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Improved shear strength performance of compacted rubberized clays treated with sodium alginate biopolymer

- Soltani, Amin, Raeesi, Ramin, Taheri, Abbas, Deng, An, Mirzababaei, Mehdi

  • Authors: Soltani, Amin , Raeesi, Ramin , Taheri, Abbas , Deng, An , Mirzababaei, Mehdi
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Polymers Vol. 13, no. 5 (2021), p. 1-21
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  • Description: This study examines the potential use of sodium alginate (SA) biopolymer as an environmentally sustainable agent for the stabilization of rubberized soil blends prepared using a high plasticity clay soil and tire-derived ground rubber (GR). The experimental program consisted of uniaxial compression and scanning electron microscopy (SEM) tests; the former was performed on three soil–GR blends (with GR-to-soil mass ratios of 0%, 5% and 10%) compacted (and cured for 1, 4, 7 and 14 d) employing distilled water and three SA solutions—prepared at SA-to-water (mass-tovolume) dosage ratios of 5, 10 and 15 g/L—as the compaction liquid. For any given GR content, the greater the SA dosage and/or the longer the curing duration, the higher the uniaxial compressive strength (UCS), with only minor added benefits beyond seven days of curing. This behaviour was attributed to the formation and propagation of so-called “cationic bridges” (developed as a result of a “Ca2+/Mg2+

Influence of silica nano-additives on performance and emission characteristics of soybean biodiesel fuelled diesel engine

- Gavhane, R., Kate, A., Soudagar, Manzoore, Wakchaure, V., Shahabuddin, M.

  • Authors: Gavhane, R. , Kate, A. , Soudagar, Manzoore , Wakchaure, V. , Shahabuddin, M.
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Energies Vol. 14, no. 5 (Mar 2021), p. 16
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  • Description: The present study examines the effect of silicon dioxide (SiO2) nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. The morphology of nano-additives was studied using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The Ultrasonication process was used for the homogeneous blending of nano-additives with biodiesel, while surfactant was used for the stabilisation of nano-additives. The physicochemical properties of pure and blended fuel samples were measured as per ASTM standards. The performance and emissions characteristics of different fuel samples were measured at different loading conditions. It was found that the brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) increased by 3.48-6.39% and 5.81-9.88%, respectively, with the addition of SiO2 nano-additives. The carbon monoxide (CO), hydrocarbon (HC) and smoke emissions for nano-additive added blends were decreased by 1.9-17.5%, 20.56-27.5% and 10.16-23.54% compared to SBME25 fuel blends. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “M. Shahabuddin” is provided in this record**

Land-use changes concerning the riparian vegetation in Galela Lake, North Maluku, Indonesia

- Soeprobowati, Tri, Jumari, Jumari, Saraswati, Tyas, Suhry, Hendro, Gell, Peter

  • Authors: Soeprobowati, Tri , Jumari, Jumari , Saraswati, Tyas , Suhry, Hendro , Gell, Peter
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Ecological Engineering Vol. 170, no. (2021), p.
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  • Description: Galela Lake is the largest lake in the Halmahera Utara Regency, North Maluku Province, Indonesia, and is used for domestic water supply, irrigation, aquaculture, and tourism. However, its catchment has been cleared and developed for intensive agriculture. Therefore, there is a need to examine the land-use changes impacting the riparian vegetation of Galela Lake to establish a basis for the development of lake management plans. Research has been conducted at four research sites within the forest (Site I), farmland (Site II), a tourism area (Site III), and near the river inlet (Site IV). In each site, the riparian vegetation was surveyed from the lake banks to 500 m into the neighboring terrestrial habitat. Evidence for changes in the spatial distribution of land-use types in the vicinity of Galela Lake in 2002, 2015, and 2019 were obtained from Landsat images. Galela Lake (302.05 Ha) is exposed to various kinds of human use. During 2002–2019, barren land had been converted into build-up land, and much of the water body of the lake was converted into a riparian zone through the pressures of population growth. Ten of 89 plant species observed within the riparian vegetation around Galela Lake are included in The International Union for Conservation of Nature and Natural Resources (IUCN) Red List threatened species 2010. Coconut (Cocos nucifera), sugar palm (Arenga pinnata), fig (Ficus variegata), nutmeg (Myristica fragrans), and mango (Mangifera indica) were found to be the most important riparian trees. The second generation stands of riparian vegetation (pole and sapling strata) of several species exhibit potential as conservation plants for land and aquatic ecosystems, such as guest tree (Kleinhovia hospita), bamboo (Bambusa vulgaris), the figs (Ficus septica and F. variegata), and sugar palm (A. pinnata). © 2021 Elsevier B.V.

Livestock data – is it there and is it FAIR? A systematic review of livestock farming datasets in Australia

- Bahlo, Christiane, Dahlhaus, Peter

  • Authors: Bahlo, Christiane , Dahlhaus, Peter
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Computers and Electronics in Agriculture Vol. 188, no. (2021), p.
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  • Description: The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts. © 2021 Elsevier B.V.

Model predictive control of microgrids – An overview

- Hu, Jiefeng, Shan, Yinghao, Guerrero, Josep, Ioinovici, Adrian, Chan, Ka, Rodriguez, Jose

  • Authors: Hu, Jiefeng , Shan, Yinghao , Guerrero, Josep , Ioinovici, Adrian , Chan, Ka , Rodriguez, Jose
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Renewable and Sustainable Energy Reviews Vol. 136, no. (2021), p.
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  • Description: The development of microgrids is an advantageous option for integrating rapidly growing renewable energies. However, the stochastic nature of renewable energies and variable power demand have created many challenges like unstable voltage/frequency and complicated power management and interaction with the utility grid. Recently, predictive control with its fast transient response and flexibility to accommodate different constraints has presented huge potentials in microgrid applications. This paper provides a comprehensive review of model predictive control (MPC) in individual and interconnected microgrids, including both converter-level and grid-level control strategies applied to three layers of the hierarchical control architecture. This survey shows that MPC is at the beginning of the application in microgrids and that it emerges as a competitive alternative to conventional methods in voltage regulation, frequency control, power flow management and economic operation optimization. Also, some of the most important trends in MPC development have been highlighted and discussed as future perspectives. © 2020 Elsevier Ltd
  • Description: This work was supported by School of Engineering, IT and Physical Sciences, Federation University Australia , under Project RGS20-5 .

Optical characterisation of alumina–mullite materials for solar particle receiver applications

- Chen, Jiangjing, Wheeler, Vincent, Liu, Boqing, Kumar, Apurv, Coventry, Joe, Lipiński, Wojciech

  • Authors: Chen, Jiangjing , Wheeler, Vincent , Liu, Boqing , Kumar, Apurv , Coventry, Joe , Lipiński, Wojciech
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Solar Energy Materials and Solar Cells Vol. 230, no. (2021), p.
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  • Description: Alumina–mullite particles are used in high-temperature solar thermal applications such as solar particle receivers. In this study, optical properties of alumina–mullite materials with variable content of alumina and mullite are determined in the spectral range of 0.193–1.69 μm. Variable angle spectroscopic ellipsometry is performed for alumina–mullite thin films, which are fabricated by magnetron sputtering. The thin films are characterised by scanning electron microscopy, atomic force microscopy, and energy dispersive spectroscopy methods. The B-spline model is employed to generate ellipsometric parameters to fit the measured data and to obtain the optical properties. The investigated materials of variable content of alumina and mullite have a similar refractive index in the considered spectral range. The absorptive index of the alumina–mullite materials in the spectral range of 0.193–0.4 μm is higher than in the range 0.4–1.69 μm. The absorptive index decreases with increasing content of alumina in the spectral range of 0.193–0.4 μm. The material composed of similar proportions of alumina and mullite yields the highest absorptive index in the spectral range of 0.4–1.1 μm. The optical properties determined for the alumina–mullite materials are applied to obtain the radiative properties of spherical homogeneous particles. Mie theory is used to calculate absorption and scattering efficiency factors, as well as the scattering phase function. In addition, the scattering phase functions are obtained using the Henyey–Greenstein approximation and the transport approximation. The Monte Carlo ray-tracing method is employed to study the radiative transfer in a model one-dimensional particle curtain containing polydisperse particles exposed to high-flux solar irradiation. It is found that the overall reflectance, absorptance and transmittance of the particles only weakly depend on the optical properties of the materials investigated. © 2021 Elsevier B.V. Corrigendum to “Optical characterisation of alumina–mullite materials for solar particle receiver applications” [Solar Energy Mater. Solar Cell. 230 (2021) 111170] (Solar Energy Materials and Solar Cells (2021) 230, (S0927024821002130), (10.1016/j.solmat.2021.111170)) Solar Energy Materials and Solar Cells, Volume 231, October 2021, Article number 111225. https://doi.org/10.1016/j.solmat.2021.111225
  • Description: Alumina–mullite particles are used in high-temperature solar thermal applications such as solar particle receivers. In this study, optical properties of alumina–mullite materials with variable content of alumina and mullite are determined in the spectral range of 0.193–1.69 μm. Variable angle spectroscopic ellipsometry is performed for alumina–mullite thin films, which are fabricated by magnetron sputtering. The thin films are characterised by scanning electron microscopy, atomic force microscopy, and energy dispersive spectroscopy methods. The B-spline model is employed to generate ellipsometric parameters to fit the measured data and to obtain the optical properties. The investigated materials of variable content of alumina and mullite have a similar refractive index in the considered spectral range. The absorptive index of the alumina–mullite materials in the spectral range of 0.193–0.4 μm is higher than in the range 0.4–1.69 μm. The absorptive index decreases with increasing content of alumina in the spectral range of 0.193–0.4 μm. The material composed of similar proportions of alumina and mullite yields the highest absorptive index in the spectral range of 0.4–1.1 μm. The optical properties determined for the alumina–mullite materials are applied to obtain the radiative properties of spherical homogeneous particles. Mie theory is used to calculate absorption and scattering efficiency factors, as well as the scattering phase function. In addition, the scattering phase functions are obtained using the Henyey–Greenstein approximation and the transport approximation. The Monte Carlo ray-tracing method is employed to study the radiative transfer in a model one-dimensional particle curtain containing polydisperse particles exposed to high-flux solar irradiation. It is found that the overall reflectance, absorptance and transmittance of the particles only weakly depend on the optical properties of the materials investigated. © 2021 Elsevier B.V. Corrigendum to “Optical characterisation of alumina–mullite materials for solar particle receiver applications” [Solar Energy Mater. Solar Cell. 230 (2021) 111170] (Solar Energy Materials and Solar Cells (2021) 230, (S0927024821002130), (10.1016/j.solmat.2021.111170)) Solar Energy Materials and Solar Cells, Volume 231, October 2021, Article number 111225

Optimal placement of synchronized voltage traveling wave sensors in a radial distribution network

- Tashakkori, Ali, Abu-Siada, Ahmed, Wolfs, Peter, Islam, Syed

  • Authors: Tashakkori, Ali , Abu-Siada, Ahmed , Wolfs, Peter , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 65380-65387
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  • Description: A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers. © 2013 IEEE.

Optimizing 3d printed metallic object’s postprocessing : a case of gamma‐tial alloys

- Chowdhury, M. A. K., Sharif Ullah, A., Teti, Roberto

  • Authors: Chowdhury, M. A. K. , Sharif Ullah, A. , Teti, Roberto
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Materials Vol. 14, no. 5 (2021), p. 1-15
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  • Description: Gamma‐TiAl (γ‐TiAl) alloys can be used in high‐end products relevant to the aerospace, defense, biomedical, and marine industries. Fabricating objects made of γ‐TiAl alloys needs an additive manufacturing process called Electron Beam Melting (EBM) or other similar processes because these alloys are difficult‐to‐cut materials. An object fabricated by EBM exhibits poor surface finish and must undergo postprocessing. In this study, cylindrical specimens were fabricated by EBM and post‐processed by turning at different cutting conditions (cutting speed, depth of cut, feed rate, insert radius, and coolant flowrate). The EBM conditions were as follows: average powder size 110 μm, acceleration voltage 60 kV, beam current 10 mA, beam scanning speed 2200 mm/s, and beam focus offset 0.20 mm. The surface roughness and cutting force were recorded for each set of cutting conditions. The values of the cutting conditions were set by the L36 Design of Experiment approach. The effects of the cutting conditions on surface roughness and cutting force are elucidated by constructing the possibility distributions (triangular fuzzy numbers) from the experimental data. Finally, the optimal cutting conditions to improve the surface finish of specimens made of γ‐TiAl alloys are determined using the possibility distributions. Thus, this study’s outcomes can be used to develop intelligent systems for optimizing additive manufacturing processes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

- Vamplew, Peter, Foale, Cameron, Dazeley, Richard, Bignold, Adam

  • Authors: Vamplew, Peter , Foale, Cameron , Dazeley, Richard , Bignold, Adam
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Engineering Applications of Artificial Intelligence Vol. 100, no. (2021), p.
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  • Description: The concept of impact-minimisation has previously been proposed as an approach to addressing the safety concerns that can arise from utility-maximising agents. An impact-minimising agent takes into account the potential impact of its actions on the state of the environment when selecting actions, so as to avoid unacceptable side-effects. This paper proposes and empirically evaluates an implementation of impact-minimisation within the framework of multiobjective reinforcement learning. The key contributions are a novel potential-based approach to specifying a measure of impact, and an examination of a variety of non-linear action-selection operators so as to achieve an acceptable trade-off between achieving the agent's primary task and minimising environmental impact. These experiments also highlight a previously unreported issue with noisy estimates for multiobjective agents using non-linear action-selection, which has broader implications for the application of multiobjective reinforcement learning. © 2021

Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment

- Qi, Lianyong, Hu, Chunhua, Zhang, Xuyun, Khosravi, Mohammad, Pang, Shaoning

  • Authors: Qi, Lianyong , Hu, Chunhua , Zhang, Xuyun , Khosravi, Mohammad , Pang, Shaoning
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4159-4167
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  • Description: As one of the cyber-physical-social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial-temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones. © 2005-2012 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**

Process modelling for the production of hydrogen-rich gas from gasification of coal using oxygen, CO2 and steam reactants

- Shahabuddin, M., Bhattacharya, Sankar

  • Authors: Shahabuddin, M. , Bhattacharya, Sankar
  • Date: 2021
  • Type: Text , Journal article
  • Relation: International Journal of Hydrogen Energy Vol. 46, no. 47 (2021), p. 24051-24059
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  • Description: This process modelling studied the effect of different reactants on syngas composition and gasifier heat duty (heat energy required to carry out the operation) and the downstream treatment of CO rich syngas to maximise hydrogen yield. The process modelling was validated against experimental data obtained from a large bench-scale entrained flow gasifier. Results show that considering the H2/CO ratio, the steam-O2 reactant favours the most compared to those of the pure oxygen and oxygen-CO2 reactants. Under comparable operating conditions, the highest H2/CO ratio of 0.74 was determined using steam-O2 reactant compared to that of 0.31 and 0.33 using steam-CO2 and pure oxygen reactant. The catalytic water-gas shift reaction (WGSR) favours the yield of H2 with complete CO conversion at a temperature of 400 °C using the steam/coal ratio of 1.2. Supplying steam in the gasifier requires more heat energy to be supplied to drive endothermic gasification reaction and maintain the gasifier temperature. Under complete carbon conversion, steam-CO2 and steam-oxygen reactants require 5–65 kW more energy than pure oxygen. © 2021 Hydrogen Energy Publications LLC

Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors

- Zhou, Jian, Chen, Chao, Wang, Mingzheng, Khandelwal, Manoj

  • Authors: Zhou, Jian , Chen, Chao , Wang, Mingzheng , Khandelwal, Manoj
  • Date: 2021
  • Type: Text , Journal article
  • Relation: International Journal of Mining Science and Technology Vol. 31, no. 5 (2021), p. 799-812
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  • Description: Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines. To address this issue, a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database. Four assessment indexes are used in the model, which are the dynamic failure duration (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC). Four membership functions, including linear (L), parabolic (P), S and Weibull (W) functions, are proposed to measure the uncertainty level of individual index. The corresponding weights are determined through information entropy (EN), analysis hierarchy process (AHP) and synthetic weights (CW). Simultaneously, the classification criteria, including unascertained cluster (UC) and credible identification principle (CIP), are analyzed. The combination algorithm, consisting of P function, CW and CIP (P-CW-CIP), is selected as the optimal classification model in function of theory analysis and to train the samples. Ultimately, the established ensemble model is further validated through test samples with 100% accuracy. The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines. © 2021

Reduced switch multilevel inverter topologies for renewable energy sources

- Sarebanzadeh, Maryam, Hosseinzadeh, Mohammad, Garcia, Cristian, Babaei, Ebrahim, Islam, Syed, Rodriguez, Jose

  • Authors: Sarebanzadeh, Maryam , Hosseinzadeh, Mohammad , Garcia, Cristian , Babaei, Ebrahim , Islam, Syed , Rodriguez, Jose
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 120580-120595
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  • Description: This article proposes two generalized multilevel inverter configurations that reduce the number of switching devices, isolated DC sources, and total standing voltage on power switches, making them suitable for renewable energy sources. The main topology is a multilevel inverter that handles two isolated DC sources with ten power switches to create 25 voltage levels. Based on the main proposed topology, two generalized multilevel inverters are introduced to provide flexibility in the design and to minimize the number of elements. The optimal topologies for both extensive multilevel inverters are derived from different design objectives such as minimizing the number of elements (gate drivers, DC sources), achieving a large number of levels, and minimizing the total standing voltage. The main advantages of the proposed topologies are a reduced number of elements compared to those required by other existing multilevel inverter topologies. The power loss analysis and standalone PV application of the proposed topologies are discussed. Experimental results are presented for the proposed topology to demonstrate its correct operation. © 2013 IEEE.

Robust image classification using a low-pass activation function and DCT augmentation

- Hossain, Md Tahmid, Teng, Shyh, Sohel, Ferdous, Lu, Guojun

  • Authors: Hossain, Md Tahmid , Teng, Shyh , Sohel, Ferdous , Lu, Guojun
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 86460-86474
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  • Description: Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the frequency domain, i.e., High Frequency corruptions (HFc, e.g., noise) and Low Frequency corruptions (LFc, e.g., blur). Although a simple solution to HFc is low-pass filtering, ReLU - a widely used Activation Function (AF), does not have any filtering mechanism. In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc. To deal with LFc, we complement LP-ReLU with Discrete Cosine Transform based augmentation. LP-ReLU, coupled with DCT augmentation, enables a deep network to tackle the entire spectrum of corruption. We use CIFAR-10-C and Tiny ImageNet-C for evaluation and demonstrate improvements of 5% and 7.3% in accuracy respectively, compared to the State-Of-The-Art (SOTA). We further evaluate our method's stability on a variety of perturbations in CIFAR-10-P and Tiny ImageNet-P, achieving new SOTA in these experiments as well. To further strengthen our understanding regarding CNN's lack of robustness, a decision space visualisation process is proposed and presented in this work. © 2013 IEEE.

Rock-burst occurrence prediction based on optimized naïve bayes models

- Ke, Bo, Khandelwal, Manoj, Asteris, Panagiotis, Skentou, Athanasia, Mamou, Anna, Armaghani, Danial

  • Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
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  • Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.

Tissue programmed hydrogels functionalized with GDNF improve human neural grafts in Parkinson's disease

- Hunt, Cameron, Penna, Vanessa, Gantner, Carlos, Moriarty, Niamh, Long, Benjamin

  • Authors: Hunt, Cameron , Penna, Vanessa , Gantner, Carlos , Moriarty, Niamh , Long, Benjamin
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Advanced Functional Materials Vol. 31, no. 47 (2021), p.
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  • Description: The survival and synaptic integration of transplanted dopaminergic (DA) progenitors are essential for ameliorating motor symptoms in Parkinson's disease (PD). Human pluripotent stem cell (hPSC)-derived DA progenitors are, however, exposed to numerous stressors prior to, and during, implantation that result in poor survival. Additionally, hPSC-derived grafts show inferior plasticity compared to fetal tissue grafts. These observations suggest that a more conducive host environment may improve graft outcomes. Here, tissue-specific support to DA progenitor grafts is provided with a fully characterized self-assembling peptide hydrogel. This biomimetic hydrogel matrix is programmed to support DA progenitors by i) including a laminin epitope within the matrix; and ii) shear encapsulating glial cell line-derived neurotrophic factor (GDNF) to ensure its sustained delivery. The biocompatible hydrogel biased a 51% increase in A9 neuron specification—a subpopulation of DA neurons critical for motor function. The sustained delivery of GDNF induced a 2.7-fold increase in DA neurons and enhanced graft plasticity, resulting in significant improvements in motor deficits at 6 months. These findings highlight the therapeutic benefit of stepwise customization of tissue-specific hydrogels to improve the physical and trophic support of human PSC-derived neural transplants, resulting in improved standardization, predictability and functional efficacy of grafts for PD. © 2021 Wiley-VCH GmbH. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Benjamin Long” is provided in this record**

Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Islam, Syed

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 2 (2021), p. 961-970
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  • Description: To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster-Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles. © 2005-2012 IEEE.

Unidirectional ablation minimizes unwanted thermal damage and promotes better thermal ablation efficacy in time-based switching bipolar radiofrequency ablation

- Ooi, Ean H., Ooi, Ean Tat

  • Authors: Ooi, Ean H. , Ooi, Ean Tat
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Computers in Biology and Medicine Vol. 137, no. (2021), p.
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  • Description: Switching bipolar radiofrequency ablation (bRFA) is a thermal treatment modality used for liver cancer treatment that is capable of producing larger, more confluent and more regular thermal coagulation. When implemented in the no-touch mode, switching bRFA can prevent tumour track seeding; a medical phenomenon defined by the deposition of cancer cells along the insertion track. Nevertheless, the no-touch mode was found to yield significant unwanted thermal damage as a result of the electrodes’ position outside the tumour. It is postulated that the unwanted thermal damage can be minimized if ablation can be directed such that it focuses only within the tumour domain. As it turns out, this can be achieved by partially insulating the active tip of the RF electrodes such that electric current flows in and out of the tissue only through the non-insulated section of the electrode. This concept is known as unidirectional ablation and has been shown to produce the desired effect in monopolar RFA. In this paper, computational models based on a well-established mathematical framework for modelling RFA was developed to investigate if unidirectional ablation can minimize unwanted thermal damage during time-based switching bRFA. From the numerical results, unidirectional ablation was shown to produce treatment efficacy of nearly 100%, while at the same time, minimizing the amount of unwanted thermal damage. Nevertheless, this effect was observed only when the switch interval of the time-based protocol was set to 50 s. An extended switch interval negated the benefits of unidirectional ablation. © 2021 Elsevier Ltd

A distributed and anonymous data collection framework based on multilevel edge computing architecture

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza, Xu, Min, He, Xiangjian, Chen, Jinjun

  • Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
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  • Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.

A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices

- Solyman, Ahmad, Attar, Hani, Khosravi, Mohammad, Menon, Varun, Jolfaei, Alireza, Balasubramanian, Venki, Selvaraj, Buvana, Tavallali, Pooya

  • Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
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  • Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.