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations', IEEE Transactions on Fuzzy Systems, vol. Early Access.
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Liu, J, Wu, C, Li, J, Su, Y & Chen, X 2018, 'Numerical investigation of reactive powder concrete reinforced with steel wire mesh against high-velocity projectile penetration', Construction and Building Materials, vol. 166, pp. 855-872.
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© 2018 Elsevier Ltd This paper numerically investigates the effects of steel wire mesh reinforcement on reactive powder concrete (RPC) targets subjected to high-velocity projectile penetration. A numerical model based on a computer program called LS-DYNA was validated with experimental data concerning the depth of penetration (DOP) and crater diameter of reinforced RPC targets. With the validated numerical model, a series of parametric studies are conducted to investigate how the variables of steel wire mesh reinforcement such as the configuration of steel wire meshes, number of layers, space between layers, space between steel wires per layer, as well as the diameter and tensile strength of steel wires affect DOP and crater diameter of reinforced RPC targets. Moreover, the energy evolution of projectile and steel wire meshes during the projectile penetration is discussed. Based on the results of parametric studies, an empirical equation derived from the simulation data is proposed to predict DOP of reinforced RPC targets.
Liu, J, Wu, C, Su, Y, Li, J, Shao, R, Chen, G & Liu, Z 2018, 'Experimental and numerical studies of ultra-high performance concrete targets against high-velocity projectile impacts', Engineering Structures, vol. 173, pp. 166-179.
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© 2018 Elsevier Ltd Ultra-high performance concrete (UHPC) which is known for high strength, high toughness, excellent ductility and good energy absorption capacity can be adopted as an ideal material in the impact resistant design of structures. In the present study, impact responses of UHPC targets with 3 vol-% ultra-high molecular weight polyethylene (UHMWPE) fibres and UHPC targets with 3 vol-% steel fibres are experimentally investigated subjected to high-velocity projectile penetration, and plain concrete targets under the same loading scenarios are also tested as control specimens for comparative purpose. In addition, numerical studies are conducted to simulate the projectile penetration process into UHPC targets with the assistance of a computer program LS-DYNA. The numerical results in terms of the depth of penetration (DOP) and crater diameter as well as projectile abrasions and damages are compared with the experimental results. Moreover, DOPs of these two types of UHPC targets obtained from tests are compared with the previously proposed empirical model.
Liu, K, Li, Q, Wu, C, Li, X & Li, J 2018, 'A study of cut blasting for one-step raise excavation based on numerical simulation and field blast tests', International Journal of Rock Mechanics and Mining Sciences, vol. 109, pp. 91-104.
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© 2018 Elsevier Ltd Over the past several decades, raise excavation has been widely employed in underground mining, civil engineering and military engineering. One-step raise excavation with burn cuts, where all the boreholes are pre-drilled and detonated at one time and no workers need to be underneath the freshly blasted and dangerous ground, is an important and promising method in raise excavation. Cut parameters, especially the parameters of prime cut which used empty hole as a free surface and swelling space, have significant influence on the effect of raise formed. In this study, two small-scale experimental methods, spiral hole spacing method and observation hole method, are designed to determine the prime cut parameters such as hole spacing (L), stemming length (Ls1, Ls2) and air deck length (La) which are normally determined by empirical formula. In order to study the feasibility of the two methods, numerical analysis and experimental tests are conducted in V zone of Sandaozhuang molybdenum mine (SMM), in which there are large numbers of underground goafs need to be controlled by filled raise. The Riedel–Hiermaier–Thoma (RHT) material model, which considers compression damage and tension damage effect under blasting loading, is employed in the LS-DYNA software to study the rock damage zone. Meanwhile, the field tests are carried out according to the two small-scale experimental methods. The comparison results show that the damage zone of numerical simulation has a good agreement with the experimental data. Further, the optimal prime cut parameters obtained from experimental tests are applied in one-step filled-raise excavation, and a 23 m raise that meets the design requirements is formed through the proposed technology. The results indicate that these cut parameters determined by the small-scale experiments are suited for one-step raise excavation. This study can provide two simple field experiments to determine the important prime cut parameters of one...
Liu, K., Law, S.S. & Zhu, X.Q. 2018, 'A layered beam element for modeling de-bonding of steel bars in concrete and its detection using static measurements', Structural Control and Health Monitoring, vol. 25, no. 4.
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Copyright © 2018 John Wiley & Sons, Ltd. In the formulation of finite elements, the variation of elemental internal forces and displacements are interpolated. The force interpolation functions are known to reproduce the variations of forces better than the interpolation functions on the displacements. Layered section beam model is not as complicated as the fiber model, and yet, it is much more accurate than ordinary beam model. The force-based finite element is revisited in this paper with its application in the numerical studies of a damage detection strategy for a reinforced concrete beam under static load. Two kinds of damages are studied including the cracking or other local damage of the concrete and the bonding between the concrete and the steel bar. Both kinds of damages in an element can be detected separately or in combinations with the proposed strategy. The force-based layered finite element is shown to be a practical, accurate, and efficient representation of the bonding damage of steel bars in concrete structures.
Liu, M, Xu, C, Luo, Y, Xua, C, Wen, Y & Tao, D 2018, 'Cost-Sensitive Feature Selection by Optimizing F-measures', IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1323-1333.
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IEEE Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the costsensitive feature selection (CSFS) by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world datasets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.
Liu, P, Liu, J & Merigó, JM 2018, 'Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making', Applied Soft Computing Journal, vol. 62, pp. 395-422.
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© 2017 Elsevier B.V. Heronian mean (HM) operator has the advantages of considering the interrelationships between parameters, and linguistic intuitionistic fuzzy number (LIFN), in which the membership and non-membership are expressed by linguistic terms, can more easily describe the uncertain and the vague information existing in the real world. In this paper, we propose the partitioned Heronian mean (PHM) operator which assumes that all attributes are partitioned into several parts and members in the same part are interrelated while in different parts there are no interrelationships among members, and develop some new operational rules of LIFNs to consider the interactions between membership function and non-membership function, especially when the degree of non-membership is zero. Then we extend PHM operator to LIFNs based on new operational rules, and propose the linguistic intuitionistic fuzzy partitioned Heronian mean (LIFPHM) operator, the linguistic intuitionistic fuzzy weighted partitioned Heronian mean (LIFWPHM) operator, the linguistic intuitionistic fuzzy partitioned geometric Heronian mean (LIFPGHM) operator and linguistic intuitionistic fuzzy weighted partitioned geometric Heronian mean (LIFWPGHM) operator. Further, we develop two methods to solve multi-attribute group decision making (MAGDM) problems with the linguistic intuitionistic fuzzy information. Finally, we give some examples to verify the effectiveness of two proposed methods by comparing with the existing
Liu, Q, Gao, R, Tam, VWY, Li, W & Xiao, J 2018, 'Strain monitoring for a bending concrete beam by using piezoresistive cement-based sensors', Construction and Building Materials, vol. 167, pp. 338-347.
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© 2018 Elsevier Ltd Graphite nanoplatelets (GNPs), promising in improving electrical properties of cement-based materials and its smartness, were used to prepare piezoresistive cement-based strain sensors (PCSSs) in this study. Their piezoresistive responses along vertical, horizontal and inclined directions were measured during applying a vertical cyclic compression. After calibrating free PCSSs by analyzing their gauge factors, three PCSSs are embedded in a four-point bending beam at different stress zones, i.e. uniaxial compression, uniaxial tension and combined shear and compression. In addition to investigating piezoresistive responses of PCSSs embedded in the beam, traditional strain gauges and finite element method (FEM) were also used to grasp the strains at relevant positions for comparison. For free PCSSs, It was found that the electrical resistances along vertical, horizontal and diagonal directions drop by amplitudes of 5.5%, 1.8% and 6.7% respectively, as the increasing of vertical compression. The gauge factor along loading direction was calculated to be −160.8, which illustrated a better sensitivity. In the four-point bending beam, the PCSSs in compressive zone and tensile zone can be used to presume the strain variation by considering the gauge factor obtained from the free PCSS. The reaction of the PCSS in shear zone can illustrate its strain features because a slight volume variation happened in this area, which can also be testified to be only 0.012‰with FEM analysis.
Liu, Q, Shen, H, Wu, Y, Xia, Z, Fang, J & Li, Q 2018, 'Crash responses under multiple impacts and residual properties of CFRP and aluminum tubes', Composite Structures, vol. 194, pp. 87-103.
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© 2018 Elsevier Ltd This study aimed to explore the impact responses and residual properties of thin-walled carbon fiber reinforced plastics (CFRP) tubes and aluminum (Al) tubes subjected to multiple axial impacts. Five repeated impacts with the same impact energy were first conducted to evaluate the effect of repeated impact number, and then the crushing tests were performed to explore the post-impact residual behavior. Regardless of number of repeated impacts, the progressive end crushing modes for the CFRP tubes and stable progressive folding mode for aluminum tubes were identified under repeated dynamic impacts. The CFRP tubes exhibited the highest specific energy absorption (SEA) under the 1st impact, then the similar SEA values in the other four subsequent impacts; whereas the SEA of aluminum tubes fluctuated with the repeated impact numbers which were related to formation of different folds. The quasi-static crushing tests revealed that the residual SEAs of the CFRP tubes and aluminum tubes were not much influenced by the impact number, only within a difference of 5% under the 5 repetitive impacts conducted. It was demonstrated that the CFRP tubes had much better performance in energy absorption capability in comparison with the aluminum tubes in terms of repeated impacts and residual crushing tests.
Liu, Q, Wu, R, Chen, E, Xu, G, Su, Y, Chen, Z & Hu, G 2018, 'Fuzzy Cognitive Diagnosis for Modelling Examinee Performance', ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 4, pp. 1-26.
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Liu, Q., Ghosh, S., Li, J., Wong, L. & Ramamohanarao, K. 2018, 'Discovering pan-correlation patterns from time course data sets by efficient mining algorithms', Computing, vol. 100, no. 4, pp. 421-437.
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely.
Liu, Q., Thoms, J.A.I., Nunez, A.C., Huang, Y., Knezevic, K., Packham, D., Poulos, R.C., Williams, R., Beck, D., Hawkins, N.J., Ward, R.L., Wong, J.W.H., Hesson, L.B., Sloane, M.A. & Pimanda, J.E. 2018, 'Disruption of a -35 kb Enhancer Impairs CTCF Binding and MLH1 Expression in Colorectal Cells.', Clinical cancer research : an official journal of the American Association for Cancer Research.
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Purpose:MLH1 is a major tumor suppressor gene involved in the pathogenesis of Lynch syndrome and various sporadic cancers. Despite their potential pathogenic importance, genomic regions capable of regulating MLH1 expression over long distances have yet to be identified.Experimental Design: Here, we use chromosome conformation capture (3C) to screen a 650-kb region flanking the MLH1 locus to identify interactions between the MLH1 promoter and distal regions in MLH1-expressing and nonexpressing cells. Putative enhancers were functionally validated using luciferase reporter assays, chromatin immunoprecipitation, and CRISPR-Cas9-mediated deletion of endogenous regions. To evaluate whether germline variants in the enhancer might contribute to impaired MLH1 expression in patients with suspected Lynch syndrome, we also screened germline DNA from a cohort of 74 patients with no known coding mutations or epimutations at the MLH1 promoter.Results: A 1.8-kb DNA fragment, 35 kb upstream of the MLH1 transcription start site enhances MLH1 gene expression in colorectal cells. The enhancer was bound by CTCF and CRISPR-Cas9-mediated deletion of a core binding region impairs endogenous MLH1 expression. A total of 5.4% of suspected Lynch syndrome patients have a rare single-nucleotide variant (G > A; rs143969848; 2.5% in gnomAD European, non-Finnish) within a highly conserved CTCF-binding motif, which disrupts enhancer activity in SW620 colorectal carcinoma cells.Conclusions: A CTCF-bound region within the MLH1-35 enhancer regulates MLH1 expression in colorectal cells and is worthy of scrutiny in future genetic screening strategies for suspected Lynch syndrome associated with loss of MLH1 expression. Clin Cancer Res; 1-10. ©2018 AACR.
Liu, T., Zhang, W., McLean, P., Ueland, M., Forbes, S.L. & Su, S.W. 2018, 'Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines', International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1309-1320.
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© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs) to detect the target odor. GAs are applied to select the informative features and the optimal model parameters of FSVMs. FSVMs are adopted as fitness evaluation criterion and the sequent odor classifier, which can reduce the outlier effects and provide a robust and accurate classification. This proposed algorithm has been compared with some commonly used learning algorithms, such as support vector machine, the k-nearest neighbors and other combination algorithms. This study is based on experimental data collected from the response of the UTS NOS.E, which is the electronic nose system developed by the University of Technology Sydney NOS.E team. In comparison with other approaches, the experiment results show that the proposed odor classification algorithm can significantly improve the classification accuracy by selecting high-quality features and reach to 92.05% classification accuracy.
Liu, W., Xu, D., Tsang, I. & Zhang, W. 2018, 'Metric Learning for Multi-output Tasks', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor (kNN) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of kNN depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to learn an appropriate distance metric for multi-output problems. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method, which shows that our method is able to tighten the excess risk bounds. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.
Liu, X, He, Y, Zeng, G, Zhang, J & Zheng, X 2018, 'State-of-Power Estimation of Li-Ion Batteries Considering the Battery Surface Temperature', Energy Technology, vol. 6, no. 7, pp. 1352-1360.
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© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim The state-of-power (SOP) of a Li-ion battery (LIB) is an important evaluation index for safety control and energy recovery in electric vehicles. Major state estimation methods are only applicable to fixed room temperature at 20 °C, although the battery capacity and resistance vary dramatically with a change in battery surface temperature in the working process of the battery. This causes significant errors in state estimation if only room temperature is assumed. The inaccurate state may cause further excessive current at high or low temperatures to affect safety and life cycle of the battery. Therefore, a novel state estimation method applicable to various battery surface temperatures is developed here. This method uses capacity–temperature and resistance–temperature relationships from experimental data to more accurately predict battery capacity and resistance over the full temperature range. It then applies the extended Kalman filter (EKF) technique to estimate the state-of-charge, and a method of constrained multi-parameter algorithm (CMPA) to estimate the SOP of LIBs. Simulation and experimental results show that the proposed method can obtain accurate SOP results at different temperatures.
Liu, X, Ni, SQ, Guo, W, Wang, Z, Ahmad, HA, Gao, B & Fang, X 2018, 'N2O emission and bacterial community dynamics during realization of the partial nitrification process', RSC Advances, vol. 8, no. 43, pp. 24305-24311.
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© 2018 The Royal Society of Chemistry. In this study, greenhouse gas emissions and microbial community succession during the realization of the partial nitrification (PN) process were studied. The results show that N2O emission mainly occurred in the aerobic stage and the PN reactor released about 20 mg of N2O gas each cycle. There is a positive correlation between the dissolved N2O concentration and the temperature of a typical cycle. High-throughput sequencing was used to illustrate succession in the microbial community structure. The most significant microfloral change during the PN startup process was that some aerobic bacteria were relatively enriched and some anaerobic bacteria were weeded out. The ammonia oxidizing bacteria (AOB) like Nitrosomonadaceae were enriched on account of the suitable external environment. Pseudomonas whose main function is denitrification declined and Planctomyces (anammox) showed the same tendency. This study comprehensively demonstrates the fluctuations of dissolved and emitted N2O while researching the succession of the microbial community in the culture of the PN process.
Liu, X, Xu, Q, Wang, D, Zhao, J, Wu, Y, Liu, Y, Ni, B-J, Wang, Q, Zeng, G, Li, X & Yang, Q 2018, 'Improved methane production from waste activated sludge by combining free ammonia with heat pretreatment: Performance, mechanisms and applications.', Bioresource technology, vol. 268, pp. 230-236.
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Anaerobic digestion of waste activated sludge (WAS) is often limited by low hydrolysis efficiencies and poor methane potentials. This work presents a novel pretreatment technology for WAS anaerobic digestion, i.e., combining free ammonia with heat pretreatment (CFHP). Experimental results showed that compared with control, solo free ammonia (135.4 mg NH3-N/L) and solo heat (70 °C) pretreatment, the combined free ammonia and heat (135.4 mg NH3-N/L with 70 °C) obtained 52.2%, 25.5% and 30.2% faster in hydrolysis rate and 25.2%, 17.9% and 16.5% higher in biochemical methane potential, respectively. Mechanism investigations showed that the combined pretreatment not only largely facilitated the disintegration of WAS but also increased the proportion of biodegradable organic matters, thereby providing better contract between biodegradable organics and the anaerobic microbes for methane production. Considering its effectiveness and renewability, the combined pretreatment is an attractive technology for the application in real-world situations.
Liu, Y, Cheng, J, Da Xu, K, Yang, S, Liu, QH & Guo, YJ 2018, 'Reducing the Number of Elements in the Synthesis of a Broadband Linear Array with Multiple Simultaneous Frequency-Invariant Beam Patterns', IEEE Transactions on Antennas and Propagation.
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IEEE The problem of reducing the number of elements in a broadband linear array with multiple simultaneous cross-over frequency-invariant (FI) patterns is considered. Different from the single FI pattern array case, every element channel in the multiple FI pattern array is divided and followed by multiple finite-impulse-response (FIR) filters, and each of the multiple FIR-filters has a set of coefficients. In this situation, a collective filter coefficient vector and its energy bound are introduced for each element, and then the problem of reducing the number of elements is transformed as minimizing the number of active collective filter coefficient vectors. Additionally, the radiation characteristics including beam-pointing direction, mainlobe FI property, sidelobe level and space-frequency notching requirement for each of the multiple patterns can be formulated as multiple convex constraints. The whole synthesis method is implemented by performing an iterative second-order cone programming (SOCP). This method can be considered as a significant extension of the original SOCP for synthesizing broadband sparse array with single FI pattern. Numerical synthesis results show that the proposed method by synthesizing multiple discretized cross-over FI patterns can save more elements than the original iterative SOCP by using a single continuously scannable FI pattern for covering the same space range. Moreover, even for multiple FIpatterns case with complicated space-frequency notching, the proposed method is still effective in the reduction of the number of elements.
Liu, Y, Li, C, Lackner, S, Wagner, M & Horn, H 2018, 'The role of interactions of effective biofilm surface area and mass transfer in nitrogen removal efficiency of an integrated fixed-film activated sludge system', Chemical Engineering Journal, vol. 350, pp. 992-999.
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© 2018 Elsevier B.V. A reaction-diffusion biofilm model was implemented to simulate the nitrification/denitrification performance of a lab-scale integrated fixed-film activated sludge (IFAS) reactor. The model was capable of representing the system performance, i.e. changes in organic load and decrease in sludge age. Furthermore, nitrification batch tests with sludge and carrier material could also be simulated successfully with the model. Model simulation revealed that the diffusive fluxes into biofilm depended strongly on substrate loading as well as sludge age. The microbial composition in the biofilm matrix was mainly influenced by the diffusive flux of chemical oxygen demand (COD) into biofilm. When COD removal started to switch to biofilm, heterotrophic bacteria quickly replaced the previously dominating autotrophic bacteria. Running a set of simulations with a range of effective biofilm surface area and different mass transfer coefficients revealed the strong influence of these two parameters on the IFAS performance. The analysis showed that both parameters were dominating factors for ammonium removal. The optimum mass transfer coefficient was in the range of 3–4 m d−1and the effective biofilm surface was around 63–88% of the theoretical carrier surface.
Liu, Y, Liu, Q, Li, J, Ngo, HH, Guo, W, Hu, J, Gao, M-T, Wang, Q & Hou, Y 2018, 'Effect of magnetic powder on membrane fouling mitigation and microbial community/composition in membrane bioreactors (MBRs) for municipal wastewater treatment.', Bioresource technology, vol. 249, pp. 377-385.
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This study aims to investigate the usefulness of magnetic powder addition in membrane bioreactors (MBRs) for membrane fouling mitigation and its effect on microbial community and composition. The comparison between the two MBRs (one with magnetic powder (MAS-MBR) and one without magnetic powder (C-MBR)) was carried out to treat synthetic municipal wastewater. Results showed that bioflocculation and adsorption of magnetic powder contributed only minimally to membrane fouling mitigation while the slower fouling rate might be ascribed to magnetic bio-effect. The macromolecules (larger than 500 kDa and 300-500 kDa) of soluble microbial product from the MAS-MBR were reduced by 24.06% and 11.11%, respectively. High-throughput sequencing demonstrated the most abundant genera of biofilm sludge indicated lower abundance in bulk sludge from the MAS-MBR compared to the C-MBR. It is possible that less membrane fouling is connected to reductions in large molecules and pioneer bacteria from bulk sludge.
Liu, Y, Xu, W, Zhu, J & Blaabjerg, F 2018, 'Sensorless Control of Standalone Brushless Doubly-Fed Induction Generator Feeding Unbalanced Loads in Ship Shaft Power Generation System', IEEE Transactions on Industrial Electronics.
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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.
Liu, Y, Zhang, X, Hao Ngo, H, Guo, W, Wen, H, Deng, L, Li, Y & Guo, J 2018, 'Specific approach for membrane fouling control and better treatment performance of an anaerobic submerged membrane bioreactor.', Bioresource technology, vol. 268, pp. 658-664.
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This paper investigated a strategy to minimize membrane fouling and increase treatment efficiency through an investigation of a specific approach by adding sponges into a conventional submerged anaerobic membrane bioreactor (CAnSMBR). During the operation, the protein-based soluble microbial products as the main factor affecting the membrane fouling could be reduced by sponge addition in the CAnSMBR (SAnSMBR). Furthermore, reducing HRT from 18 h to 12 h could shorten the membrane fouling cycle to 62% and 87% in CAnSMBR and SAnSMBR, respectively. At the initial of COD/NO3 ratio ranges from 5 to 4, only 88% of nitrogen in CAnSMBR was removed, while the SAnSMBR could remove more than 90%. TOC removal efficiency could reach more than 95% under a good stirring scenario. It is evident that the SAnSMBR is a promising solution for improving overall CAnSMBR performance and substantially mitigating membrane fouling.
Liu, Y., Bai, J., Xu, K.D., Xu, Z., Han, F., Liu, Q.H. & Jay Guo, Y. 2018, 'Linearly Polarized Shaped Power Pattern Synthesis With Sidelobe and Cross-Polarization Control by Using Semidefinite Relaxation', IEEE Transactions on Antennas and Propagation, vol. 66, no. 6, pp. 3207-3212.
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© 1963-2012 IEEE. In this communication, the problem of synthesizing a linearly polarized shaped power pattern with accurate control on both sidelobe and cross-polarization (XP) levels is considered. For a user-defined desired polarization direction, the definitions of realizable co-polarization (COP) and XP directions for an arbitrary propagation direction in the shaped pattern are presented. With the help of such definitions, the considered problem is formulated as finding appropriate excitations so as to produce a shaped power pattern in which the realizable COP component meets prescribed lower and upper bounds, the realizable XP component and the total power pattern are less than their upper bounds in the regions of interest. The semidefinite relaxation method in the literature is then extended to solve this vectorial pattern synthesis problem. The proposed method can include the mutual coupling and platform effects by using vectorial active element patterns of an antenna array. A set of synthesis examples with different array geometries and radiation requirements are conducted to validate the effectiveness and advantages of the proposed method.
Liu, Y., Lan, C., Blumenstein, M. & Li, J. 2018, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y., Li, J., Guo, W., Ngo, H.H., Hu, J. & Gao, M.-.T. 2018, 'Use of magnetic powder to effectively improve the performance of sequencing batch reactors (SBRs) in municipal wastewater treatment.', Bioresource Technology, vol. 248, no. Part B, pp. 135-139.
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This study aims to investigate the effect of adding magnetic powder in the sequencing batch reactor (SBR) on the reactor performance and microbial community. Results indicated that, the magnetic activated sludge sequencing batch reactor (MAS-SBR) had 7.76% and 4.76% higher ammonia nitrogen (NH4(+)-N) and chemical oxygen demand (COD) removal efficiencies than that of the conventional SBR (C-SBR). The MAS-SBR also achieved 6.86% sludge reduction compared with the C-SBR. High-throughput sequencing demonstrated that the dominant phyla of both SBRs (present as ≥1% of the sequence reads) were Protebacteria, Bacteroidetes, Chloroflexi, Saccharibacteria, Chlorobi, Firmicutes, Actinobactoria, Acidobacteria, Planctomycetes and unclassified_Bacteria. The relative abundance of Protebacteria and Bacteroidetes simultaneously declined whereas the other 8 phyla increased following the addition of magnetic powder. Adding magnetic powder in the SBR significantly affected the microbial diversity and richness of activated sludge, consequently affecting the reactor performance.
Liu, Y., Ngo, H.H., Guo, W., Peng, L., Chen, X., Wang, D., Pan, Y. & Ni, B.-.J. 2018, 'Modeling electron competition among nitrogen oxides reduction and N2 O accumulation in hydrogenotrophic denitrification.', Biotechnology and Bioengineering, vol. 115, no. 4, pp. 978-988.
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Hydrogenotrophic denitrification is a novel and sustainable process for nitrogen removal, which utilizes hydrogen as electron donor, and carbon dioxide as carbon source. Recent studies have shown that nitrous oxide (N2 O), a highly undesirable intermediate and potent greenhouse gas, can accumulate during this process. In this work, a new mathematical model is developed to describe nitrogen oxides dynamics, especially N2 O, during hydrogenotrophic denitrification for the first time. The model describes electron competition among the four steps of hydrogenotrophic denitrification through decoupling hydrogen oxidation and nitrogen reduction processes using electron carriers, in contrast to the existing models that couple these two processes and also do not consider N2 O accumulation. The developed model satisfactorily describes experimental data on nitrogen oxides dynamics obtained from two independent hydrogenotrophic denitrifying cultures under various hydrogen and nitrogen oxides supplying conditions, suggesting the validity and applicability of the model. The results indicated that N2 O accumulation would not be intensified under hydrogen limiting conditions, due to the higher electron competition capacity of N2 O reduction in comparison to nitrate and nitrite reduction during hydrogenotrophic denitrification. The model is expected to enhance our understanding of the process during hydrogenotrophic denitrification and the ability to predict N2 O accumulation.
Liu, YT, Pal, NR, Marathe, AR & Lin, CT 2018, 'Weighted Fuzzy Dempster-Shafer Framework for Multimodal Information Integration', IEEE Transactions on Fuzzy Systems, vol. 26, no. 1, pp. 338-352.
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© 1993-2012 IEEE. This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster-Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain-computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods.
Liu, Z., Wang, D., Liang, J., Wu, F. & Wu, C. 2018, 'The fast multi-pole indirect BEM for solving high-frequency seismic wave scattering by three-dimensional superficial irregularities', Engineering Analysis with Boundary Elements, vol. 90, pp. 86-99.
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© 2018 Elsevier Ltd Taking full advantage of the indirect boundary element method (IBEM) and fast multi-pole expansion algorithm, this paper proposes a fast multi-pole indirect boundary element method (FM-IBEM) to solve the scattering of high-frequency seismic waves by three-dimensional (3-D) superficial irregularities or heterogeneity in a solid half-space. First, IBEM utilizes an exact dynamic Green's function for a full-space to construct the scattered wave field. Subsequently, by employing plane waves expansion of 3-D potential functions of compressional and shear waves, the multi-pole expansion and local expansion coefficients were derived. Implementation of FM-IBEM is presented in detail for wave-scattering problems. Numerical examples illustrate that the proposed FM-IBEM can reduce the memory required by more than an order of magnitude and also greatly improve the computing efficiency, retaining excellent accuracy as well. Ultimately, several high-frequency plane wave scattering problems of 3-D superficial irregularities in a solid half-space are illustrated, and several important scattering characteristics are described based on the high-precision numerical results.
Liyanaarachchi, S., Muthukumaran, S., Kaiser, J., Rogers, P., Shu, L., Shon, H.K. & Jegatheesan, V. 2018, 'Computing the effective diffusion coefficient of solutes in a multi-salts solutions during forward osmosis (FO) membrane filtration: Experiments and mathematical modelling', Journal of Environmental Management, vol. 214, pp. 215-223.
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© 2018 Elsevier Ltd Diffusion coefficient of solutes through a porous membrane media is different from diffusion coefficient through a free homogenous media. Porosity, tortuosity and the thickness of the membrane significantly affect the diffusion through a specific thickness of a membrane and therefore it is termed as effective diffusion coefficient (Deff) which is lower than the actual diffusion coefficient, D. The Deffof single or dual solutes through a porous membrane layer are well documented but not for multiple salts. Therefore, in this study, single, dual and multiple salt mixtures were passed through a flat sheet cellulose triacetate Forward Osmosis (FO) membrane to obtain a semi-empirical relationship with the Deffand its water flux. This will allow computing the structural coefficient of FO membranes. Research community have spent tremendous efforts in membrane modification to reduce the structural coefficient to improve FO process efficiency. Our finding suggests that optimising the draw solution chemistry can achieve this goal.
Llopis-Albert, C., Merigó, J.M., Liao, H., Xu, Y., Grima-Olmedo, J. & Grima-Olmedo, C. 2018, 'Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators', Water Resources Management, vol. 32, no. 2, pp. 497-510.
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© 2017, Springer Science+Business Media B.V. There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders’ preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders’ satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders’ preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders’ satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.
Llopis-Albert, C., Merigó, J.M., Xu, Y. & Liao, H. 2018, 'Application of Fuzzy Set/Qualitative Comparative Analysis to Public Participation Projects in Support of the EU Water Framework Directive.', Water environment research : a research publication of the Water Environment Federation, vol. 90, no. 1, pp. 74-83.
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This study analyzes the level of satisfaction of stakeholders in the public participation process (PPP) of water resources management, which is mandatory according to the EU Water Framework Directive (WFD). The methodology uses a fuzzy set/qualitative comparative analysis (fsQCA), which allows the identification of a combination of factors that lead to the outcome that is stakeholders' satisfaction. It allows dealing with uncertain environments due to the heterogeneous nature of stakeholders and factors. The considered causes range from environmental objectives pursued, actual capacity of efficiently carrying out those objectives, socioeconomic development of the region, level of involvement and means of participation of the stakeholders engaged in the PPP, and alternative policies and measures that should be performed. Results support the argument that different causal paths explain the stakeholders' satisfaction. The methodology may help in the implementation of the WFD and conflict resolution since it leads to greater fairness, social equity, and consensus among stakeholders.
Loganathan, P, Shim, WG, Sounthararajah, DP, Kalaruban, M, Nur, T & Vigneswaran, S 2018, 'Modelling equilibrium adsorption of single, binary, and ternary combinations of Cu, Pb, and Zn onto granular activated carbon', Environmental Science and Pollution Research, vol. 25, no. 17, pp. 16664-16675.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Elevated concentrations of heavy metals in water can be toxic to humans, animals, and aquatic organisms. A study was conducted on the removal of Cu, Pb, and Zn by a commonly used water treatment adsorbent, granular activated carbon (GAC), from three single, three binary (Cu-Pb, Cu-Zn, Pb-Zn), and one ternary (Cu-Pb-Zn) combination of metals. It also investigated seven mathematical models on their suitability to predict the metals adsorption capacities. Adsorption of Cu, Pb, and Zn increased with pH with an abrupt increase in adsorption at around pH 5.5, 4.5, and 6.0, respectively. At all pHs tested (2.5–7.0), the adsorption capacity followed the order Pb > Cu > Zn. The Langmuir and Sips models fitted better than the Freundlich model to the data in the single-metal system at pH 5. The Langmuir maximum adsorption capacities of Pb, Cu, and Zn (mmol/g) obtained from the model’s fits were 0.142, 0.094, and 0.058, respectively. The adsorption capacities (mmol/g) for these metals at 0.01 mmol/L equilibrium liquid concentration were 0.130, 0.085, and 0.040, respectively. Ideal Adsorbed Solution (IAS)-Langmuir and IAS-Sips models fitted well to the binary and ternary metals adsorption data, whereas the Extended Langmuir and Extended Sips models’ fits to the data were poor. The selectivity of adsorption followed the same order as the metals’ capacities and affinities of adsorption in the single-metal systems.
Long, G, Liu, H, Ma, K, Xie, Y & Li, W 2018, 'Development of high-performance self-compacting concrete applied as the filling layer of high-speed railway', Journal of Materials in Civil Engineering, vol. 30, no. 2.
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© 2017 American Society of Civil Engineers. The filling layer of a China rail track system III (CRTS III) type ballastless track structure of a high-speed railway is a complicated structure typically constructed from self-compacting concrete (SCC). Excellent properties of SCC are of great importance to ensure the quality of construction technology and long-term service performance of the filling layer. In this study, preparation methodologies and properties of SCC applied as a filling layer are systematically investigated by series of experiments. The results indicate that high-performance SCC with high stability in a fresh state and low deformation in a hardened state was successfully achieved by optimizing aggregates and binder components. Use of viscosity-enhancing compounds can not only effectively improve the workability of fresh SCC, but also significantly enhance mechanical properties and decrease drying shrinkage and creep of hardened concrete.
Lopez, A.M., Quevedo, D.E., Aguilera, R.P., Geyer, T. & Oikonomou, N. 2018, 'Limitations and Accuracy of a Continuous Reduced-Order Model for Modular Multilevel Converters', IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 6292-6302.
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© 1986-2012 IEEE. This paper analyzes the limitations of a reduced-order model for modular multilevel converters (MMCs) by elucidating the relation between its accuracy, operating frequency, and converter parameters. A reduced-order simplifies the analysis of the MMC and thereby may provide additional information about the converter behavior. However, the accuracy of such model depends on several factors. In this paper, the effect of approximating the converter as a continuous system by neglecting quantization issues associated with having a finite number of modules is studied in detail. The analysis is done based on Fourier-series approximations with which it is possible to elucidate the relationship between the resonant frequencies of the MMC and the error of the reduced-order model. With the Fourier approximation, it is also possible to characterize resonant frequencies of the converter, both numerically and analytically, in terms of the converter parameters. The results can serve as a tool to identify situations when the reduced-order model produces good and also less accurate approximations especially when a low number of modules is available.
Lozano, F.J., Lozano, R., Freire, P., Jiménez-Gonzalez, C., Sakao, T., Ortiz, M.G., Trianni, A., Carpenter, A. & Viveros, T. 2018, 'New perspectives for green and sustainable chemistry and engineering: Approaches from sustainable resource and energy use, management, and transformation', Journal of Cleaner Production, vol. 172, pp. 227-232.
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© 2017 Elsevier Ltd The special volume on green and sustainable chemistry and engineering has fourteen papers that were considered relevant to the present day issues and discussion, such as adequate use of raw materials and efficient energy, besides considering renewable sources for materials and energy; and changing economical canons towards circular economy. Businesses, governments and Society are facing a number of challenges to tread the sustainability path and provide wellbeing for future generations. This special volume relevance provides discussions and contributions to foster that desirable future. Chemicals are ubiquitous in everyday activities. Their widespread presence provides benefits to societies’ wellbeing, but can have some deleterious effects. To counteract such effect, green engineering and sustainable assessment in industrial processes have been gathering momentum in the last thirty years. Green chemistry, green engineering, eco-efficiency, and sustainability are becoming a necessity for assessing and managing products and processes in the chemical industry. This special volume presents fourteen articles related to sustainable resource and energy use (five articles), circular economy (one article), cleaner production and sustainable process assessment (five article), and innovation in chemical products (three articles). Green and sustainable chemistry, as well as sustainable chemical engineering and renewable energy sources are required to foster and consolidate a transition towards more sustainable societies. This special volume present current trends in chemistry and chemical engineering, such as sustainable resource and energy use, circular economy, cleaner production and sustainable process assessment, and innovation in chemical products. This special volume provides insights in this direction and complementing other efforts towards such transition.
Lu, C, Yu, JX & Wei, H 2018, 'Exploring Triangle-Free Dense Structures', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 2, pp. 278-291.
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© 2017 IEEE. Triadic closure is ubiquitous in social networks, which refers to the property among three individuals, A, B, and C, such thatif there exist strong ties between A-B and A-C, then there must be a strong or weak tie between B-C. Related to triadic closure, thenumber of triangles has been extensively studied since it can be effectively used as a metric to analyze the structure and function of anetwork. In this paper, from a different viewpoint, we study triangle-free dense structures which have received little attention. We focuson K3;3 where there are two subsets of three vertices, a vertex in a subset has an edge connected to every vertex in another subsetwhile it does not have an edge to any other vertex in the same subset. Such Kn;n in general implies a philosophy contradiction: (a) Anytwo individuals are friends if they have no common friends, and (b) Any two individuals are not friends if they have common friends.However, we find such induced K3;3 does exist frequently, and they do not disappear over time over a real academic collaborationnetwork. In addition, in the real datasets tested, nearly all edges appearing in K3;3 appear in some triangles. We analyze the expectednumbers of induced K3;3 and triangles (D) in four representative random graph models, namely, Erdos-Renyi random graph model,Watts-Strogatz small-world model, Barabasi-Albert preferential attachment model, and configuration model, and give an algorithm toenumerate all distinct K3;3 in an undirected social network. We conduct extensive experiments on both real and synthetic datasets toconfirm our findings. As an application, such K3;3 found helps to find new stars collaborated by well-known figures who themselves donot collaborate.
Lu, C., Yang, D., Guo, J., Xie, Z., Song, Y., Xing, Y., Ngo, H.H., Han, Y. & Li, H. 2018, 'The catalysis biodecolorization characteristics of novel recyclable insoluble redox mediators onto magnetic nanoparticles', Desalination and Water Treatment, vol. 107, pp. 62-71.
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© 2018 Desalination Publications. All rights reserved. The insoluble redox mediators (RMs) were prepared to overcome limitations of soluble RMs that are eluted with water flow in wastewater treatment process. Magnetic nanoparticles possess good performance, due to their high specific surface area, the absence of internal diffusion resistance and easy separation in presence of an external magnetic field. In this study, insoluble RMs were prepared by immobilizing anthraquinone-2-sulfate (AQS) onto magnetic nanoparticles. AQS modified magnetic nanoparticles (FeSi@AQS) were formed by chemical reaction between the sulfochlorides group of anthraquinone-2-sulfonyl choride and amino-modified magnetic nanoparticles, with formation confirmed by Fourier transform infrared spectra. Results of energy dispersive X-ray and thermal grav-imetric analysis showed that AQS occupied a 21.47 wt.% proportion of the FeSi@AQS complex. FeSi@ AQS was used as insoluble RMs to catalyze biodecolorization of several kinds of azo dyes. When the concentration of FeSi@AQS was as low as 40 mg/L, biodecolorization rate of reactivered K-2BP was increased by 2.18-fold. FeSi@AQ Scan then be separated and gathered from wastewater by magnetic attraction and reused for further catalysis of azo dye decolorization in a modified SBR system. These findings show that the immobilization of RMs on magnetic nanoparticle surfaces, benefits potential industrial applications of RMs.
Lu, J, Xuan, J, Zhang, G & Luo, X 2018, 'Structural property-aware multilayer network embedding for latent factor analysis', Pattern Recognition, vol. 76, pp. 228-241.
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© 2017 Elsevier Ltd Multilayer network is a structure commonly used to describe and model the complex interaction between sets of entities/nodes. A three-layer example is the author-paper-word structure in which authors are linked by co-author relation, papers are linked by citation relation, and words are linked by semantic relation. Network embedding, which aims to project the nodes in the network into a relatively low-dimensional space for latent factor analysis, has recently emerged as an effective method for a variety of network-based tasks, such as collaborative filtering and link prediction. However, existing studies of network embedding both focus on the single-layer network and overlook the structural properties of the network, e.g., the degree distribution and communities, which are significant for node characterization, such as the preferences of users in a social network. In this paper, we propose four multilayer network embedding algorithms based on Nonnegative Matrix Factorization (NMF) with consideration given to four structural properties: whole network (NNMF), community (CNMF), degree distribution (DNMF), and max spanning tree (TNMF). Experiments on synthetic data show that the proposed algorithms are able to preserve the desired structural properties as designed. Experiments on real-world data show that multilayer network embedding improves the accuracy of document clustering and recommendation, and the four embedding algorithms corresponding to the four structural properties demonstrate the differences in performance on these two tasks. These results can be directly used in document clustering and recommendation systems.
Lu, ZH, Lun, PY, Li, W, Luo, Z, Li, Y & Liu, P 2018, 'Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments', Advances in Structural Engineering.
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© 2018, The Author(s) 2018. The corrosion rate of reinforcing steel is an important factor to determine the corrosion propagation of reinforced concrete structures in the chloride-laden environments. Since the corrosion rate of reinforcing steel is affected by several coupled parameters, the efficient prediction of which remains challenging. In this study, a total of 156 experimental data on corrosion rate from the literature were collected and compared. Seven empirical models for predicting the corrosion rate were reviewed and investigated using the collected experimental data. Based on the investigations, a new empirical model is proposed for predicting the corrosion rate in corrosion-affected reinforced concrete structures considering parameters including concrete resistivity, temperature, relative humidity, corrosion duration and concrete chloride content. The comparison between the experimental data and those predicted using the new empirical model demonstrates that the new model gives a good prediction of the corrosion rate. Furthermore, the uncertainty and probability characteristics of these empirical models are also investigated. It is found that the probability distributions of the model errors can be described as lognormal, normal, Weibull or Gumbel distributions. As a result, the new empirical model can provide an efficient prediction of the corrosion rate of reinforcing steel, and the model error analysis results can be utilized for reliability-based service life prediction of reinforced concrete structures under chloride-laden environments.
Luo, F, Du, B, Zhang, L, Zhang, L & Tao, D 2018, 'Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image', IEEE Transactions on Cybernetics.
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IEEE Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are most based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
Luo, L., Jiang, Z., Wei, D., Wang, X., Zhou, C. & Huang, Q. 2018, 'Micro-hydromechanical deep drawing of metal cups with hydraulic pressure effects', Frontiers of Mechanical Engineering, vol. 13, no. 1, pp. 66-73.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany. Micro-metal products have recently enjoyed high demand. In addition, metal microforming has drawn increasing attention due to its net-forming capability, batch manufacturing potential, high product quality, and relatively low equipment cost. Micro-hydromechanical deep drawing (MHDD), a typical microforming method, has been developed to take advantage of hydraulic force. With reduced dimensions, the hydraulic pressure development changes; accordingly, the lubrication condition changes from the macroscale to the microscale. A Voronoi-based finite element model is proposed in this paper to consider the change in lubrication in MHDD according to open and closed lubricant pocket theory. Simulation results agree with experimental results concerning drawing force. Changes in friction significantly affect the drawing process and the drawn cups. Moreover, defined wrinkle indexes have been shown to have a complex relationship with hydraulic pressure. High hydraulic pressure can increase the maximum drawing ratio (drawn cup height), whereas the surface finish represented by the wear is not linearly dependent on the hydraulic pressure due to the wrinkles.
Luo, M, Chang, X, Nie, L, Yang, Y, Hauptmann, AG & Zheng, Q 2018, 'An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition', IEEE Transactions on Cybernetics, vol. 48, no. 2, pp. 648-660.
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© 2017 IEEE. Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough high-quality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality. Additionally, the predetermined graph separates itself from the procedure of feature selection, which might lead to downgraded performance for video semantic recognition. In this paper, we exploit a novel semisupervised feature selection meth od from a new perspective. The primary assumption underlying our model is that the instances with similar labels should have a larger probability of being neighbors. Instead of using a predetermined similarity graph, we incorporate the exploration of the local structure into the procedure of joint feature selection so as to learn the optimal graph simultaneously. Moreover, an adaptive loss function is exploited to measure the label fitness, which significantly enhances model's robustness to videos with a small or substantial loss. We propose an efficient alternating optimization algorithm to solve the proposed challenging problem, together with analyses on its convergence and computational complexity in theory. Finally, extensive experimental results on benchmark datasets illustrate the effectiveness and superiority of the proposed approach on video semantic recognition related tasks.
Luo, M, Nie, F, Chang, X, Yang, Y, Hauptmann, AG & Zheng, Q 2018, 'Adaptive Unsupervised Feature Selection with Structure Regularization', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 944-956.
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© 2012 IEEE. Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsupervised feature selection lies in how to characterize the geometry structure of original feature space and produce a faithful feature subset, which preserves the intrinsic structure accurately. In this paper, we characterize the intrinsic local structure by an adaptive reconstruction graph and simultaneously consider its multiconnected-components (multicluster) structure by imposing a rank constraint on the corresponding Laplacian matrix. To achieve a desirable feature subset, we learn the optimal reconstruction graph and selective matrix simultaneously, instead of using a predetermined graph. We exploit an efficient alternative optimization algorithm to solve the proposed challenging problem, together with the theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on clustering task are conducted over several benchmark data sets to verify the effectiveness and superiority of the proposed unsupervised feature selection algorithm.
Luo, W, Xie, M, Song, X, Guo, W, Ngo, HH, Zhou, JL & Nghiem, LD 2018, 'Biomimetic aquaporin membranes for osmotic membrane bioreactors: Membrane performance and contaminant removal.', Bioresource technology, vol. 249, pp. 62-68.
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In this study, we investigated the performance of an osmotic membrane bioreactor (OMBR) enabled by a novel biomimetic aquaporin forward osmosis (FO) membrane. Membrane performance and removal of 30 trace organic contaminants (TrOCs) were examined. Results show that the aquaporin FO membrane had better transport properties in comparison with conventional cellulose triacetate and polyamide thin-film composite FO membranes. In particular, the aquaporin FO membrane exhibited much lower salt permeability and thus smaller reverse salt flux, resulting in a less severe salinity build-up in the bioreactor during OMBR operation. During OMBR operation, the aquaporin FO membrane well complemented biological treatment for stable and excellent contaminant removal. All 30 TrOCs selected here were removed by over 85% regardless of their diverse properties. Such high and stable contaminant removal over OMBR operation also indicates the stability and compatibility of the aquaporin FO membrane in combination with activated sludge treatment.
Luo, Y, Pan, J, Zhang, JA & Huang, S 2018, 'Worst-Case Performance Optimization Beamformer with Embedded Array’s Active Pattern', International Journal of Antennas and Propagation, vol. 2018, pp. 1-5.
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Luo, Y, Wen, Y & Tao, D 2018, 'Heterogeneous Multitask Metric Learning Across Multiple Domains', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE Distance metric learning plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in a target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multitask metric learning (MTML), which can be regarded as a special case of TML, performs transfer across all related domains. Current TML tools usually assume that the same feature representation is exploited for different domains. However, in real-world applications, data may be drawn from heterogeneous domains. Heterogeneous transfer learning approaches can be adopted to remedy this drawback by deriving a metric from the learned transformation across different domains. However, they are often limited in that only two domains can be handled. To appropriately handle multiple domains, we develop a novel heterogeneous MTML (HMTML) framework. In HMTML, the metrics of all different domains are learned together. The transformations derived from the metrics are utilized to induce a common subspace, and the high-order covariance among the predictive structures of these domains is maximized in this subspace. There do exist a few heterogeneous transfer learning approaches that deal with multiple domains, but the high-order statistics (correlation information), which can only be exploited by simultaneously examining all domains, is ignored in these approaches. Compared with them, the proposed HMTML can effectively explore such high-order information, thus obtaining more reliable feature transformations and metrics. Effectiveness of our method is validated by the extensive and intensive experiments on text categorization, scene classification, and social image annotation.
Luo, Y., Wen, Y., Liu, T. & Tao, D. 2018, 'Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Ly, QV, Nghiem, LD, Cho, J & Hur, J 2018, 'Insights into the roles of recently developed coagulants as pretreatment to remove effluent organic matter for membrane fouling mitigation', Journal of Membrane Science, vol. 564, pp. 643-652.
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© 2018 Elsevier B.V. Membrane fouling by dissolved organic matter (EfOM) in secondary treated effluent is a problematic and inevitable issue during wastewater reclamation using low pressure membrane filtration. This study evaluates the performance of coagulation/flocculation (C/F) using two recently developed coagulants (namely TiCl4and ZrCl4) in comparison to conventional alum (i.e. Al2(SO4)3) as pretreatment to remove EfOM for subsequent ultrafiltration (UF) membrane fouling mitigation. At the optimal dosage, TiCl4-based C/F pretreatment showed the greatest performance in membrane fouling mitigation, followed by ZrCl4and then alum. The underlying mechanisms were well explained by classical fouling models and the extended Derjaguin-Landau-Verwey-Overbeek (xDLVO) theory, highlighting a dominant role of standard blocking in the fouling potential of the C/F treated EfOM. The interfacial free energy of cohesion and adhesion showed that C/F pretreatment using TiCl4and ZrCl4as coagulant can lower the binding affinity between EfOM molecules and between EfOM molecules and membrane surface, ultimately reduce membrane fouling. The results of size exclusion chromatography (SEC) and fluorescence excitation emission matrix- parallel factor analysis (EEM-PARAFAC) also supported the classical fouling mechanisms, providing additional insights into the potential roles of chemical interactions in the preferential removal of certain organic substances by C/F pretreatment and the chemical composition of subsequent membrane foulants. Protein-like components were highly associated with reversible fouling after the C/F, while the reversibility of humic-like substances was enhanced upon C/F pretreatment. After C/F pretreatment, small sized EfOM molecules became the dominant fraction responsible for UF membrane fouling.
Ly, QV, Nghiem, LD, Sibag, M, Maqbool, T & Hur, J 2018, 'Effects of COD/N ratio on soluble microbial products in effluent from sequencing batch reactors and subsequent membrane fouling.', Water research, vol. 134, pp. 13-21.
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The relative ratios of chemical oxygen demand (COD) to nitrogen (N) in wastewater are known to have profound effects on the characteristics of soluble microbial products (SMP) from activated sludge. In this study, the changes in the SMP characteristics upon different COD/N ratios and the subsequent effects on ultrafiltration (UF) membrane fouling potentials were examined in sequencing batch reactors (SBR) using excitation emission matrix-parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). Three unique fluorescent components were identified from the SMP samples in the bioreactors operated at the COD/N ratios of 100/10 (N rich), 100/5 (N medium), and 100/2 (N deficient). The tryptophan-like component (C1) was the most depleted at the N medium condition. Fulvic-like (C2) and humic-like (C3) components were more abundant with N rich wastewater. Greater abundances of large size biopolymer (BP) and low molecular weight neutrals (LMWN) were found under the N deficient and N rich conditions, respectively. SMPs from various COD/N exhibited a greater degree on membrane fouling following the order of 100/2 > 100/10 > 100/5. C1 and C2 had close associations with reversible and irreversible fouling, respectively, while the reversible fouling potential of C3 depended on the COD/N ratios. No significant impact of COD/N ratio was observed on the relative contributions of SMP size fractions to either reversible or irreversible fouling potential. However, the COD/N ratios likely altered the BP foulants' composition with greater contribution of proteinaceous substances to reversible fouling under the N deficient condition than at other N richer conditions. The opposite trend was observed for irreversible fouling. Our results provided further insight into changes in different SMP constitutes and their membrane fouling in response to microbial activities under different COD/N ratios.
Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2018, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Most existing solutions for supergraph search follow the pruning-and-verification framework. However, they are not scalable due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationship to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the high scalability of our algorithms.
Lyu, X, Ren, C, Ni, W, Tian, H & Liu, RP 2018, 'Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing', IEEE Journal on Selected Areas in Communications, vol. 36, no. 3, pp. 574-586.
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© 1983-2012 IEEE. Fog computing enables resource-limited network devices to help each other with computationally demanding tasks, but has yet to be implemented in large scales due to sophisticated control and network inhomogeneity. This paper presents a new fully distributed online optimization to asymptotically minimize the time-average cost of fog computing, where tasks are selected to be offloaded and processed independently between different links and devices by measuring their cost effectiveness at each time slot. A key contribution is that we optimize the cost-effectiveness measures which achieve the asymptotic optimality over infinite time. Another contribution is that we optimize placeholders at the devices; which create collaborative computing regions of tasks in the vicinity of the point of capture, prevent tasks being offloaded beyond, preserve the asymptotic optimality and reduce delay. This is achieved in a distributed fashion by discovering the optimal substructure of the placeholders. Simulations show that the average size of collaborative regions is only 3.2 out of total 500 servers, and the system income increases by 43% as compared with existing techniques.
Lyu, X, Tian, H, Ni, W, Zhang, Y, Zhang, P & Liu, RP 2018, 'Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing', IEEE Transactions on Communications, vol. 66, no. 6, pp. 2603-2616.
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© 1972-2012 IEEE. Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1 - ∈)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ∈ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(∈),O(1/∈)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.
Ma, B, Lei, G, Zhu, J & Guo, Y 2018, 'Design Optimization of a Permanent Magnet Claw Pole Motor with Soft Magnetic Composite Cores', IEEE Transactions on Magnetics, vol. 54, no. 3.
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© 2017 IEEE. In our previous study, the claw pole motor (CPM) shows promising magnetic concentrating performance for permanent magnet motors with soft magnetic composite cores. Meanwhile, its relatively complex structure also increases the core loss and cogging torque. To conduct the high dimensional optimization of the CPM with comprehensive analysis models, this paper presents an improved multilevel strategy of high efficiency. Since the holistic performances, including output torque, efficiency, cogging torque, and back electromotive force, are considered, an orthogonal design is utilized for the surrogate model building for increasing the optimization efficiency. A full factor sample method for the surrogate model is also conducted for comparison. The similar optimization results with the two kinds of models prove the effectiveness of the proposed method and optimal design.
Ma, B, Lei, G, Zhu, J, Guo, Y & Liu, C 2018, 'Application-Oriented Robust Design Optimization Method for Batch Production of Permanent Magnet Motors', IEEE Transactions on Industrial Electronics, vol. 65, no. 2.
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