Dou, W, Xu, X & Yu, S 2023, Intelligent Industrial Internet Systems, Springer Nature Singapore. View/Download from: Publisher's site
Hoang, DT, Huynh, NV, Nguyen, DN, Hossain, E & Niyato, D 2023, Deep Reinforcement Learning for Wireless Communications and Networking, Wiley, New Jersey. View/Download from: Publisher's site View description>>
Deep Reinforcement Learning for Wireless Communications and Networking
Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems
Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking.
Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design.
Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as:
Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning
Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security
Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association
Network layer applications, covering traffic routing, network classification, and network slicing
With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for ...
Khuat, TT, Kedziora, DJ & Gabrys, B 2023, The Roles and Modes of Human Interactions with Automated Machine Learning Systems: A Critical Review and Perspectives, Now Publishers. View/Download from: Publisher's site
Scriven, A, Kedziora, DJ, Musial, K & Gabrys, B 2023, The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry, Now Publishers. View/Download from: Publisher's site
Wang, T, Li, B, Chen, M & Yu, S 2023, Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore. View/Download from: Publisher's site
Yu, S & Cui, L 2023, Security and Privacy in Federated Learning, Springer Nature Singapore. View/Download from: Publisher's site
Afsari, M, Shon, HK & Tijing, LD 2023, 'Characterization of nanofibers and nanofiber membranes' in Electrospun and Nanofibrous Membranes, Elsevier, pp. 295-322. View/Download from: Publisher's site
Alam, SL & Gill, AQ 2023, 'Analyzing social interactions and conflicting goals: Australian government ecosystem context' in Digitalization and Sustainability, Edward Elgar Publishing, pp. 128-145. View/Download from: Publisher's site View description>>
There is an increasing use of social media platforms by public agencies for active interactions with community. In particular, the Australian public agencies are the major users of Facebook pages for actively interacting with the community. While active interactivity seems useful, however, this also marks the need for understanding and addressing tension and conflicts between government and community goals for effective interactions and mutual benefits. There is limited analysis and research into the use and impact of Facebook pages on the Australian government and community expectations. This chapter aims to address this gap and reports findings from the analysis of the Australian government Facebook pages. It is anticipated that findings from this analysis can be used by agencies to identity and address conflicting goals when redesigning their social media platforms such as Facebook pages for interactions with the community.
Aldini, S & Liu, D 2023, 'Human-Robot Collaboration in Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9. View/Download from: Publisher's site
Aldini, S & Liu, D 2023, 'Human-Robot Collaboration in Agriculture' in Encyclopedia of Digital Agricultural Technologies, Springer International Publishing, pp. 622-629. View/Download from: Publisher's site
Alhussein, A, Kocaballi, B & Prasad, M 2023, 'Work from Home in Smart Home Technology During and After Covid-19 and Role of IOT' in Proceedings in Adaptation, Learning and Optimization, Springer Nature Switzerland, pp. 568-579. View/Download from: Publisher's site
Al-Shaeli, M, Al-Juboori, RA, Makarem, MA, Alsalhy, QF, Altaee, A & Ladewig, BP 2023, 'Carbon Capture With Fixed-Carrier Membranes' in Reference Module in Earth Systems and Environmental Sciences, Elsevier. View/Download from: Publisher's site
Altaee, A 2023, 'Fate of Nanoparticles in Soiland Water' in Nanoparticles as Sustainable Environmental Remediation Agents, Royal Society of Chemistry. View/Download from: Publisher's site View description>>
Engineered nanoparticles (ENPs) have already been discharged into theenvironment, including soil and water, as a result of their mass manufactureand ubiquitous use. This chapter outlines the transition and persistence ofENPs in soil and water after giving a brief summary of the fate of ENPs in theenvironment. The main elements that influence how long ENPs are retained,transported, and released into soils and groundwater are described. Furthermore,we also provide a thorough assessment of how the fate andtransport of ENPs in soil and groundwater systems might be impacted by thephysicochemical characteristics of media, nanoparticles, and flow. The finalsection of this chapter presents the research directions and perspectives forthe fate and transport of ENPs in soils and water.
Altaee, A 2023, 'Nanoparticles as Sustainable Environmental Remediation Agents' in Simeonidis, K & Mourdikoudis, S (eds), Nanoparticles as Sustainable Environmental Remediation Agents, Royal Society of Chemistry. View/Download from: Publisher's site View description>>
The expanding use of nanoparticles in a wide range of applications has brought to light the need to adopt an integrated approach regarding their synthesis, use, recovery and handling.This book covers the intense research field of nanoparticle utilization as remediation agents for toxic pollutants, and pays special attention to their post-application recovery, the monitoring of their fate when released, and life cycle analysis. The reader may therefore evaluate the prospects and limitations of these technologies through the prism of sustainability demands.Several chapters summarize successful applications of single or multi-phase nanoparticles for drinking water purification, wastewater and gas-stream treatment and soil consolidation. Importantly, they evaluate the potential scale-up for real-world applications that need to compete with traditional treatment methods.However, the risk of uncontrolled release into the environment can be a significant drawback to the extended use of nanoparticles. For this reason, a detailed analysis is given to aspects of their post-use recycling and regeneration, determination of release pathways, risk assessment methods and life cycle evaluation studies, highlighting the importance of preventing the unintended release of nanoparticles into the environment.This book will be a valuable resource for anyone looking at the development of nanoparticles with a view to environmental remediation strategies.
Alzahrani, M, Karimi, F, Bharathy, G & Prasad, M 2023, 'Intention of MOOCs Adoption, Completion and Continued Use.' in Xie, H, Lai, C-L, Chen, W, Xu, G & Popescu, E (eds), Advances in Web-Based Learning - ICWL 2023 - 22nd International Conference, ICWL 2023, Sydney, NSW, Australia, November 26-28, 2023, Proceedings, Springer, pp. 3-12.
Aqeel, A, Zafar, J, Mohammadi, P, Tabatabaei, M, Aghbashlo, M, Mahlia, TMI & Nizami, A-S 2023, 'Biodiesel: the fundamentals' in Sustainable Biodiesel, Elsevier, UK, pp. 1-20. View/Download from: Publisher's site View description>>
An upsurge in industrialization and the human population has led to increasing energy demands most met by fossil-oriented energy carriers, resulting in the emission of pollutants and greenhouse gases. The environmental and public health damages caused by these emissions have triggered a global transition toward replacing fossil fuels with their renewable and green counterparts. Biodiesel is a promising alternative to fossil diesel and can be used in existing diesel engines with no or minor modifications. Transesterification is the most commonly used process among the various techniques used for biodiesel production. The worldwide boost in biodiesel production has led to the establishment of various methods used in a real-world setting to increase biodiesel yield and transesterification efficiency. This introductory chapter explains the fundamentals of industrial biodiesel production, including statistics, approaches, and costs. Despite improvements over the years, the biodiesel production industry still needs further improvements and advances to enhance the production process and meet increasing global demands.
Arachchige, CMK, Indraratna, B, Rujikiakamjorn, C, Qi, Y & Siddiqui, AR 2023, 'Use of waste rubber inclusions for ballasted railway construction – A real-life case study' in Smart Geotechnics for Smart Societies, CRC Press, pp. 2567-2570. View/Download from: Publisher's site
Bailo, F 2023, 'Social media' in Elgar Encyclopedia of Political Sociology, Edward Elgar Publishing, pp. 546-549. View/Download from: Publisher's site
Beni, HM, Mortazavi, H, Paul, G, Islam, MS & Zarei, AA 2023, 'Numerical simulation of the aortic arch behavior' in Digital Human Modeling and Medicine, Elsevier, UK, pp. 289-303. View/Download from: Publisher's site View description>>
Pressoreceptor reflex or baroreflex is an automatic control mechanism of the cardiovascular system that helps keep homeostasis or maintain blood pressure at a constant level. It is crucial to consider viscoelastic aortic arterial behavior in the pulse wave blood flow to determine the exact distributed location of the aortic arch pressoreceptors. This chapter presents a three-dimensional (3D) healthy man computer tomography model of the aortic arch. Computational Fluid Dynamic and Fluid–Structure Interaction methods were used to obtain the velocity–pressure field in the fluid domain and the stress–strain distribution in the solid domain. We found an increase in the pressure and velocity of the fluid in the aortic arch domain with a maximum of 14.9 kPa and 1.4 m/s in the systole, respectively. Endothelial cells in the artery wall concave located at the junctures of the supraaortic branches sense the wall shear stress with a value of 20 Pa in the systole. Deformation in the aortic artery wall convex reaches over 1 mm. The highest normal stress at the supraaortic branches root when sensed through the adventitia layer pressoreceptor is 188 kPa. Increasing age of a person leads to an increase in the elastic modulus and adds to the amount of normal stress in the pressoreceptor.
Boyd-Weetman, B, Thomas, P, DeSilva, P & Sirivivatnanon, V 2023, 'Accelerated Mortar Bar Test to Assess the Effect of Alkali Concentration on the Alkali–Silica Reaction' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 233-239. View/Download from: Publisher's site View description>>
AbstractWe report the outcomes of a study into the influence of alkali concentration on expansion induced by the alkali–silica reaction (ASR), a deleterious reaction that causes cracking and durability loss in concrete structures. We assessed the effect of alkali concentration on mortar bar expansion using a modified form of AS1141.60.1, the accelerated mortar bar test (AMBT). Mortar prisms were prepared with a reactive aggregate and immersed in alkali solutions of varying concentrations (from 0.4 to 1.0 M NaOH) and saturated limewater at 80 °C. Expansion was monitored for 28 days. The degree of expansion was observed to increase with increasing alkali concentration and an induction period prior to expansion was observed for the 0.4 M NaOH. No expansion was observed for mortar bars immersed in the control saturated lime water bath. Additionally, no expansion was observed for mortars using blended cements containing fly ash (FA) and ground granulated blast furnace slag, suggesting the AMBT is a viable technique for demonstrating the efficacy of mitigation strategies.
Bragar, EP, Pronozin, YA, Zhussupbekov, AZ, Gerber, AD & Indraratna, B 2023, 'Evaluation of the strength parameters of clay loams during freezing–thawing cycles' in Smart Geotechnics for Smart Societies, CRC Press, pp. 2036-2041. View/Download from: Publisher's site
Chen, C, Chen, Z, Li, Y, Liu, X & Guo, Y 2023, 'A Novel Self-tuning Speed Control Scheme for the Permanent Magnet Synchronous Motor' in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 649-661. View/Download from: Publisher's site View description>>
For the permanent magnet synchronous motor (PMSM), it is challenging to control the speed under the varying conditions, such as wide-speed range and sudden load. Therefore, a novel self-tuning speed controller is proposed for the current source inverter fed three-phase PMSM. This scheme includes two parts: the single-neuron based controller and the parameter self-tuning algorithm in real time. The controller takes the error as the input, and combines the proportional item and the modified active function to meet the actual requirements. Based on the stochastic gradient descent algorithm and the PMSM mathematical model, the real-time online self-tuning of the parameters including the weight and bias is achieved, and the other parameters like the proportional coefficient and learning rate can be easily obtained. With the proposed speed control scheme, the dynamic response and adaptive ability can be improved. Especially under the condition of the wide speed range, the settle time changes little, and the overshoot of the system decreases with the increase of speed. And the parameters of this scheme can be self-tuned in online. The comparison experiments have been executed to verify the proposed ST controller.
Cheng, D, Ngo, HH, Guo, W, Pandey, A, Varjani, S, Zhang, Z & Awasthi, MK 2023, 'Sustainability considerations of biochar production in biowaste management' in Current Developments in Biotechnology and Bioengineering, Elsevier, Netherlands, pp. 41-62. View/Download from: Publisher's site View description>>
Large volumes of biowastes including municipal solid waste, agricultural residues, animal manure, and biosolids are generated continually with the growth of global population. These biowastes can pose a huge threat to the ecosystem and human health if managed inappropriately. Commonly, conventional strategies for biowastes management such as burning, open dumpling, and landfilling can cause a lot of environmental issues like greenhouse gas (GHG) emissions, water, and soil pollutions that are hazardous to all living beings including humans. However, these biowastes can be considered as valuable resources if they are handled responsibly that will not only reduce the problem of biowastes management, but also generate value-added products and bioenergy to meet the ever-increasing resource and energy demands. The conversion of biowastes to biochar with the production of bio-oil as byproducts has been considered as a potential alternative for biowastes management, which is economically viable and environmentally sustainable. This chapter focuses on sources of biowastes, the comparison of environmental impacts of biowaste management between biochar productions and the conventional management methods, as well as technologies for the sustainable biochar production from biowastes. Furthermore, environmental benefits of biochar production from biowastes are discussed in this chapter as well. Future perspectives on commercial biochar production from biowastes are discussed.
Daher, J, Correll, P, Kennedy, PJ & Drake, B 2023, 'Understanding the Impact of Patient Journey Patterns on Health Outcomes for Patients with Diabetes' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 1-26. View/Download from: Publisher's site
Dangol, S, Li, J, Sirivivatnanon, V & Kidd, P 2023, 'Influence of Reinforcement on the Loading Capacity of Geopolymer Concrete Pipe' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 165-175. View/Download from: Publisher's site View description>>
AbstractGeopolymer concrete is emerging as a sustainable construction material due to utilization of industrial by-products, which greatly reduces its carbon footprint. Past studies of the mechanical properties and resistance to sulfuric acid reaction of cement-less geopolymer concrete indicated its suitability for precast concrete pipes over ordinary Portland cement (OPC) concrete. In the present study, a three-dimensional finite element (FE) model of reinforced concrete pipe was developed using commercial software ANSYS-LSDYNA. The load-carrying capacity of reinforced and non-reinforced geopolymer concrete pipes under the three-edge bearing (TEB) test was investigated and compared with OPC concrete pipes. The results indicated geopolymer concrete with comparable compressive strength to OPC concrete showed higher loading capacity in a pipe structure due to its better tensile performance. The effect of steel reinforcement area on the loading capacity of geopolymer concrete pipes was quantitatively analyzed, and they met the specified strength requirement for OPC concrete in the ASTM standard, with up to 20% reduction in the reinforcement area.
Deng, L, Ngo, HH, Ni, B-J, Wei, W, Wang, Q & Guo, W 2023, 'Engineered membrane processes for nutrient removal and microalgae harvesting' in Current Developments in Biotechnology and Bioengineering, Elsevier, Netherlands, pp. 267-292. View/Download from: Publisher's site View description>>
Microalgae harvesting methods are generally not efficient and cost-effective due to the inherent characteristics of microalgae. These problems can be addressed by membrane technology for effective microalgae harvesting and nutrient removal. This chapter focuses on the performance of membrane-based systems, membrane fouling, and fouling control approaches during the microalgae harvesting process. Operational parameters (i.e., hydraulic retention time, light conditions, etc.) should be optimized to obtain better performance. Alginate-immobilized microalgae beads and solid carriers accelerate microalgae growth and mitigate membrane fouling by the adsorption and degradation of foulants. A membrane bioreactor combined with microalgae photobioreactor effectively treats various types of wastewaters and enhances microalgae cultivation. Membrane modification and in situ physical cleaning methods (i.e., patterned membrane, turbulent jet-assisted microfiltration, etc.) remove foulants from the membrane surface and improve membrane permeability, thereby reducing the specific energy demand and harvesting cost. Development of novel carriers and techno-economic analyses of different membrane-based systems is needed in future studies.
Deng, S, Ngo, HH, Guo, W, You, N & Peng, S 2023, 'Biological nutrient recovery from wastewater for circular economy' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 355-412. View/Download from: Publisher's site
Di, X, Qin, J, Sun, Y & Su, QP 2023, 'Visualize the Distribution and Dynamics of Mitochondrial DNA (mtDNA) Nucleoids with Multiple Labeling Strategies' in Methods in Molecular Biology, Springer US, pp. 79-88. View/Download from: Publisher's site
Dickson-Deane, C, Heggart, K & Vanderburg, R 2023, 'Designing Learning Design Pedagogy: Proactively Integrating Work-Integrated Learning to Meet Expectations' in Design Education Across Disciplines, Springer International Publishing, pp. 125-142. View/Download from: Publisher's site View description>>
As more and more organisations examine the validity and suitability of online and blended models of learning and development the pandemic has only accelerated this demand. While the initial response was more in line with emergency remote teaching (Heggart in Global perspectives on educational innovations for emergency situations. Springer, 2022), more carefully planned models, often requiring design expertise, are now being trialled in different contexts. Contexts are the foundation and thus have a direct dependency when one thinks of designing for learning and performance. Understanding the factors that influence how a context shapes the learning experience thus assists with a much improved outcome for institutions and individuals (de Alvarez & Dickson-Deane in TechTrends 62:1–9, 2018; Romero-Hall, E., Correia, A. P., Branch, R. M. (Rob), Cevik, Y. D., Dickson-Deane, C., Chen, B., Liu, J. C., Tang, H., Vasconcelos, L., Pallitt, N., & Thankachan, B in Research methods in learning design and technology. Routledge, 2020). Work-integrated learning, also known as cooperative education in some geographies, depends on the integration of the disciplinary and societal context to add the value needed for the learning experience (Saunders in JADARA 6, 2019). Knowing how this may look from a design perspective and then measuring it against the outcomes that are achieved to see if they meet the needs of industry and society at large is the next step to have meaningful translation (Carr-Chellman & Carr-Chellman, in TechTrends 64:704–709, 2020).
Dipto, SM, Reza, MT, Rahman, MNJ, Parvez, MZ, Barua, PD & Chakraborty, S 2023, 'An XAI Integrated Identification System of White Blood Cell Type Using Variants of Vision Transformer' in Lecture Notes in Networks and Systems, Springer Nature Switzerland, pp. 303-315. View/Download from: Publisher's site
Do, QC, Tran, TN, Tran, TH, La, DD, Ngo, HH, Thanh, BX, Chang, SW & Nguyen, DD 2023, 'Sustainable production and application of biochar for energy storage and conversion' in Current Developments in Biotechnology and Bioengineering, Elsevier, Netherlands, pp. 333-364. View/Download from: Publisher's site View description>>
Efficient solutions for storing and converting energy sources with sustainable and environment-friendly materials play an increasingly important role in ensuring energy security and promoting the development of sustainable energy sectors. Biochar-based materials have been known as promising materials for energy storage and conversion applications owing to their superior structural properties (e.g., the porosity and large surface area) and diversity of functional groups, which can be exploited for safe, modern, and environment-friendly materials. In addition, owing to the abundance of raw materials (in particular, waste biomass sources), biochar-based materials for energy storage and conversion applications can be produced at a reasonable cost. This chapter presents a critical overview of the importance of biochar for energy storage and conversion applications and the presented biochar production and modification techniques. The potential applications and challenges of these biochar materials in the energy storage and conversion field are also discussed to make relevant judgments for the future.
Do, T-TN, Duong, NMH & Lin, C-T 2023, 'Integrated Sensing Devices for Brain-Computer Interfaces' in More-than-Moore Devices and Integration for Semiconductors, Springer International Publishing, pp. 241-258. View/Download from: Publisher's site View description>>
Brain-computer interfaces (BCI) help users to interact with machines via brain activity and without the use of muscles. Among the many components of BCI frameworks, sensor technology helps to make the systems highly efficient and robust. As a source of input for BCI systems, sensors also provide high-quality signals that can support downstream tasks, such as noise removal and feature extraction. This book chapter explores the fundamental concepts and configurations of the non-invasive sensor technology that are commonly used in BCI systems.
Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Influence of microscale cohesive contacts on the macro-behaviour of soils through DEM investigation' in Smart Geotechnics for Smart Societies, CRC Press, pp. 229-243. View/Download from: Publisher's site View description>>
Microscale contacts are inherent in most geotechnical failures such as soil liquefaction, landslides and internal instability, thus study of these failures based on microscale concepts can significantly enhance our intrinsic understanding as well as the quality of prediction and designs. Despite rapidly increasing investigation on micro-mechanisms of soil failures, especially based on Discrete Element Method (DEM), cohesive behaviours of particles are usually ignored and simplified, resulting in incomplete and/or inaccurate understanding. My study aims to overcome this imperative limitation and improve our modelling capability by investigating the influence of microscale cohesive contacts on fundamental soil behaviours such as the formation of angle of repose and direct shear. Different degrees of cohesion between particles are incorporated into DEM models and the results are validated against experimental data. The results show cohesive contacts can significantly affect soil behaviour and the predicted outcomes if they are not considered properly. The prediction capability of this DEM model can be further applied to study the cohesive behaviour of geomaterials in various geotechnical problems such as soil clogging and debris flow.
Dong, WK, Li, WG, Lin, XQ & Shah, SP 2023, 'Investigation on Superhydrophobicity and Piezoresistivity of Self-sensing Cement-Based Sensors Using Silane Surface Treatment' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 17-22. View/Download from: Publisher's site View description>>
AbstractCement-based sensors are highly susceptible to the effects of watery environments due to the hydrophilic properties of the cement matrix. In this paper, we applied a surface treatment using a silane/isopropanol solution to graphene/cement-based sensors to achieve superhydrophobicity and mitigate piezoresistive instability in watery environments. After treatment, impressive water contact angles of 163.4° and 142.0° were achieved for the surface and inner cement-based sensors, respectively. Moreover, the piezoresistivity of the coated cement-based sensors exhibited greater stability compared to their untreated counterparts. These results provide valuable insights into the piezoresistivity of hydrophobic cement-based sensors in moist environments, offering promising prospects for future structural health monitoring applications.
Duong, HC, Cao, HT, Nghiem, LD, Ansari, AJ, Le, NL, Doan, TO & Nguyen, NC 2023, 'Membrane distillation-liquid desiccant air-conditioning for thermal comfort in buildings' in Current Developments in Biotechnology and Bioengineering, Elsevier, Netherlands, pp. 387-409. View/Download from: Publisher's site View description>>
Increasing demand for thermal comfort in buildings together with the urgent need for reducing greenhouse gas emissions has resulted in significant technological advancement in the air-conditioning industry, most notably including the development of the liquid desiccant air-conditioning (LDAC) process. This innovative process involves two stages, namely air dehumidification and liquid desiccant solution regeneration, and its dehumidification capacity is regulated by the efficiency of liquid desiccant solution regeneration. Membrane distillation (MD) has been increasingly explored for regeneration of liquid desiccant solutions in LDAC systems owing to its notable advantages including excellent membrane rejection, resilience to hypersalinity, and effective incorporation of low-grade waste heat and solar thermal energy. Despite these advantages, MD regeneration of liquid desiccant solutions has been demonstrated only at the lab-scale level using direct contact membrane distillation (DCMD) and vacuum membrane distillation (VMD) configurations for manifestation of their technical feasibility. Although these lab-scale demonstrations have arguably paved the way for progress on the MD regeneration of liquid desiccant solutions, more studies at the pilot or large-scale level are required to realize this MD strategic application.
Fattah, IMR, Mujtaba, MA, Veza, I & Smaisim, GF 2023, 'Microwave-assisted Catalytic Biodiesel Production' in Advances in Microwave-assisted Heterogeneous Catalysis, Royal Society of Chemistry, pp. 190-216. View/Download from: Publisher's site View description>>
Microwave-enhanced biodiesel synthesis is a favoured approach due to various advantages such as decreased energy usage, a significant reduction in reaction durations and solvent needs, higher selectivity, and improved conversions with generation of fewer byproducts. Because of society’s increased concern for sustainability, the conversion of bio-based feedstocks into biodiesel is an important study topic. Various technologies have been used for biodiesel production, one of which, the application of microwaves, has been shown to hold a lot of promise. Microwaves are part of the electromagnetic spectrum, with wavelengths ranging from 1 cm to 1 m (30 GHz to 300 MHz). This work investigates the use of microwave radiation to produce biodiesel at a frequency of 2.45 GHz, the normal operating range available for commercially accessible microwave applicators. It is possible to accelerate the rate of reactions and improve selectivity by using microwave heating instead of conventional heating. Several parameters, including the catalyst type and concentration, microwave irradiation power, reaction temperature, type of alcohol and alcohol-to-oil ratio, the water content of oil, and stirring rate, could all influence microwave-assisted biodiesel production. As a result, it is critical to gain a thorough understanding of the effects of these parameters on the biodiesel production process.
Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-Based Energy Efficient Supply Chain Management' in Blockchain in Supply Chain Digital Transformation, CRC Press, USA, pp. 195-218. View/Download from: Publisher's site View description>>
This book explores real-world supply chain use cases from a range of industries to demonstrate the digital transformative capabilities of blockchain and DLT technologies"--
Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-enabled humanitarian supply chain management: sustainability and responsibility' in Blockchain in a Volatile-Uncertain-Complex-Ambiguous World, Elsevier, Amsterdam, Netherlands, pp. 251-276. View/Download from: Publisher's site View description>>
Blockchain technology has lately garnered traction as a viable supply chain management system. This paper aims to create a complete theoretical framework for blockchain adoption in supply chain management by identifying the drivers and empirically analyzing their interdependencies and influence on adoption. This paper fills the present research gap on the Blockchain's potential implications for HSCM by proposing a theoretical framework built on the foundations of 10 organizational theories: social exchange theory, resource-based view, transaction cost theory, knowledge-based view, strategic choice theory, agency theory, institutional theory, systems theory, network theory, network perspective theory, and materials logistics theory. This theoretical framework can help decision-makers develop HSCM that can react adaptively to both desirable and undesirable changes (the agility angle), recuperate, and endure in the face of global shocks (short term and long term) accompanied by social and economic crises.
Hernandez Moreno, V, Carmichael, MG & Deuse, J 2023, 'Towards Learning by Demonstration for Industrial Assembly Tasks' in Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022, Springer International Publishing, pp. 229-239. View/Download from: Publisher's site View description>>
AbstractIn recent times, learning by demonstration has seen tremendous progress in robotic assembly operations. One of the most prominent trajectory-level task models applied is Dynamic Movement Primitives (DMP). However, it lacks the ability to tackle complex operations as often encountered in industrial assembly. Augmenting low-level models with a high-level framework in which different movement segments are deliberately parameterised is considered promising for such scenarios. This paper investigates the combination of trajectory-level DMPs with Methods-Time Measurement (MTM). We demonstrate how the MTM-1 system is utilised to establish distinguished DMP models for five of its basic elements, paving the way to benefitting from the sophisticated MTM system. The evaluation of the framework is conducted on a generic pick and place operation. Compared to a one-model-fits-all DMP approach for the whole task, the proposed method shows the advantage of appropriate temporal scaling, accuracy levelling and force consideration at adequate times.
Hoang, DT, Nguyen, DN, Nguyen, CT, Hossain, E & Niyato, D 2023, 'Prefce', pp. xxiv-xxiv.
Indraratna, B, Qi, Y & Ngo, T 2023, 'Sustainable Transport Infrastructure Adopting Energy-Absorbing Waste Materials' in Springer Transactions in Civil and Environmental Engineering, Springer Nature Singapore, pp. 159-173. View/Download from: Publisher's site
Indraratna, B, Qi, Y, Rujikiatkamjorn, C, Tawk, M, Mehmood, F, Navaratnarajah, SK, Neville, T & Grant, J 2023, 'Smart Solutions in a Circular Economy for Advancing Railroad Design and Construction Using Recycled Materials' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 1-32. View/Download from: Publisher's site
Indraratna, B, Vinod, JS & Athukorala, R 2023, 'Chemically Treated Soils' in DSC/HISS Modeling Applications for Problems in Mechanics, Geomechanics, and Structural Mechanics, CRC Press, pp. 149-164. View/Download from: Publisher's site
Kang, K, Li, L & Namisango, F 2023, 'Dissimilar Social Settings Impact on User Motivations and Activities on Live-Streaming Digital Platforms' in E-Service Digital Innovation, IntechOpen. View/Download from: Publisher's site View description>>
This chapter delves into the motivations and activities of users within various social contexts on live digital platforms. It introduces an innovative research model that employs the well-established Achievement Motivation Theory to investigate how three fundamental needs relate to the motivation of live streamers during their live-streaming activities. The study aims to illuminate the underlying drivers that influence live streamers’ engagement and behavior within the dynamic landscape of live digital content. Live-streaming digital platforms have become prominent channels for user engagement and content creation, enabling individuals to broadcast live videos and connect with audiences in real time. However, user motivations and behaviors on these platforms can significantly differ based on their social settings. This research explores the impact of diverse social backgrounds on user motivations and activities on live-streaming digital platforms, shedding light on the intricacies that shape user behavior across various contexts. Influence of Social Settings: Social settings encompass cultural norms, societal values, economic conditions, and technological infrastructure. These factors shape users’ attitudes, preferences, and aspirations on live-streaming platforms, ultimately influencing their motivations and activities. Drawing on the Achievement Motivation Theory by McClelland, this chapter examines motivating factors for live-streaming activities, focusing on the need for achievement, power, and affiliation. The study employs variance-based structural equation modeling (SEM), specifically partial least squares (PLS), to analyze these elements. The findings highlight the positive impact of these factors on live streamers’ motivation to create live-streaming content, offering theoretical insights and practical implications for scholars and practitioners engaged in live-streaming activities. This research aids in understanding the live-streamer ...
Karetla, GR, Catchpoole, DR & Nguyen, QV 2023, 'Hybrid Framework for Genomic Data Classification Using Deep Learning: QDeep_SVM' in Algorithms for Intelligent Systems, Springer Nature Singapore, pp. 451-463. View/Download from: Publisher's site
Karetla, GR, Nguyen, QV, Simoff, SJ, Catchpoole, DR & Kennedy, PJ 2023, 'Feature-Ranking Methods for RNA Sequencing Data' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 129-145. View/Download from: Publisher's site
Kim, IS, Choi, C, Oh, BS, Yoon, S, Shon, H, Lee, S & Hong, S 2023, 'Wastewater Reclamation' in Water Sustainability, Springer US, pp. 149-167. View/Download from: Publisher's site
Kim, J 2023, 'Swarm and Fleet for Agriculture: Scalable Information Fusion Framework' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-10. View/Download from: Publisher's site
Kim, J 2023, 'Swarm and Fleet for Agriculture: Scalable Information Fusion Framework' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9. View/Download from: Publisher's site
Lammers, T, Guertler, M, Sick, N & Deuse, J 2023, 'It’s Coming Home Down Under – The Potential of Digital Work to Overcome Australia’s Challenges in Reshoring Manufacturing' in New Digital Work, Springer International Publishing, pp. 161-170. View/Download from: Publisher's site View description>>
AbstractOver the past decades, the world has seen a continuous increase of globalisation and interconnectedness – in part supported by advances in digital communication and production technologies. In the case of industrial production, this trend has led to global, integrated supply chains in order to provide the most competitive and innovative products utilising the most competitive market conditions. In Australia, due to its remote geographic location and socioeconomic conditions, such as high labour costs and negative economics of scale, this has resulted in a loss of domestic manufacturing capabilities. With recent changes in the geopolitical environment (trade wars, actual wars, Covid-19, climate crisis etc.) calls to produce local are becoming louder again. In this article, we therefore explore the potential of digital technologies to overcome Australia’s challenges in reshoring its manufacturing capabilities. Findings indicate that a highly skilled digital workforce is needed to leverage the country’s potential in world-leading niche manufacturing. The Associate Degree of Advanced Manufacturing, developed and delivered by the Centre for Advanced Manufacturing at the University of Technology Sydney (UTS), is presented as an example of how to upskill the manufacturing workforce.
Larpruenrudee, P, Paul, G, Saha, SC, Husain, S, Mortazavy Beni, H, Lawrence, C, He, X, Gu, Y & Islam, MS 2023, 'Ultrafine particle transport to the lower airways: airway diameter reduction effects' in Digital Human Modeling and Medicine, Elsevier, pp. 253-274. View/Download from: Publisher's site
Leon-Castro, E, Sahni, M, Blanco-Mesa, F, Alfaro-Garcia, V & Merigo, J 2023, 'Preface', p. xiii.
Li, B, Guo, T, Wang, Y & Chen, F 2023, 'Data‐Driven Delay Analysis with Applications to Railway Networks' in Advances in Data Science and Analytics: Concepts and Paradigms, Wiley, USA, pp. 115-143. View/Download from: Publisher's site View description>>
Reliability is one of the key evaluation criteria in railway service. Many factors contribute to the measure, such as delays spanning over spatiotemporal dimensions. A common method used to improve reliability is to design a better timetable to reduce the mutual influence between trains. Recently, machine learning shows great potential in improving the effectiveness and efficiency of decision-making to increase operational performance. For railway system management, this seems like an opportunity worth further exploration. In this chapter, we focus on analyzing railway disruptions and their impact on the traffic in the whole network. Specifically, we build three different data-driven models: (1) a conditional Bayes Net Model with the introduction of Markov property for delay propagation prediction, (2) a primary delay tracking back model, and (3) a dwell improvement evaluation model which can estimate the potential network benefits of the dwell improvement on single or multiple platforms. All the delay analysis models have been validated and deployed to the railway network in the Great Sydney area and the outcome of this application of the intelligent delay analysis technology significantly reduces delay-caused losses, increases the operation efficiency, and enables the train operating system to meet performance metrics and recover from incidents.
Li, B, Guo, T, Zhu, X, Wang, Y & Chen, F 2023, 'ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation' in Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, pp. 369-386. View/Download from: Publisher's site View description>>
An essential weakness of existing personalized recommender systems is that the learning is biased and dominated by popular items and users. Existing methods, particularly graph-based approaches, primarily focus on the “heterogeneous interaction” between user-item, leading to a disproportionately large influence of popular nodes during the graph learning process. Recently, popularity debiasing models have been proposed to address this issue, but they excessively concentrate on considering cause-effect or re-weighting the item/user popularity. These approaches artificially alter the nature of the data, inadvertently downplaying the representation learning of popular items/users. Consequently, balancing the trade-off between global recommendation accuracy and unpopular items/users exposure is challenging. In this paper, we advocate the concept of “homogeneous effect” from both user and item perspectives to explore the intrinsic correlation and alleviate the bias effects. Our core theme is to simultaneously factorize user-item interactions, user-user similarity, and item-item correlation, thereby learning balanced representations for items and users. To pursue well-balanced representations, we propose a Consensus factorized Graph Convolution neural Network (ConGCN), which leverages graph-based nonlinear representation learning and manifold constraints to regulate the embedding learning. An inherent advantage of ConGCN is its consensus optimization nature, where item-item and user-user relationships ensure that unpopular items are well preserved in the embedding space. The experiments on four real-world datasets demonstrate that ConGCN outperforms existing single-task-oriented methods on two typical tasks with opposite goals (global recommendation and popularity debiasing recommendation), indicating that our model can perform a balanced recommendation with both higher global and debiasing recommendation accuracy with greater long-tail item/user exposure.
It is critical to continuously maintain the high quality of drinking water in water distribution network management. However, it remains challenging to predict water demand and optimize dosage to ensure safe drinking water. This paper details solutions that utilize data analytics and machine learning to provide water demand forecasting and chemical dosing optimization. Key environmental factors are used to build a Bayesian linear model to predict the water demand for each supply zone. To ensure safe drinking water, chlorine residual is one of the most important indicators. However, as the water age becomes high, the chlorine residual decays very fast. Thus, we provide a data-driven solution to determine the chlorine set points and optimize the ammonia dosage for reservoirs. The optimization results can determine the set point of the dosage devices to be installed at reservoirs. Evaluation results demonstrate that the solutions achieve reasonably consistent results.
Lindsay, ED, Hadgraft, RG, Boyle, F & Ulseth, R 2023, 'Disrupting Engineering Education' in International Handbook of Engineering Education Research, Routledge, pp. 115-133. View/Download from: Publisher's site
Liu, T, Awasthi, SK, Zhou, Y, Varjani, S, Zhang, Z, Pandey, A, Ngo, HH & Awasthi, MK 2023, 'Biochar for sustainable agriculture' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 299-331. View/Download from: Publisher's site View description>>
The characteristic of sustainable agriculture is to minimize damage to the environment while being profitable, which is not easy to achieve. “Sustainable agriculture” tries to explore a series of protective agricultural practices that can decrease the adverse pressures of land intensification. Various raw materials have different physiochemical properties of biochar. The common approaches for biochar generation include pyrolysis, gasification, and hydrothermal carbonization. Biochar can be modified by purification of acids, alkalis, oxidants, gases, and so on. Additionally, the practical application of biochar for environmental remediation should pay attention to the mobilization of heavy metals and organic pollution from biochar in the environment. The main objective of this chapter is to provide an important scientific report of the present knowledge about the impact of biochar on soil characteristics, changes, and functions. A wider range of issues were discussed, including atmospheric gases emissions related to biochar production and treatment, and the impact of application on soil respiration.
Liu, X, Deng, L, Chen, Z, Ngo, HH, Guo, W & Wang, D 2023, 'Sustainability assessment of biochar applications' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 415-441. View/Download from: Publisher's site View description>>
Due to characteristics of easy access, stability, high organic carbon content, high specific surface area, high porosity, etc., biochar has been increasingly prepared and used for multiple applications in the environmental field over the world. Although the number of biochar-related studies is booming, the long-term sustainability behaviors of biochar were much less explored. The sustainability of the biochar could affect its practical applications and further development and improvement of technologies and management strategies. This study investigates the biochar applications in different environmental fields and gives notable illustrations for environmental pollution control and emission reduction. Afterward, a comprehensive framework for sustainability assessment of biochar was proposed, which addresses the concerns from technical, environmental, economic, and social aspects. The associated indices for different parts were further demonstrated, and the use of possible evaluation tools such as life cycle analysis, net present value, and multiple attribute decision making for quantification and comparison was highlighted. Finally, more field investigations on the applicability and effectiveness of biochar in different environmental conditions are further needed. The results from the study can provide theoretical and applicable information for the comprehensive evaluation and management of biochar toward sustainability.
Ma, B, Zhao, Y, Wang, X, Liu, Z, Lin, X, Wang, Z, Ni, W & Liu, RP 2023, 'Vehicle Trajectory Obfuscation and Detection' in Advanced Sciences and Technologies for Security Applications, Springer International Publishing, pp. 121-134. View/Download from: Publisher's site
Mannina, G, Barbara, L, Cosenza, A & Ni, B-J 2023, 'Advances in technologies for sewage sludge management' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 137-156. View/Download from: Publisher's site
Marynowsky, W, Knowles, J, Bown, O & Ferguson, S 2023, 'Sonic Robotics: Musical Genres as Platforms for Understanding Robotic Performance as Cultural Events' in Springer Series on Cultural Computing, Springer International Publishing, Switzerland, pp. 219-235. View/Download from: Publisher's site View description>>
This edited collection approaches the field of social robotics from the perspective of a cultural ecology, fostering a deeper examination of the reach of robotic technology into the lived experience of diverse human populations, as well as ...
Mathur, S, Sankaran, S, MacAulay, S & Tsang, I 2023, 'Minimum viable governance for data science initiatives' in Research Handbook on the Governance of Projects, Edward Elgar Publishing, UK, pp. 445-456. View/Download from: Publisher's site View description>>
This cutting-edge Research Handbook provides a comprehensive overview of research on the governance of projects.
Matthews, N, Ly, S, West, D & Gentile, C 2023, '3D Bioprinting for Cardiovascular Applications' in Muñoz, JLP, Martínez, LSDB, Ochoa, GP & Sesma, JZ (eds), 3D Printing and Bioprinting for Pharmaceutical and Medical Applications, CRC Press, Boca Raton, pp. 169-190. View/Download from: Publisher's site View description>>
Cardiovascular disease remains one of the major causes of death globally, and a heart transplant is the gold standard treatment for end-stage heart failure patients. However, a successful transplantation is dependent on organ donor’s limited availability and poses several clinical and economical risk factors for the patient, their families and more broadly the whole health system. 3D bioprinting technologies combined with tissue engineering and regenerative medicine have shown promising results in better recapitulating the natural microenvironment of the human body and in providing a future alternative for organ transplants. In this approach, the precise deposition of cellular material in permissive biomaterials suitable for tissue-specific cells has demonstrated advancements for cardiac tissue transplantation. In particular, the use of patient-derived stem cells for 3D bioprinting provides a personalised treatment specific for each patient, which also prevents the risk of rejection and may lead to faster recovery following the surgical procedure. This book chapter presents the current state of 3D bioprinting technologies applied for personalised cardiac patches. First, an overview of human cardiac anatomy and the impact of myocardial infarction are then followed by a comparison of 3D bioprinting methods, commonly used biomaterials, and the characteristics of cells and cellular spheroids. This is followed by a closer look at animal studies using 3D bioprinted cardiac patches to date and highlights major challenges for the translation of this approach to the clinic. Finally, considerations around biological and design requirements to develop personalised cardiac patches are introduced for future potential directions in cardiovascular 3D bioprinting.
Merenda, A, Kumari, P, Dumée, LF, Lee, AF & Wilson, K 2023, 'Stimuli-responsive catalytic membrane reactors: current challenges and future outlook in water treatment technologies' in Green Membrane Technologies towards Environmental Sustainability, Elsevier, pp. 265-293. View/Download from: Publisher's site View description>>
This book is written for chemical and polymer engineers, materials scientists, professors, graduate students, as well as general readers at universities, research institutions and R&D departments in industries who are engaged in sustainable ...
Mihaita, AS, Li, Z, Singh, H, Sharma, N, Tuo, M & Ou, Y 2023, 'Using machine learning and deep learning for traffic congestion prediction: A review' in Handbook on Artificial Intelligence and Transport, pp. 124-153.
The controller is the key element of a control system to compare the controlled value to the desired value. From the comparison, if any deviation exists, the controller adjusts it. It is basically a device that generates the control signals to alleviate the deviation of the real value from the required value to either zero or an inconsiderable level. The controller ensures the plant’s efficiency and smooth run. Indeed, the frequency is a sensitive parameter of the power system, which needs to be maintained within rigid limits. Thus to control the frequency, AGC is considered an essential part of the power system, where the controller plays an important role. This chapter explains various control techniques with mathematical modeling and block diagram illustrations. Further, error functions are discussed, which have been used as an objective function in AGC.
Mishra, DK, Li, L, Zhang, J & Hossain, MJ 2023, 'Challenges and viewpoints of load frequency control in deregulated power system' in Power System Frequency Control, Elsevier, pp. 117-132. View/Download from: Publisher's site View description>>
In the last few decades, the power system restructure is undergoing a significant phenomenon due to the participation of different power producers in the energy market. It helps to operate the power delivery in an economical manner. While considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and unified power flow controller have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and contract violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation.
Automatic generation control (AGC) plays a significant role in the control process in a power network to achieve the equilibrium between generation and load in the most cost-effective manner. Moreover, the AGC approach deals with frequency regulation and control of the power exchange and economic dispatch. This chapter discusses the fundamentals of load frequency control with mathematical modeling and error functions. In addition, the transfer function of the single and multiarea AGC models is presented.
Mojiri, A & Zhou, J 2023, 'Production of biochar from biowaste and its application in wastewater treatment' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 149-193. View/Download from: Publisher's site
Mortazavi, H, Mortazavy Beni, H, Islam, MS & Paul, G 2023, 'Aerosolized airborne bacteria and viruses inhalation: Micro-bioaerosols deposition effects through upper nasal airway inhalation' in Paul, G (ed), Digital Human Modeling and Medicine, Elsevier, pp. 275-288. View/Download from: Publisher's site
Ngo, HH, Guo, W, Pandey, A, Varjani, S & Tsang, DCW 2023, 'Preface', pp. xix-xx. View/Download from: Publisher's site
Ngo, HH, Nguyen, TT, Guo, W, Deng, L, Varjani, S & Liu, Y 2023, 'Sustainability assessment of biochar for climate change mitigation' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 443-462. View/Download from: Publisher's site
Nguyen, HT, Nguyen, NC, Chen, S-S, Ngo, HH, Bui, X-T & Nguyen, P-T 2023, 'Application of forward osmosis membrane technology in nutrient recovery and water reuse' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 493-508. View/Download from: Publisher's site
Nguyen, QV, Kennedy, PJ, Simoff, SJ & Catchpoole, DR 2023, 'Visual Communication and Trust in the Health Domain' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 215-226. View/Download from: Publisher's site
Nguyen, QV, Qu, Z, Lau, CW, Tegegne, Y, Tran, J, Karetla, GR, Kennedy, PJ, Simoff, SJ & Catchpoole, DR 2023, 'Biomedical Data Analytics and Visualisation—A Methodological Framework' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 174-196. View/Download from: Publisher's site
Ni, B-J, Wu, L, Shi, X & Wei, W 2023, 'Application of biochar for improving sewage sludge treatment' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 229-257. View/Download from: Publisher's site View description>>
Seeking energy alternatives to replace nonreusable fossil fuels is essential to achieve circular economic in the future. Anaerobic digestion (AD) is an efficient technology for recovering such energy alternatives and valuable products from wastes including sewage sludge. However due to the inherent obstacles of this biological method, the yield of wanted products are still not sufficient. Dosing biochar into AD is an effective optimization strategy for accelerating the sludge reduction via dewatering or composting and improving the production of methane, hydrogen and volatile fatty acids (VFAs) from AD. In an attempt to understand the role of biochar in enhancing the sustainability of sludge treatment, this chapter will (1) evaluate the dewatering performance of sludge after dosing biochar; (2) discuss the role of biochar in enhancing methane from sludge digestion; (3) understand the mechanisms of applying biochar for producing higher hydrogen and VFAs; and (4) introduce the effects of biochar on sludge reduction. The outlooks about improving sustainable sludge treatment by dosing biochar are then finally proposed.
Ni, BJ, Xu, Q & Wei, W 2023, 'Preface', pp. xvii-xviii.
Nsiah-Baafi, E, Tapas, MJ, Vessalas, K, Thomas, P & Sirivivatnanon, V 2023, 'Characterization of the Nano- and Microscale Deterioration Mechanism of the Alkali–Silica Reaction in Concrete Using Neutron and X-ray Scattering Techniques: A Review' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 469-477. View/Download from: Publisher's site View description>>
AbstractAlkali–silica reaction (ASR) is one of the most recognized chemical reactions that lead to the deterioration and premature failure of concrete. The severity of ASR is largely dependent on the expansive nature of the reaction product (ASR gel). As such, it is important to expound the developed knowledge on the formation, structure, composition, and swelling mechanism of ASR gel, to provide a greater understanding of ASR deterioration and to facilitate the development of more reliable prediction and mitigation methods. We present a summary of existing methods for assessing ASR and the state-of-the-art techniques that use neutron and X-ray scattering methods to characterize the nano- and microstructural properties of concrete and elucidate the potential transport dynamics of reactants that determine the mechanism and extent of ASR.
Pandey, AK, Gaur, VK, Varjani, S, Pandey, A, Kumar, S, Ngo, HH, Guo, W, Awasthi, MK & Wong, JWC 2023, 'Role of biochar in polyaromatic hydrocarbons remediation and environment management' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 365-385. View/Download from: Publisher's site
Pathak, N, Badeti, U, Sohn, W, Phuntsho, S & Shon, HK 2023, 'Reverse osmosis (RO) membrane development and industrial applications' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 411-435. View/Download from: Publisher's site
Pathak, N, Shon, H, Yu, H, Choo, Y, Naidu, G, Akther, N & Han, D-S 2023, 'Membrane technology for brine management and valuable resource recovery' in Green Membrane Technologies towards Environmental Sustainability, Elsevier, pp. 415-441. View/Download from: Publisher's site
Peng, X, Long, G, Yan, P, Tang, W & Clarke, A 2023, 'COVID-19 Impact Analysis on Patients with Complex Health Conditions' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 27-63. View/Download from: Publisher's site
Polmear, M, Chance, S, Hadgraft, RG & Shaw, C 2023, 'Informal Learning as Opportunity for Competency Development and Broadened Engagement in Engineering' in International Handbook of Engineering Education Research, Routledge, pp. 312-335. View/Download from: Publisher's site
Poostchi, H & Piccardi, M 2023, '6 Persian Named Entity Recognition with Structural Prediction Methods' in Persian Computational Linguistics and NLP, De Gruyter, pp. 149-184. View/Download from: Publisher's site
Presti, D, Ni, B-J & Mannina, G 2023, 'Production of volatile fatty acids from sewage sludge fermentation' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 61-94. View/Download from: Publisher's site
Qu, Z, Simoff, SJ, Kennedy, PJ, Catchpoole, DR & Nguyen, QV 2023, 'Visualisation for Explainable Machine Learning in Biomedical Data Analysis' in Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall/CRC, pp. 197-214. View/Download from: Publisher's site
Rad, HS, Shiravand, Y, Radfar, P, Ladwa, R, Warkiani, ME, O’Byrne, K & Kulasinghe, A 2023, 'Spatial Transcriptomic Approaches for Understanding the Tumor Microenvironment (TME)' in Interdisciplinary Cancer Research, Springer Nature Switzerland, pp. 49-77. View/Download from: Publisher's site
Radfar, P, Es, HA, Kulasinghe, A, Thiery, JP & Warkiani, ME 2023, 'Circulating Tumour Cell Isolation and Molecular Profiling; Potential Therapeutic Intervention' in Circulating Tumor Cells, Springer International Publishing, pp. 359-385. View/Download from: Publisher's site View description>>
Comprehensive tumour characterisation is indispensable for patients to receive targeted therapy. The use of liquid biopsy, particularly circulating tumour cells (CTC), has shown great promise in the treatment and management of cancer patients. An in-depth understanding of CTCs at the cellular and molecular level can provide clues as to the mechanisms of cancer dissemination and the pathways responsible for conferring intrinsic and acquired resistance to therapeutic agents. Herein, we discuss the current methods of CTC isolation and analysis at the single-cell resolution for therapeutic applications in the management of cancer.
Ramu, YK, Thomas, PS, Vessalas, K & Sirivivatnanon, V 2023, 'Submicroscopic Evaluation Studies to Minimize Delayed Ettringite Formation in Concrete for a Sustainable Industry and Circular Economy' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 445-455. View/Download from: Publisher's site View description>>
AbstractThe high cost of maintenance, repair and retrofitting of concrete infrastructure to keep these structures durable and serviceable is not sustainable, so the design process needs to consider all aspects of deterioration mechanism/s that can potentially occur in a concrete structure. The ideal solution should contribute to sustainability by enhancing the durability of concrete elements and supporting a circular economy. We studied delayed ettringite formation (DEF), a potential deterioration mechanism, including mitigation measures, in various heat-cured cementitious systems. The results showed that continuously connected pore/crack paths at the submicroscopic level favor the transportation of DEF-causing ions in heat-cured systems. DEF increases the chance of developing cracks, which is a durability concern. To mitigate DEF, fly ash produced from an Australian bituminous coal-burning power station was incorporated in the binder to support the circular economy concept. Changes in heat-cured cementitious systems were evaluated using expansion, electrical resistivity, dynamic modulus, and microstructural studies. The pozzolanicity of fly ash was found to greatly enhance the formation of denser calcium-silica-hydrate, which in turn restricted the transportation of DEF-causing ions at the submicron level, leading to less DEF occurrence and enhancement of the durability and sustainability of concrete in field structures.
Raza, MR, Hussain, W & Rajaeian, M 2023, 'Analysing Trust, Security and Cost of Cloud Consumer's Reviews using RNN, LSTM, and GRU' in Advances in Complex Decision Making, Chapman and Hall/CRC, pp. 52-65. View/Download from: Publisher's site
Rizwanul Fattah, IM, Ashrafur, RSM & Ahmed, A 2023, 'Black cumin (Nigella sativa L.) essential oil and aroma quality' in Biochemistry, Nutrition, and Therapeutics of Black Cumin Seed, Elsevier, pp. 71-87. View/Download from: Publisher's site
Sadaf, A, Mathieson, L & Musial, K 2023, 'Effects of Global and Local Network Structure on Number of Driver Nodes in Complex Networks' in Lecture Notes in Social Networks, Springer Nature Switzerland, pp. 81-98. View/Download from: Publisher's site View description>>
This book offers an excellent source of knowledge for readers who are interested in keeping up with the developments in the field of cyber security and social media analysis.
The future development of smart materials in the areas, such as self-tuning vibrating energy harvesting devices, self-sustainable networks, and earthquake protection devices, is of significant importance. With the great potential to develop smart structures, smart material includes shape memory alloy, piezoelectric, magnetorheological (MR) and electrorheological (ER) materials, which possess stimulus responses. Accordingly, smart materials can be regarded as promising candidates for adaptive device development for structural vibration mitigation application. In this chapter, the smart material classification and potential applications are reviewed first. Then, a case study is conducted to comprehensively illustrate application of MR material-based semi-active control device in structural seismic protection. Finally, the challenges and future work on this topic are briefly analysed and discussed.
Sengul, K & Bailo, F 2023, 'Twenty-First Century Populism in Australia and Italy: A Comparative Analysis' in Italy and Australia, Springer Nature Singapore, pp. 213-239. View/Download from: Publisher's site
Shafei, H, Li, L & Aguilera, RP 2023, 'A Comprehensive Review on Cyber-Attack Detection and Control of Microgrid Systems' in Power Systems Cybersecurity, Springer International Publishing, pp. 1-45. View/Download from: Publisher's site View description>>
Due to the fast progress of Microgrid (MG) systems and the development of advanced computing technologies and communication networks—all of which enhance the efficiency and reliability of power networks—MGs are at the risk of various cyber-attacks which can eventually lead to different glitches in the power distribution networks. There are many different kinds of cyber-attacks, some of which are the False Data Injection Attack, Denial of Service, Stealth Attack, and Covert Attack. The common goals of these attacks are to cause power outage, economic loss, and even system instability. Cyber-attacks could infiltrate MGs through the communication links, local controllers, or master control channels. In this chapter, a thorough review of the types of cyber-attacks and the problems caused by them in MGs has been presented, and some methods of cyber-attack detection, resilient control system design, and countermeasures against such attacks have been discussed. Numerous research works have already investigated the subject of cyber-attacks on both the Direct-Current (DC) and Alternating-Current (AC) MG systems. These studies can be divided into two main categories: (a) detection and mitigation approaches, and (b) resilient control system designs. Several subclasses of each of these categories, along with their advantages and disadvantages has been thoroughly investigated in this chapter. In the first category, after detecting a compromised agent, an active or passive mitigation mechanism is activated to prevent the spread of the agent’s destructive effects to the whole system. This may impose some strict limitations on the MGs. In the second category, by developing the distributed attack-resilient control protocols, the resilience of a MG system against potential attacks/faults/noises is enhanced to the point where no detection and mitigation action will be required.
Sharma, R & Mehndiratta, S 2023, 'Recent Advancements in Biomedical Sciences and Their Healthcare Applications' in Biomedical Research, Medicine, and Disease, CRC Press, USA, pp. 3-8. View/Download from: Publisher's site View description>>
Today’s biomedical science is a product of consistency, multidiscipline collaborations of researchers to bring out the best in health services. While conventionally structured research laboratories and clinical studies are the basic and most essential components that led to breakthroughs, today’s biomedical field comprises of multi-dimensional approach with the use of artificial intelligence (AI). Technological developments have accelerated the speed of research in the biomedical field and have provided a new horizon to the concept of biomedical sciences. Use of AI to develop tools of bio-informatics and health-informatics helps in development, production, and testing of medical ailments and their treatment in a quick and affordable manner. Moreover, these advancements can enhance capacity to provide a link between early molecular or cellular detection of disease outcomes. This could provide a mechanistic understanding of human disease processes and a way to deal with them (e.g., development of AI-assisted neuron networks for studying the basic mechanisms of brain and bio-informatics-guided in-silico assays for drug testing to obsolete the need for animals). Considering the plethora of research being conducted in the field of biomedical sciences, this chapter will focus on biomedical technological advancements and its applications in health care such as signaling pathway-based approaches, human-on-a-chip, organoid models, and in-silico modeling via AI and machine learning tools.
Singh, AK & Lin, C-T 2023, 'Content Augmentation in Virtual Reality with Cognitive-Conflict-Based Brain-Computer Interface' in Handbook of Neuroengineering, Springer Nature Singapore, pp. 1901-1922. View/Download from: Publisher's site
Singh, AK & Zhu, H 2023, 'Human Computer Interface in Smart Agriculture' in Encyclopedia of Digital Agricultural Technologies, Springer International Publishing, pp. 605-613. View/Download from: Publisher's site View description>>
Our reference work takes full advantage of this feature, which allows for continuous improvement or revision of published content electronically. The Editorial BoardDr. Irwin R. Donis-Gonzalez, University of California Davis, Dept.
Singh, AK & Zhu, H 2023, 'Human Computer Interface in Smart Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-8. View/Download from: Publisher's site
Soar, J, Lih, OS, Wen, LH, Ward, A, Sharma, E, Deo, RC, Barua, PD, Tan, R-S, Rinen, E & Acharya, UR 2023, 'Deep Image Analysis for Microalgae Identification' in Information Integration and Web Intelligence, Springer Nature Switzerland, pp. 280-292. View/Download from: Publisher's site
Sun, X, Jiang, G, Keller, J, Bond, P & Li, X 2023, 'Testing of Sulfide Uptake Rate (SUR) and Its Applications' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, Switzerland, pp. 37-58. View/Download from: Publisher's site View description>>
This chapter introduces a methodology to measure the rate of gaseous H2S transferring from sewer atmosphere to the exposed concrete surface. This facilitates the monitoring of sulfide-induced corrosion processes on concrete at various corrosion stages. In comparison to many existing methods, this methodology has the advantages of rapid measurement and non-destruction of the concrete sample. The H2S uptake rate (SUR) for a concrete coupon can be determined by measuring the gaseous H2S concentrations over time in a temperature- and humidity-controlled gas-tight reactor. The reliability of this method was evaluated by carrying out repeated tests on concrete coupons previously exposed to different corrosion conditions. The method could be applied to perform various research activities related to microbiologically influenced concrete corrosion, for instance, (1) understand sulfide uptake activity by concrete; (2) differentiate chemical and biological driven sulfide uptake activity; (3) evaluate the effectiveness of concrete control techniques; (4) estimate concrete corrosion rate in sewer systems; (5) investigate important factors affecting sulfide-induced concrete corrosion, particularly temperature, fluctuating gaseous H2S concentrations, oxygen concentrations, surface pH and relative humidity (RH).
Syberg, M, West, N, Schwenken, J, Adams, R & Deuse, J 2023, 'Requirements for the Development of a Collaboration Platform for Competency-Based Collaboration in Industrial Data Science Projects' in Marquez, FPG, Ramirez, IS, Sanchez, PJB & DelRio, AM (eds), Lecture Notes on Data Engineering and Communications Technologies, Springer International Publishing, pp. 64-69. View/Download from: Publisher's site View description>>
The ongoing digitization of online learning resources has led to a proliferation of collaboration platforms for specific areas of application and disciplines. Simultaneously, the need for collaboration in the field of data analysis and knowledge management increases, especially for manufacturing companies. In this paper, collaborative and competency-based requirements for applying industrial data analytics are derived into tangible specifications for implementing a collaboration platform. The currently absent requirements of Industrial Data Science (IDS) projects are determined and then translated into platform-specific functions. Using the practical implementation of an ongoing research project, the defined requirements are transformed into features and applied in an online platform. The validation in a system of dynamic value networks serves to confirm the practical suitability of the specified requirements. The innovation of the platform is its clear focus on IDS project practitioners, who are typically comprised of several different domains. They often use a variety of analytics tools and aim for long-term use of deployed solutions. A pilot implementation of the developed platform is available online and additionally serves to validate the platform suitability.
Tapas, MJ, Yan, A, Thomas, P, Holt, C & Sirivivatnanon, V 2023, 'Effect of Carbonation on the Microstructure and Phase Development of High-Slag Binders' in Lecture Notes in Civil Engineering, Springer Nature Singapore, Switzerland, pp. 213-221. View/Download from: Publisher's site View description>>
AbstractThe drive for sustainable concrete production favors the use of high replacement levels of supplementary cementitious materials (SCMs) in the concrete mix. The use of SCMs such as fly ash and slag, however, although they improve the sustainability of concrete production as well as most concrete durability properties, increases the carbonation rate. Carbonation decreases the pH of the concrete pore solution, making the steel reinforcement susceptible to corrosion. The effect of carbonation is, however, not confined to the change in pH of the pore solution. We investigated changes in the microstructure and phases of high-slag binders due to carbonation. The carbonation resistance of mortars with 50 and 70% slag replacement were investigated at exposure conditions of 2%CO2, 50%RH, 23 °C. The carbonated and non-carbonated parts of the mortars were subjected to various characterization techniques to investigate the effect of carbonation on microstructure and phase development. Results confirmed the absence of portlandite in all the carbonated regions (“colorless” by phenolphthalein test, which indicated that the change in color of the phenolphthalein solution was due to the absence of portlandite to buffer the pH). Significant reduction in the amount of C-S-H, as well as increase in the amount of calcium carbonate, were been observed in the carbonated regions. Aragonite, a polymorph of CaCO3, was very prominent in all the carbonated mortars.
Tian, H, Nghiem, J & Chen, F 2023, 'Electrical Network‐Related Incident Prediction Based on Weather Factors' in Advances in Data Science and Analytics Concepts and Paradigms, Wiley, pp. 233-245. View/Download from: Publisher's site View description>>
ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across ...
Tran, VS, Ngo, HH, Guo, W & Nguyen, MK 2023, 'Sustainable application of biochar for storm water reuse' in Current Developments in Biotechnology and Bioengineering, Elsevier, Netherlands, pp. 259-276. View/Download from: Publisher's site View description>>
This book chapter reviews the storm water reuse, harvesting issues, characterization of storm water, potential of reuse, and the quantity and characteristics of lignocellulosic biomass, and its modified products. Storm water is a resource that can help solve the global water scarcity problem. The pollutants such as heavy metals, organic pollutants, pathogens that exist in storm water, however, prevent it from becoming more readily used in drinking and other applications. Subsequently, the modifying techniques for biochar production involving various target contaminants removal will be presented. The application of biochar for storm water is also discussed in detail. Finally, conclusions and future perspectives on the sustainable application of biochar for storm water reuse will be showed.
Verma, H, Gupta, A, Singh Kirar, J, Prasad, M & Lin, CT 2023, 'Introduction to computational methods' in Computational Intelligence Aided Systems for Healthcare Domain, CRC Press, pp. 1-32. View/Download from: Publisher's site
Vu, L, Nguyen, QU, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2023, 'A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks' in Emerging Trends in Cybersecurity Applications, Springer International Publishing, Switzerland, pp. 45-65. View/Download from: Publisher's site View description>>
Internet of Things, or IoT has been playing an important part in human’s lives. Nonetheless, the rapid growth of IoT-based services may lead to an increase in IoT-based attacks that compromise sensitive user data. IoT-based attack detection and prevention is thus essential for the development of IoT-based network technologies. However, this is a difficult task because the data transmitted between IoT devices is often complex and heterogeneous while IoT-based threats/attacks change rapidly over time. Machine learning-based threat detection methods, which are trained with old/existing labelled data, may become less effective in the future. For that, it is critical to develop effective learning models that can leverage both labelled and unlabelled data to timely adapt with the fast-varying attack methods. To this end, this chapter proposes a Deep Transfer Learning (DTL) model to build an effective IoT attack detection model from both labelled and unlabelled data collected from multiple IoT devices. The proposed solution combines the Multi-Maximum Mean Discrepancy (M2D) distance and two AutoEncoder (AE) networks named as Multi-Maximum Mean Discrepancy AutoEncoder (M2DA). The first AE is trained on labelled IoT data, while the second AE is trained on unlabelled IoT data. The purpose of the training process is to transfer the learned label information from the first AE to the second AE by the M2D distance. As a result, the second AE can efficiently classify and detect anomaly (attacks) even on unlabelled IoT network data. We further study the performance of the proposed M2DA framework using nine well-known commercial IoT datasets with different botnet families and attacks.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Fundamentals of Machine Learning in Data Center Networks' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 9-14. View/Download from: Publisher's site View description>>
In this chapter, we will briefly review the common learning paradigms of ML and some preliminary knowledge about data collection and processing. Furthermore, to better assess the strengths and weaknesses of the existing research work, we design a multi-dimensional and multi-perspective quality assessment criteria, called REBEL-3S.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Insights, Challenges and Opportunities' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 101-108. View/Download from: Publisher's site View description>>
Through systematic research and analysis, we found that ML has been gradually introduced and applied to various fields of data center network, and has made certain achievements. However, the current researches are still in its infancy and need to be further improved in various areas.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Introduction' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 1-8. View/Download from: Publisher's site View description>>
As the storage and computation progressively migrate to the cloud, the data center (DC) as the core infrastructure of cloud computing provides vital technical and platform support for enterprise and cloud services. However, with the rapid rise of the data center scale, the network optimization, resource management, operation and maintenance, and data center security have become more and more complicated and challenging.
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Machine Learning Empowered Intelligent Data Center Networking' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 15-99. View/Download from: Publisher's site View description>>
Machine learning has been widely studied and practiced in data center networks, and a large number of achievements have been made. In this chapter, we will review, compare, and discuss the existing work in the following research areas: flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, network security, and new intelligent networking concepts.
Xing, R, Yen, P-Y & Liu, H 2023, 'Residential Choices of the Elderly Under Medical and Aged Care Integration: Evidence from Shanghai' in Gaps and Actions in Health Improvement from Hong Kong and Beyond, Springer Nature Singapore, pp. 495-506. View/Download from: Publisher's site
Ye, Y, Ngo, HH, Guo, W, Ding, A, Deng, S, Nguyen, DD & Bui, XT 2023, 'Application of Sewage Sludge as an Agricultural Soil Amendment' in Sustainable Treatment and Management of Sewage Sludge, CRC Press, pp. 69-84. View/Download from: Publisher's site
Ye, Y, Ngo, HH, Guo, W, Kang, J, Jiang, W, Ren, Y & Liu, D 2023, 'Biochar for sustainable remediation of soil' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 277-297. View/Download from: Publisher's site
Yuan Zhu, H & Lin, C 2023, 'Virtual/Augmented/Mixed Reality Technologies for Enabling Metaverse' in Metaverse Communication and Computing Networks Applications, Technologies, and Approaches, Wiley, pp. 125-155. View/Download from: Publisher's site View description>>
Metaverse Communication and Computing Networks Understand the future of the Internet with this wide-ranging analysis “Metaverse” is the term for applications that allow users to assume digital avatars to interact with other humans and ...
Yusoff, MNAM, Imran, S, Kalam, MA, Zulkifli, NW & Masjuki, HH 2023, 'Future needs of the biodiesel industry' in Sustainable Biodiesel, Elsevier, pp. 373-383. View/Download from: Publisher's site
Zhang, JA, Ni, Z, Wu, K, Huang, X & Guo, YJ 2023, 'Perceptive Mobile Networks' in Integrated Sensing and Communications, Springer Nature Singapore, pp. 355-384. View/Download from: Publisher's site
Zhou, J & Chen, F 2023, 'Artificial Intelligence in Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9. View/Download from: Publisher's site
Zhou, J & Chen, F 2023, 'Artificial Intelligence in Agriculture' in Encyclopedia of Digital Agricultural Technologies, Springer International Publishing, pp. 84-92. View/Download from: Publisher's site
Zhou, J & Chen, F 2023, 'Artificial Intelligence in Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9. View/Download from: Publisher's site
Abdi, Y, Momeni, E & Armaghani, DJ 2023, 'Elastic modulus estimation of weak rock samples using random forest technique', Bulletin of Engineering Geology and the Environment, vol. 82, no. 5. View/Download from: Publisher's site
Abdollahi, A & Pradhan, B 2023, 'Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model', Science of The Total Environment, vol. 879, pp. 163004-163004. View/Download from: Publisher's site
Abdollahi, A, Pradhan, B & Alamri, A 2023, 'Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach', International Journal on Smart Sensing and Intelligent Systems, vol. 16, no. 1. View/Download from: Publisher's site View description>>
AbstractAccurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.
Abdollahi, A, Pradhan, B, Alamri, A & Lee, C-W 2023, 'Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights', Journal of Sensors, vol. 2023, pp. 1-14. View/Download from: Publisher's site View description>>
The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat-8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt-and-pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat-8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat-8 data ensures that decision makers have the resources they need to track land cover throughout the year.
Abharian, S, Sarfarazi, V, Marji, MF, Rasekh, H & Sadrekarimi, A 2023, 'Effect of geogrid reinforcement on tensile failure of high-strength self-compacted concrete', Magazine of Concrete Research, vol. 75, no. 8, pp. 379-401. View/Download from: Publisher's site View description>>
In this study, the tensile strength, failure mechanism and ductile behaviour of geogrid-reinforced high-strength self-compacting concrete discs subjected to both the Brazilian tensile strength test and a biaxial compressive test are studied. To determine the combined effects of geogrid layer numbers and inclination angle on the ultimate tensile strength of concrete samples, 21 experiments were conducted with up to three layers of geogrids inclined at angles of 0° to 90°, at increments of 15°. In addition, discrete-element numerical simulations were conducted using two-dimensional particle flow code to examine the failure behaviour of geogrid-reinforced high-strength self-compacting concrete discs. The numerical models were first calibrated by the experimental results and then the failure behaviour of models containing geogrids was investigated. Both experimental and numerical results demonstrate that augmenting the concrete discs with geogrids increases the ductility of specimens, especially after failure. As the number of geogrid layers increased, the tensile strength of specimens also increased, whereas the tensile strength and absorbed energy were the same for specimens with different numbers of geogrid layers and inclination angles of 75° and 90°. The specimen with three horizontal geogrid layers had the highest tensile strength, biaxial compression strength and ductility of all specimens tested.
Aboughaly, M & Fattah, IMR 2023, 'Environmental Analysis, Monitoring, and Process Control Strategy for Reduction of Greenhouse Gaseous Emissions in Thermochemical Reactions', Atmosphere, vol. 14, no. 4, pp. 655-655. View/Download from: Publisher's site View description>>
This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products.
Abounahia, N, Ibrar, I, Kazwini, T, Altaee, A, Samal, AK, Zaidi, SJ & Hawari, AH 2023, 'Desalination by the forward osmosis: Advancement and challenges', Science of The Total Environment, vol. 886, pp. 163901-163901. View/Download from: Publisher's site
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2023, 'Reinforcement Learning-Based News Recommendation System', IEEE Transactions on Services Computing, vol. 16, no. 6, pp. 4493-4502. View/Download from: Publisher's site
Abraham, MT, Vaddapally, M, Satyam, N & Pradhan, B 2023, 'Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India', CATENA, vol. 223, pp. 106948-106948. View/Download from: Publisher's site
Abu, SM, Hannan, MA, Hossain Lipu, MS, Mannan, M, Ker, PJ, Hossain, MJ & Mahlia, TMI 2023, 'State of the art of lithium-ion battery material potentials: An analytical evaluations, issues and future research directions', Journal of Cleaner Production, vol. 394, pp. 136246-136246. View/Download from: Publisher's site
Abu, SM, Hannan, MA, Ker, PJ, Mansor, M, Tiong, SK & Mahlia, TMI 2023, 'Recent progress in electrolyser control technologies for hydrogen energy production: A patent landscape analysis and technology updates', Journal of Energy Storage, vol. 72, pp. 108773-108773. View/Download from: Publisher's site View description>>
Alternative low-to-zero carbon technologies must be developed to facilitate the clean energy transition rather than only concentrating on one or a few specific technology trajectories. The hydrogen electrolyser has many benefits over traditional energy storage technologies, making it a competitive alternative to the current fossil fuel combustion-based energy generation system. To better understand the impact and developments of electrolyser control technologies for hydrogen production, this study aims to shed light on current research and patent trends. The research was conducted by performing extensive keyword searches on electrolyser control methods for hydrogen generation in the Lens database and then extracting the bibliometric data from the 107 patent publications selected based on keywords, family filtering and material exclusion. An up-to-date technical overview is provided with a bibliographic study of patent growth, key players and innovators, patent distribution across jurisdictions and technological sectors, and patent categorization using the cooperative patent classification (CPC) code. Key owners, inventors, and jurisdictional hierarchies in patent publications are also identified, and the potential for further study is assessed. These selected patent documents and their landscape analysis aim to provide a systematic foundation for future developments in electrolyser technologies and materials related to hydrogen production and to propose emerging research and commercialization prospects for future researchers.
Acharya, R, Aleiner, I, Allen, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Basso, J, Bengtsson, A, Boixo, S, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Y, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Higgott, O, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Kelly, J, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, Malone, FD, Marshall, J, Martin, O, McClean, JR, McCourt, T, McEwen, M, Megrant, A, Meurer Costa, B, Mi, X, Miao, KC, Mohseni, M, Montazeri, S, Morvan, A, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Neven, H, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Opremcak, A, Platt, J, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Roushan, P, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smelyanskiy, V, Smith, WC, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff Heidweiller, C, White, T, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y & Zhu, N 2023, 'Suppressing quantum errors by scaling a surface code logical qubit', Nature, vol. 614, no. 7949, pp. 676-681. View/Download from: Publisher's site View description>>
AbstractPractical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per cycle floor set by a single high-energy event (1.6 × 10−7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Adeoti, OS, Kandasamy, J & Vigneswaran, S 2023, 'Water infrastructure sustainability in Nigeria: a systematic review of challenges and sustainable solutions', Water Policy, vol. 25, no. 11, pp. 1094-1111. View/Download from: Publisher's site View description>>
AbstractUsing the PRISMA method, this systematic literature review synthesized findings from 15 studies to elucidate the key factors contributing to water infrastructure failure in Nigeria and propose evidence-based sustainable solutions. The study identified technical, financial, environmental, social, political, and institutional factors as predominant challenges in achieving water infrastructure sustainability. In response to these challenges, the researcher proposes a comprehensive ‘Sustainability Framework for Water Infrastructure’. This framework is designed to guide every stage of water infrastructure development, starting from pre-construction with an emphasis on inclusive project planning, followed by the construction phase where suitable techniques are utilized, and extending to the post-construction stage, focusing on efficient monitoring and management mechanisms. The study highlights the complexity of water infrastructure sustainability in Nigeria and underscores the urgent need for a structured and comprehensive approach to address this pressing issue.
Adhikari, S, Thapa, S, Naseem, U, Lu, HY, Bharathy, G & Prasad, M 2023, 'Explainable hybrid word representations for sentiment analysis of financial news.', Neural Networks, vol. 164, pp. 115-123. View/Download from: Publisher's site View description>>
Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.
Adibi, T, Razavi, SE, Ahmed, SF, Hassanpour, H, Saha, SC & Muyeen, SM 2023, 'Predicting airfoil stalling dynamics using upwind numerical solutions to non-viscous equations', Results in Engineering, vol. 20, pp. 101472-101472. View/Download from: Publisher's site
The interplay between CO2 input and light intensity is investigated to provide new insight to optimise microalgae growth rate in photobioreactors for environmental remediation, carbon capture, and biomass production. Little is known about the combined effect of carbon metabolism and light intensity on microalgae growth. In this study, carbonated water was transferred to the microalgae culture at different rates and under different light intensities for observing the carbon composition and growth rate. Results from this study reveal opposing effects from CO2 input and light intensity on the culture solution pH and ultimately microalgae growth rate. Excessive CO2 concentration can inhibit microalgae growth due to acidification caused by CO2 dissolution. While increasing light intensity can increase pH because the carboxylation process consumes photons and transfers hydrogen ions into the cell. This reaction is catalysed by the enzyme RuBisCO, which functions optimally within a specific pH range. By balancing CO2 input and light intensity, high microalgae growth rate and carbon capture could be achieved. Under the intermittent CO2 transfer mode, at the optimal condition of 850 mg/L CO2 input and 1089 μmol/m2/s light intensity, leading to the highest microalgae growth rate and carbon fixation of 4.2 g/L as observed in this study.
Aditya, L, Vu, HP, Nguyen, LN, Mahlia, TMI, Hoang, NB & Nghiem, LD 2023, 'Microalgae enrichment for biomass harvesting and water reuse by ceramic microfiltration membranes', Journal of Membrane Science, vol. 669, pp. 121287-121287. View/Download from: Publisher's site View description>>
Harvesting and water reuse are two critical issues for large-scale microalgae cultivation. Using two representative microalgae species, namely C. vulgaris and Scenedesmus sp., this study evaluates the performance of a ceramic microfiltration membrane to extract clean water for reuse and pre-concentrate the microalgae solution for subsequent harvesting. The results show that fouling was specific to each individual microalgae species due to the difference in cell properties (e.g. size, shape, and cell membrane). Importantly, membrane fouling could be efficiently mitigated by aeration and regular backwashing without any chemical addition. Aeration reduced the transmembrane pressure when filtering C. vulgaris and Scenedesmus sp. by 56 and 38%, respectively. In long-term performance experiments, C. vulgaris showed considerable membrane fouling over time; by contrast, Scenedesmus sp. showed negligible fouling. The results reaffirmed that membrane filtration efficiency was microalgae species-specific. Permeate water reuse for growing another batch of microalgae was also demonstrated using both species. Results reported here suggest that ceramic microfiltration membrane can simultaneously enrich the microalgae solution and recycle permeated water for microalgae cultivation.
Adnan Farooq, M & Nimbalkar, S 2023, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366, pp. 130260-130260. View/Download from: Publisher's site
Afroz, S, Kim, T & Castel, A 2023, 'Distinct Effect of Hydration of Calcined Kaolinitic Clay–Limestone Blended Cement on Microstructure and Autogenous Shrinkage', Journal of Materials in Civil Engineering, vol. 35, no. 12. View/Download from: Publisher's site
Afroz, S, Nguyen, QD, Zhang, Y, Kim, T & Castel, A 2023, 'Cracking of limestone calcined clay blended concrete and mortar under restrained shrinkage', Construction and Building Materials, vol. 386, pp. 131599-131599. View/Download from: Publisher's site
Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2023, 'Shrinkage of blended cement concrete with fly ash or limestone calcined clay', Materials and Structures, vol. 56, no. 1, p. 15. View/Download from: Publisher's site View description>>
AbstractThis study investigates the shrinkage of two sustainable aluminosilicate blends with fly ash or limestone-calcined clay (LC3). Paste and concrete were prepared using these SCMs for the highest possible replacement of binder without compromising the strength. The chemical and autogenous shrinkage were assessed for paste samples and further investigation were conducted on hydration by thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). Opting for an engineering approach, comparison among different segments of shrinkage i.e., autogenous, drying and total shrinkage of concrete having a specific compressive strength were considered. The initial investigation on paste samples highlighted the dissimilarities in shrinkage and hydration of fly ash and calcined clay. LC3 hydrated faster compared to fly ash leading to greater autogenous shrinkage. The high autogenous shrinkage in the LC3 blend was compensated by a low drying shrinkage for a specific compressive strength. Considering the replacement level of cement, shrinkage, and ecological impact, LC3 proved to be a more sustainable and eco-friendly concrete compared to fly ash.
Afsari, M, Jiang, J, Phuntsho, S, Shon, HK & Tijing, LD 2023, 'Ammonia recovery from source-separated hydrolyzed urine via a dual-membrane distillation in-series process', Chemical Engineering Journal, vol. 470, pp. 144215-144215. View/Download from: Publisher's site
Afsari, M, Li, Q, Karbassiyazdi, E, Shon, HK, Razmjou, A & Tijing, LD 2023, 'Electrospun nanofiber composite membranes for geothermal brine treatment with lithium enrichment via membrane distillation', Chemosphere, vol. 318, pp. 137902-137902. View/Download from: Publisher's site View description>>
In this study, a composite electrospun nanofiber membrane was fabricated and used to treat a geothermal brine source with lithium enrichment. An in-situ growth technique was applied to incorporate silica nanoparticles on the surface of nanofibers with (3-Aminopropyl) triethoxysilane as the nucleation site. The fabricated composite nanofiber membrane was heat pressed to enhance the integration of the membrane and its mechanical stability. The fabricated membranes were tested to evaluate their performance in feedwater containing different concentrations of NaCl in the range of 0-100 g/L, and the wetting resistivity of the membranes was examined. Finally, the optimal membrane was applied to treat the simulated geothermal brine. The experimental results revealed that the in-situ growth of nanoparticles and coating of flourosilane agent dramatically improved the separation performance of the membrane with high salt rejection, and adequate flux was achieved. The heat-pressed membrane obtained >99% salt rejection and flux of 14-19 L/m2h at varying feedwater salinity (0-100 g/L), and the concentration of the Li during the 24 h test reached >1100 ppm from the initial 360 ppm. Evaluation of the energy efficiency of the membranes showed that the heat-pressed membrane obtained the optimum energy efficiency in the high concentration of salts. Additionally, the economic analysis indicated that MD could achieve a levelized cost of 2.9 USD/m3 of lithium brine concentration as the heat source is within the feed. Overall, this technology would represent a viable alternative to the solar pond to concentrate Li brine, enabling a compact, efficient, and continuous operating system.
Afsari, M, Park, MJ, Kaleekkal, NJ, Motsa, MM, Shon, HK & Tijing, L 2023, 'Janus Distillation Membrane via Mussel-Inspired Inkjet Printing Modification for Anti-Oil Fouling Membrane Distillation', Membranes, vol. 13, no. 2, pp. 191-191. View/Download from: Publisher's site View description>>
In this work, inkjet printing technology was used to print a thin layer of a hydrophilic solution containing polydopamine as a binder and polyethyleneimine as a strong hydrophilic agent on a commercial hydrophobic membrane to produce a Janus membrane for membrane distillation. The pristine and modified membranes were tested in a direct-contact membrane distillation system with mineral oil-containing feedwater. The results revealed that an integrated and homogenous hydrophilic layer was printed on the membrane with small intrusions in the pores. The membrane, which contained three layers of inkjet-printed hydrophilic layers, showed a high underwater oil contact angle and a low in-air water contact angle. One-layer inkjet printing was not robust enough, but the triple-layer coated modified membrane maintained its anti-oil fouling performance even for a feed solution containing 70 g/L NaCl and 0.01 v/v% mineral oil concentration with a flux of around 20 L/m2h. This study implies the high potential of the inkjet printing technique as a facile surface modification strategy to improve membrane performance.
Afsari, M, Shirazi, MMA, Ghorbani, AH, Sayar, O, Shon, HK & Tijing, LD 2023, 'Triple-layer nanofiber membrane with improved energy efficiency for treatment of hypersaline solution via membrane distillation', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110638-110638. View/Download from: Publisher's site
Ahmadi, S, Ebrahimi Warkiani, M, Rabiee, M, Iravani, S & Rabiee, N 2023, 'Carbon-based nanomaterials against SARS-CoV-2: Therapeutic and diagnostic applications', OpenNano, vol. 10, pp. 100121-100121. View/Download from: Publisher's site
Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 2023, 'Novel Dual-Band Metascreen for Dual-Band Near-Field Phase Correction', IEEE Transactions on Antennas and Propagation, vol. 71, no. 7, pp. 5591-5604. View/Download from: Publisher's site
Ahmed, F, Hayat, T, Afzal, MU, Zhang, S, Esselle, KP & Whittow, WG 2023, '3-D Printable Synthetic Metasurface to Realize 2-D Beam-Steering Antenna', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 506-519. View/Download from: Publisher's site View description>>
This article presents highly radio-frequency (RF) transparent phase gradient synthetic metasurfaces made of sub-wavelength-sized 3D printable meta-atoms with tailored permittivity that cannot be achieved with off-the-shelf, commercially available materials. The synthesized meta-atoms design uses one dielectric block of PREPERM ® ABS 1000 material with air and metallic inclusions to make low- and high-permittivity materials. The inclusions' size and height are varied to achieve a complete phase range from 0 to 360°, while maintaining transmission magnitudes greater than -3.0 dB. A two-dimensional array of meta-atoms forms a metasurface, which can be used for phase-shifting structures. Such metasurfaces can manipulate RF waves by introducing progressive phase delay into array elements. The proposed meta-atoms are employed to create highly RF transparent phase-gradient metal-dielectric composite metasurfaces (MDCMs) operating at 11 GHz. These MDCMs can be implemented through 3D printed technology using low-cost thermoplastics or polymers with composite filaments and minimal human intervention. A pair of MDCMs are combined with an array of microstrip patches to demonstrate 2D beam steering functionality numerically. The antenna system provides a peak directivity of 19.9 dBi with a maximum conical scanning angle of 114° and a directivity variation of less than 3 dB.
This article reviews the latest developments of beam steering antennas that are entirely passive to realize interference-free, power-efficient, and highly secured end-to-end wireless communication. We briefly introduce metamaterials and metasurfaces, a timely advanced topic in electromagnetics (EM) and optics. Mathematical formulas associated with the design of beam steering metasurfaces have been numerically explained. In addition, reflect and transmit array antennas are also discussed for an in-depth understanding of beam scanning principles in elevation and azimuth planes. We then provide intuitive design examples and discuss three broad classes of the latest beam scanning antenna systems, namely i) Reflectarrays, ii) Transmitarrays, and iii) Near-Field Meta-Steering antennas that are available in up-to-date literature. The third category’s unprecedented scanning performance and aesthetically compact size are elucidated compared to previous antenna systems, such as reflector dishes or large phased arrays. Alongside the working principles, the trade-offs for the scanning techniques, operation, and physical size of each antenna type are also discussed. Towards the end, an evaluative conclusion with a comparative discussion on the beam-steering antenna systems is provided. Future research directions considering mass-market demands are also indicated.
Ahmed, SF, Alam, MSB, Hassan, M, Rozbu, MR, Ishtiak, T, Rafa, N, Mofijur, M, Shawkat Ali, ABM & Gandomi, AH 2023, 'Deep learning modelling techniques: current progress, applications, advantages, and challenges', Artificial Intelligence Review, vol. 56, no. 11, pp. 13521-13617. View/Download from: Publisher's site View description>>
AbstractDeep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world.
Ahmed, SF, Debnath, JC, Mehejabin, F, Islam, N, Tripura, R, Mofijur, M, Hoang, AT, Rasul, MG & Vo, D-VN 2023, 'Utilization of nanomaterials in accelerating the production process of sustainable biofuels', Sustainable Energy Technologies and Assessments, vol. 55, pp. 102894-102894. View/Download from: Publisher's site View description>>
Around 84 % of the global energy needs are met by fossil fuels, notwithstanding their several drawbacks. Dependence on fossil fuels can be reduced when biofuels become more widely used. Compared to fossil fuel, biofuel is substantially less combustible and derived from renewable resources. Biofuel production from non-edible feedstocks can be enhanced by utilizing nanotechnology. Biofuel research to date has produced promising results, but very few recent studies have underlined the use of nanotechnology to enhance the biofuel production process. This study comprehensively reviews the potential use of nanotechnology in improving biofuel production processes. It also highlights the factors that affect nanomaterial performance in the biofuel production process. The nickel oxide (NiO) nanoparticles (NPs) are shown to be highly efficient, with harvesting Chlorella vulgaris biomass at an efficiency of 98.75 % in 1 min at pH 7. In terms of cost-effectiveness, naked modified magnetic nanoparticles (MNPs) cost significantly less, ranging from £3-500 to £0.5–108 per kg following nanoparticle reactivation. Due to their toxicity, nanomaterials used in biofuel production systems have several detrimental effects on living organisms, the environment, and the economy. Developing non-toxic nanomaterials, utilizing cheaper nanoparticles, and doing additional research might increase knowledge availability and understanding to address the current issues.
Ahmed, SF, Islam, N, Kumar, PS, Hoang, AT, Mofijur, M, Inayat, A, Shafiullah, GM, Vo, D-VN, Badruddin, IA & Kamangar, S 2023, 'Perovskite solar cells: Thermal and chemical stability improvement, and economic analysis', Materials Today Chemistry, vol. 27, pp. 101284-101284. View/Download from: Publisher's site View description>>
Perovskite solar cells (PSCs) are highly efficient and are comparatively cheaper than the large silicon crystals primarily used in solar cells. Their outstanding photovoltaic performance makes them a potential alternative to silicon solar cells. While efficiency and photovoltaic performance have been investigated in recent decades, a knowledge gap on the degradation, economic feasibility and stability of PSCs exists, and their poor stability remains a barrier to commercialization. Thus, this review aims to fill this knowledge gap by focusing on approaches to improve PSCs’ thermal and chemical stability, and their economic viability under different conditions. The structure and manufacture of PSCs are also discussed along with an economic analysis of different perovskite devices. Improvements in thermal stability can be reached by incorporating inorganic materials into the PSC. A PSC model optimized with ZnO improves chemical stability by 8% and works well under low temperatures. To make PSCs more economically feasible, certain parts like counter electrodes (CE) and hole transport materials (HTMs) can be replaced with alternative elements like carbon and inorganic HTMs, respectively. PSCs with long durability and high conversion efficiency will expand the commercial prospects for this material. To bridge the lack of knowledge, further investigation is required on the sustainability and longevity of PSCs.
Ahmed, SF, Kabir, M, Mehjabin, A, Oishi, FTZ, Ahmed, S, Mannan, S, Mofijur, M, Almomani, F, Badruddin, IA & Kamangar, S 2023, 'Waste biorefinery to produce renewable energy: Bioconversion process and circular bioeconomy', Energy Reports, vol. 10, pp. 3073-3091. View/Download from: Publisher's site
Ahmed, SF, Rafa, SJ, Mehjabin, A, Tasannum, N, Ahmed, S, Mofijur, M, Lichtfouse, E, Almomani, F, Badruddin, IA & Kamangar, S 2023, 'Bio-oil from microalgae: Materials, production, technique, and future', Energy Reports, vol. 10, pp. 3297-3314. View/Download from: Publisher's site
Ahmed, T, Cha, JS, Park, C-G, Shon, HK, Han, DS & Park, H 2023, 'Activated Carbon-Embedded Reduced Graphene Oxide Electrodes for Capacitive Desalination', Journal of Electrochemical Science and Technology, vol. 14, no. 3, pp. 222-230. View/Download from: Publisher's site View description>>
Capacitive deionization of saline water is one of the most promising water purification technologies due to its high energy efficiency and cost-effectiveness. This study synthesizes porous carbon composites composed of reduced graphene oxide (rGO) and activated carbon (AC) with various rGO/AC ratios using a facile chemical method. Surface characterization of the rGO/AC composites shows a successful chemical reduction of GO to rGO and incorporation of AC into rGO. The optimized rGO/AC composite electrode exhibits a specific capacitance of ~243 F g<sup>−1</sup> in a 1 M NaCl solution. The galvanostatic charging-discharging test shows excellent reversible cycles, with a slight shortening in the cycle time from the ~260<sup>th</sup> to the 530<sup>th</sup> cycle. Various monovalent sodium salts (NaF, NaCl, NaBr, and NaI) and chloride salts (LiCl, NaCl, KCl, and CsCl) are deionized with the rGO/AC electrode pairs at a cell voltage of 1.3 V. Among them, NaI shows the highest specific adsorption capacity of ~22.2 mg g<sup>−1</sup>. Detailed surface characterization and electrochemical analyses are conducted.
Ahsan, F, Dana, NH, Sarker, SK, Li, L, Muyeen, SM, Ali, MF, Tasneem, Z, Hasan, MM, Abhi, SH, Islam, MR, Ahamed, MH, Islam, MM, Das, SK, Badal, MFR & Das, P 2023, 'Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review', Protection and Control of Modern Power Systems, vol. 8, no. 1. View/Download from: Publisher's site View description>>
AbstractMeteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efficient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efficient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.
Akbal, E, Barua, PD, Dogan, S, Tuncer, T & Acharya, UR 2023, 'Explainable automated anuran sound classification using improved one-dimensional local binary pattern and Tunable Q Wavelet Transform techniques', Expert Systems with Applications, vol. 225, pp. 120089-120089. View/Download from: Publisher's site
Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 2023, 'Acoustic radiation force-induced push-pull particle oscillations', Journal of the Acoustical Society of America.
Akhtar, R, Hamza, A, Razzaq, L, Hussain, F, Nawaz, S, Nawaz, U, Mukaddas, Z, Jauhar, TA, Silitonga, AS & Saleel, CA 2023, 'Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques', Heliyon, vol. 9, no. 11, pp. e22031-e22031. View/Download from: Publisher's site
Alabdali, SA, Pileggi, SF & Cetindamar, D 2023, 'Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review', Sustainability, vol. 15, no. 10, pp. 7908-7908. View/Download from: Publisher's site View description>>
Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. On one side, trends show a lack of appeal for rural environments as people prefer to move to cities, looking for better opportunities and lifestyles. On the other side, recent studies and reports show that the attractiveness of rural areas as places with opportunities is increasing. Sustainable solutions are needed to enhance development in the rural context, and technological innovation is expected to lead and support the stability for people and organizations in rural regions. While Smart City is progressively becoming a reality and a successful model for integrating Smart Technology into different aspects of everyday life, its effective application in a rural context according to a Sustainable Development approach is not yet completely defined. This study adopts comparative and categorial content analysis to address the different applications and the specific characteristics of rural regions, which often present significant peculiarities depending on the country and the context. The main goal is to investigate and discuss how the Smart City model may be adopted and effectively applied within rural contexts, looking at major gaps and challenges. Additionally, because of the complexity of the topic, we provide an overview of the current adoption of Smart Technology in the different applications in rural areas, including farming, education, business, healthcare, and governance. The study highlights the huge difficulties in rural life and the potentiality of Smart Technology to enhance their Sustainable Development, which is still challenging. While the holistic analysis clearly points out a gap, there is no specific strategic roadmap to re-u...
Alajlouni, DA, Bliuc, D, Tran, TS, Blank, RD & Center, JR 2023, 'Muscle strength and physical performance contribute to and improve fracture risk prediction in older people: A narrative review', Bone, vol. 172, pp. 116755-116755. View/Download from: Publisher's site
Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 156-184. View/Download from: Publisher's site
Alalyan, MS, Jaafari, NA, Hussain, FK & Gill, AQ 2023, 'A systematic review of blockchain adoption in education institutions.', Int. J. Web Grid Serv., vol. 19, no. 2, pp. 156-184. View/Download from: Publisher's site
Alam, MM, Farhangi, M, D.-C.LU, D, Siwakoti, YP & Aljarajreh, H 2023, 'Design, Implementation and Reliability Assessment of a Fault-Tolerant Three-Port Converter', IEEE Transactions on Industry Applications, pp. 1-12. View/Download from: Publisher's site
Alarkawi, D, Tran, T, Chen, W, March, LM, Blyth, FM, Blank, RD, Bliuc, D & Center, JR 2023, 'Denosumab and Mortality in a Real-World Setting: A Comparative Study', Journal of Bone and Mineral Research, vol. 38, no. 12, pp. 1757-1770. View/Download from: Publisher's site View description>>
ABSTRACTDenosumab (Dmab) is increasingly prescribed worldwide. Unlike bisphosphonates (BPs), its effect on mortality has yet to be well explored. This study examined the association between Dmab and all-cause mortality compared with no treatment in subjects with a fracture and BPs in subjects without a fracture. The study population was from the Sax Institute's 45 and Up Study (n = 267,357), a prospective population-based cohort with questionnaire data linked to hospital admissions (Admitted Patients Data Collection [APDC] data were linked by the Centre for Health Record Linkage), medication records (Pharmaceutical Benefits Scheme [PBS] provided by Services Australia), and stored securely (secure data access was provided through the Sax Institute's Secure Unified Research Environment [SURE]). The new-user cohort design with propensity-score (PS) matching was implemented. In the fracture cohort, Dmab and oral BP users were matched 1:2 to no treatment (Dmab: 617 women, 154 men; oral BPs: 615 women, 266 men). In the no-fracture cohort, Dmab users were matched 1:1 with oral BPs and zoledronic acid (Zol) users (Dmab:oral BPs: 479 men, 1534 women; Dmab:Zol: 280 men, 625 women). Mortality risk was measured using sex-specific pairwise multivariable Cox models. In the fracture cohort, compared with no treatment, Dmab was associated with 48% lower mortality in women (hazard ratio [HR] = 0.52, 95% confidence interval [CI] 0.36–0.72) but not in men. Oral BPs were associated with 44% lower mortality in both sexes (women HR = 0.56, 95% CI 0.42–0.77; men HR = 0.56, 95% CI 0.40–0.78). In the no-fracture cohort, compared with BPs, Dmab was associated with 1.5- to 2.5-fold higher mortality than oral BPs (women HR = 1.49, 95% CI 1.13–1.98; men HR = 2.74; 95% CI 1.82–4.11) but similar mortality to Zol. Dmab in women and oral BPs were associated with lower post-fracture mortality than no treatment. However, Dmab users had gen...
Aldini, S, Singh, AK, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 2023, 'Detection and Estimation of Cognitive Conflict During Physical Human–Robot Collaboration', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 2, pp. 959-968. View/Download from: Publisher's site View description>>
Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.
Al-Doghman, F, Moustafa, N, Khalil, I, Sohrabi, N, Tari, Z & Zomaya, AY 2023, 'AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 1485-1504. View/Download from: Publisher's site View description>>
The paradigm of edge computing has formed an innovative scope within the domain of IoT through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in AI, and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology.
Alfouneh, M, Hoang, V-N, Luo, Z & Luo, Q 2023, 'Topology optimization for multi-layer multi-material composite structures', Engineering Optimization, vol. 55, no. 5, pp. 773-790. View/Download from: Publisher's site View description>>
This article investigates topology optimization of multi-layer multi-material composite structures under static loading. A moving iso-surface threshold optimization method, previously well defined for single or cellular materials, is extended to multi-layer multi-material structures using a physical response function discrepancy scheme. It is also integrated with an alternating active-phase algorithm as an alternative procedure. The proposed methods are applied to three types of objective functions, namely, minimizing compliance, maximizing mutual strain energy and minimizing full-stress designs. The corresponding response functions relevant to each optimization problem according to the proposed topology optimization methods are strain energy density, mutual strain energy density and von Mises stress, respectively. Examples are presented and compared with those available in the literature to verify the derived formulations on topology optimization for multi-layer multi-material structures. Highlights Optimization by integrating MIST with alternating active phase for multi-materials Extended MIST to topology optimization for multi-layer and multi-materials Multimaterial design to maximize mutual energy, minimize compliance and full stress.
Alghamdi, AM, Pileggi, SF & Sohaib, O 2023, 'Social Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review', Sustainability, vol. 15, no. 13, pp. 9957-9957. View/Download from: Publisher's site View description>>
Although knowledge management relying on data from social networks has become an integral part of common practices, there needs to be a well-defined body of knowledge that explicitly addresses the process and the value generated. Sustainable knowledge management practices, which promote responsible and ethical knowledge sharing between different stakeholders, can also be facilitated through social media. This can foster a culture of continuous learning and innovation while considering the social implications of knowledge sharing. The main goal of this study is to critically and holistically discuss the impact of social media analysis in the knowledge management process holistically and maximize its value in a given context. More concretely, we conducted a systematic literature review (2012–2022) based on the PRISMA guidelines. We first approached the ideal phases of the knowledge management process and then discussed key issues and challenges from an application perspective. Overall, the study points out the positive impact of social network analysis on knowledge sharing, creativity and productivity, knowledge formulation, building trust, and cognitive capital. Additionally, value is provided in knowledge acquisition by simplifying and massively gathering information, reducing uncertainty and ambiguity, and organizing knowledge through storage, retrieval, and classification practices. At an application level, such knowledge may improve the quality of services and encourage creativity. Finally, this study analyzed specific domains, such as healthcare, marketing, politics, tourism, and event management, focusing on the potential and added value.
Alhosaini, H, Alharbi, S, Wang, X & Xu, G 2023, 'API Recommendation For Mashup Creation: A Comprehensive Survey', The Computer Journal. View/Download from: Publisher's site View description>>
AbstractMashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.
AlJaberi, FY, Ahmed, SA, Makki, HF, Naje, AS, Zwain, HM, Salman, AD, Juzsakova, T, Viktor, S, Van, B, Le, P-C, La, DD, Chang, SW, Um, M-J, Ngo, HH & Nguyen, DD 2023, 'Recent advances and applicable flexibility potential of electrochemical processes for wastewater treatment', Science of The Total Environment, vol. 867, pp. 161361-161361. View/Download from: Publisher's site
Almadani, MS, Alotaibi, S, Alsobhi, H, Hussain, OK & Hussain, FK 2023, 'Blockchain-based multi-factor authentication: A systematic literature review', Internet of Things, vol. 23, pp. 100844-100844. View/Download from: Publisher's site
Al-Maliki, S, Bouanani, FE, Ahmad, K, Abdallah, M, Hoang, DT, Niyato, D & Al-Fuqaha, A 2023, 'Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks', IEEE Transactions on Reliability, vol. 72, no. 4, pp. 1367-1382. View/Download from: Publisher's site View description>>
Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.
Al-Najar, JA, Al-Humairi, ST, Lutfee, T, Balakrishnan, D, Veza, I, Soudagar, MEM & Fattah, IMR 2023, 'Cost-Effective Natural Adsorbents for Remediation of Oil-Contaminated Water', Water, vol. 15, no. 6, pp. 1186-1186. View/Download from: Publisher's site View description>>
Oil-contaminated water is among the most significant environmental challenges from various industries and manufacturing processes. Oily water poses a severe environmental threat and is toxic to many forms of life. This study aims to investigate the potential of natural adsorbents, namely animal bones (ABs) and anise residues (ARs), for removing oil from water using a batch adsorption process. The effects of adsorbent dosage (0.2–2 g), oil concentration (200–1000 mg/L), and contact time (30–120 min) on the adsorption process were evaluated. This study is the first to employ ABs and ARs as adsorbents for oil removal, and their efficacy for this purpose has not been previously reported. The results indicate that ABs exhibit superior oil removal capacity compared to ARs. Specifically, ABs removed 45 mg/g of oil from water, while ARs removed only 30 mg/g of oil. Furthermore, ABs achieved a percentage removal rate of 94%, whereas ARs had a percentage removal rate of 70%. The adsorbents were characterised using Fourier transform infrared (FTIR) spectrometry, contact angle measurements before and after adsorption, and thermogravimetric analysis (TGA). In addition to the experimental analysis, several kinetic and adsorption models were employed to investigate the adsorption process. The pseudo-first-order and pseudo-second-order models were used to represent the kinetics of the reaction, while the Langmuir and Freundlich isotherm models were used to represent the adsorption isotherm. Marquardt’s percent standard deviation (MPSD) error function was used to confirm the fit of the experimental data with the isotherm model, in addition to the correlation coefficient R2. The isotherm studies indicated that the experimental data of the two adsorbents used with the Langmuir isotherm model were consistent with one another. The kinetics study demonstrated that the adsorption process using the two adsorbents adheres to a pseudo-second-order kinetics model.
Al-Najjar, HAH, Pradhan, B, Beydoun, G, Sarkar, R, Park, H-J & Alamri, A 2023, 'A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset', Gondwana Research, vol. 123, pp. 107-124. View/Download from: Publisher's site View description>>
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it is important to understand how decisions are made. Fairness, and transparency becomes ever more vital due to ethical concerns and trust. Despite the popularity of machine learning (ML) algorithms in landslide modeling, the explainability of these methods are often considered as black box. This paper aims to propose an explainable artificial intelligence (XAI) for landslide prediction using synthetic-aperture radar (SAR) time-series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives. We employed a Shapley Additive Explanations (SHAP) approach to understand how and what decisions ML-based models are making. 37 features were extracted from various sources such as ALOS-PALSAR (ALOS Phased Array type L-band Synthetic Aperture Radar), ALOS-2 (SAR), Landsat-8, topographic maps, and DEM for landslide susceptibility mapping in a landslide prone area in Chukha, Bhutan as a test site. The result was then compared using two standard ML methods: random forest (RF) and support vector machine (SVM). As per results, the RF model outperformed (0.914) the SVM. Moreover, the higher reliability of the RF model was proved by the area under the curve (AUC) of 0.941. XAI results revealed, features like altitude, aspect, NDVI-2014, NDVI-2017, and NDVI-2018 were the most effective features for landslide prediction by both models. Interestingly, among those features, NDVI-2014, aspect, and NDVI-2017 negatively correlated with the landslide prediction; whereas positively correlated when SVM was utilized. This interpretation ability indicates the advantages of XAI over the conventional methods as it measures the impact, interaction and correlation of conditioning factors within a model. The current research finding can provide more transparency and explainability when working with MLs ...
Alsobhi, HA, Alakhtar, RA, Ubaid, A, Hussain, OK & Hussain, FK 2023, 'Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review', Knowledge-Based Systems, vol. 265, pp. 110238-110238. View/Download from: Publisher's site View description>>
A micro-credential is a proof of the student's knowledge, skills, and experience that can be used to progress towards a larger credential or degree that focuses on a particular field of study in the shortest amount of time. Micro-credentials are a new area in the education sector that has expanded significantly over recent years and have become a popular idea in the higher education sector. Since the Covid-19 pandemic, micro-credentials are the most recent innovation in online education, gaining traction in public and private universities throughout the world. This has resulted in many universities developing strategies to offer micro-credential-driven courses. Higher education institutions (HEIs) need to validate micro-credentials, but the validation is a long-drawn-out and cumbersome process, so blockchain technology can be used to easily validate the detailed information on each students’ micro-credentials. Unfortunately, to date, only scant scholarly research has been conducted on blockchain-based micro-credentialing systems in HEIs. This study provides a detailed overview of the state-of-the-art in the field of managing micro-credentials using blockchain technology. We start by outlining the various requirements that need to be met in a blockchain-based micro-credentialing system. We then use a systematic literature review (SLR) to retrieve relevant studies published between 2016–2022 and compare them to the defined requirements. We also analyse the relevant studies to determine the research gaps. This review will offer insight into micro-credentialing systems that have been proposed for HEIs over recent years.
Alsoibi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Non-profit Organizations', Asia Pacific Journal of Information Systems (APJIS), vol. 33, no. 1, pp. 33-68. View/Download from: Publisher's site View description>>
Nonprofit organisations (NPOs) use data analytics and corresponding visualisations to discover and interpretpatterns of donations and donor behaviours, predict future funds, and analyse time series to undertake decisionsand resolve issues. Further detailed understanding of these activities in the context of NPOs is required forefficient and effective utilisation of data analytics. This article reports a systematic review of available literatureon data analytics applications in NPOs to answer three research questions: (1) What are the proposed approachesand frameworks for adopting and applying data analytics in NPOs? (2) What aspects of data analytics are usedfor NPO activities and missions? (3) What challenges and barriers face NPOs regarding the adoption and applicationof data analytics for their missions? We answered the three research questions by collecting and examiningdata and using it to develop a new taxonomy. The results show the utilisation of data analytics applicationsby NPOs has not been examined in depth, indicating the need for further research. This study contributesto the literature by providing insights on the existing use of data analytics applications in various domains,and their benefits and drawbacks for NPOs. This study also presents future research directions.
Alsolbi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Nonprofit Organisations', Asia Pacific Journal of Information Systems, vol. 33, no. 1, pp. 39-68. View/Download from: Publisher's site
Alsolbi, I, Shavaki, FH, Agarwal, R, Bharathy, GK, Prakash, S & Prasad, M 2023, 'Big data optimisation and management in supply chain management: a systematic literature review', Artificial Intelligence Review, vol. 56, no. S1, pp. 253-284. View/Download from: Publisher's site View description>>
AbstractThe increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.
Alsouda, F, Bennett, NS, Saha, SC, Salehi, F & Islam, MS 2023, 'Vapor Compression Cycle: A State-of-the-Art Review on Cycle Improvements, Water and Other Natural Refrigerants', Clean Technologies, vol. 5, no. 2, pp. 584-608. View/Download from: Publisher's site View description>>
Air conditioning and refrigeration have become necessary in modern life, accounting for more than 7.8% of greenhouse gases (GHG) emitted globally. Reducing the environmental impact of these systems is crucial for meeting the global GHG emission targets. Two principal directions must be considered to reduce the environmental impact of air conditioning systems. Firstly, reducing the direct effect by looking at less harmful refrigerants and secondly, reducing the indirect effect by searching for options to improve the system efficiency. This study presents the latest developments in the vapor compression cycle and natural refrigerants, focusing on water as a refrigerant. Natural refrigerants, and especially water, could be the ultimate solution for the environmental problems associated with the operation of vapor compression cycle (VCC) cooling systems, including ozone depletion (OD) and global warming (GW). Reducing the environmental impact of building cooling systems is essential, and the recent system improvements made to enhance the system coefficient of performance (COP) are thoroughly discussed in this paper. Though the cycle improvements discussed in this work are essential and could increase the system efficiency, they still need to solve the direct environmental impact of refrigerants. Accordingly, this paper suggests that natural refrigerants, including water, are the most suitable strategic choice to replace the current refrigerants in the refrigeration and air conditioning industry. Finally, this study reviews the latest VCC system improvements and natural refrigerants in order to guide interested researchers with solutions that may reduce the environmental impact of VCC systems and suggest future research areas.
Altaf, T, Wang, X, Ni, W, Liu, RP & Braun, R 2023, 'NE-GConv: A lightweight node edge graph convolutional network for intrusion detection', Computers & Security, vol. 130, pp. 103285-103285. View/Download from: Publisher's site
Altaf, T, Wang, X, Ni, W, Yu, G, Liu, RP & Braun, R 2023, 'A new concatenated Multigraph Neural Network for IoT intrusion detection', Internet of Things, vol. 22, pp. 100818-100818. View/Download from: Publisher's site
Alyami, A, Pileggi, SF & Hawryszkiewycz, I 2023, 'Knowledge development, technology and quality of experience in collaborative learning: a perspective from Saudi Arabia universities', Quality & Quantity, vol. 57, no. 4, pp. 3085-3104. View/Download from: Publisher's site View description>>
AbstractTechnology has recently gained relevance within collaborative learning environments to provide robustness, agility and flexibility. Several recent studies have investigated the role of technology, as well as researchers have defined different metrics to assess learning outcomes and experience along the collaborative knowledge development process. More recently, technology has played a key role to face the new challenges related to COVID-19, which forced to move on remote or hybrid learning. This research focuses on the quality of learning experience in terms of academic performance and perceived satisfaction. From a methodological point of view, a conceptual framework has been proposed and a quantitative study has been conducted among undergraduate and postgraduate students that are undertaking programs related to System Design in Saudi Arabia universities. 152 responses have been collected through an online survey and analysed using SPSS and SmartPLS. Results show a positive impact of technology along the collaborative knowledge development process and a strong correlation among the different quality of learning experience parameters considered. Indeed, despite some challenges, an integrated use of technology seems to properly support the most pressing needs in terms of quality experience, while the well-known social/educational issues related to the COVID-19 pandemic are not object of this study. Those findings are expected to contribute to the Saudi Arabia’s vision 2030 and, more holistically, to the assessment of collaborative learning environments that extensively rely on technology.
System design is an essential subject taught in information systems and has become a core course in its curriculum. Unified modelling language (UML) has been broadly adopted, and it is common to support the system design process using different diagrams. Each diagram serves a purpose by focusing on a specific part of a particular system. Design consistency ensures a seamless process, as the diagrams are generally interrelated. However, creating a well-designed system takes a lot of work, especially for university students with work experience. To overcome this challenge, aligning the concepts across diagrams is essential, which can help achieve better consistency and management of the design system, especially in an educational setting. This article is an extension of our previous work, as we have discussed a simple scenario of Automated teller machines to demonstrate the alignment concepts between UML diagrams. From a more technical perspective, the current contribution provides a Java program that aligns concepts by converting text-based use cases to text-based sequence diagrams. Then, the text is transformed in PlantUML to generate its graphical representation. The developed alignment tool is expected to contribute to helping students and instructors during the system design phases to be more consistent and practical. Limitations and future work are presented.
Alzoubi, YI & Aljaafreh, A 2023, 'Blockchain-Fog Computing Integration Applications: A Systematic Review', Cybernetics and Information Technologies, vol. 23, no. 1, pp. 3-37. View/Download from: Publisher's site View description>>
AbstractThe Fog computing concept has been introduced to aid in the data processing of Internet of things applications using Cloud computing. Due to the profitable benefits of this combination, several papers have lately been published proposing the deployment of Blockchain alongside Fog computing in a variety of fields. A comprehensive evaluation and synthesis of the literature on Blockchain-Fog computing integration applications that have emerged in recent years is required. Although there have been several articles on the integration of Blockchain with Fog computing, the applications connected with this combination are still fragmented and require further exploration. Hence, in this paper, the applications of Blockchain-Fog computing integration are identified using a systematic literature review technique and tailored search criteria generated from the study objectives. This article found and evaluated 144 relevant papers. The findings of this article can be used as a resource for future Fog computing research and designs.
Amini, E, Nasiri, M, Pargoo, NS, Mozhgani, Z, Golbaz, D, Baniesmaeil, M, Nezhad, MM, Neshat, M, Astiaso Garcia, D & Sylaios, G 2023, 'Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach', Energy Conversion and Management: X, vol. 19, pp. 100371-100371. View/Download from: Publisher's site
Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$$\times$$4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.
Amirkhani, F, Dashti, A, Jokar, M, Mohammadi, AH, Gholamzadeh Chofreh, A, Varbanov, PS & Zhou, JL 2023, 'Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture', Journal of Cleaner Production, vol. 430, pp. 139435-139435. View/Download from: Publisher's site
Ampah, JD, Jin, C, Rizwanul Fattah, IM, Appiah-Otoo, I, Afrane, S, Geng, Z, Yusuf, AA, Li, T, Mahlia, TMI & Liu, H 2023, 'Investigating the evolutionary trends and key enablers of hydrogen production technologies: A patent-life cycle and econometric analysis', International Journal of Hydrogen Energy, vol. 48, no. 96, pp. 37674-37707. View/Download from: Publisher's site View description>>
With rapid industrialization, rising fossil fuel consumption, and environmental concerns, developing clean and green energy is an inescapable option. Hydrogen has emerged as a significant potential energy carrier and a viable future replacement fuel for fossil fuels due to its renewable and pollution-free properties. Previous review papers have significantly contributed to the body of literature on the various technologies for producing hydrogen by revealing key insights into their working principles and conditions, as well as the economic and environmental aspects. In addition, they also highlighted the potential pathways to enable the application of these technologies in the context of carbon neutrality. However, these studies have not broken down the evolutionary patterns and developmental progress of either fossil fuel-based or renewable energy-based technologies used to produce hydrogen. In addition, the currently available literature does not contain the most recent research that focuses on the evolution and life cycle of each technology category from a chronological point of view. The key drivers, countries/regions, and their contributions to the field's development have received little attention. As a result, it is critical to monitor technological advances in hydrogen energy production and investigate the key enablers of these advancements. Against this backdrop, the current study employs patent analysis tools to achieve four primary goals: (1) to track the development trends in the field of hydrogen production from 2000 to 2019; (2) to identify and compare the recent development trends in the last five years according to the feedstock, i.e., fossil fuel, water, and biomass-based technologies; (3) to predict the technology life cycle of the two main groups of hydrogen production technologies (fossil and renewable); (4) to identify and compare the key drivers of hydrogen production technologies from a statistical standpoint. The findings of the ...
An, Y, Lam, H-K & Ling, SH 2023, 'Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network.', Neural Comput. Appl., vol. 35, no. 16, pp. 12001-12027. View/Download from: Publisher's site View description>>
AbstractIn recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been made on MI classification. What seems to be lacking, however, is multiple MI classification. This paper develops a single-channel-based convolutional neural network to tackle multi-classification motor imagery tasks. For multi-classification, a single-channel learning strategy can extract effective information from each independent channel, making the information between adjacent channels not affect each other. A data evaluation method and a mutual information-based regularization parameters auto-selection algorithm are also proposed to generate effective spatial filters. The proposed method can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters and invalid training data. To illustrate the merits of the proposed methods, we used the tenfold cross-validation accuracy and kappa as the evaluation measures to test two data sets. BCI4-2a and BCI3a data sets have four mental classes. For the BCI4-2a data set, the average accuracy is 79.01%, and the kappa is 0.7202 using data evaluation-based auto-selected filter bank regularized common spatial pattern voting (D-ACSP-V) and single-channel series convolutional neural network (SCS-CNN). Compared to traditional FBRCSP, the proposed method improved accuracy by 7.14% for the BCI4-2a data set. By using the BCI3a data set, the proposed method improved accuracy by 9.54% compared with traditional FBRCSP, the average accuracy of the proposed method is 83.70%, and the kappa is 0.7827.
Anaissi, A, Zandavi, SM, Suleiman, B, Naji, M & Braytee, A 2023, 'Multi-objective variational autoencoder: an application for smart infrastructure maintenance', Applied Intelligence, vol. 53, no. 10, pp. 12047-12062. View/Download from: Publisher's site View description>>
AbstractMulti-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way laboratory-based and real-life structural datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney, a reinforced concrete cantilever beam which replicates one of the major structural components on the Sydney Harbour Bridge and a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.
Anand, V, Singh, V, Guo, X, Sathik, MAJ, Siwakoti, YP, Mekhilef, S & Blaabjerg, F 2023, 'Seventeen Level Switched Capacitor Inverters With the Capability of High Voltage Gain and Low Inrush Current', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 4, pp. 1138-1150. View/Download from: Publisher's site
Andersen, TI, Lensky, YD, Kechedzhi, K, Drozdov, IK, Bengtsson, A, Hong, S, Morvan, A, Mi, X, Opremcak, A, Acharya, R, Allen, R, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Babbush, R, Bacon, D, Bardin, JC, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Chou, C, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Dau, AG, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hilton, J, Hoffmann, MR, Huang, T, Huff, A, Huggins, WJ, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Lucero, E, Malone, FD, Martin, O, McClean, JR, McCourt, T, McEwen, M, Miao, KC, Mieszala, A, Mohseni, M, Montazeri, S, Mount, E, Movassagh, R, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Kim, E-A, Aleiner, I & Roushan, P 2023, 'Non-Abelian braiding of graph vertices in a superconducting processor', Nature, vol. 618, no. 7964, pp. 264-269. View/Download from: Publisher's site View description>>
AbstractIndistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date—including fermions, bosons and Abelian anyons—this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4–8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9–22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
Appiahene, P, Chaturvedi, K, Asare, JW, Donkoh, ET & Prasad, M 2023, 'CP-AnemiC: A conjunctival pallor dataset and benchmark for anemia detection in children', Medicine in Novel Technology and Devices, vol. 18, pp. 100244-100244. View/Download from: Publisher's site
Arango, E, Nogal, M, Jiménez, P, Sousa, HS, Stewart, MG & Matos, JC 2023, 'Policies towards the resilience of road-based transport networks to wildfire events. The Iberian case', Transportation Research Procedia, vol. 71, pp. 61-68. View/Download from: Publisher's site
Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2023, 'GIS-based methodology for prioritization of preparedness interventions on road transport under wildfire events', International Journal of Disaster Risk Reduction, vol. 99, pp. 104126-104126. View/Download from: Publisher's site
Arango, E, Nogal, M, Yang, M, Sousa, HS, Stewart, MG & Matos, JC 2023, 'Dynamic thresholds for the resilience assessment of road traffic networks to wildfires', Reliability Engineering & System Safety, vol. 238, pp. 109407-109407. View/Download from: Publisher's site
Aregawi, BH, Nguyen, HC, Fu, C-C, Ong, HC, Barrow, CJ, Su, C-H, Wu, S-J, Juan, H-Y & Wang, F-M 2023, 'Biodiesel Production through Electrolysis Using an Ionic Liquid, 1-Ethyl-3-Methylimidazolium Chloride as a Supporting Electrolyte', International Journal of Energy Research, vol. 2023, pp. 1-11. View/Download from: Publisher's site View description>>
Electrolysis is a promising approach for biodiesel production. However, low electrical conductivity of a reaction mixture results in a low reaction rate. Thus, this study developed a novel catalyst-free electrolysis process using an ionic liquid as a supporting electrolyte for biodiesel production. Various ionic liquids were assessed, and 1-ethyl-3-methylimidazolium chloride ([Emim]Cl) exhibited the highest electrical conductivity (4.59 mS/cm) and the best electrolytic performance for transesterification. Electrolysis in the presence of [Emim]Cl was subsequently optimized using response surface methodology to maximize biodiesel yield. A maximum biodiesel yield of 97.76% was obtained under the following optimal reaction conditions: electrolysis voltage, 19.42 V; [Emim]Cl amount, 4.43% (); water content, 1.62% (); methanol to oil molar ratio, 26.38 : 1; and reaction time, 1 h. Notably, [Emim]Cl could be efficiently reused for at least three cycles with a corresponding biodiesel yield of 94.81%. Moreover, the properties of the synthesized biodiesel complied with EN and ASTM standards. The findings of this study indicate that catalyst-free electrolysis using [Emim]Cl as a supporting electrolyte is an eco-friendly and efficient method for biodiesel production.
Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Begum, RA, Hossain, MJ, Ker, PJ & Mahlia, TMI 2023, 'Hydrogen electrolyser technologies and their modelling for sustainable energy production: A comprehensive review and suggestions', International Journal of Hydrogen Energy, vol. 48, no. 72, pp. 27841-27871. View/Download from: Publisher's site
Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Hossain, MJ, Begum, RA, Ker, PJ, Salehi, F & Muttaqi, KM 2023, 'Hydrogen electrolyser for sustainable energy production: A bibliometric analysis and future directions', International Journal of Hydrogen Energy, vol. 48, no. 13, pp. 4960-4983. View/Download from: Publisher's site
Asadabadi, MR, Saberi, M, Sadghiani, NS, Zwikael, O & Chang, E 2023, 'Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment', Journal of Enterprise Information Management, vol. 36, no. 1, pp. 275-302. View/Download from: Publisher's site View description>>
PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.FindingsThe paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.Originality/valueOnline product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.
Aseeri, M & Kang, K 2023, 'Organisational culture and big data socio-technical systems on strategic decision making: Case of Saudi Arabian higher education', Education and Information Technologies, vol. 28, no. 7, pp. 8999-9024. View/Download from: Publisher's site
Ashfaque Ahmed, S, Elahi M. Soudagar, M, Rahamathullah, I, Sadhik Basha, J, Yunus Khan, TM, Javed, S, Elfasakhany, A & Kalam, MA 2023, 'Investigation of ternary blends of animal fat biodiesel-diethyl ether-diesel fuel on CMFIS-CI engine characteristics', Fuel, vol. 332, pp. 126200-126200. View/Download from: Publisher's site View description>>
The experimental study aims at investigation of the ternary blends of animal fat oil biodiesel, diesel and diethyl ether in-terms of performance, emission and combustion analysis in single cylinder four stroke diesel engine. The animal (mono) fat biodiesel was prepared through transesterification process by using alcohol (6:1) and potassium hydroxide (4:1) at 50 °C –55 °C and obtained 90 % of biodiesel. The prepared biodiesel was blended with neat diesel to get the tested blend, animal fat biodiesel (AFB20). To improve the performance parameters of AFB20, the prepared biodiesel blend AFB20 was again blend with diethyl ether (DEE) in various proportions by 10 % and 20 % to obtain the ternary blend AFB20DEE10 and AFB20DEE20. Addition of DEE to AFB20 enhances the physicochemical properties of the biodiesel blends. The first stage of this study was that the neat diesel was examined in the single cylinder four stroke diesel engine to obtain the reference readings. The second and third stage of this investigation was the prepared animal fat biodiesel blend AFB20 and AFB20DEE10 and AFB20DEE20 were examined in the diesel engine. The fourth stage of this investigation was that, the obtained results of neat diesel was compared with the other tested blends. The experimental outcome reveals that, AFB20DEE20 blend perform better than that of the other tested blends. 4.8 % higher fuel is consumed, and 7.1 % lowered brake thermal efficiency and exhaust gas temperature was found in the blend AFB20DEE20 compared to neat diesel. The blend AFB20DEE20 exhibits higher cylinder pressure by 70.43 bar and lower heat release rate by 35.23 J/deg., compared to neat diesel. 0.15 % lower CO emission and 37.8 % lower UBHC emission were found in AFB20DEE20 compared with ND. Reduction of NOx emission by 4.18 % and higher smoke emissions were found in the blend AFB20DEE20 compared to neat diesel.
Asikin-Mijan, N, Juan, JC, Taufiq-Yap, YH, Ong, HC, Lin, Y-C, AbdulKareem-Alsultan, G & Lee, HV 2023, 'Towards sustainable green diesel fuel production: Advancements and opportunities in acid-base catalyzed H2-free deoxygenation process', Catalysis Communications, vol. 182, pp. 106741-106741. View/Download from: Publisher's site
Aworanti, OA, Agbede, OO, Agarry, SE, Ajani, AO, Ogunkunle, O, Laseinde, OT, Rahman, SMA & Fattah, IMR 2023, 'Decoding Anaerobic Digestion: A Holistic Analysis of Biomass Waste Technology, Process Kinetics, and Operational Variables', Energies, vol. 16, no. 8, pp. 3378-3378. View/Download from: Publisher's site View description>>
The continual generation and discharge of waste are currently considered two of the main environmental problems worldwide. There are several waste management options that can be applied, though anaerobic digestion (AD) process technology seems to be one of the best, most reliable, and feasible technological options that have attracted remarkable attention due to its benefits, including the generation of renewable energy in the form of biogas and biomethane. There is a large amount of literature available on AD; however, with the continuous, progressive, and innovative technological development and implementation, as well as the inclusion of increasingly complex systems, it is necessary to update current knowledge on AD process technologies, process variables and their role on AD performance, and the kinetic models that are most commonly used to describe the process-reaction kinetics. This paper, therefore, reviewed the AD process technologies for treating or processing organic biomass waste with regard to its classification, the mechanisms involved in the process, process variables that affect the performance, and the process kinetics. Gazing into the future, research studies on reduced MS-AD operational cost, integrated or hybrid AD-biorefinery technology, integrated or hybrid AD-thermochemical process, novel thermochemical reactor development, nutrient recovery from integrated AD-thermochemical process, and solid and liquid residual disposal techniques are more likely to receive increased attention for AD process technology of biomass wastes.
Aydemir, E, Baygin, M, Dogan, S, Tuncer, T, Barua, PD, Chakraborty, S, Faust, O, Arunkumar, N, Kaysi, F & Acharya, UR 2023, 'Mental performance classification using fused multilevel feature generation with EEG signals', International Journal of Healthcare Management, vol. 16, no. 4, pp. 574-587. View/Download from: Publisher's site
Azizivahed, A, Gholami, K, Li, L & Zhang, J 2023, 'Accurate optimal power flow for active distribution networks via floating tangent surface', Electric Power Systems Research, vol. 217, pp. 109167-109167. View/Download from: Publisher's site
Ba, X, Sun, X, Gong, Z, Guo, Y, Zhang, C & Zhu, J 2023, 'A Generalized Per-Phase Equivalent Circuit Model of the PMSM With Predictable Core Loss', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 3, pp. 1512-1521. View/Download from: Publisher's site
Bagirov, A, Seifollahi, S, Piccardi, M, Zare Borzeshi, E & Kruger, B 2023, 'SMGKM: An Efficient Incremental Algorithm for Clustering Document Collections', pp. 314-328. View/Download from: Publisher's site
Bahrami, H, Sichetti, F, Puppo, E, Vettori, L, Liu Chung Ming, C, Perry, S, Gentile, C & Pietroni, N 2023, 'Physically-based simulation of elastic-plastic fusion of 3D bioprinted spheroids', Biofabrication, vol. 15, no. 4, pp. 045021-045021. View/Download from: Publisher's site View description>>
AbstractSpheroids are microtissues containing cells organized in a spherical shape whose diameter is usually less than a millimetre. Depending on the properties of the environment they are placed in, some nearby spheroids spontaneously fuse and generate a tissue. Given their potential to mimic features typical of body parts and their ability to assemble by fusing in permissive hydrogels, they have been used as building blocks to 3D bioprint human tissue parts. Parameters controlling the shape and size of a bioprinted tissue using fusing spheroid cultures include cell composition, hydrogel properties, and their relative initial position. Hence, simulating, anticipating, and then controlling the spheroid fusion process is essential to control the shape and size of the bioprinted tissue. This study presents the first physically-based framework to simulate the fusion process of bioprinted spheroids. The simulation is based on elastic-plastic solid and fluid continuum mechanics models. Both models use the ‘smoothed particle hydrodynamics’ method, which is based on discretizing the continuous medium into a finite number of particles and solving the differential equations related to the physical properties (e.g. Navier–Stokes equation) using a smoothing kernel function. To further investigate the effects of such parameters on spheroid shape and geometry, we performed sensitivity and morphological analysis to validate our simulations with in-vitro spheroids. Through our in-silico simulations by changing the aforementioned parameters, we show that the proposed models appropriately simulate the range of the elastic-plastic behaviours of in-vitro fusing spheroids to generate tissues of desired shapes and sizes. Altogether, this study presented a physically-based simulation that can provide a framework for monitoring and controlling the ...
Bahrami, N, Nikoo, MR, Al-Rawas, G, Al-Jabri, K & Gandomi, AH 2023, 'Optimal Treated Wastewater Allocation Among Stakeholders Based on an Agent-based Approach', Water Resources Management, vol. 37, no. 1, pp. 135-156. View/Download from: Publisher's site View description>>
Using unconventional water resources, such as treated wastewater (TWW), is an excellent alternative to meet excess water demands. Policymakers should consider optimal and equitable allocation of TWW to relieve conflicts among stakeholders. In the current research, an agent-based model (ABM) is integrated with a multi-objective optimization method (MOM) to fairly distribute water among different beneficiaries in Tehran Province, Iran. In ABM there are two groups of agents: water users and managers. Water users seek to minimize water shortages, and water managers are responsible for allocating water to the users fairly. Managers also assess different bankruptcy scenarios (BSs) for allocating TWW to each stakeholder, and the most agreeable scenario is selected. The Conditional Value-at-Risk (CVaR)-based objective functions are used to assess the risk of uncertainties under different confidence levels. Then, to prioritize the Pareto-optimal solutions, a novel multi-criteria decision-making (MCDM) method, named R-method, is utilized. Results show that considering stakeholders’ objectives and interactions can lead to finding a more equitable solution. Interactions among beneficiaries can diminish water shortages in the study area through an investment by the industrial sector in the agricultural sector to improve the efficiency of agricultural activities.
Bai, H, Cheng, R, Yazdani, D, Tan, KC & Jin, Y 2023, 'Evolutionary Large-Scale Dynamic Optimization Using Bilevel Variable Grouping', IEEE Transactions on Cybernetics, vol. 53, no. 11, pp. 6937-6950. View/Download from: Publisher's site View description>>
Variable grouping provides an efficient approach to large-scale optimization, and multipopulation strategies are effective for both large-scale optimization and dynamic optimization. However, variable grouping is not well studied in large-scale dynamic optimization when cooperating with multipopulation strategies. Specifically, when the numbers/sizes of the variable subcomponents are large, the performance of the algorithms will be substantially degraded. To address this issue, we propose a bilevel variable grouping (BLVG)-based framework. First, the primary grouping applies a state-of-the-art variable grouping method based on variable interaction analysis to group the variables into subcomponents. Second, the secondary grouping further groups the subcomponents into variable cells, that is, combination variable cells and decomposition variable cells. We then tailor a multipopulation strategy to process the two types of variable cells efficiently in a cooperative coevolutionary (CC) way. As indicated by the empirical study on large-scale dynamic optimization problems (DOPs) of up to 300 dimensions, the proposed framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization.
Bai, Y, Henry, J, Cheng, E, Perry, S, Mawdsley, D, Wong, BBM, Kaslin, J & Wlodkowic, D 2023, 'Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology', Environmental Science & Technology, vol. 57, no. 48, pp. 19453-19462. View/Download from: Publisher's site
Bailo, F, Johns, A & Rizoiu, M-A 2023, 'Riding Information Crises: The Performance of Far-Right Twitter Users in Australia During the 2019–20 Bushfires and the COVID-19 Pandemic', Information Communication & Society, vol. ahead-of-print, no. ahead-of-print, pp. 1-19. View/Download from: Publisher's site
Bakhanova, E, Garcia, JA, Raffe, WL & Voinov, A 2023, 'Gamification Framework for Participatory Modeling: A Proposal', Group Decision and Negotiation, vol. 32, no. 5, pp. 1167-1182. View/Download from: Publisher's site View description>>
AbstractProblem structuring methods imply the involvement of stakeholders and aim to create a shared understanding of the problem and commitment among them. The process and outcomes of such interventions entirely depend on the stakeholder’s level of engagement and willingness to contribute to the discussion. Gamification, in its turn, has been extensively used to increase engagement in an activity and nudge certain behaviors. Several gamification frameworks exist for stakeholder engagement; however, none fully considers the context of the modeling workshops with stakeholders.In this paper, we focus on a specific method for problem structuring, called Participatory Modeling (PM), and aim to explore the essential components and steps to gamify the PM process. We look at the literature on gamification, stakeholder engagement, problem structuring methods and, specifically, PM. Based on this analysis, we propose a gamification framework for PM, which includes the steps commonly mentioned in other existing frameworks and more nuanced features within each step that are specific to the PM context. Emphasis is given to analyzing the context of the gamified activity, including such aspects as participants, group interaction, and modeling. In addition, consideration of ethical points and potential risks of gamification is suggested as a necessary step to prevent undesired side effects during the gamified PM process.The gamification framework for PM leads to a variety of ways in which gamified intervention can be designed and incorporated into the process. Further research on the appropriateness of gamification use, practical applications, their evaluation, and risks associated with gamified interventions can contribute to the extension and clarification of the proposed framework.
Bala, D, Hossain, MS, Hossain, MA, Abdullah, MI, Rahman, MM, Manavalan, B, Gu, N, Islam, MS & Huang, Z 2023, 'MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification', Neural Networks, vol. 161, pp. 757-775. View/Download from: Publisher's site View description>>
The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The 'MSID' dataset, short form of 'Monkeypox Skin Images Dataset', which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.
Balakrishnan, HK, Dumée, LF, Merenda, A, Aubry, C, Yuan, D, Doeven, EH & Guijt, RM 2023, '3D Printing Functionally Graded Porous Materials for Simultaneous Fabrication of Dense and Porous Structures in Membrane‐Integrated Fluidic Devices', Small Structures, vol. 4, no. 5. View/Download from: Publisher's site View description>>
3D printing provides access to complex multilevel architectures, though the capability to routinely print and integrate structures of controlled porosity is limited. Herein, grayscale digital light projection 3D printing of a polymerization‐induced phase separation ink is introduced to directly 3D print functionally graded porous within a single layer from the same ink formulation. The structural properties of materials printed from a single ink are tuned from an effectively dense to a porous material with interconnected pores up to 250 nm. Heterostructures with the physically dense structure of porosity 0.8% and porous structures with up to 23% can be concurrently formed within a layer, with high spatial resolution inherent of this 3D printing technique. Materials with densities from 1.01 to 1.21 g cm−3 are 3D printed in a wicking device and show wicking rates (H2O) from complete diffusion blockage up to 4.5 mm h−1. Furthermore, a proof‐of‐concept membrane‐integrated fluidic device is used for the elemental metal sensing of iron in soil. The presented single‐step fabrication of functionally graded materials with pixel‐based control within a single layer holds potential for manufacturing and integrating membranes or sorbents for environmental, biotechnology, and healthcare applications.
Bansal, P, Mirjalili, S & Wen, S 2023, 'Special issue on soft computing for high-dimensional data analytics and optimization', Soft Computing, vol. 27, no. 18, pp. 13517-13518. View/Download from: Publisher's site
Barua, PD, Aydemir, E, Dogan, S, Erten, M, Kaysi, F, Tuncer, T, Fujita, H, Palmer, E & Acharya, UR 2023, 'Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels', Neural Computing and Applications, vol. 35, no. 8, pp. 6065-6077. View/Download from: Publisher's site
Barua, PD, Aydemir, E, Dogan, S, Kobat, MA, Demir, FB, Baygin, M, Tuncer, T, Oh, SL, Tan, R-S & Acharya, UR 2023, 'Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals', International Journal of Machine Learning and Cybernetics, vol. 14, no. 5, pp. 1651-1668. View/Download from: Publisher's site
Barua, PD, Dogan, S, Kavuran, G, Tuncer, T, Tan, R-S & Rajendra Acharya, U 2023, 'NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers', Neural Computing and Applications, vol. 35, no. 30, pp. 22253-22263. View/Download from: Publisher's site View description>>
AbstractIn the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: “biological,” “fibers,” “film-coated surfaces,” “MEMS devices and electrodes,” “nanowires,” “particles,” “pattern surfaces,” “porous sponge,” “powder,” and “tips,” respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each...
Barua, PD, Keles, T, Dogan, S, Baygin, M, Tuncer, T, Demir, CF, Fujita, H, Tan, R-S, Ooi, CP & Rajendra Acharya, U 2023, 'Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database', Biomedical Signal Processing and Control, vol. 79, pp. 104055-104055. View/Download from: Publisher's site
Barua, PD, Kobayashi, M, Tanabe, M, Baygin, M, Paul, JK, Iype, T, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Innovative Fibromyalgia Detection Approach Based on Quantum-Inspired 3LBP Feature Extractor Using ECG Signal', IEEE Access, vol. 11, pp. 101359-101372. View/Download from: Publisher's site
Barua, PD, Yildiz, AM, Canpolat, N, Keles, T, Dogan, S, Baygin, M, Tuncer, I, Tuncer, T, Tan, R-S, Fujita, H & Acharya, UR 2023, 'An accurate automated speaker counting architecture based on James Webb Pattern', Engineering Applications of Artificial Intelligence, vol. 119, pp. 105821-105821. View/Download from: Publisher's site
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Blaabjerg, F & Siwakoti, YP 2023, 'A Novel Active Neutral Point-Clamped Five-Level Inverter With Single-Stage-Integrated Dynamic Voltage Boosting Feature', IEEE Transactions on Power Electronics, vol. 38, no. 6, pp. 7796-7809. View/Download from: Publisher's site
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Liserre, M & Siwakoti, YP 2023, 'New Family of Dual-Mode Active Neutral Point-Clamped Five-Level Converters', IEEE Transactions on Power Electronics, vol. 38, no. 10, pp. 12236-12253. View/Download from: Publisher's site
Basak, S, Agrawal, H, Jena, S, Gite, S, Bachute, M, Pradhan, B & Assiri, M 2023, 'Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems', Computer Modeling in Engineering & Sciences, vol. 135, no. 2, pp. 1053-1089. View/Download from: Publisher's site
Basavaraja, BM, Bantwal, RP, Tripathi, A, Hegde, G, John, NS, Thapa, R, Hegde, G, Balakrishna, RG, Saxena, M, Altaee, A & Samal, AK 2023, 'Functionalized Silver Nanocubes for the Detection of Hazardous Analytes through Surface-Enhanced Raman Scattering: Experimental and Computational Studies', ACS Sustainable Chemistry & Engineering, vol. 11, no. 29, pp. 10605-10619. View/Download from: Publisher's site
Baygin, M, Barua, PD, Chakraborty, S, Tuncer, I, Dogan, S, Palmer, E, Tuncer, T, Kamath, AP, Ciaccio, EJ & Acharya, UR 2023, 'CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals', Physiological Measurement, vol. 44, no. 3, pp. 035008-035008. View/Download from: Publisher's site View description>>
AbstractObjective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals. Approach. In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels. Main results. The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier. Significance. Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.
Baygin, M, Tuncer, I, Dogan, S, Barua, PD, Tuncer, T, Cheong, KH & Acharya, UR 2023, 'Automated facial expression recognition using exemplar hybrid deep feature generation technique', Soft Computing, vol. 27, no. 13, pp. 8721-8737. View/Download from: Publisher's site
Baygin, N, Aydemir, E, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S & Rajendra Acharya, U 2023, 'Automated mental arithmetic performance detection using quantum pattern- and triangle pooling techniques with EEG signals', Expert Systems with Applications, vol. 227, pp. 120306-120306. View/Download from: Publisher's site
Bazli, M, Ashrafi, H, Rajabipour, A & Kutay, C 2023, '3D printing for remote housing: Benefits and challenges', Automation in Construction, vol. 148, pp. 104772-104772. View/Download from: Publisher's site
Beck, AT & Stewart, MG 2023, 'Risk-based cost-benefit analysis of structural strengthening to mitigate disproportionate collapse of buildings under abnormal blast loading', Structures, vol. 57, pp. 105103-105103. View/Download from: Publisher's site
Beck, AT, Ribeiro, LDR, Costa, LGL & Stewart, MG 2023, 'Comparison of risk-based robustness indices in progressive collapse analysis of building structures', Structures, vol. 57, pp. 105295-105295. View/Download from: Publisher's site
Beena, KS, Sandeep, MN, Indraratna, B & Malisetty, RS 2023, 'Near-field vibrations in railway track on soft subgrades for semi high-speed trains', Transportation Engineering, vol. 12, pp. 100176-100176. View/Download from: Publisher's site
Begum, Z, Saleem, M, Islam, SU & Saha, SC 2023, 'Numerical Study of Natural Convection Flow in Rectangular Cavity with Viscous Dissipation and Internal Heat Generation for Different Aspect Ratios', Energies, vol. 16, no. 14, pp. 5267-5267. View/Download from: Publisher's site View description>>
Numerical simulations have been performed to investigate the influence of constant volumetric heat generation and viscous dissipation on the unsteady natural convection flow of an incompressible Newtonian fluid contained in a rectangular cavity. The left vertical wall of the cavity is cooled, while the right vertical wall is heated, and the bottom and top walls are adiabatic. A numerical technique based on the implicit finite difference method (IFDM), along with an upwind finite difference scheme and an iterative successive over relaxation (SOR) technique, is employed to solve the governing equations numerically. The effect of physical parameters, namely the modified Rayleigh number (103≤Ra≤107), aspect ratio (1≤A≤4), Prandtl number (Pr=0.7, 1.0, 6.2, 15), volumetric internal heat generation parameter (Qλ=0, 1), and Eckert number (0≤Ec≤10−6), on the streamlines and isotherms are discussed graphically. Variations of maximum stream function, as well as average and local Nusselt number, are also discussed. The results show that the increase in Eckert number from 0 to 10−4 causes the average heat transfer to decrease, while Pr=0.71, Ra=104, and Qλ=0. Additionally, the average heat transfer decreases as the cavity width increases from 1 to 4, while Pr=0.71, Ra = 5×104, Ec=10−6 and Qλ=1. The results of the numerical model used here are in excellent accord with earlier findings.
Benkhaya, S, Lgaz, H, Tang, H, Altaee, A, Haida, S, Vatanpour, V & Xiao, Y 2023, 'Investigating the effects of polypropylene-TiO2 loading on the performance of polysulfone/polyetherimide ultrafiltration membranes for azo dye removal: Experimental and molecular dynamics simulation', Journal of Water Process Engineering, vol. 56, pp. 104317-104317. View/Download from: Publisher's site
Bennett, NS & Lim, B 2023, 'Assessing the Potential of Heat Pumps to Reduce the Radiator Size on Small Satellites', Energies, vol. 16, no. 10, pp. 4010-4010. View/Download from: Publisher's site View description>>
Future small satellites will demand high-performance on-board electronics, requiring sophisticated approaches to heat rejection beyond simply increasing the radiator surface area. An interesting alternative approach is to increase the surface temperature of the radiator, using a heat pump. In this study, calculations were carried out to compute the theoretical radiator size reduction potential enacted by having a heat pump as part of a satellite’s thermal management system. The practical likelihood of a ‘typical’ vapor compression cycle (VCC) heat pump satisfying theoretical requirements was considered. In agreement with theoretical calculations, employing a ‘typical’ VCC heat pump could either increase or decrease the required radiator surface area. The choice of heat pump and its design is therefore crucial. A heat pump with a large temperature lift is essential for satellite radiator cooling applications, with the coefficient of performance (COP) being less important. Even with a low COP, such as 2.4, a ‘typical’ heat pump providing a large temperature lift, close to 60 °C, could reduce the satellite’s radiator surface area by a factor close to 1.4. This is a significant potential reduction. The decision on whether to pursue this approach compared to alternatives, such as deployable radiators, should consider the relative complexity, cost, weight, size, reliability, etc., of the two options. The focus of this study is VCC heat pumps; however, the results provide performance targets for less mature heat pump technologies, e.g., caloric devices, which could ultimately be applied in space.
Bennett, NS, Hawchar, A & Cowley, A 2023, 'Thermal Control of CubeSat Electronics Using Thermoelectrics', Applied Sciences, vol. 13, no. 11, pp. 6480-6480. View/Download from: Publisher's site View description>>
A feasibility study is presented exploring the possibility of using thermoelectric devices for the thermal control of CubeSat on-board electronics. A simple thermoelectric architecture is devised and an empirical model for how such a system would perform is constructed, using the performance data of a commercially available thermoelectric module. This is used to calculate the temperature to which the system could cool a computer chip, as a function of thermal resistance and heat rejection. As a baseline scenario, the temperature of the system without the thermoelectric device is compared and the benefit, or otherwise, of using a thermoelectric module is calculated. Analysis shows that in some circumstances introducing a thermoelectric device would actually increase the temperature of the electronics being cooled. This is most common when the quantity of heat being removed, or the thermal resistance of the system, is high. Nevertheless, thermoelectric cooling is beneficial for a range of conditions, such as for cooling the computer chip below ambient temperature, however a good quality radiator is required. This constraint could undermine the thermoelectric device’s potential benefit in many cases, due to the need for an unrealistically large radiator.
Beyhan, B, Akcomak, IS & Cetindamar, D 2023, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, pp. 1-37. View/Download from: Publisher's site View description>>
PurposeThis paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.Design/methodology/approachThis research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.FindingsThe analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category.Originality/valueThis study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.
Bhattad, A, Rao, BN, Atgur, V, Veza, I, Zamri, MFMA & Fattah, IMR 2023, 'Thermal Performance Evaluation of Plate-Type Heat Exchanger with Alumina–Titania Hybrid Suspensions', Fluids, vol. 8, no. 4, pp. 120-120. View/Download from: Publisher's site View description>>
This paper aims to develop models for the thermal conductivity and viscosity of hybrid nanofluids of aluminium oxide and titanium dioxide (Al2O3-TiO2). The study investigates the impact of fluid temperature (283 K–298 K) on the performance of a plate heat exchanger using Al2O3-TiO2 hybrid nanofluids with different particle volume ratios (0:5, 1:4, 2:3, 3:2, 4:1, and 5:0) prepared with a 0.1% concentration in deionised water. Experimental evaluations were conducted to assess the heat transfer rate, Nusselt number, heat transfer coefficient, Prandtl number, pressure drop, and performance index. Due to the lower thermal conductivity of TiO2 nanoparticles compared to Al2O3, a rise in the TiO2 ratio decreased the heat transfer coefficient, Nusselt number, and heat transfer rate. Inlet temperature was found to decrease pressure drop and performance index. The Al2O3 (5:0) nanofluid demonstrated the maximum enhancement of around 16.9%, 16.9%, 3.44%, and 3.41% for the heat transfer coefficient, Nusselt number, heat transfer rate, and performance index, respectively. Additionally, the TiO2 (0:5) hybrid nanofluid exhibited enhancements of 0.61% and 2.3% for pressure drop and Prandtl number, respectively. The developed hybrid nanofluids enhanced the performance of the heat exchanger when used as a cold fluid.
Bhol, P, Patil, SA, Barman, N, Siddharthan, EE, Thapa, R, Saxena, M, Altaee, A & Samal, AK 2023, 'Design and fabrication of cobaltx nickel(1-x) telluride microfibers on nickel foam for battery-type supercapacitor and oxygen evolution reaction study', Materials Today Chemistry, vol. 30, pp. 101557-101557. View/Download from: Publisher's site
Bhola, B, Kumar, R, Priyadarshini, I, So-In, C, Padhy, T, Slowik, A & Gandomi, AH 2023, 'Internet-of-Things-Based Sensor Module for Respiratory Tracking System', IEEE Sensors Journal, vol. 23, no. 16, pp. 18664-18674. View/Download from: Publisher's site
Bi, S, Wang, C, Shen, J, Xiang, W, Ni, W, Wang, X, Wu, B & Gong, Y 2023, 'A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System', Computer Modeling in Engineering & Sciences, vol. 135, no. 1, pp. 527-538. View/Download from: Publisher's site
Bi, Z, Zhang, L, He, X & Zhai, Y 2023, 'Effect of oblique incidence angle and frequency content of P and SV waves on the dynamic behavior of liquid tanks', Soil Dynamics and Earthquake Engineering, vol. 171, pp. 107929-107929. View/Download from: Publisher's site
Bibin, C, Sheeja, R, Devarajan, Y, Raja, T, Hossain, I, Ouladsmane, M & Kalam, MA 2023, 'Process optimization study on the feedstock derived from Cerbera odollam seeds', Biomass Conversion and Biorefinery, vol. 13, no. 17, pp. 16253-16262. View/Download from: Publisher's site
Bisui, S, Pradhan, B, Roy, S, Sengupta, D, Bhunia, GS & Shit, PK 2023, 'Estimating Forest-Based Livelihood Strategies Focused on Accessibility of Market Demand and Forest Proximity', Small-scale Forestry, vol. 22, no. 3, pp. 537-556. View/Download from: Publisher's site
Biswas, K, Shivakumara, P, Pal, U, Lu, T, Blumenstein, M & Lladós, J 2023, 'Classification of aesthetic natural scene images using statistical and semantic features', Multimedia Tools and Applications, vol. 82, no. 9, pp. 13507-13532. View/Download from: Publisher's site
Blamires, S, Lozano-Picazo, P, Bruno, AL, Arnedo, M, Ruiz-León, Y, González-Nieto, D, Rojo, FJ, Elices, M, Guinea, GV & Pérez-Rigueiro, J 2023, 'The Spider Silk Standardization Initiative (S3I): A powerful tool to harness biological variability and to systematize the characterization of major ampullate silk fibers spun by spiders from suburban Sydney, Australia', Journal of the Mechanical Behavior of Biomedical Materials, vol. 140, pp. 105729-105729. View/Download from: Publisher's site
Bliuc, D, Tran, T, Chen, W, Alarkawi, D, Alajlouni, DA, Blyth, F, March, L, Blank, RD & Center, JR 2023, 'Antiresorptive Medication Use Is not Associated With Acute Cardiovascular Risk: An Observational Study', The Journal of Clinical Endocrinology & Metabolism, vol. 108, no. 5, pp. e110-e119. View/Download from: Publisher's site View description>>
AbstractContextBisphosphonates have been reported to be cardioprotective in some, but not all, studies. It is unknown whether denosumab (Dmab) use protects against cardiovascular events (CVEs).ObjectiveTo determine whether oral bisphosphonate (oBP) or Dmab use is associated with CVEs in persons with incident fracture.MethodsParticipants with an incident minimal trauma fracture from the Sax Institute’s 45 and Up Study, a population-based cohort from NSW, Australia, were followed between 2005/2009 and 2017. Questionnaire data were linked to hospital admissions (Admitted Patients Data Collection [APDC]) by the Centre for Health Record Linkage). Medicare Benefit Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data sets were provided by Services Australia. Data was stored in a secure computing environment (Secure Unified Research Environment). Fractures, CVEs, and comorbidities were identified from the APDC and oBP and Dmab medication from the PBS. oBP and Dmab users were matched to never users (NoRx) by propensity scores. The main outcome measures were association between oBP and Dmab with CVE (acute myocardial infarction, unstable angina, cerebrovascular accident, and transient ischemic attack) and were determined using a stratified Cox's proportional hazards model.ResultsThere were 880 pairs of oBP and NoRx (616 women) and 770 pairs of Dmab and NoRx (615 women) followed for ∼4.3 years. CVE risk was similar for oBP and NoRx Hazard Ratios (HR) women, 0.88 [95% CI 0.65-1.18]; men, 1.07 [95% CI 0.72-1.57]). Similar findings were obtained for Dmab (Hazard Ratios (HR) women, 1.08 [95% CI 0.78-1.50]; men, 1.55 [95% CI 0.96-2.48]).Conclusion...
Boshir Ahmed, M, Alom, J, Hasan, MS, Asaduzzaman, M, Rahman, MS, Hossen, R, Abu Hasan Johir, M, Taufiq Alam, M, Zhou, JL, Zhu, Y & Zargar, M 2023, 'Front Cover: General Doping Chemistry of Carbon Materials (ChemNanoMat 4/2023)', ChemNanoMat, vol. 9, no. 4. View/Download from: Publisher's site
AbstractCarbon has an extraordinary ability to bind with itself and other elements, resulting in unique structures for a wide range of applications. Recently, intensive research has been focused on the properties of carbon‐based materials (CBMs) and on increasing their performance by doping them with metals and non‐metallic elements. While materials with excellent performance have been experimentally achieved, a fundamental knowledge of the relationship between the electronic, physical, and electrochemical properties and their structural features, particularly the chemistry of carbon‐based materials remains a top challenge. This review begins with the doping chemistries of CBMs, covering the role of electron affinity, orbital chemistry, the chemistry of band gap, conductivity, bonding type, spin redistribution, and conducting relevant comparisons. These will lead to providing an in‐depth understanding of the overall picture in the CBMs doping chemistry particularly as catalysts. The future research prospects and challenges for doped CBMs are highlighted.
Brown, A, Lamb, E, Deo, A, Pasin, D, Liu, T, Zhang, W, Su, S & Ueland, M 2023, 'The use of novel electronic nose technology to locate missing persons for criminal investigations', iScience, vol. 26, no. 4, pp. 106353-106353. View/Download from: Publisher's site View description>>
The search for missing persons is a major challenge for investigations involving presumed deceased individuals. Currently, the most effective tool is the use of cadaver-detection dogs; however, they are limited by their cost, limited operation times, and lack of granular information reported to the handler. Thus, there is a need for discrete, real-time detection methods that provide searchers explicit information as to whether human-decomposition volatiles are present. A novel e-nose (NOS.E) developed in-house was investigated as a tool to detect a surface-deposited individual over time. The NOS.E was able to detect the victim throughout most stages of decomposition and was influenced by wind parameters. The sensor responses from different chemical classes were compared to chemical class abundance confirmed by two-dimensional gas chromatography - time-of-flight mass spectrometry. The NOS.E demonstrated its ability to detect surface-deposited individuals days and weeks since death, demonstrating its utility as a detection tool.
Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2023, 'SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain', Knowledge-Based Systems, vol. 269, pp. 110501-110501. View/Download from: Publisher's site
Büscher, J, Paranjape, A, Möhle, R, Polikarpov, M, Plettenberg, N, Zwinkau, R, Deuse, J & Schmitt, RH 2023, 'Bauteile ressourceneffizient reinigen mithilfe von KI', JOT Journal für Oberflächentechnik, vol. 63, no. 1, pp. 40-43. View/Download from: Publisher's site
Cai, P, Li, H, Guo, Q & Huang, X 2023, 'UAMP-Based Equalization for Dual Pulse Shaping Transmission Systems', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1164-1168. View/Download from: Publisher's site
Cai, Z, Yang, F, Song, Y, Liu, Y, Liu, W, Wang, Q & Sun, X 2023, 'Semiconducting mineral induced photochemical conversion of PAHs in aquatic environment: Mechanism study and fate prediction', Science of The Total Environment, vol. 860, pp. 160382-160382. View/Download from: Publisher's site View description>>
Semiconducting minerals (such as iron sulfides) are highly abundant in surface water, but their influences on the natural photochemical process of contaminants are still unknown. By simulating the natural water environment under solar irradiation, this work comprehensively investigated the photochemical processes of anthracene (a typical Polycyclic Aromatic Hydrocarbons) in both freshwater and seawater. The results show that the natural pyrite (NP) significantly promotes the degradation of anthracene under solar illumination via 1) NP induced photocatalytic degradation of anthracene, and 2) Fenton reaction due to the NP induced photocatalytic generation of H2O2. The material characterization and theoretical calculation reveal that the natural impurity in NP enlarges its band gap, which limits the utilization of solar spectra to shorter wavelength. The contribution of generated reactive intermediates on anthracene degradation follows the order of 1O2 >OH > O2- in freshwater and O2- >1O2 >OH in seawater. The photochemically generated H2O2 is a vital source for OH generation (from Fenton reaction). The steady-state concentration of OH, 1O2 and O2- in freshwater were monitored as 3.0 × 10-15 M, 1.1 × 10-13 M, and 4.5 × 10-14 M, respectively. However, the OH concentration in seawater can be negligible due to the quenching effects by halides, and the 1O2 and O2- concentrations are higher than that in freshwater. An anthracene degradation kinetic model was built based on the experimentally determined reactive intermediates concentration and its second order rate constant with anthracene. Moreover, the anthracene degradation pathway was proposed based on intermediates analysis and DFT calculation, and its toxicity evolution during the photochemical process was assessed by quantitative structure-activity relationship (QSAR) based prediction. This finding suggests that the natural semiconducting minerals can affect the fate and environmental risks of contaminants...
Cancino, CA, Merigó, JM, Urbano, D & Amorós, JE 2023, 'Evolution of the entrepreneurship and innovation research in Ibero-America between 1986 and 2015', Journal of Small Business Management, vol. 61, no. 2, pp. 322-352. View/Download from: Publisher's site View description>>
Cao, C, Nogueira, L, Zhu, H, Keller, J, Best, G, Garg, R, Kohanbash, D, Maier, J, Zhao, S, Yang, F, Cujic, K, Darnley, R, DeBortoli, R, Drozd, B, Sun, P, Higgins, I, Willits, S, Armstrong, G, Zhang, J, Hollinger, G, Travers, M & Scherer, S 2023, 'Exploring the Most Sectors at the DARPA Subterranean Challenge Finals', Field Robotics, vol. 3, no. 1, pp. 801-836. View/Download from: Publisher's site View description>>
Autonomous robot navigation in austere environments is critical to missions like “search and rescue”, yet it remains difficult to achieve. The recent DARPA Subterranean Challenge (SubT) highlights prominent challenges including GPS-denied navigation through rough terrains, rapid exploration in large-scale three-dimensional (3D) space, and interrobot coordination over unreliable communication. Solving these challenges requires both mechanical resilience and algorithmic intelligence. Here, we present our approach that leverages a fleet of custom-built heterogeneous robots and an autonomy stack for robust navigation in challenging environments. Our approach has demonstrated superior navigation performance in the SubT Final Event, resulting in the fastest traversal and most thorough exploration of the environment, which won the “Most Sectors Explored Award.” This paper details our approach from two aspects: mechanical designs of a marsupial ground-and-aerial system to overcome mobility challenges and autonomy algorithms enabling collective rapid exploration. We also provide lessons learned in the design, development, and deployment of complex but resilient robotic systems to overcome real-world navigation challenges.
Cao, J-S, Wang, S-N, Xu, R-Z, Luo, J-Y, Ni, B-J & Fang, F 2023, 'Phosphorus recovery from synthetic anaerobic fermentation supernatant via vivianite crystallization: Coupling effects of various physicochemical process parameters', Science of The Total Environment, vol. 897, pp. 165416-165416. View/Download from: Publisher's site
Cao, K, Dong, F, Ma, L, Khan, NM, Alarifi, SS, Hussain, S & Armaghani, DJ 2023, 'Infrared radiation constitutive model of sandstone during loading fracture', Infrared Physics & Technology, vol. 133, pp. 104755-104755. View/Download from: Publisher's site
Cao, K, Xu, Y, Khan, NM, Li, X, Cui, R, Hussain, S, Jahed Armaghani, D & Alarifi, SS 2023, 'A comprehensive model for evaluating infrared radiation and acoustic emission characteristics of sandstone fracture', Engineering Fracture Mechanics, vol. 283, pp. 109217-109217. View/Download from: Publisher's site
Cao, L 2023, 'AI and data science for smart emergency, crisis and disaster resilience', International Journal of Data Science and Analytics, vol. 15, no. 3, pp. 231-246. View/Download from: Publisher's site View description>>
The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, classic ECD management, ECD data complexities, and an AISDR research landscape are presented in this article. Translational disaster AI is essential to enable smart disaster resilience.
Cao, L 2023, 'AI in Finance: Challenges, Techniques, and Opportunities', ACM Computing Surveys, vol. 55, no. 3, pp. 1-38. View/Download from: Publisher's site View description>>
AI in finance refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades, with both classic and modern AI techniques applied to increasingly broader areas of finance, economy, and society. In contrast to reviews on discussing the problems, aspects, and opportunities of finance benefited from specific or some new-generation AI and data science (AIDS) techniques or the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance over the past decades. The challenges of financial businesses and data are first outlined, followed by a comprehensive categorization and a dense overview of the decades of AIDS research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. A comparison, criticism, and discussion of classic versus modern AIDS techniques for finance follows. Finally, the open issues and opportunities to address future AIDS-empowered finance and finance-motivated AIDS research are discussed.
Cao, L 2023, 'Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science', International Journal of Data Science and Analytics, vol. 15, no. 2, pp. 119-132. View/Download from: Publisher's site View description>>
AbstractAfter the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary, and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.
Cao, MX & Tomamichel, M 2023, 'Comments on “Channel Coding Rate in the Finite Blocklength Regime”: On the Quadratic Decaying Property of the Information Rate Function', IEEE Transactions on Information Theory, vol. 69, no. 9, pp. 5528-5531. View/Download from: Publisher's site
Cao, X, Liu, W & Tsang, IW 2023, 'Data-Efficient Learning via Minimizing Hyperspherical Energy', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15. View/Download from: Publisher's site
Cao, Y, Cheng, H, Gu, N, Ou, K, Wang, Z, Liu, Q, Guan, R, Fu, Q & Sun, Y 2023, 'Excellent mechanical durability of superhydrophobic coating by electrostatic spraying', Materials Chemistry and Physics, vol. 301, pp. 127658-127658. View/Download from: Publisher's site
Cao, Y, Zhao, L, Zhong, Q, Wen, S, Shi, K, Xiao, J & Huang, T 2023, 'Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights', Neural Networks, vol. 163, pp. 28-39. View/Download from: Publisher's site
Cao, Y, Zhao, L, Zhong, Q, Zhu, S, Guo, Z & Wen, S 2023, 'Adaptive PI control for H∞ synchronization of multiple delayed coupled neural networks', Neurocomputing, vol. 560, pp. 126855-126855. View/Download from: Publisher's site
Cao, Z & Lin, C-T 2023, 'Reinforcement Learning From Hierarchical Critics', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1066-1073. View/Download from: Publisher's site View description>>
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.
Caruana, A, Bandara, M, Musial, K, Catchpoole, D & Kennedy, PJ 2023, 'Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications', Artificial Intelligence in Medicine, vol. 144, pp. 102642-102642. View/Download from: Publisher's site View description>>
Machine learning provides many powerful and effective techniques foranalysing heterogeneous electronic health records (EHR). Administrative HealthRecords (AHR) are a subset of EHR collected for administrative purposes, andthe use of machine learning on AHRs is a growing subfield of EHR analytics.Existing reviews of EHR analytics emphasise that the data-modality of the EHRlimits the breadth of suitable machine learning techniques, and pursuablehealthcare applications. Despite emphasising the importance of data modality,the literature fails to analyse which techniques and applications are relevantto AHRs. AHRs contain uniquely well-structured, categorically encoded recordswhich are distinct from other data-modalities captured by EHRs, and they canprovide valuable information pertaining to how patients interact with thehealthcare system. This paper systematically reviews AHR-based research, analysing 70 relevantstudies and spanning multiple databases. We identify and analyse which machinelearning techniques are applied to AHRs and which health informaticsapplications are pursued in AHR-based research. We also analyse how thesetechniques are applied in pursuit of each application, and identify thelimitations of these approaches. We find that while AHR-based studies aredisconnected from each other, the use of AHRs in health informatics research issubstantial and accelerating. Our synthesis of these studies highlights theutility of AHRs for pursuing increasingly complex and diverse researchobjectives despite a number of pervading data- and technique-based limitations.Finally, through our findings, we propose a set of future research directionsthat can enhance the utility of AHR data and machine learning techniques forhealth informatics research.
Ceballos‐González, CF, Bolívar‐Monsalve, EJ, Quevedo‐Moreno, DA, Chávez‐Madero, C, Velásquez‐Marín, S, Lam‐Aguilar, LL, Solís‐Pérez, ÓE, Cantoral‐Sánchez, A, Neher, M, Yzar‐García, E, Zhang, YS, Gentile, C, Boccaccini, AR, Alvarez, MM & Trujillo‐de Santiago, G 2023, 'Plug‐and‐Play Multimaterial Chaotic Printing/Bioprinting to Produce Radial and Axial Micropatterns in Hydrogel Filaments', Advanced Materials Technologies, vol. 8, no. 17. View/Download from: Publisher's site View description>>
AbstractNature abounds with micro‐architected materials containing layered multi‐material patterns that often transition within the very same monolithic piece. Fabricating these complex materials using current technologies is challenging. Multimaterial chaotic printing is presented—an extrusive printing method based on the use of chaotic advection—that can fabricate microstructured hydrogels with well‐defined multimaterial and multilayered micropatterns. Printheads containing internal Kenics static mixing (KSM) elements and top‐ and lateral‐positioned inlets are used to produce a wide repertoire of multilayered hydrogel filaments. In this plug‐and‐play system, the radial and axial micropatterns can be designed ad hoc by defining the printhead configuration (i.e., the number of KSM elements and inlets, and the inlet positions) and the flow program (i.e., activation/deactivation of the ink‐flow through each inlet). Computational fluid dynamics simulations closely predict the microstructure obtained by a given printhead configuration. The application of this platform is illustrated for easy fabrication of fibers with radial microgradients, bacterial ecosystems, structured emulsions, micro‐channeled hydrogel filaments, a pre‐vascularized tumor niche model, and skeletal muscle‐like tissues with axial and radial transitions of bioactive glass compartments. It is envisioned that multimaterial chaotic printing will be a valuable addition to the toolbox of additive manufacturing for the rational fabrication of advanced materials.
Ceballos‐González, CF, Bolívar‐Monsalve, EJ, Quevedo‐Moreno, DA, Chávez‐Madero, C, Velásquez‐Marín, S, Lam‐Aguilar, LL, Solís‐Pérez, ÓE, Cantoral‐Sánchez, A, Neher, M, Yzar‐García, E, Zhang, YS, Gentile, C, Boccaccini, AR, Alvarez, MM & Trujillo‐de Santiago, G 2023, 'Plug‐and‐Play Multimaterial Chaotic Printing/Bioprinting to Produce Radial and Axial Micropatterns in Hydrogel Filaments (Adv. Mater. Technol. 17/2023)', Advanced Materials Technologies, vol. 8, no. 17. View/Download from: Publisher's site
Cetindamar, D & Phaal, R 2023, 'Technology Management in the Age of Digital Technologies', IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2507-2515. View/Download from: Publisher's site
Challis, VJ, Xu, X, Halfpenny, A, Cramer, AD, Saunders, M, Roberts, AP & Sercombe, TB 2023, 'Understanding the effect of microstructural texture on the anisotropic elastic properties of selective laser melted Ti-24Nb-4Zr-8Sn', Acta Materialia, vol. 254, pp. 119021-119021. View/Download from: Publisher's site
Chandrakant Nikam, K, Jathar, L, Shelare, SD, Shahapurkar, K, Dambhare, S, Soudagar, MEM, Mubarak, NM, Ahamad, T & Kalam, MA 2023, 'Parametric analysis and optimization of 660 MW supercritical power plant', Energy, vol. 280, pp. 128165-128165. View/Download from: Publisher's site
Chang, X, Ren, P, Xu, P, Li, Z, Chen, X & Hauptmann, A 2023, 'A Comprehensive Survey of Scene Graphs: Generation and Application', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1-26. View/Download from: Publisher's site View description>>
Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images; instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also know the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and find similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present.
This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.
Bio-based alternatives for existing construction materials can be used to reduce the carbon footprint of the built environment. Hempcrete is one of these materials and is both an excellent hygric/thermal regulator and is carbon negative. However, this novel material is still incompletely researched, especially its fungal growth potential specifically within warm and humid environments. The incorporation of significant biological material within hempcrete can enable it to act as a microbial growth medium, with the corresponding potential for the release of bioaerosols. The aim of this research was thus to investigate the overall practicality of hempcrete implementation in a humid climate. To achieve this, the endogenous fungal genera on a sample of hempcrete were identified, fungal propagules aerosolized from a hempcrete sample enumerated, and a range of temperatures tested to determine their effect on fungi growth determined. Trials were performed to determine whether hempcrete can be effectively decontaminated with common materials to manage microbial growth. Under high humidity, fungal propagule emissions were high with low diversity, with potentially allergenic fungi detected. Disinfection of high fungal load hempcrete samples was able to reduce ∼94% of the fungal observations and reduce aerosolized counts to average background tropical fungal counts. The range of temperatures tested were not found to effect fungal growth, contrary to the consensus of the literature. Overall, these properties make hempcrete suited to humid areas, however, further research to investigate the potential effects of fungi on the material remains lacking.
Chauhan, R & Yafi, E 2023, 'Applicability of classifier to discovery knowledge for future prediction modelling', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10347-10362. View/Download from: Publisher's site View description>>
AbstractThe immense growth of new technological interventions has forced researchers and scientists around the globe to adopt the widely anticipated technology of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI have generously prospected itself from the past decade in the discovery of knowledge from databases. Several ML and AI based adoptive technologies have emerged in varied application domains, and are thus widely opening a new era of knowledge in decision making. Moreover, ML and AI are techniques that can improve the treatment and diagnosis of diseases. In the current study, we have designed and deployed a “PROCLAVE”. The tool was designed in varied layers of structure, where each layer plays a significant role in determining the patterns. We have applied several libraries for the processing of a prototype to develop a visualization interface. The tool forecasts health vulnerability, makes a comparison among variable classifiers and visualize the results for end users. Moreover, the proposed architecture is based on the concepts of conceptualization and visualization to detect the overall dashboard. Furthermore, the current approach was synthesized and populated with a database that allows the end users to select the variable features and relatively determine the interactive patterns for the number of cases. The database was collected from the National Institute of Health Stroke (NIHS) in the United States. Data was gathered for stroke patients who were diagnosed with stroke from 1950 to 2015. The study was based on several attributes which included causes of death, sex, race, Hispanic origin and others to discover unknown patterns for future decision making.
Chauhan, R, Shighra, S, Madkhali, H, Nguyen, L & Prasad, M 2023, 'Efficient Future Waste Management: A Learning-Based Approach with Deep Neural Networks for Smart System (LADS)', Applied Sciences, vol. 13, no. 7, pp. 4140-4140. View/Download from: Publisher's site View description>>
Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods.
Chen, C, Liu, Y, Chen, L & Zhang, C 2023, 'Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6913-6925. View/Download from: Publisher's site View description>>
Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS). Existing methods focus on spatial-temporal dependency modeling, while two intrinsic properties of the traffic forecasting problem are overlooked. First, the complexity of diverse forecasting tasks is nonuniformly distributed across various spaces (e.g., suburb versus downtown) and times (e.g., rush hour versus off-peak). Second, the recollection of past traffic conditions is beneficial to the prediction of future traffic conditions. Based on these properties, we propose a bidirectional spatial-temporal adaptive transformer (Bi-STAT) for accurate traffic forecasting. Bi-STAT adopts an encoder-decoder architecture, where both the encoder and the decoder maintain a spatial-adaptive transformer and a temporal-adaptive transformer structure. Inspired by the first property, each transformer is designed to dynamically process the traffic streams according to their task complexities. Specifically, we realize this by the recurrent mechanism with a novel dynamic halting module (DHM). Each transformer performs iterative computation with shared parameters until DHM emits a stopping signal. Motivated by the second property, Bi-STAT utilizes one decoder to perform the present → past recollection task and the other decoder to perform the present → future prediction task. The recollection task supplies complementary information to assist and regularize the prediction task for a better generalization. Through extensive experiments, we show the effectiveness of each module in Bi-STAT and demonstrate the superiority of Bi-STAT over the state-of-the-art baselines on four benchmark datasets. The code is available at https://github.com/chenchl19941118/Bi-STAT.git.
Chen, D, Wu, C & Li, J 2023, 'Assessment of modeling methods for predicting load resulting from hydrogen-air detonation', Process Safety and Environmental Protection, vol. 180, pp. 752-765. View/Download from: Publisher's site
Chen, D, Wu, C, Li, J & Liao, K 2023, 'An overpressure-time history model of methane-air explosion in tunnel-shape space', Journal of Loss Prevention in the Process Industries, vol. 82, pp. 105004-105004. View/Download from: Publisher's site View description>>
This study investigated methane-air explosion in tunnel-shape space and developed an overpressure-time history model based on numerical results. The findings revealed that for the progressively vented gas explosion with movable steel obstacles in a 20 m long tunnel, the inner peak overpressure increased as the activation pressure of the tunnel top cover got higher but remained below 6 bar. However, as the activation pressure increased to 8 bar or higher, the peak inner overpressure remained unchanged. As the segment cover panel became wider, the peak pressure was almost unchanged, but the pressure duration and impulse declined significantly. The peak pressure and impulse increased as the tunnel length vary from 10 to 30 m. With fixed tunnel length, higher blast pressure but lower impulse was observed as the inner obstacles were closer or the activation pressure of obstacles was higher. It is also found that a local enlarged space in the tunnel enhanced the peak pressure significantly. An overpressure time history model for the tunnel with fixed top cover and enlarged end zone was established. The model considered activation pressure of vent cover, area and length of vent opening, methane concentration, number and blockage ratio of fixed obstacles was developed to calculate the overpressure and corresponding time at characteristic points of the pressure-history curve. The cubic Hermite interpolation algorithm and a specially tuned formula consisting of the power and exponential function were used to interpolate pressure values between characteristic points. The proposed model can predict both the peak pressure and the overpressure time history with acceptable accuracy.
Chen, D, Zhuang, Y, Shen, Z, Yang, C, Wang, G, Tang, S & Yang, Y 2023, 'Cross-Modal Data Augmentation for Tasks of Different Modalities', IEEE Transactions on Multimedia, vol. 25, pp. 7814-7824. View/Download from: Publisher's site
Chen, F, Zhou, J, Holzinger, A, Fleischmann, KR & Stumpf, S 2023, 'Artificial Intelligence Ethics and Trust: From Principles to Practice', IEEE Intelligent Systems, vol. 38, no. 6, pp. 5-8. View/Download from: Publisher's site
Chen, H, Tian, K, Qing, T, Liu, X, Mao, J, Qin, J & Jiang, S 2023, 'Efficient removal of tetrabromobisphenol A through persulfate activation by α-MnO2 nanofiber coated with graphene oxide', Applied Surface Science, vol. 641, pp. 158445-158445. View/Download from: Publisher's site
Chen, J, Hao, D, Chen, W, Liu, Y, Yin, Z, Hsu, H, Ni, B, Wang, A, Lewis, SW & Jia, G 2023, 'Engineering Colloidal Metal‐Semiconductor Nanorods Hybrid Nanostructures for Photocatalysis†', Chinese Journal of Chemistry, vol. 41, no. 22, pp. 3050-3062. View/Download from: Publisher's site View description>>
Comprehensive SummaryEmerging engineering strategies of colloidal metal‐semiconductor nanorod hybrid nanostructures spanning from type, size, dimension, and location of both metal nanoparticles and semiconductors, co‐catalyst, band gap structure, surface ligand to hole scavenger are elaborated symmetrically to rationalize the design of this type of intriguing materials for efficient photocatalytic applications.
Chen, J, Indraratna, B, Vinod, JS, Ngo, T & Liu, Y 2023, 'Discrete element modelling of the effects of particle angularity on the deformation and degradation behaviour of railway ballast', Transportation Geotechnics, vol. 43, pp. 101154-101154. View/Download from: Publisher's site View description>>
Railroad ballast exhibits distinct morphological characteristics represented by shape irregularity, corner angularity, and surface texture. Upon repeated train loading, the morphology of ballast undergoes inevitable degradation, particularly in terms of its corner sharpness, which can affect track performance and even pose a substantial threat to operational safety. These aspects have rarely been captured insightfully in most DEM studies on ballast. In contrast, this study examines the influence of particle angularity on the deformation and degradation behaviour of railway ballast upon repeated loading using the discrete element method (DEM). The angularity of ballast particles is captured and quantified using the CT scanning technology in conjunction with an image-based processing strategy, after which the irregularly shaped particles are reconstructed in the DEM. In this numerical procedure, aggregates with varying angularities are created by incorporating a particle degradation subroutine to capture corner abrasion and surface attrition of ballast to mimick real-life field processes. The macro-response of a typical ballasted track subjected to cyclic rail loading is investigated, and the results show that as the angularity increases, the permanent deformation of the track corresponds to a lower permanent strain rate and a higher resilient modulus. However, the opposite behaviour is observed if excessive breakage of the aggregates occurs that reduces the angularity of the individual particles. In this study, detailed microscopic analysis based on DEM in terms of interparticle interaction and associated vibration velocity has also been performed. The results offer distinct clarity to the essential micro-mechanisms embracing particle angularity, and the accompanying influence on the deformation and degradation characteristics of ballast is elucidated with greater insight.
Chen, J, Vinod, JS, Indraratna, B, Ngo, T & Liu, Y 2023, 'DEM study on the dynamic responses of a ballasted track under moving loading', Computers and Geotechnics, vol. 153, pp. 105105-105105. View/Download from: Publisher's site View description>>
This paper presents the discrete element modeling of the dynamic response of a ballasted track under moving loads. The DEM model, consisting of sleepers, ballast, and sub-ballast, has been calibrated using field and laboratory data. This model was further used to examine the dynamic responses of the ballasted track subjected to a series of moving traffic loading representing various train axle loads and speeds. The results show that the permanent settlement of the sleeper, the breakage of ballast, and the dynamic stresses in the track substructure increase with an increase in train axle load and speed. As the train moves, the magnitudes of dynamic stresses and the orientations of principal stress axes in the track change continuously, and a more pronounced principal stress rotation is observed at sleeper edges than those underneath sleepers. The capping layer is found to play a critical role in reducing train-induced stress and further alleviating the disturbance from the trains to the subgrade. The interparticle contacts and the vibration of ballast during the movement of the train including the influences of train axle load and speed on the dynamic responses of ballasted railway tracks are captured and analyzed from a micromechanical perspective.
Chen, K, He, X, Liang, F & Sheng, D 2023, 'Contribution of capillary pressure to effective stress for unsaturated soils: Role of wet area fraction and water retention curve', Computers and Geotechnics, vol. 154, pp. 105140-105140. View/Download from: Publisher's site
Chen, K, Wang, Q, Long, P & Ying, M 2023, 'Unitarity Estimation for Quantum Channels.', IEEE Trans. Inf. Theory, vol. 69, no. 8, pp. 5116-5134. View/Download from: Publisher's site
Chen, L, Chen, L, Ge, Z, Sun, Y & Zhu, X 2023, 'A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 8, pp. 3178-3186. View/Download from: Publisher's site
Chen, L, Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2023, 'A Wideband Balanced Amplifier Using Edge-Coupled Quadrature Couplers in 0.13-μm SiGe HBT Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 2, pp. 631-641. View/Download from: Publisher's site
Chen, L, Far, H, Mortazavi, M & Ragab, AE 2023, 'Comparative Study in Design of Fiber-Reinforced Concrete at Elevated Temperatures by Numerical Evaluation through Developed Hybrid Metaheuristic Algorithms', Buildings, vol. 13, no. 8, pp. 2045-2045. View/Download from: Publisher's site View description>>
Fibrous concrete has good properties such as high ductility, high strength, suitable energy absorption and cracking resistance, which can be useful in many applications. This type of concrete is one of the best materials used in the construction of impact-resistant masonries, such as burial masonry structures, and explosive masonry warehouses. In this study, an artificial intelligence assessment based on the experimental test data from a laboratory has been performed on the fibrous concrete to evaluate the behavior of the samples at elevated temperatures and determine the most governing parameter on the mechanical properties of the fibrous concrete at elevated temperatures. For the first time, a hybrid intelligence algorithm has been developed based on the neural network structure using both genetic and swarm optimization algorithms. The ANFIS-PSO-GA (APG) algorithm was trained with experimental data and evaluated the flexural load and deflection of the samples. In order to detect the most prominent feature in the fire resistance of the fibrous concrete, five different subdatasets were designed. The results of the APG algorithm have been challenged with the ANFIS-PSO algorithm, which is a well-known hybrid numerical evaluation algorithm. As per the results, the newly designed APG algorithm has been successfully performed on both deflection and flexural prediction phases. Based on the numerical achievements, fiber features such as the fiber content and fiber mechanical properties are governing factors on the fibrous concrete resistance at elevated temperatures.
Chen, L, Liu, Y, Yang, S, Hu, J & Guo, YJ 2023, 'Synthesis of Wideband Frequency-Invariant Beam Patterns for Nonuniformly Spaced Arrays by Generalized Alternating Projection Approach', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1099-1104. View/Download from: Publisher's site
Chen, L, Zhang, Y, Liang, J, Li, Y, Zhang, J, Fang, W, Zhang, P, Zhang, G & Hao Ngo, H 2023, 'Improvement of anaerobic digestion containing sulfur with conductive materials: Focusing on recent advances and internal biological mechanisms', Chemical Engineering Journal, vol. 472, pp. 144867-144867. View/Download from: Publisher's site
Chen, N, Zhang, X, Du, Q, Huo, J, Wang, H, Wang, Z, Guo, W & Ngo, HH 2023, 'Advancements in swine wastewater treatment: Removal mechanisms, influential factors, and optimization strategies', Journal of Water Process Engineering, vol. 54, pp. 103986-103986. View/Download from: Publisher's site
Chen, P, Ouyang, L, Lang, C, Zhong, H, Liu, J, Wang, H, Huang, Z & Zhu, M 2023, 'All-pH Hydrogen Evolution by Heterophase Molybdenum Carbides Prepared via Mechanochemical Synthesis', ACS Sustainable Chemistry & Engineering, vol. 11, no. 9, pp. 3585-3593. View/Download from: Publisher's site
Chen, Q, Rong, H, Tao, G, Nimbalkar, S & Xie, K 2023, 'Fatigue characteristics of nano-SiO2 cemented soil under coupled effects of dry-wet cycle and seawater corrosion', Construction and Building Materials, vol. 401, pp. 132579-132579. View/Download from: Publisher's site
Chen, Q, Xie, K, Tao, G, Nimbalkar, S, Peng, P & Rong, H 2023, 'Laboratory investigation of microstructure, strength and durability of cemented soil with Nano-SiO2 and basalt fibers in freshwater and seawater environments', Construction and Building Materials, vol. 392, pp. 132008-132008. View/Download from: Publisher's site
Chen, Q, Yu, R, Gaoliang, T & Nimbalkar, S 2023, 'Microstructure, strength and durability of nano-cemented soils under different seawater conditions: laboratory study', Acta Geotechnica, vol. 18, no. 3, pp. 1607-1627. View/Download from: Publisher's site
Chen, Q, Zhang, H, Ye, J, Tao, G & Nimbalkar, S 2023, 'Corrosion Resistance and Compressive Strength of Cemented Soil Mixed with Nano-Silica in Simulated Seawater Environment', KSCE Journal of Civil Engineering, vol. 27, no. 4, pp. 1535-1550. View/Download from: Publisher's site
Chen, R, Pye, JS, Li, J, Little, CB & Li, JJ 2023, 'Multiphasic scaffolds for the repair of osteochondral defects: Outcomes of preclinical studies', Bioactive Materials, vol. 27, pp. 505-545. View/Download from: Publisher's site
Chen, S, Zhang, P, Xie, G-S, Peng, Q, Cao, Z, Yuan, W & You, X 2023, 'Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 824-837. View/Download from: Publisher's site
Chen, S-L, Song, L-Z, Karmokar, DK, Jones, B & Guo, YJ 2023, 'Wideband Fixed-Beam Single-Piece Leaky Wave Antenna With Controlled Dispersion Slope', IEEE Transactions on Antennas and Propagation, vol. 71, no. 11, pp. 8429-8440. View/Download from: Publisher's site
Chen, W-H, Biswas, PP, Ong, HC, Hoang, AT, Nguyen, T-B & Dong, C-D 2023, 'A critical and systematic review of sustainable hydrogen production from ethanol/bioethanol: Steam reforming, partial oxidation, and autothermal reforming', Fuel, vol. 333, pp. 126526-126526. View/Download from: Publisher's site
Chen, W-H, Biswas, PP, Ubando, AT, Kwon, EE, Lin, K-YA & Ong, HC 2023, 'A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks', Fuel, vol. 345, pp. 128243-128243. View/Download from: Publisher's site
Chen, X, Chen, X, Zeng, RJ, Nie, W-B, Yang, L, Wei, W & Ni, B-J 2023, 'Instrumental role of bioreactors in nitrate/nitrite-dependent anaerobic methane oxidation-based biotechnologies for wastewater treatment: A review', Science of The Total Environment, vol. 857, no. Pt 3, pp. 159728-159728. View/Download from: Publisher's site View description>>
Recently, the nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) processes have become a research hotspot in the field of wastewater treatment. The n-DAMO processes could not only mitigate direct and indirect carbon emissions from wastewater treatment plants but also strengthen biological nitrogen removal. However, the applications of n-DAMO-based biotechnologies face practical difficulties mainly caused by the distinctive properties of n-DAMO microorganisms and the limited/availability of methane with poor solubility. In this sense, the choice of bioreactors will play important roles that influence the growth and functioning of n-DAMO microorganisms, thus enabling dedicated development of the n-DAMO processes and efficient applications of n-DAMO-based biotechnologies. Therefore, this paper aims to discuss the three commonly-applied types of bioreactors, covering the individual working principle and state-of-the-art removal performance of nitrogen as well as dissolved methane observed when adopted for n-DAMO-based biotechnologies. With noted limitations for each bioreactor type, several key perspectives were proposed which hopefully would inspire future investigation and practical applications of the n-DAMO processes.
Chen, X, Chen, Z, Ngo, HH, Mao, Y, Cao, K, Shi, Q, Lu, Y & Hu, H-Y 2023, 'Comparison of inactivation characteristics between Gram-positive and Gram-negative bacteria in water by synergistic UV and chlorine disinfection', Environmental Pollution, vol. 333, pp. 122007-122007. View/Download from: Publisher's site
Chen, X, Feng, Z, Andrew Zhang, J, Wei, Z, Yuan, X & Zhang, P 2023, 'Sensing-Aided Uplink Channel Estimation for Joint Communication and Sensing', IEEE Wireless Communications Letters, vol. 12, no. 3, pp. 441-445. View/Download from: Publisher's site
Chen, X, Feng, Z, Wei, Z, Yuan, X, Zhang, P, Zhang, JA & Yang, H 2023, 'Multiple Signal Classification Based Joint Communication and Sensing System', IEEE Transactions on Wireless Communications, vol. 22, no. 10, pp. 6504-6517. View/Download from: Publisher's site
Chen, X, Wang, D, Nie, W-B, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Impacts of Biofilm Properties on the Start-Up and Performance of a Membrane Biofilm Reactor Performing Anammox and Nitrate/Nitrite-Dependent Anaerobic Methane Oxidation Integrated Processes: A Model-Based Investigation', ACS ES&T Water, vol. 3, no. 4, pp. 1141-1149. View/Download from: Publisher's site View description>>
Even though the membrane biofilm reactor (MBfR) performing the anammox and nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) integrated processes has been known to enable complete nitrogen removal, the effects of biofilm properties on such an MBfR are yet to be disclosed. In this work, a biofilm model was constructed to investigate the effects of the initial microbial composition of the biofilm, the initial biofilm thickness, the boundary layer thickness of the biofilm, and the diffusivity of solutes in the biofilm structure on the start-up process and steady-state performance of the MBfR performing anammox/n-DAMO. The results showed that the four biofilm properties would not affect the steady-state performance but would significantly regulate the start-up time of the MBfR. Unless the MBfR was operated under undesired operational conditions, inoculation of sludge comprised mainly of anammox bacteria or/and n-DAMO archaea to form a thin initial biofilm would accelerate the start-up process of the MBfR. Moreover, measures could be taken to reduce the boundary layer thickness and the diffusivity of solutes in the biofilm structure, the latter of which would also enhance methane utilization. This work would provide valuable practical guidance for the rapid establishment of the MBfR with a high-level treatment capacity based on anammox/n-DAMO.
Chen, X, Yao, L, McAuley, J, Zhou, G & Wang, X 2023, 'Deep reinforcement learning in recommender systems: A survey and new perspectives', Knowledge-Based Systems, vol. 264, pp. 110335-110335. View/Download from: Publisher's site
Chen, X, Yao, L, Wang, X, Sun, A & Sheng, QZ 2023, 'Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 9878-9889. View/Download from: Publisher's site
Chen, X, Zhang, Y, Tsang, IW, Pan, Y & Su, J 2023, 'Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation', ACM Transactions on Information Systems, vol. 41, no. 1, pp. 1-31. View/Download from: Publisher's site View description>>
Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.
Chen, X, Zhao, Q, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Impacts of granular sludge properties on the bioreactor performing nitrate/nitrite-dependent anaerobic methane oxidation/anammox processes', Bioresource Technology, vol. 386, pp. 129510-129510. View/Download from: Publisher's site
Chen, Y & Jupp, JR 2023, 'Challenges to requirements management in complex rail transport projects', International Journal of Product Lifecycle Management, vol. 15, no. 2, pp. 139-177. View/Download from: Publisher's site
Chen, Y, Huang, W, Jiang, X, Zhang, T, Wang, Y, Yan, B, Wang, Z, Chen, Q, Xing, Y, Li, D & Long, G 2023, 'UbiMeta: A Ubiquitous Operating System Model for Metaverse', International Journal of Crowd Science, vol. 7, no. 4, pp. 180-189. View/Download from: Publisher's site
Chen, Y, Li, C, Yang, T, Ekimov, EA, Bradac, C, Ha, ST, Toth, M, Aharonovich, I & Tran, TT 2023, 'Real-Time Ratiometric Optical Nanoscale Thermometry', ACS Nano, vol. 17, no. 3, pp. 2725-2736. View/Download from: Publisher's site
Chen, Y, Lin, S, Qin, Y, Surawski, NC & Huang, X 2023, 'Carbon distribution and multi-criteria decision analysis of flexible waste biomass smouldering processing technologies', Waste Management, vol. 167, pp. 183-193. View/Download from: Publisher's site View description>>
Waste biomass treatment is a globally urgent matter which highly relates to environmental quality and human health. Here, a flexible suite of smouldering-based waste biomass processing technologies is developed and four processing strategies: (a) full smouldering, (b) partial smouldering, (c) full smouldering with a flame, and (d) partial smouldering with a flame, are proposed. The gaseous, liquid, and solid products of each strategy are quantified under various airflow rates. Then, a multi-criteria analysis in terms of environmental impact, carbon sequestration, waste removal efficiency, and by-product value is performed. The results show that full smouldering achieves the highest removal efficiency but generates significant greenhouse and toxic gases. Partial smouldering effectively generates stable biochar, sequesters over 30% carbon, and therefore reduces the greenhouse gases to the atmosphere. By applying a self-sustained flame, the toxic gases are significantly reduced to clean smouldering emissions. Finally, the process of partial smouldering with a flame is recommended to process the waste biomass that can sequester more carbon as biochar, minimize carbon emissions and mitigate the pollution. And the process of full smouldering with a flame is preferred to maximally reduce the waste volume with minimum environmental impact. This work enriches strategies for carbon sequestration and environmentally friendly waste biomass processing technologies.
Chen, Y, White, S, Ekimov, EA, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2023, 'Ultralow-Power Cryogenic Thermometry Based on Optical-Transition Broadening of a Two-Level System in Diamond', ACS Photonics, vol. 10, no. 8, pp. 2481-2487. View/Download from: Publisher's site View description>>
Cryogenic temperatures are the prerequisite for many advanced scientific applications and technologies. The accurate determination of temperature in this range and at the submicrometer scale is, however, nontrivial. This is due to the fact that temperature reading in cryogenic conditions can be inaccurate due to optically induced heating. Here, we present an ultralow-power, optical thermometry technique that operates at cryogenic temperatures. The technique exploits the temperature-dependent linewidth broadening measured by resonant photoluminescence of a two-level system: a germanium-vacancy color center in a nanodiamond host. The proposed technique achieves a relative sensitivity of ∼20% K-1, at 5 K. This is higher than any other all-optical nanothermometry method. Additionally, it achieves such sensitivities while employing excitation powers of just a few tens of nanowatts, several orders of magnitude lower than other traditional optical thermometry protocols. To showcase the performance of the method, we demonstrate its ability to accurately read out local differences in temperatures at various target locations of a custom-made microcircuit. Our work is a step toward the advancement of nanoscale optical thermometry at cryogenic temperatures.
Chen, Y, Zhou, X, Ni, W, Hossain, E & Wang, X 2023, 'Optimal Power Allocation for Multiuser Photon-Counting Underwater Optical Wireless Communications Under Poisson Shot Noise', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2230-2245. View/Download from: Publisher's site
Chen, Y, Zhou, X, Ni, W, Wang, X & Hanzo, L 2023, 'Underwater Photon-Counting Systems Under Poisson Shot Noise: Rate Analysis and Power Allocation', IEEE Transactions on Communications, vol. 71, no. 9, pp. 5152-5168. View/Download from: Publisher's site View description>>
Photon counting is an effective detection technique for weak optical signals in underwater optical wireless communications (UOWC). This paper proposes a new approach for power allocation in an uplink M-ary pulse position modulation (PPM), photo-counting non-orthogonal multiple-access (PhC-NOMA) system. Different from existing techniques in photon-counting systems, the new approach supports consistent duty cycles across underwater devices and adjusts the transmit rates of the devices through their transmit powers, thereby avoiding the delays of duty cycle adjustments and supporting high-speed transmissions. Power allocation is non-trivial in photon-counting systems due to signal-dependent Poisson shot noises. As a key contribution, we derive the exact and asymptotic expressions for the achievable rate of the M-ary PPM PhC-NOMA system with the signal-dependent Poisson shot noise and multiuser interference considered. With the expressions, we reveal the received power at the base station (BS) is minimized when their minimum data rate requirements are delivered and can be solved using an incremental algorithm. We also asymptotically maximize the photon efficiency of the devices while preventing the saturation of the receiving photon detector, using Karush-Kuhn-Tucker (KKT) conditions. Simulations show that our approach can reduce the received power at the BS by up to 25% and double the photon efficiency, as compared to the existing techniques.
Chen, Y, Zhu, S, Mu, C, Liu, X & Wen, S 2023, 'Improved Criteria for Stability of a Class of Recurrent Neural Networks With Generalized Piecewise Constant Argument', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 7246-7255. View/Download from: Publisher's site
Chen, Y-N, Ding, C, Zhu, H & Liu, Y 2023, 'A ±45°-Polarized Antenna System With Four Isolated Channels for In-Band Full-Duplex (IBFD)', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3000-3010. View/Download from: Publisher's site
Chen, Z, Han, N, Zheng, R, Ren, Z, Wei, W & Ni, B 2023, 'Design of earth‐abundant amorphous transition metal‐based catalysts for electrooxidation of small molecules: Advances and perspectives', SusMat, vol. 3, no. 3, pp. 290-319. View/Download from: Publisher's site View description>>
AbstractElectrochemical oxidation of small molecules (e.g., water, urea, methanol, hydrazine, and glycerol) has gained growing scientific interest in the fields of electrochemical energy conversion/storage and environmental remediation. Designing cost‐effective catalysts for the electrooxidation of small molecules (ESM) is thus crucial for improving reaction efficiency. Recently, earth‐abundant amorphous transition metal (TM)‐based nanomaterials have aroused souring interest owing to their earth‐abundance, flexible structures, and excellent electrochemical activities. Hundreds of amorphous TM‐based nanomaterials have been designed and used as promising ESM catalysts. Herein, recent advances in the design of amorphous TM‐based ESM catalysts are comprehensively reviewed. The features (e.g., large specific surface area, flexible electronic structure, and facile structure reconstruction) of amorphous TM‐based ESM catalysts are first analyzed. Afterward, the design of various TM‐based catalysts with advanced strategies (e.g., nanostructure design, component regulation, heteroatom doping, and heterostructure construction) is fully scrutinized, and the catalysts’ structure‐performance correlation is emphasized. Future perspectives in the development of cost‐effective amorphous TM‐based catalysts are then outlined. This review is expected to provide practical strategies for the design of next‐generation amorphous electrocatalysts.
Chen, Z, Shi, X, Zhang, J, Wu, L, Wei, W & Ni, B-J 2023, 'Nanoplastics are significantly different from microplastics in urban waters', Water Research X, vol. 19, pp. 100169-100169. View/Download from: Publisher's site View description>>
Microplastics (MPs) and nanoplastics (NPs) are ubiquitous and intractable in urban waters. Compared with MPs, the smaller NPs have shown distinct physicochemical features, such as Brownian motion, higher specific surface area, and stronger interaction with other pollutants. Therefore, the qualitative and quantitative analysis of NPs is more challenging than that of MPs. Moreover, these characteristics endow NPs with significantly different environmental fate, interactions with pollutants, and eco-impacts from those of MPs in urban waters. Herein, we critically analyze the current advances in the difference between MPs and NPs in urban waters. Analytical challenges, fate, interactions with surrounding pollutants, and eco-impacts of MPs and NPs are comparably discussed., The characterizations and fate studies of NPs are more challenging compared to MPs. Furthermore, NPs in most cases exhibit stronger interactions with other pollutants and more adverse eco-impacts on living things than MPs. Subsequently, perspective in this field is proposed to stimulate further size-dependent studies on MPs and NPs. This review would benefit the understanding of the role of NPs in the urban water ecosystem and guide future studies on plastic pollution management.
Chen, Z, Wei, W, Chen, H & Ni, B-J 2023, 'Eco-designed electrocatalysts for water splitting: A path toward carbon neutrality', International Journal of Hydrogen Energy, vol. 48, no. 16, pp. 6288-6307. View/Download from: Publisher's site View description>>
Realizing sustainable hydrogen fuel production through water electrolysis is crucial to achieving carbon neutrality. However, the development of cost-effective electrocatalysts continues to be a challenge. Eco-designed electrocatalysts derived from wastes and naturally abundant materials have recently received increasing attention. The development of eco-designed electrocatalysts is of great environmental and economic significance and makes green hydrogen more accessible to the wider community. Here, recent advances in eco-designed electrocatalysts for water splitting are summarized. Eco-design strategies such as pyrolysis, ball milling, wet-chemical methods, and electrochemical treatment are first analyzed. Recent achievements in eco-designed electrocatalysts for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water splitting (OWS) are then detailed, with an emphasis on analyzing the eco-design strategy-catalyst property-catalytic performance correlation. Perspectives in this blooming field for a greener hydrogen economy are finally outlined.
Chen, Z, Yun, S, Wu, L, Zhang, J, Shi, X, Wei, W, Liu, Y, Zheng, R, Han, N & Ni, B-J 2023, 'Waste-Derived Catalysts for Water Electrolysis: Circular Economy-Driven Sustainable Green Hydrogen Energy', Nano-Micro Letters, vol. 15, no. 1, p. 4. View/Download from: Publisher's site View description>>
AbstractThe sustainable production of green hydrogen via water electrolysis necessitates cost-effective electrocatalysts. By following the circular economy principle, the utilization of waste-derived catalysts significantly promotes the sustainable development of green hydrogen energy. Currently, diverse waste-derived catalysts have exhibited excellent catalytic performance toward hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water electrolysis (OWE). Herein, we systematically examine recent achievements in waste-derived electrocatalysts for water electrolysis. The general principles of water electrolysis and design principles of efficient electrocatalysts are discussed, followed by the illustration of current strategies for transforming wastes into electrocatalysts. Then, applications of waste-derived catalysts (i.e., carbon-based catalysts, transitional metal-based catalysts, and carbon-based heterostructure catalysts) in HER, OER, and OWE are reviewed successively. An emphasis is put on correlating the catalysts’ structure–performance relationship. Also, challenges and research directions in this booming field are finally highlighted. This review would provide useful insights into the design, synthesis, and applications of waste-derived electrocatalysts, and thus accelerate the development of the circular economy-driven green hydrogen energy scheme.
Chen, Z, Zheng, R, Bao, T, Ma, T, Wei, W, Shen, Y & Ni, B-J 2023, 'Dual-Doped Nickel Sulfide for Electro-Upgrading Polyethylene Terephthalate into Valuable Chemicals and Hydrogen Fuel', Nano-Micro Letters, vol. 15, no. 1, p. 210. View/Download from: Publisher's site View description>>
AbstractElectro-upcycling of plastic waste into value-added chemicals/fuels is an attractive and sustainable way for plastic waste management. Recently, electrocatalytically converting polyethylene terephthalate (PET) into formate and hydrogen has aroused great interest, while developing low-cost catalysts with high efficiency and selectivity for the central ethylene glycol (PET monomer) oxidation reaction (EGOR) remains a challenge. Herein, a high-performance nickel sulfide catalyst for plastic waste electro-upcycling is designed by a cobalt and chloride co-doping strategy. Benefiting from the interconnected ultrathin nanosheet architecture, dual dopants induced up-shifting d band centre and facilitated in situ structural reconstruction, the Co and Cl co-doped Ni3S2 (Co, Cl-NiS) outperforms the single-doped and undoped analogues for EGOR. The self-evolved sulfide@oxyhydroxide heterostructure catalyzes EG-to-formate conversion with high Faradic efficiency (> 92%) and selectivity (> 91%) at high current densities (> 400 mA cm−2). Besides producing formate, the bifunctional Co, Cl-NiS-assisted PET hydrolysate electrolyzer can achieve a high hydrogen production rate of 50.26 mmol h−1 in 2 M KOH, at 1.7 V. This study not only demonstrates a dual-doping strategy to engineer cost-effective bifunctional catalysts for electrochemical conversion processes, but also provides a green and sustainable way for plastic waste upcycling and simultaneous energy-saving hydrogen production.
Chen, Z, Zheng, R, Zou, H, Wang, R, Huang, C, Dai, W, Wei, W, Duan, L, Ni, B-J & Chen, H 2023, 'Amorphous iron-doped nickel boride with facilitated structural reconstruction and dual active sites for efficient urea electrooxidation', Chemical Engineering Journal, vol. 465, pp. 142684-142684. View/Download from: Publisher's site
Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell', Energy, vol. 278, pp. 127982-127982. View/Download from: Publisher's site View description>>
In order to reveal the quantitative impacts of multi-factors on the performance of high-temperature proton exchange membranes fuel cell (HT-PEMFC), in this work, Taguchi experimental design, grey relational analysis, and analysis of variance are combined to explore the influence of working temperature, working pressure, anode stoichiometric ratio, GDL porosity, and membrane thickness on power density, system efficiency, and exergy efficiency of HT-PEMFC. Firstly, by Taguchi experimental design, the highest power density, system efficiency and exergy efficiency of HT-PEMFC arrives at 0.6895 W cm−2, 38.19% and 48.65%, respectively. Secondly, based on grey relational analysis and analysis of variance, the impact order of multi-factors on power density, system efficiency, and exergy efficiency of HT-PEMFC is determined. Finally, it is discovered that the membrane thickness makes the most significant contribution on the power density of HT-PEMFC, while the anode stoichiometry ratio makes the most significant contribution on the system efficiency and exergy efficiency of HT-PEMFC. This work provides a significant reference and valuable guidance for designing HT-PEMFC.
Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II', Energy Conversion and Management, vol. 277, pp. 116691-116691. View/Download from: Publisher's site View description>>
This study optimized the performance of a proton exchange membrane fuel cell by combining the response surface methodology and non-dominated ranking genetic algorithm. Firstly, the design variables are determined, including operating pressure (p), operating temperature (T), Anode stoichiometry ratio (λa), thickness of the proton exchange membrane (Hmem) and gas diffusion layer (GDL) porosity (εGDL). The objective functions are also identified, including power density (P), system efficiency (η) and exergy efficiency. Then, the Box-Behnken design is employed to arrange the numerical investigations. Analysis of variance is used to verify the appropriateness and reliability of the constructed regression models. Response surface analysis is used to show the interaction between each pair of design parameters. Finally, the Pareto optimal frontier is obtained by non-dominated ranking genetic algorithm II and the regression models constructed by response surface methodology. The Pareto optimal solution offers a power density of 0.6327 W·cm−2, a system efficiency of 26.16% and an exergy efficiency of 43.94 %, which is 13.18 %, 7.06 % and 20.29 % better than the initial direct current channel, respectively. The corresponding design variables is p = 2.6498 atm, T = 341.621 K, λa = 1.1808, Hmem = 0.0577 mm and εGDL = 0.4908. This work provides a new multi-objective optimization method for designing more efficient proton exchange membrane fuel cells.
Cheng, D, Ye, Y, Xiang, S, Ma, Z, Zhang, Y & Jiang, C 2023, 'Anti-Money Laundering by Group-Aware Deep Graph Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12444-12457. View/Download from: Publisher's site
Cheng, H-C, Winter, A & Yu, N 2023, 'Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting', Communications in Mathematical Physics, vol. 404, no. 1, pp. 151-183. View/Download from: Publisher's site
Cheng, W, Wan, C, Li, X, Chai, H, Yang, Z, Wei, S, Su, J, Tang, X & Wu, Y 2023, 'Waste to wealth: Oxygen-nitrogen-sulfur codoped lignin-derived carbon microspheres from hazardous black liquors for high-performance DSSCs', Journal of Energy Chemistry, vol. 83, pp. 549-563. View/Download from: Publisher's site
Cheng, Z, Zhu, T, Zhu, C, Ye, D, Zhou, W & Yu, PS 2023, 'Privacy and evolutionary cooperation in neural-network-based game theory', Knowledge-Based Systems, vol. 282, pp. 111076-111076. View/Download from: Publisher's site
Chi, K, Li, J & Wu, C 2023, 'Numerical simulation of buried steel pipelines subjected to ground surface blast loading', Thin-Walled Structures, vol. 186, pp. 110716-110716. View/Download from: Publisher's site View description>>
Steel gas pipelines are important component in energy sector. Due to its easy accessibility and importance, shallow-buried pipelines are becoming targets of intentional attack. Therefore, it is urgent to investigate the failure mechanism of buried pipelines subjected to ground surface blast loadings and carry out quantitative damage assessment of pipelines. The present study performs numerical simulation on the resistance of buried pipelines subjected to ground surface explosion. The simulated ground shock propagation in the soil medium was validated with technical manual TM5-855-1 as well as experimental data. The effects of charge weight, stand-off distance, explosive position offset, pipe diameter, pipe wall thickness, buried depth, and steel grade as well as different soil types were investigated. It was found that for the grade X70 pipe with the same buried depth 760 mm, the cross-sectional flattening ratio under charge weight 227 kg (typical sedan bomb) was nearly 544 times greater than the case in 2.3 kg charge weight (typical pipe bomb). The flattening ratio decreased 99.9% because of the buried depth increased from 300 mm to 1800 mm. The decrease in pipe diameter from 860 mm to 350 mm caused 89.6% reduction in flattening ratio. The increase in wall thickness from 4.80 mm to 12.7 mm caused 99.7% decline in flattening ratio. Similarly, it showed the flattening ratio decreased 29.3% when the steel grade increased from X42 (yield strength 290 MPa) to X80 (yield strength 580 MPa). The blast resistance was the worst when the pipeline was buried in clay soil, in which the flattening ratio was 74.8% and 40.3% greater as compared with sandy loam and soil medium. An analytical formula was derived to predict the flattening ratio of pipelines against surface explosion.
Chinh Nguyen, H, Hagos Aregawi, B, Fu, C-C, Chyuan Ong, H, Barrow, CJ, Su, C-H, Wu, S-J, Juan, H-Y & Wang, F-M 2023, 'Biodiesel production through electrolysis in the presence of choline chloride-based deep eutectic solvent: Optimization by response surface methodology', Journal of Molecular Liquids, vol. 379, pp. 121633-121633. View/Download from: Publisher's site
Choi, W, Fang, J, Kim, J, Love, N, Saeys, M & Wong, M 2023, 'ACS ES&T Engineering’s 2022 Excellence in Review Awards', ACS ES&T Engineering, vol. 3, no. 8, pp. 1053-1054. View/Download from: Publisher's site
Choi, WWY, Sánchez, C, Li, JJ, Dinarvand, M, Adomat, H, Ghaffari, M, Khoja, L, Vafaee, F, Joshua, AM, Chi, KN, Guns, EST & Hosseini-Beheshti, E 2023, 'Extracellular vesicles from biological fluids as potential markers in castration resistant prostate cancer', Journal of Cancer Research and Clinical Oncology, vol. 149, no. 8, pp. 4701-4717. View/Download from: Publisher's site View description>>
AbstractPurposeExtracellular vesicles (EV) secreted from cancer cells are present in various biological fluids, carrying distinctly different cellular components compared to normal cells, and have great potential to be used as markers for disease initiation, progression, and response to treatment. This under-utilised tool provides insights into a better understanding of prostate cancer.MethodsEV from serum and urine of healthy men and castration-resistant prostate cancer (CRPC) patients were isolated and characterised by transmission electron microscopy, particle size analysis, and western blot. Proteomic and cholesterol liquid chromatography-mass spectrometry (LC–MS) analyses were conducted.ResultsThere was a successful enrichment of small EV/exosomes isolated from serum and urine. EV derived from biological fluids of CRPC patients had significant differences in composition when compared with those from healthy controls. Analysis of matched serum and urine samples from six prostate cancer patients revealed specific EV proteins common in both types of biological fluid for each patient.ConclusionSome of the EV proteins identified from our analyses have potential to be used as CRPC markers. These markers may depict a pattern in cancer progression through non-invasive sample collection.
Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2023, 'Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778-5793. View/Download from: Publisher's site View description>>
Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of IoT data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this paper introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms such as Q-learning and deep Q-learning can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.
Chu, NH, Nguyen, DN, Hoang, DT, Pham, Q-V, Phan, KT, Hwang, W-J & Dutkiewicz, E 2023, 'AI-Enabled mm-Waveform Configuration for Autonomous Vehicles With Integrated Communication and Sensing', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16727-16743. View/Download from: Publisher's site View description>>
Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.
Chugh, D, Mittal, H, Saxena, A, Chauhan, R, Yafi, E & Prasad, M 2023, 'Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering', Algorithms, vol. 16, no. 1, pp. 28-28. View/Download from: Publisher's site View description>>
Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.
Ci, J, Guo, Z, Long, H, Wen, S & Huang, T 2023, 'Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching', Neural Networks, vol. 157, pp. 11-25. View/Download from: Publisher's site View description>>
This paper presents theoretical results on multiple asymptotical ω-periodicity of a state-dependent switching fractional-order neural network with time delays and sigmoidal activation functions. Firstly, by combining the geometrical properties of activation functions with the range of switching threshold, a partition of state space is given. Then, the conditions guaranteeing that the solutions can approach each other infinitely in each positive invariant set are derived. Furthermore, the S-asymptotical ω-periodicity and the convergence of solutions in positive invariant sets are discussed. It is worth noting that the number of attractors increases to 3n from 2n in a neural network without switching. Finally, three numerical examples are given to substantiate the theoretical results.
Ci, Q, Wang, Y, Wu, B, Coy, E, Li, JJ, Jiang, D, Zhang, P & Wang, G 2023, 'Fe‐Doped Carbon Dots as NIR‐II Fluorescence Probe for In Vivo Gastric Imaging and pH Detection', Advanced Science, vol. 10, no. 7, pp. e2206271-2206271. View/Download from: Publisher's site View description>>
AbstractCarbon dots (CDs) with excellent cytocompatibility, tunable optical properties, and simple synthesis routes are highly desirable for use in optical bioimaging. However, the majority of existing CDs are triggered by ultraviolet/blue light, presenting emissions in the visible/first near‐infrared (NIR‐I) regions, which do not allow deep tissue penetration. Emerging research into CDs with NIR‐II emission in the red region has generated limited designs with poor quantum yield, restricting their in vivo imaging applications due to low penetration depth. Developing novel CDs with NIR‐II emissions and high quantum yield has significant and far‐reaching applications in bioimaging and photodynamic therapy. Here, it is developed for the first time Fe‐doped CDs (Fe‐CDs) exhibiting the excellent linear relationship between 900–1200 nm fluorescence‐emission and pH values, and high quantum yield (QY‐1.27%), which can be used as effective probes for in vivo NIR‐II bioimaging. These findings demonstrate reliable imaging accuracy in tissue as deep as 4 mm, reflecting real‐time pH changes comparable to a standard pH electrode. As an important example application, the Fe‐CDs probe can non‐invasively monitor in vivo gastric pH changes during the digestion process in mice, illustrating its potential applications in aiding imaging‐guided diagnosis of gastric diseases or therapeutic delivery.
Consoli, NC, Tebechrani Neto, A, Khajeh, A, Salimi, M, Specht, LP, Vestena, PM & da Rocha, CG 2023, 'Dynamic Properties of Reclaimed Asphalt Pavement–Green Cement Blends for Road Base Layer', Geotechnical and Geological Engineering, vol. 41, no. 6, pp. 3495-3511. View/Download from: Publisher's site
Crowther, CA, Ashwood, P, Middleton, PF, McPhee, A, Tran, T, Harding, JE, Alsweiler, J, Baker, E, Eaglen, D, Groom, K, Hauch, H, Mackay, L, Pacella, MJ, Preest, A, Taylor, J, Williamson, K, Tottman, A, Austin, N, Darlow, BA, Dixon, B, Ellis, N, Graham, P, Gullam, J, Leishman, D, Van Dyk, MM, Broadbent, R, Dawson, P, Devenish, C, Douglas, J, McCaffrey, F, Carey, R, Marshall, P, Morris, S, Nguyen, T, Gaerty, K, Grupp, O, Boddice, G, Green, A, Mahomed, K, Turner, L, Baldwin, M, Dennis, A, Fisher, E, Gee, K, Gee, M, Strong, D, Asadi, S, Burakevych, N, Griffth, R, Kendaragama, A, Ksionda, O, Kurkchi, K, Paine, C, Philipsen, S, Rogers, J, Samuel, D, Shah, R, Slabkevich, N, Stewart, H, Vasilenko, A, Beckman, M, Bolton, E, Chaplin, J, Cooper, C, Fox, J, Gray, P, Hawley, G, Hickey, J, Hoey, J, Hurrion, E, Jardine, L, Kan, J, Lynn, L, McHale, T, Poad, D, Poulsen, L, Warhurst, K, Bice, C, Davis, N, Duff, J, Jones, A, Kelly, EA, Magrath, E, Malcolm, D, O'Connor, K-A, Opie, G, Turner, A-M, Walker, S, Williamson, A, Woods, H, Hou, D, Kippen, M, Schroder, J, Thesing, AJ, Wadsworth, S, Camadoo, L, Dyer, C, Jones, S, Kothari, A, Markovic, V, Owens, J, Shallcross, M, Butterley, K, Davis, C, De Paoli, A, Dodson, S, Holmes, M, Kenchapla, H, Matzolic, T, McGregor, A, Patel, S, Simic, S, Andrijic, V, Biggs, V, Brandrick, S, Goldstein, S, Lainchbury, A, Lui, K, Lyons, S, Shand, A, Sutton, L, Barnes, L, Bowen, J, Harvey, L, Jacobs, C, Milligan, J, Morris, J, Nippita, T, Sau-Harvey, R, Sparks, A, Wegener, A, Burnett, A, Callanan, K, Cheong, J, De Luca, C, Doyle, L, du Plessis, J, Duff, J, Hutchinson, E, Kane, SC, Kelly, E, Kornman, L, Maxwell, D, McDonald, M, Poth, M, Arcus, JC, Cruickshank, M, Devoy, B, Fanning, MJ, Henriksen, K, Morse, F, Schiller, A, Tomlinson, PA, Davis, G, Dosen, A, Roberts, L, Rowe, C, Creen, J, Gee, K, Hurley, T, Pallett, L, Smitheram, C, Thompson, A, Weaver, E, Lynch, L-A, Pszczola, R, Said, J, Shekleton, J, Craine, K, Fergus, J, Ford, J, Harris, A, Kummer, M, Thurnell, C, Boniface, C, Davis, A, Dickinson, C, Ireland, S, Lawrence, A, Mandell, K, Menon, S, Watson, D, Bennett, M, Elder, R, Hayne, P, Massov, L, Miller, H, Sandler, ME, Schenk, V, Wilkes, N, Sibanda, T, Davis, W, Dill, N, Espinoza, N, Kunjunju, A, Wright, I, Anderson, C, Ball, V, Bhatia, V, Burford-Rice, R, Gagliardi, D, Gooding, ML, Han, S, Headley, B, Holst, C, Keir, A, Khong, TY, Kochar, A & et al. 2023, 'Prenatal Intravenous Magnesium at 30-34 Weeks’ Gestation and Neurodevelopmental Outcomes in Offspring', JAMA, vol. 330, no. 7, pp. 603-603. View/Download from: Publisher's site View description>>
ImportanceIntravenous magnesium sulfate administered to pregnant individuals before birth at less than 30 weeks’ gestation reduces the risk of death and cerebral palsy in their children. The effects at later gestational ages are unclear.ObjectiveTo determine whether administration of magnesium sulfate at 30 to 34 weeks’ gestation reduces death or cerebral palsy at 2 years.Design, Setting, and ParticipantsThis randomized clinical trial enrolled pregnant individuals expected to deliver at 30 to 34 weeks’ gestation and was conducted at 24 Australian and New Zealand hospitals between January 2012 and April 2018.InterventionIntravenous magnesium sulfate (4 g) was compared with placebo.Main Outcomes and MeasuresThe primary outcome was death (stillbirth, death of a live-born infant before hospital discharge, or death after hospital discharge before 2 years’ corrected age) or cerebral palsy (loss of motor function and abnormalities of muscle tone and power assessed by a pediatrician) at 2 years’ corrected age. There were 36 secondary outcomes that assessed the health of the pregnant individual, infant, and child.ResultsOf the 1433 pregnant individuals enrolled (mean age, 30.6 [SD, 6.6] years; 46 [3.2%] self-identified as Aboriginal or Torres Strait Islander, 237 [16.5%] as Asian, 82 [5.7%] as Māori, 61 [4.3%] as Pacific, and 966 [67.4%] as White) and their 1679 infants, 1365 (81%) offspring (691 in the magnesium group and 674 in the placebo group) were included in the primary outcome analysis. Death or cerebral palsy at 2 years’ corrected age was not significantly different between the magnesium and placebo gr...
Crowther, CA, Samuel, D, McCowan, LME, Edlin, R, Tran, T & McKinlay, CJ 2023, 'Lower Versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes', Obstetrical & Gynecological Survey, vol. 78, no. 2, pp. 85-87. View/Download from: Publisher's site View description>>
ABSTRACTGestational diabetes mellitus (GDM) in pregnancy is a global health problem. Maternal risks associated with this condition include higher rates of induced labor, cesarean delivery, and preeclampsia. Fetal exposure to GDM can increase the risk of large for gestational age (LGA), as well as operative birth, shoulder dystocia, and birth injuries. To reduce these risks, GDM is managed with nutritional therapy, blood glucose monitoring, and pharmacologic treatment. However, the criteria to diagnose GDM in pregnant individuals vary globally. Although the International Association of Diabetes and Pregnancy Study Groups (IADPSG) recommends using a lower glycemic threshold to diagnose maternal hyperglycemia, its adoption has been mixed. The aim of this study was to evaluate whether lower glycemic criteria to diagnosis GDM would improve perinatal and maternal outcomes.This was a randomized trial conducted in 2 health boards in New Zealand between April 2015 and August 2020. Included were women with singleton pregnancies who had a 75-g oral glucose tolerance test (OGTT) at 24 to 32 weeks of gestation. Excluded were those with diabetes mellitus or a history of GDM. The women were randomly assigned to be evaluated for GDM using lower glycemic criteria for diagnosis or higher criteria in a 1:1 ratio. The lower criteria were defined as a fasting plasma glucose level ≥5.1 mmol/L, a 1-hour level ≥10.1 mmol/L, or a 2-hour level ≥8.5 mmol/L. The higher criterion was a fasting plasma glucose level ≥5.5 mmol/L or a 2-hour level ≥9 mmol/L. The primary outcome was LGA.A total of 4050 women and their infants were randomized and included in the analysis—2019 in the lower criteria group and 2013 in the higher criteria group. There were 8.8% LGA infants born in the lower criteria group and 8.9% in the higher criteria group (unadjusted relative risk, 0.99; 95% confidence int...
Cui, J, Rao, P, Li, J, Chen, Q & Nimbalkar, S 2023, 'Time-dependent evolution of bearing capacity of driven piles in clays', Proceedings of the Institution of Civil Engineers - Geotechnical Engineering, vol. 176, no. 4, pp. 402-418. View/Download from: Publisher's site View description>>
Analysis of the time-dependent variation in the axial capacity of driven piles is difficult yet critical for geotechnical engineers. In this work, to investigate the short-term evolution of the bearing capacity of driven piles, a two-dimensional finite-element (FE) model was developed using the Abaqus program. Pile installation, soil consolidation and loading were incorporated in an integrated FE model. Changes in the excess pore pressure and the void ratio of the surrounding soil were investigated to evaluate the consolidation mechanism. The findings revealed that excess pore water pressure dissipation was the primary cause of the short-term evolution of the pile's bearing capacity. The dissipation of excess pore water pressure lowered the void ratio and increased the strength and stiffness of the surrounding soil. The effect of the permeability coefficient was also assessed. The permeability coefficient was found to affect the rate of evolution but not its magnitude. A centrifuge model test was used to verify the numerical results. The findings of this study may serve as a guide for improved design and construction of driven piles.
Cui, L, Ma, J, Zhou, Y & Yu, S 2023, 'Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses', IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 910-920. View/Download from: Publisher's site View description>>
Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.
Cui, Y, Lv, T, Ni, W & Jamalipour, A 2023, 'Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems', IEEE Journal on Selected Areas in Communications, vol. 41, no. 10, pp. 3175-3190. View/Download from: Publisher's site
Cui, Y, Miao, MZ, Wang, M, Su, QP, Qiu, K, Arbeeva, L, Chubinskaya, S, Diekman, BO & Loeser, RF 2023, 'Yes-associated protein nuclear translocation promotes anabolic activity in human articular chondrocytes', Osteoarthritis and Cartilage, vol. 31, no. 8, pp. 1078-1090. View/Download from: Publisher's site View description>>
OBJECTIVE: Yes-associated protein (YAP) has been widely studied as a mechanotransducer in many cell types, but its function in cartilage is controversial. The aim of this study was to identify the effect of YAP phosphorylation and nuclear translocation on the chondrocyte response to stimuli relevant to osteoarthritis (OA). DESIGN: Cultured normal human articular chondrocytes from 81 donors were treated with increased osmolarity media as an in vitro model of mechanical stimulation, fibronectin fragments (FN-f) or IL-1β as catabolic stimuli, and IGF-1 as an anabolic stimulus. YAP function was assessed with gene knockdown and inhibition by verteporfin. Nuclear translocation of YAP and its transcriptional co-activator TAZ and site-specific YAP phosphorylation were determined by immunoblotting. Immunohistochemistry and immunofluorescence to detect YAP were performed on normal and OA human cartilage with different degrees of damage. RESULTS: Chondrocyte YAP/TAZ nuclear translocation increased under physiological osmolarity (400 mOsm) and IGF-1 stimulation, which was associated with YAP phosphorylation at Ser128. In contrast, catabolic stimulation decreased the levels of nuclear YAP/TAZ through YAP phosphorylation at Ser127. Following YAP inhibition, anabolic gene expression and transcriptional activity decreased. Additionally, YAP knockdown reduced proteoglycan staining and levels of type II collagen. Total YAP immunostaining was greater in OA cartilage, but YAP was sequestered in the cytosol in cartilage areas with more severe damage. CONCLUSIONS: YAP chondrocyte nuclear translocation is regulated by differential phosphorylation in response to anabolic and catabolic stimuli. Decreased nuclear YAP in OA chondrocytes may contribute to reduced anabolic activity and promotion of further cartilage loss.
Cui, Z, Sun, X, Pan, L, Liu, S & Xu, G 2023, 'Event-based incremental recommendation via factors mixed Hawkes process', Information Sciences, vol. 639, pp. 119007-119007. View/Download from: Publisher's site
Cui, Z, Zhang, M, Yuan, Y, Jia, H, Hao Ngo, H & Wang, J 2023, 'Study on pre-concentration of trace heavy metal ions in water quality detection using FO-electroosmotic integrated chip', Chemical Engineering Journal, vol. 472, pp. 144968-144968. View/Download from: Publisher's site
Cullen, M, Ji, JC & Parnell, J 2023, 'Real time GMAW weld bead profile mapping using acoustic sensing', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A284-A284. View/Download from: Publisher's site View description>>
Monitoring the penetration profile of Gas Metal Arc Welding (GMAW) is critical in determining the overall quality and structural integrity of the produced weld bead. However, due to the instability and complexity of the GMAW process, it can be difficult to accurately monitor the weld bead formation and penetration profile. Due to this complexity, traditional methods for modeling the welding process, such as CFD analysis, are unable to be computed in real time. In this work, a new analytical method of estimating the weld bead penetration profile in real time using the sound signal is proposed. This method monitors the sound signal generated during the droplet transfer process to estimate both the heat input and material deposition into the weld pool. Using this estimation, a digital twin of the welding process is produced, allowing for the operator to monitor the weld bead formation in real time, guaranteeing the structural integrity of the final weld bead.
Curtis, K, Brown, J, Sharwood, LN, Risi, D, Eager, D, Holland, AJA, Beck, B, Erskine, C, Lockhart, K, Cooke, K, Adams, S, Teague, WJ & Mitchell, R 2023, 'Playground injury prevention: the need for consistent and national implementation of Australian safety standards', Australian and New Zealand Journal of Public Health, vol. 47, no. 2, pp. 100023-100023. View/Download from: Publisher's site View description>>
OBJECTIVES: Hospitalisation rates for injury, including at playgrounds, have not changed in the past decade. There are nine Australian Standards specific to playgrounds. The impact (if any) of these standards on playground injury resulting in hospitalisation is unknown. METHODS: Retrospective data for patients under 18 years presenting to emergency departments and/or admitted between October 2015 and December 2019 due to an injury documented as occurring at a playground were retrieved by the Illawarra Shoalhaven Local Health District Planning, Information and Performance Department. Maintenance and Australian Standard (AS) compliance data for the 401 local playgrounds were requested from the four Local Governments in Illawarra Shoalhaven Local Health District. Descriptive statistics were used. RESULTS: A total of 548 children were treated in emergency departments and/or admitted following playground injury. There was an overall increase of 39.3% in playground injury across the study period, and expenditure rose from $43,478 in 2011 to $367,259 in 2019 (a 744.7% increase). CONCLUSIONS: Playground injury has not decreased in the Illawarra Shoalhaven. Data regarding maintenance and AS compliance are lacking. This is not unique to our region. IMPLICATIONS FOR PUBLIC HEALTH: Without a national approach to adequately resource and monitor playground injury, it is not possible to assess the impact of Australian Standards or any injury prevention program.
Cuzmar, RH, Mora, A, Pereda, J, Aguilera, RP, Poblete, P & Neira, S 2023, 'Computationally Efficient MPC for Modular Multilevel Matrix Converters Operating With Fixed Switching Frequency', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 748-761. View/Download from: Publisher's site
da Rocha, CG, Wijayaratna, K, Jong, K & Haller, D 2023, 'Examining Off-Site Construction from a Flow Viewpoint', Journal of Construction Engineering and Management, vol. 149, no. 3. View/Download from: Publisher's site
Dang, B-T, Bui, X-T, Nguyen, T-T, Ngo, HH, Nghiem, LD, Huynh, K-P-H, Vo, T-K-Q, Vo, T-D-H, Lin, C & Chen, S-S 2023, 'Effect of biomass retention time on performance and fouling of a stirred membrane photobioreactor', Science of The Total Environment, vol. 864, pp. 161047-161047. View/Download from: Publisher's site View description>>
Co-culture of microalgae-activated sludge has the potential to purify wastewater while reduce energy demand from aeration. In this work, a mechanically stirred membrane photobioreactor (stirred-MPBR) was used to evaluate the impact of the biomass retention time (BRT) on the treatment performance and membrane fouling. Results showed that stirred-MPBR was affected by BRT during treating domestic wastewater at a flux of 16.5 L m-2 h-1. The highest productivity was attained at BRT 7d (102 mg L-1 d-1), followed by BRT 10d (86 mg L-1 d-1), BRT 5d (85 mg L-1 d-1), and BRT 3d (83 mg L-1 d-1). Statistical analysis results showed that BRT 7d had a higher COD removal rate than BRT 10d, however, there is no difference in total nitrogen removal rate. The highest TP removal occurred when the biomass operated at BRT as short as 3d. Reduced BRTs caused a change in the microalgae-activated sludge biomass fraction that encouraged nitrification activity while simultaneously contributing to a higher fouling rate. The bound protein concentrations dropped from 31.35 mg L-1 (BRT 10d) to 10.67 mg L-1 (BRT 3d), while soluble polysaccharides increased from 0.99 to 1.82 mg L-1, respectively. The concentrations of extracellular polymeric substance fractions were significantly altered, which decreased the mean floc size and contributed to the escalating fouling propensity. At the optimum BRT of 7d, the stirred-MPBR showed sufficient access to light and nutrients exchange for mutualistic interactions between the microalgae and activated sludge.
Dang, KB, Pham, HH, Nguyen, TN, Giang, TL, Pham, TPN, Nghiem, VS, Nguyen, DH, Vu, KC, Bui, QD, Pham, HN, Nguyen, TT & Ngo, HH 2023, 'Monitoring the effects of urbanization and flood hazards on sandy ecosystem services', Science of The Total Environment, vol. 880, pp. 163271-163271. View/Download from: Publisher's site
Dang, Z, Luo, M, Jia, C, Yan, C, Chang, X & Zheng, Q 2023, 'Counterfactual Generation Framework for Few-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 8, pp. 3747-3758. View/Download from: Publisher's site View description>>
Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based methods, these models fail to maintain the discrimination and diversity of the generated samples due to the distribution shift and intra-class bias caused by the data scarcity, therefore greatly undermining the performance. To this end, we use causal mechanisms, which are constant among independent variables across data distribution, to alleviate such effects. In this sense, we decompose the image information into two independent components: sample-specific and class-agnostic information, and further propose a novel Counterfactual Generation Framework (CGF) to learn the underlying causal mechanisms to synthesize faithful samples for FSL. Specifically, based on the counterfactual inference, we design a class-agnostic feature extractor to capture the sample-specific information, together with a counterfactual generation network to simulate the data generation process from a causal perspective. Moreover, to leverage the power of CGF in counterfactual inference, we further develop a novel classifier that classifies samples based on their distributions of counterfactual generations. Extensive experiments demonstrate the effectiveness of CGF on four FSL benchmarks, e.g., 80.12/86.13% accuracy on 5-way 1/5-shot miniImageNet FSL tasks, significantly improving the performance. Our codes and models are available at https://github.com/eric-hang/CGF.
Darroch, MM, Cooper-Woolley, B & Halkon, BJ 2023, 'Design and development of SiteHive MEMS based system for real-time vibration monitoring', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A303-A303. View/Download from: Publisher's site View description>>
Construction projects need to proactively manage their works that may cause vibration impacts to nearby structures and stakeholders. Risks of vibration include cosmetic and structural damage to buildings and threats to human comfort. The advent of MEMS accelerometers offers significant opportunities to improve on traditional vibration monitoring practices based on geophones. Geophones measure velocity which preclude acceleration based measurements and calculations like vibration dose value (the measure for human comfort), groundborne noise, and auto-levelling. The inability to capture these results means additional monitoring devices are required to capture all key measurements. MEMS-based vibration monitoring systems can be much cheaper, smaller, and more power efficient than traditional vibration monitoring systems. This enables easier installation, greater mobility, and more monitoring to be conducted. SiteHive has worked extensively with the National Measurement Institute (NMI) and the University of Technology Sydney (UTS) to test and validate the efficacy of the MEMS-based accelerometers and develop a calibration system for MEMS-based devices. This paper will outline the design research and findings that have gone into this development, results from field testing, and details on the value offered by this innovation.
Das, CM, Yang, F, Yang, Z, Liu, X, Hoang, QT, Xu, Z, Neermunda, S, Kong, KV, Ho, H, Ju, LA, Xiong, J & Yong, K 2023, 'Computational Modeling for Intelligent Surface Plasmon Resonance Sensor Design and Experimental Schemes for Real‐Time Plasmonic Biosensing: A Review', Advanced Theory and Simulations, vol. 6, no. 9. View/Download from: Publisher's site View description>>
AbstractThe spectacular physical phenomenon of surface plasmon resonance (SPR) is the essence of present‐day plasmonic sensors. Meanwhile, the unique properties of the interaction between light and matter have been carved out into the development of modern‐day diagnostic biosensors. Plasmons, in simple terms, are oscillating free electrons in metallic nano‐structures triggered by an incoming electromagnetic (EM) wave. With the advantages of real‐time and label‐free bio‐sensing, plasmonic sensors are being utilized in multiple diverse areas of food technology, the bio‐medical diagnostic sector, and even the chemical industry. Although this review will be brief, readers can gain a comprehensive picture of the essential elements by taking a broader look into the exploration of SPR sensor design via simulated studies and representative experimental plasmonic schemes developed for bio‐sensing. In short, the various SPR sensing schemes that researchers have explored to realize enhanced SPR sensitivity are reviewed and summarized. Different experimental plasmonic sensors are also examined in which new SPR excitation schemes have been adopted. These 'unconventional' designs, specifically those involving hybrid localized surface plasmon resonance (LSPR)‐SPR excitation, may inspire those in the plasmonic field.
Das, S, Bhattacharjee, M, Thiyagarajan, K & Kodagoda, S 2023, 'Conformable packaging of a soft pressure sensor for tactile perception', Flexible and Printed Electronics, vol. 8, no. 3, pp. 035006-035006. View/Download from: Publisher's site View description>>
AbstractHumans can perceive surface properties of an unfamiliar object without relying solely on vision. One way to achieve it is by physically touching the object. This human-inspired tactile perception is a complementary skill for robotic tactile perception. Robot perception depends on the informational quality of the tactile sensor; thus, packaging sensors and integrating them with robots plays a crucial role. In this work, we investigate the influence of conformable packaging designs on soft polydimethylsiloxane-based flexible pressure sensors that work in a variety of surface conditions and load levels. Four different 3D printed packaging designs capable of maintaining sensor trends have been developed. The low detection limits of 0.7 kPa and 0.1 kPa in the piezoresistive and piezocapacitive sensors, respectively, remain unaffected, and a performance variation as low as 30% is observed. Coefficient of variation and sensitivity studies have also been performed. Limit tests show that the designs can handle large forces ranging from 500 N to more than a 1000 N. Lastly, a qualitative study was performed, which covered prospective use-case scenarios as well as the advantages and downsides of each sensor casing design. Overall, the findings indicate that each sensor casing is distinct and best suited for tactile perception when interacting with objects, depending on surface properties.
Das, S, Bhattacharjee, M, Thiyagarajan, K & Kodagoda, S 2023, 'Nonlinear Response Analysis of a Polymer-Based Piezoresistive Flexible Tactile Sensor at Low Pressure', IEEE Sensors Letters, vol. 7, no. 11, pp. 1-4. View/Download from: Publisher's site
Dashti, A, Raji, M, Riasat Harami, H, Zhou, JL & Asghari, M 2023, 'Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection', Separation and Purification Technology, vol. 312, pp. 123399-123399. View/Download from: Publisher's site
de Couvreur, LA, Cobo, MJ, Kennedy, PJ & Ellis, JT 2023, 'Bibliometric analysis of parasite vaccine research from 1990 to 2019', Vaccine, vol. 41, no. 44, pp. 6468-6477. View/Download from: Publisher's site
Dehkordi, AA, Etaati, B, Neshat, M & Mirjalili, S 2023, 'Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations', IEEE Access, vol. 11, pp. 39269-39294. View/Download from: Publisher's site
Delle-Vergini, S, Ally, M, Eacersall, D, Dann, C & Chakraborty, S 2023, 'Teaching project management to primary school children: Exploring the perspectives of project practitioners', Issues in Educational Research, vol. 33, no. 1, pp. 41-70. View description>>
Project management knowledge and skills are an important component of the Australian Curriculum. The Australian Curriculum, Assessment and Reporting Authority calls for the explicit teaching of project management in primary school. However, it is uncertain if teachers possess the knowledge required to provide explicit teaching of project management, and to what extent it is being taught in primary school. To support the efforts of educators, seventeen project management experts were recruited to provide their perspectives. The Delphi method was used for consensus-building and the identification of core project management hard and soft skills. The findings revealed ten hard skills and twenty soft skills, ranked in order of importance and difficulty level, that children require to successfully manage projects. This study has significance for the project management profession by including the perspectives of industry practitioners on the skills required to successfully manage projects. It also provides educators with an evidence-based perspective for the design and delivery of project management content.
Deng, L, Guo, W, Ngo, HH, Zhang, X, Wei, D, Wei, Q & Deng, S 2023, 'Novel catalysts in catalytic upcycling of common polymer wastes', Chemical Engineering Journal, vol. 471, pp. 144350-144350. View/Download from: Publisher's site
Deng, R, Huo, P, Chen, X, Chen, Z, Yang, L, Liu, Y, Wei, W & Ni, B-J 2023, 'Towards efficient heterotrophic recovery of N2O via Fe(II)EDTA-NO: A modeling study', Science of The Total Environment, vol. 859, no. Pt 1, pp. 160285-160285. View/Download from: Publisher's site View description>>
Efficient recovery of nitrous oxide (N2O) through heterotrophic denitrification with the help of Fe(II)EDTA-NO as a chelating agent has been regarded as an ideal technology to treat nitric oxide (NO)-rich flue gas. In this study, an integrated NO-based biological denitrification model was developed to describe the sequential reduction of the NO fixed in Fe(II)EDTA-NO with organic carbon as the electron donor. With the inclusion of only the key pathways contributing to nitrogen transformation, the model was firstly developed and then calibrated/validated and evaluated using the data of batch tests mediated by the identified functional heterotrophic bacteria at various substrates concentrations and then used to explore the possibility of enhancing N2O recovery by altering the substrates condition and reactor setup. The results demonstrated that the optimal COD/N ratio decreased consistently from 1.5 g-COD/g-N at the initial NO concentration of 40 g-N/m3 to 1.0 g-COD/g-N at the initial NO concentration of 420 g-N/m3. Furthermore, sufficiently increasing the headspace volume of the reactor was considered an ideal strategy to obtain ideal N2O production of 86.6 % under the studied conditions. The production of high-purity N2O (98 %) confirmed the practical application potential of this integrated treatment technology to recover a valuable energy resource from NO-rich flue gas.
Deng, S, Wang, C, Ngo, HH, Guo, W, You, N, Tang, H, Yu, H, Tang, L & Han, J 2023, 'Comparative review on microbial electrochemical technologies for resource recovery from wastewater towards circular economy and carbon neutrality', Bioresource Technology, vol. 376, pp. 128906-128906. View/Download from: Publisher's site
Deng, Z, Li, W, Dong, W, Sun, Z, Kodikara, J & Sheng, D 2023, 'Multifunctional asphalt concrete pavement toward smart transport infrastructure: Design, performance and perspective', Composites Part B: Engineering, vol. 265, pp. 110937-110937. View/Download from: Publisher's site
Desroches, PE, Fraysse, KS, Silva, SM, Firipis, K, Merenda, A, Han, M, Dumée, LF, Quigley, AF, Kapsa, RMI, O'Connel, CD, Moulton, SE & Greene, GW 2023, 'A surface-tethered dopant method to achieve 3D control over the growth of a nanometers-thin and intrinsically transparent polypyrrole film', Electrochimica Acta, vol. 463, pp. 142817-142817. View/Download from: Publisher's site View description>>
The electrochemical growth of conductive polymer films is a convenient synthesis route but challenging to control due to local variability in the reaction kinetics. Here we report a new method for electropolymerizing highly reproducible conductive polypyrrole films that are just nanometers thick, highly conductive and possess intrinsic optical transparencies comparable to ITO. The synthesis method utilizes a surface-tethered dopant molecule, in this case a self-assembled monolayer of the highly anionic protein lubricin (LUB), to template and thus control the 3-dimensional growth of the polypyrrole when the electrochemical polymerization reaction is performed in a pyrrole monomer solution containing no additional dopant molecules or ions. Because the tethered dopant controls where and how much polypyrrole growth occurs, this method effectively decouples the fine film morphology, thickness, and spatial-growth from the polymerization reaction kinetics and represents a paradigm shift in the electrochemical polymerization of conductive polymer films.
Deveci, Ö, Shannon, AG & Mansımova, A 2023, 'The cyclic sequences in non-Abelian groups', Communications in Algebra, vol. 51, no. 7, pp. 2956-2962. View/Download from: Publisher's site
Dewi, OC, Putra, N, Yatim, A, Mahlia, TMI, Rahmasari, K, Hanjani, T, Siregar, R, Rangin, B & Izzatur, N 2023, 'Zoning and activity-based post occupancy evaluation of multipurpose auditorium in campus facility', Energy and Buildings, vol. 295, pp. 113319-113319. View/Download from: Publisher's site
Dharma, S, Silitonga, AS, Shamsuddin, AH, Sebayang, AH, Milano, J, Sebayang, R, Sarjianto, Ibrahim, H, Bahri, N, Ginting, B & Damanik, N 2023, 'Properties and corrosion behaviors of mild steel in biodiesel-diesel blends', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 2, pp. 3887-3899. View/Download from: Publisher's site View description>>
Global warming in relation to fossil fuel pollution and their environmental impacts have become a major global concern. Biodiesel has entered the scene as an alternative fuel but it also generated controversy associated with increased residual fuel, increased acidity, oxidation, and corrosion. The main objective of this study was to observe the corrosion behavior of the mild steel immersed in J50C50 biodiesel-diesel fuel blends for up to 800 h at ambient temperature. The results showed corrosion rate at 800-h immersion are 0.0103, 0.0044, 0.0117, 0.0155, 0.2283 and 0.02524 mm/year, respectively, for B0, B10, B20, B30, B40 and B50. Mild steel coupon surface observation using SEM showed corrosion attacks are characterized by round holes on the metal surface. The addition of J50C50 biodiesel into diesel fuel accelerated the corrosion rate and acid value. Overall, corrosion observations conducted on mild steel suggested J50C50 biodiesel-diesel fuel blend is more corrosive compared with diesel fuel.
Ding, A, Lin, W, Shi, H, Chen, R, Ngo, HH, He, X, Nan, J, Li, G & Ma, J 2023, 'Enhanced Sludge Dewaterability by Efficient Oxidation of α-Mn2O3/Peroxymonosulfate: Analysis of the Mechanism and Evaluation of Engineering Application', ACS ES&T Engineering, vol. 3, no. 2, pp. 236-247. View/Download from: Publisher's site
Ding, H & Ji, JC 2023, 'Vibration control of fluid-conveying pipes: a state-of-the-art review', Applied Mathematics and Mechanics, vol. 44, no. 9, pp. 1423-1456. View/Download from: Publisher's site View description>>
AbstractFluid-conveying pipes are widely used to transfer bulk fluids from one point to another in many engineering applications. They are subject to various excitations from the conveying fluids, the supporting structures, and the working environment, and thus are prone to vibrations such as flow-induced vibrations and acoustic-induced vibrations. Vibrations can generate variable dynamic stress and large deformation on fluid-conveying pipes, leading to vibration-induced fatigue and damage on the pipes, or even leading to failure of the entire piping system and catastrophic accidents. Therefore, the vibration control of fluid-conveying pipes is essential to ensure the integrity and safety of pipeline systems, and has attracted considerable attention from both researchers and engineers. The present paper aims to provide an extensive review of the state-of-the-art research on the vibration control of fluid-conveying pipes. The vibration analysis of fluid-conveying pipes is briefly discussed to show some key issues involved in the vibration analysis. Then, the research progress on the vibration control of fluid-conveying pipes is reviewed from four aspects in terms of passive control, active vibration control, semi-active vibration control, and structural optimization design for vibration reduction. Furthermore, the main results of existing research on the vibration control of fluid-conveying pipes are summarized, and future promising research directions are recommended to address the current research gaps. This paper contributes to the understanding of vibration control of fluid-conveying pipes, and will help the research work on the vibration control of fluid-conveying pipes attract more attention.
Ding, L, Ji, J, Li, Y, Wang, S, Noman, K & Feng, K 2023, 'A Novel Weak Feature Extraction Method for Rotating Machinery: Link Dispersion Entropy', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-12. View/Download from: Publisher's site View description>>
The entropy-based feature extraction is a promising tool for extracting weak features from rotating machinery. However, the existing research has paid little attention to the state transition process, which brings the problem of accuracy and comprehensiveness in complexity estimation. To address this issue, this paper proposes link dispersion entropy (LDE) based on the theory of the Markov chain for weak feature extraction. By calculating the transition probability of symbol patterns, the LDE can extract the fault information contained in the transition, enabling it to capture the early weak fault. Furthermore, LDE is extended to a multiscale analysis by combining it with the coarse-gaining process for comprehensive feature extraction, termed multiscale LDE (MLDE). Finally, three simulated signals and two different experimental data are utilized to verify the advantage of MLDE in extracting the weak fault features. Results demonstrate that MLDE has the best performance in fault diagnosis of rotating machinery compared with the existing five methods, namely sample entropy, fuzzy entropy, permutation entropy, dispersion entropy and symbolic dynamic entropy.
Ding, L, Oh, S, Shrestha, J, Lam, A, Wang, Y, Radfar, P & Warkiani, ME 2023, 'Scaling up stem cell production: harnessing the potential of microfluidic devices', Biotechnology Advances, vol. 69, pp. 108271-108271. View/Download from: Publisher's site
Ding, W, Liu, J, Lin, C & Mrozek, D 2023, 'Special issue on Recent Advances in Fuzzy Deep Learning for Uncertain Medicine Data', Information Sciences, vol. 642, pp. 118997-118997. View/Download from: Publisher's site
Ding, Z, Chen, X, Dong, Y, Yu, S & Herrera, F 2023, 'Consensus Convergence Speed in Social Network DeGroot Model: The Effects of the Agents With High Self-Confidence Levels', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2882-2892. View/Download from: Publisher's site View description>>
In group decision making (GDM), opinion dynamics is a useful tool to investigate consensus formation. Notably, consensus convergence speed is of key importance to manage the consensus formation in GDM with opinion dynamics. Recently, social network DeGroot (SNDG) model has been widely used in opinion dynamics. Based on this, this article dedicates to study how agents’ high self-confidence levels affect the consensus convergence speed in SNDG model. Interestingly, using theoretical analysis, we prove that: 1) the speed of consensus reaching is subject to the largest self-confidence level of opinion followers and 2) the speed of consensus reaching is also subject to the top two self-confidence levels of opinion leaders. Furthermore, through extensive simulation’, we find that the theoretical results are robust to the topological structure and the size of social networks.
Dinh, TH, Anh, VTT, Nguyen, T, Hieu Le, C, Trung, NL, Duc, ND & Lin, C-T 2023, 'Toward Vision-Based Concrete Crack Detection: Automatic Simulation of Real-World Cracks', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15. View/Download from: Publisher's site
Dinh, TQ, Nguyen, DN, Hoang, DT, Pham, TV & Dutkiewicz, E 2023, 'In-Network Computation for Large-Scale Federated Learning Over Wireless Edge Networks', IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 5918-5932. View/Download from: Publisher's site View description>>
Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This paper proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$ of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud’s traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable ...
Do, PMT, Zhang, Q, Zhang, G & Lu, J 2023, 'meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning', Procedia Computer Science, vol. 225, pp. 2536-2545. View/Download from: Publisher's site
Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Interactive Role of Rolling Friction and Cohesion on the Angle of Repose through a Microscale Assessment', International Journal of Geomechanics, vol. 23, no. 1. View/Download from: Publisher's site View description>>
Cohesion and rolling friction play key roles in governing the behavior of soil; however, only a limited number of studies have been able to assess the simultaneous contributions of these two microparameters on the macroproperties of soil. In this respect, the innovation of the current study includes an attempt to examine the interplay of these two primary parameters on the angle of repose (AoR) based on the discrete-element method (DEM). Lifting cylinder tests on cohesive wet sand have been carried out in DEM, while the cohesion and rolling friction are captured through proposed computational models. In this paper, macroparameters, such as the geometry and developmental stages of sand piles obtained in DEM simulation, are compared with experimental data, while their microevolution is quantified in detail. The results show that a large AoR can only be obtained when the cohesive and rotational frictional forces work in tandem. Increasing the cohesion and rolling friction results in smaller contact numbers, with increasing chain-like connections between particles and larger pore spaces to account for a larger AoR. For the first time, this study distinctly identifies three major stages that contribute to the AoR, based on the development of contact numbers and the transformation of energy. Accordingly, the linkage between macroscale AoR and the microstructural coordination number is formulated with varying levels of cohesion and rolling friction. The DEM results prove that the more cohesive the particles are, the greater the delay in the dissipation of kinetic energy.
Dogan, S, Baygin, M, Tasci, B, Loh, HW, Barua, PD, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals', Cognitive Neurodynamics, vol. 17, no. 3, pp. 647-659. View/Download from: Publisher's site
Dong, B, Yu, Y, Feng, Y, Wu, D, Zhao, G, Liu, A & Gao, W 2023, 'Robust numerical solution for assessing corrosion of reinforced concrete structures under external power supply', Engineering Structures, vol. 294, pp. 116724-116724. View/Download from: Publisher's site
Dong, M, Yao, L, Wang, X, Xu, X & Zhu, L 2023, 'Adversarial dual autoencoders for trust-aware recommendation', Neural Computing and Applications, vol. 35, no. 18, pp. 13065-13075. View/Download from: Publisher's site View description>>
Recommender systems face longstanding challenges in gaining users’ trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users’ social relationships for inferring the user preference, i.e., recommending items according to the preference by user’s trusted friends; or adding random noise to the input to improve the robustness of the recommender systems. However, such approaches cannot defend the real-world noises like fake ratings. The recommender model is generally built upon all the user-item interactions, which incorporates the information from fake ratings or spammer groups, that neglects the reliability of the ratings. To address the above challenges, we propose an adversarial training approach in this work. In details, our approach includes two components: a predictor that infers the user preference; and a discriminator that enforces cohort rating patterns. In particular, the predictor applies an encoder-decoder structure to learn the shared latent information from sparse users’ ratings and trust relationships; the discriminator enforces the predictor to provide ratings as coherent with the cohort rating patterns. Our extensive experiments on three real-world datasets show the advantages of our approach over several competitive baselines.
Dong, Q, Zheng, X, Fu, A, Su, M, Zhou, L & Yu, S 2023, 'DMRA: Model Usability Detection Scheme Against Model-Reuse Attacks in the Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16907-16916. View/Download from: Publisher's site
Dong, S, Mei, F, Li, JJ & Xing, D 2023, 'Global Cluster Analysis and Network Visualization in Prosthetic Joint Infection: A Scientometric Mapping', Orthopaedic Surgery, vol. 15, no. 4, pp. 1165-1178. View/Download from: Publisher's site View description>>
ObjectiveProsthetic joint infection (PJI) is the main reason of failure of total joint arthroplasty (TJA). This study aimed to investigate the global trends and network visualization in research of PJI.MethodsPublications in PJI search during 1980–2022 were extracted from the Science Citation Index‐Expanded of Web of Science Core Collection database (WoSCC). The source data was investigated and analyzed by bibliometric methodology. For network visualization, VOS viewer and R software was used to perform bibliographic coupling, co‐citation, co‐authorship and co‐occurrence analysis and to predict the publication trends in PJI research.ResultsThere were 7288 articles included. The number of publications and relative research interests increased gradually per year globally. The USA made the highest contributions in the world and with the highest H‐index and the most citations. Journal of Arthroplasty published the highest number of articles in this area. The Mayo Clinic, Thomas Jefferson University (Rothman Institute), Hospital Special Surgery and the Rush University were the most contributive institutions by network visualization. Included studies were divided into four clusters: bacterial pathogenic mechanism and antibacterial drugs study, TJA complications, risk factors and epidemiology of PJI, diagnosis of PJI, and revision surgical management. More articles in PJI could be published over the next few years.ConclusionThe number of publications about PJI will be increasing dramatically based on the global trends and network visualization. The USA made the highest contributions in PJI. Diagnosis and revision management may be the next hot spots in this field.
Dong, Y, Ran, Q, Chao, X, Li, C & Yu, S 2023, 'Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process', ACM Transactions on Internet Technology, vol. 23, no. 2, pp. 1-27. View/Download from: Publisher's site View description>>
When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.
Du, G, Pu, T, Zhou, Q, Wang, L, Lei, G & Zhu, J 2023, 'Multiphysics Comparative Study of High Speed PM Machines for Ring PM Rotor and Solid PM Rotor', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1421-1432. View/Download from: Publisher's site
Du, J, Li, X, Sugumaran, V, Hu, Y & Xue, Y 2023, 'Dynamic model averaging-based procurement optimization of prefabricated components', Neural Computing and Applications, vol. 35, no. 36, pp. 25157-25173. View/Download from: Publisher's site
Du, J, Xue, Y, Sugumaran, V, Hu, M & Dong, P 2023, 'Improved biogeography-based optimization algorithm for lean production scheduling of prefabricated components', Engineering, Construction and Architectural Management, vol. 30, no. 4, pp. 1601-1635. View/Download from: Publisher's site View description>>
PurposeFor prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.Design/methodology/approachThis paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.FindingsThis paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiven...
Du, K, Luo, X, Yang, S, Danial, JA & Zhou, J 2023, 'An insight from energy index characterization to determine the proneness of rockburst for hard rock', Geomechanics for Energy and the Environment, vol. 35, pp. 100478-100478. View/Download from: Publisher's site
Du, S, Shen, Y, Zheng, Y, Cheng, Y, Xu, X, Chen, D & Xia, D 2023, 'Systematic in vitro and in vivo study on biodegradable binary Zn-0.2 at% Rare Earth alloys (Zn-RE: Sc, Y, La–Nd, Sm–Lu)', Bioactive Materials, vol. 24, pp. 507-523. View/Download from: Publisher's site
Du, X, Chen, Z, Li, Q, Yang, S, Jiang, L, Yang, Y, Li, Y & Gu, Z 2023, 'Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence', Bio-Design and Manufacturing, vol. 6, no. 3, pp. 319-339. View/Download from: Publisher's site
Du, Z, Yang, M, Yang, Y, Zhang, X, Chen, H, Ngo, HH & Liu, Q 2023, 'Sulfur-Modified Biochar Efficiently Removes Cr(VI) from Water by Sorption and Reduction', Environmental Engineering Science, vol. 40, no. 9, pp. 362-372. View/Download from: Publisher's site
Duan, J-L, Han, Y, Feng, L-J, Ma, J-Y, Sun, X-D, Liu, X-Y, Geng, F-S, Jiang, J-L, Liu, M-Y, Sun, Y-C, Peu, P, Ni, B-J & Yuan, X-Z 2023, 'Single bubble probe atomic force microscope and impinging-jet technique unravel the interfacial interactions controlled by long chain fatty acid in anaerobic digestion', Water Research, vol. 231, pp. 119657-119657. View/Download from: Publisher's site
Duan, L, Yang, L, Liu, C, Ni, W & Wang, W 2023, 'A New Smart Contract Anomaly Detection Method by Fusing Opcode and Source Code Features for Blockchain Services', IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4354-4368. View/Download from: Publisher's site
Duan, Q, Huang, J, Hu, S, Deng, R, Lu, Z & Yu, S 2023, 'Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions', IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2892-2950. View/Download from: Publisher's site
Duan, Y, Lu, Y, Shen, S, Yu, S, Zhang, P, Zhang, W & Igorevich, KK 2023, 'NFLCS: An Service Function Chain Path Optimization Strategy Based on Network-Functional Layout Clustering', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10813-10825. View/Download from: Publisher's site
Duan, Y, Wang, Z, Li, Y, Tang, J, Wang, Y-K & Lin, C-T 2023, 'Cross task neural architecture search for EEG signal recognition', Neurocomputing, vol. 545, pp. 126260-126260. View/Download from: Publisher's site
Ebrahimi Farshchi, M, Madadian Bozorg, N, Ehsani, A, Aghdasinia, H, Chen, Z, Rostamnia, S & Ni, B-J 2023, 'Green valorization of PET waste into functionalized Cu-MOF tailored to catalytic reduction of 4-nitrophenol', Journal of Environmental Management, vol. 345, pp. 118842-118842. View/Download from: Publisher's site
Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Liu, J 2023, 'A three-way Pythagorean fuzzy correlation coefficient approach and its applications in deciding some real-life problems', Applied Intelligence, vol. 53, no. 1, pp. 226-237. View/Download from: Publisher's site View description>>
Correlation coefficient (CC) is a reliable information measure for measuring interrelationship between Pythagorean fuzzy sets (PFSs). Some approaches for calculating CC of PFSs have been considered. These hitherto approaches assess only the strength of relationship between PFSs, and are described within the interval [0,1]. This paper proposes a three-way approach for the computation of CC between PFSs by using the concepts of variance and covariance, respectively. This new approach is defined within the interval [− 1,1] akin to classical statistics, shows the strength of relationship between the considered PFSs and indicates whether the PFSs are either positively or negatively correlated. By including the three conventional parameters of PFSs in the proposed technique, the possibility of error due to information leakage is reasonably minimized. The new technique is validated with some theoretical results to show its suitability as reliable information measure. Some numerical examples are considered to show the edges of the new methods over similar methods. From the comparative analysis, the proposed methods of computing CCPFSs give more reliable and reasonable results compare to similar existing methods as presented in Table 13. Certain decision-making problems involving recognition of patterns and diagnostic medicine are resolved with the aid of the new method. The three-way technique of computing correlation coefficient between PFSs can solve decision-making problems that are multi-attributes in nature.
Eklund, M, Khalilpour, K, Voinov, A & Hossain, MJ 2023, 'Understanding the community in community microgrids: A conceptual framework for better decision-making', Energy Research & Social Science, vol. 104, pp. 103260-103260. View/Download from: Publisher's site View description>>
A community microgrid comes with the introduction of non-conventional distributed renewable energy infrastructure, affecting the behaviour of community members and their relationship with energy. The aspects of ownership, trust, collaboration and its often-discursive structure will be reflected in the cultural and social factors, such as norms and values in a community. The success of specific community microgrids is widely dependent on the community's ability to engage in various activities connected to the microgrid installation and operation. This paper conceptualises existing literature on community microgrids, focusing on the representation and inclusion of community preferences, needs and behaviour across the development stages. From this analysis, a conceptual-theoretical framework is proposed based on social capital theory for identifying community characteristics to determine key needs and considerations for microgrid adoption. The framework is divided into four components: social capital, community capability, community type and microgrid impact. Social capital, including its dimensions such as structural, cognitive, and relational capital forms the foundation of the framework and serves to evaluate the community capability and determine its type, which in turn affects its impact on the community microgrid. Finally, we present an initial step in operationalising our conceptual framework as a practical tool to guide further research in the development of community microgrids. Ultimately, this research can benefit both academia and industry by providing a comprehensive and practical approach to understanding the importance of social factors in community microgrid success.
El Majzoub, A, Rabhi, FA & Hussain, W 2023, 'Evaluating interpretable machine learning predictions for cryptocurrencies', Intelligent Systems in Accounting, Finance and Management, vol. 30, no. 3, pp. 137-149. View/Download from: Publisher's site View description>>
SummaryThis study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
Elgharabawy, A, Prasad, M & Lin, C-T 2023, 'Preference Neural Network', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 5, pp. 1362-1376. View/Download from: Publisher's site View description>>
This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (PNN) uses spearman correlation gradient ascent and two new activation functions, positive smooth staircase (PSS), and smooth staircase (SS) that accelerate the ranking by creating almost deterministic preference values. PNN is proposed in two forms, fully connected simple Three layers and Preference Net (PN), where the latter is the deep ranking form of PNN to learning feature selection using ranking to solve images classification problem. PN uses a new type of ranker kernel to generate a feature map. PNN outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and PN achieves promising results on CFAR-100 with high computational efficiency.
Elkhodr, M, Gide, E, Wu, R & Darwish, O 2023, 'ICT students' perceptions towards ChatGPT: An experimental reflective lab analysis', STEM Education, vol. 3, no. 2, pp. 70-88. View/Download from: Publisher's site View description>>
<abstract><p>This study explores the use of the Generative artificial intelligence (GenAI) tool ChatGPT in higher education. Amidst the potential benefits and the risk of misuse, this research investigates the tool's role as a classroom aid and its impact on learning outcomes and experiences. Three case studies involving undergraduate and postgraduate ICT students were conducted. Findings revealed a positive perception of ChatGPT as a useful and enjoyable learning resource. Most students indicated a willingness to use such AI tools in the future. Additionally, the study suggested improved performance in functionality, user flow, and content comprehension among students using ChatGPT, compared to those relying solely on traditional search engines.</p></abstract>
Elmakki, T, Zavahir, S, Gulied, M, Qiblawey, H, Hammadi, B, Khraisheh, M, Shon, HK, Park, H & Han, DS 2023, 'Potential application of hybrid reverse electrodialysis (RED)-forward osmosis (FO) system to fertilizer-producing industrial plant for efficient water reuse', Desalination, vol. 550, pp. 116374-116374. View/Download from: Publisher's site View description>>
This study presents an experimental investigation and a parametric analysis of the applicability of agricultural fertigation and power generation using a reverse electrodialysis-forward osmosis (RED-FO) hybrid system, with a water stream discharged from a fertilizer-producing plant. The results of this study demonstrated the possibility of achieving high salinity power generation from the RED system utilizing high-salinity brine and low-salinity ammonia solution that simulates reverse osmosis (RO) brine and wastewater streams released by the fertilizer-producing industry. The feasibility of stream dilution for fertigation application is demonstrated when the resulting moderately saline RED effluent is introduced into the FO process as a draw solution. The effect of external load addition, flow velocities variation, and concentration changes of the working solutions on the overall stack internal resistance and, thereby, RED performance was evaluated. As such, the lowest internal resistance converged to a threshold value of 4.03 Ω, giving the highest gross power density of 2.17 W/m2 when a flow velocity of 1.18 cm/s, 10 Ω external load, and 0.015 M (NH4)2SO4/1 M NaCl solution pair were utilized. In addition, the effect of the number of ion exchange membrane pairs and wastewater stream recycling was studied and optimized to amplify the osmotically generated power. As a result, the most consistent power generation was achieved when using 20 pairs of membrane cells in a single-pass flow mode operation. The applicability of the RED effluent to a subsequent FO system as a draw solution (DS) was investigated, showing a dilution rate (17 %) and a conductivity (1–2 mS/cm of DS) suitable for agricultural fertigation applications.
Elsawah, S, Bakhanova, E, Hämäläinen, RP & Voinov, A 2023, 'A Competency Framework for Participatory Modeling', Group Decision and Negotiation, vol. 32, no. 3, pp. 569-601. View/Download from: Publisher's site View description>>
AbstractParticipatory modeling (PM) is a craft that is often learned by training ‘on the job’ and mastered through years of practice. There is little explicit knowledge available on identifying and documenting the skills needed to perform PM. In the modeling literature, existing attempts to identify relevant competencies have focused on the specific technical skills required for specific technical model development. The other skills required to organize and conduct the stakeholder process seem to be more vaguely and poorly defined in this context. The situation is complicated by PM being an essentially transdisciplinary craft, with no single discipline or skill set to borrow ideas and recommendations from. In this paper, we aim to set the foundation for both the practice and capacity-building efforts for PM by identifying the relevant core competencies. Our inquiry into this topic starts with reviewing and compiling literature on competencies in problem-solving research areas related to PM (e.g., systems thinking, facilitated model building, operations research, and so forth). We augment our inquiry with results from a PM practitioners’ survey to learn how they perceive the importance of different competencies and how the scope of these competencies may vary across the various roles that participatory modellers play. As a result, we identified five core competency areas essential for PM: systems thinking, modeling, group facilitation, project management and leadership, and, more recently, designing and running virtual workshops and events.
Entezari, A, Liu, N-C, Zhang, Z, Fang, J, Wu, C, Wan, B, Swain, M & Li, Q 2023, 'Nondeterministic multiobjective optimization of 3D printed ceramic tissue scaffolds', Journal of the Mechanical Behavior of Biomedical Materials, vol. 138, pp. 105580-105580. View/Download from: Publisher's site
Erdem, K, Kobat, MA, Bilen, MN, Balik, Y, Alkan, S, Cavlak, F, Poyraz, AK, Barua, PD, Tuncer, I, Dogan, S, Baygin, M, Erten, M, Tuncer, T, Tan, R & Acharya, UR 2023, 'Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images', International Journal of Imaging Systems and Technology, vol. 33, no. 4, pp. 1144-1159. View/Download from: Publisher's site View description>>
AbstractCOVID‐19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X‐ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time‐consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid‐Patch‐Alex for automated COVID‐19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID‐19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre‐trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k‐nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.
Erten, M, Barua, PD, Tuncer, I, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Swin-LBP: a competitive feature engineering model for urine sediment classification', Neural Computing and Applications, vol. 35, no. 29, pp. 21621-21632. View/Download from: Publisher's site View description>>
AbstractAutomated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of variou...
Eskandari, M, Savkin, AV & Ni, W 2023, 'Consensus-Based Autonomous Navigation of a Team of RIS-Equipped UAVs for LoS Wireless Communication With Mobile Nodes in High-Density Areas', IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 923-935. View/Download from: Publisher's site
Faisal, SN, Do, T-TN, Torzo, T, Leong, D, Pradeepkumar, A, Lin, C-T & Iacopi, F 2023, 'Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene', ACS Applied Nano Materials, vol. 6, no. 7, pp. 5440-5447. View/Download from: Publisher's site
Fan, C, Zhang, X, Zhao, Y, Liu, Y & Yu, S 2023, 'Self-Adaptive Gradient Quantization for Geo-Distributed Machine Learning Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cloud Computing, vol. 11, no. 4, pp. 3483-3496. View/Download from: Publisher's site
Fan, H, Yang, Y & Kankanhalli, M 2023, 'Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2181-2192. View/Download from: Publisher's site
Fan, J, Li, J, Wang, Y, Li, Y, Hsieh, M-H & Du, J 2023, 'Partially Concatenated Calderbank-Shor-Steane Codes Achieving the Quantum Gilbert-Varshamov Bound Asymptotically', IEEE Transactions on Information Theory, vol. 69, no. 1, pp. 262-272. View/Download from: Publisher's site
Fan, J, Yan, J, Zhou, M, Xu, Y, Lu, Y, Duan, P, Zhu, Y, Zhang, Z, Li, W, Wang, A & Sun, D 2023, 'Heavy metals immobilization of ternary geopolymer based on nickel slag, lithium slag and metakaolin', Journal of Hazardous Materials, vol. 453, pp. 131380-131380. View/Download from: Publisher's site View description>>
To solve heavy metals leaching problem in the utilization of various industrial solid wastes, this work investigated the heavy metals immobilization of ternary geopolymer prepared by nickel slag (NS), lithium slag (LS), and metakaolin (MK). Compressive strength was measured to determine the optimum and appropriate mix proportions. The leaching characteristics of typical heavy metals (Cu (Ⅱ), Pb (Ⅱ), and Cr (Ⅲ)) in acid, alkali, and salt environments were revealed by Inductively Coupled Plasma (ICP). The heavy metals immobilization mechanism was explored by Mercury Intrusion Porosimetry (MIP), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) tests. The experimental results show that the group with a mass ratio of NS, LS and MK of 1:1:8 exhibits the highest compressive strength, which reaches 69.1 MPa at 28 d. The ternary geopolymer possesses a desirable capacity for immobilizing inherent heavy metals, where the immobilization rates of Cu and Pb reach 96.69 %, and that of Cr reaches 99.97 %. The leaching concentrations of Cr and Pb increase when the samples are exposed to acidic and alkaline environments. Cu and Pb are mainly physically encapsulated in geopolymer. Additionally, immobilization of Cr mainly involves physical encapsulation and chemical bonding.
Fan, W, Xiao, F, Cai, H, Chen, X & Yu, S 2023, 'Disjoint Paths Construction and Fault-Tolerant Routing in BCube of Data Center Networks', IEEE Transactions on Computers, vol. 72, no. 9, pp. 2467-2481. View/Download from: Publisher's site
Fan, X, Li, Y, Chen, L, Li, B & Sisson, SA 2023, 'Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1848-1861. View/Download from: Publisher's site View description>>
Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g. stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.
Fan, Z, Yan, Z & Wen, S 2023, 'Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health', Sustainability, vol. 15, no. 18, pp. 13493-13493. View/Download from: Publisher's site View description>>
Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.
Fang, C, Hu, Z, Meng, X, Tu, S, Wang, Z, Zeng, D, Ni, W, Guo, S & Han, Z 2023, 'DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 12, pp. 16195-16207. View/Download from: Publisher's site
Fang, K, Zhao, J, Li, X, Li, Y & Duan, R 2023, 'Quantum NETwork: from theory to practice', Science China Information Sciences, vol. 66, no. 8. View/Download from: Publisher's site
Fang, W & Ying, M 2023, 'SymPhase: Phase Symbolization for Fast Simulation of Stabilizer Circuits.', CoRR, vol. abs/2311.03906.
Fang, Z, Lu, J, Liu, F & Zhang, G 2023, 'Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1087-1105. View/Download from: Publisher's site View description>>
Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions. Motivated by compatibility condition in semi-supervised probably approximately correct (PAC) theory, we explain the SsHeDA problem by proving its generalization error that is, why labeled heterogeneous source data and unlabeled target data help to reduce the target risk. Guided by our theory, we devise two algorithms as proof of concept. One, kernel heterogeneous domain alignment (KHDA), is a kernel-based algorithm; the other, joint mean embedding alignment (JMEA), is a neural network-based algorithm. When a dataset is small, KHDA's training time is less than JMEA's. When a dataset is large, JMEA is more accurate in the target domain. Comprehensive experiments with image/text classification tasks show KHDA to be the most accurate among all non-neural network baselines, and JMEA to be the most accurate among all baselines.
Fang, Z, Wu, Z, Ni, W, Wang, X & Hossain, E 2023, 'Beamforming Design for Novel Relay-Assisted Multi-User Multi-Tag Symbiotic Radios', IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2253-2257. View/Download from: Publisher's site
Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lu, DD-C, Liserre, M & Siwakoti, YP 2023, 'A Single-Stage Switched-Boost Grid-Connected Five-Level Converter With Integrated Active Power Decoupling Under Polluted Grid Voltage Condition', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 328-345. View/Download from: Publisher's site
Farhart, P, Beakley, D, Diwan, A, Duffield, R, Rodriguez, EP, Chamoli, U & Watsford, M 2023, 'Intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers in cricket: a systematic review', BMC Sports Science, Medicine and Rehabilitation, vol. 15, no. 1, p. 114. View/Download from: Publisher's site View description>>
AbstractBackgroundLumbar spine injuries in fast bowlers account for the greatest missed playing time in cricket. A range of extrinsic and intrinsic variables are hypothesised to be associated with low back pain and lumbar spine injury in fast bowlers, and an improved understanding of intrinsic variables is necessary as these may alter load tolerance and injury risk associated with fast bowling. This review critically evaluated studies reporting intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers and identified areas for future investigation.MethodsOVID Medline, EMBASE, SPORTDiscus, CINAHL, Web of Science and SCOPUS databases were last searched on 3 June 2022 to identify studies investigating intrinsic variables associated with low back pain and lumbar spine injury in cricket fast bowlers. Terms relevant to cricket fast bowling, and intrinsic variables associated with lumbar spine injury and low back pain in fast bowlers were searched. 1,503 abstracts were screened, and 118 full‐text articles were appraised to determine whether they met inclusion criteria. Two authors independently screened search results and assessed risk of bias using a modified version of the Quality in Prognostic Studies tool.ResultsTwenty-five studies met the inclusion criteria. Overall, no included studies demonstrated a low risk of bias, two studies were identified as moderate risk, and twenty-three studies were identified as high risk. Conflicting results were reported amongst studies investigating associations of fast bowling kinematics and kinetics, trunk and lumbar anatomical features, anthropometric traits, age, and ne...
Farooq, U, Riaz, HH, Munir, A, Zhao, M, Tariq, A & Islam, MS 2023, 'Application of heliox for optimized drug delivery through respiratory tract', Physics of Fluids, vol. 35, no. 10. View/Download from: Publisher's site View description>>
Understanding the transportation and deposition (TD) of inhaled particles in the upper respiratory tract is crucial for predicting health risks and treating pulmonary diseases. The available literature reports highly turbulent flow in the extrathoracic (ET) region during normal breathing, which leads to higher deposition of the drug aerosol in this region. To improve the targeted deposition of inhaled drugs, in the tracheobronchial airways, it is essential to understand the flow and particle transport dynamics and reduce the turbulence behavior at the ET region. The less-dense heliox gas could reduce the turbulence behavior at the ET; however, the knowledge of heliox inhalation therapies in drug aerosol TD remains underachieved to realize the full potential for assisted breathing and drug delivery. Additionally, the impact of the inhalation of heliox mixed with other gases on particle deposition is missing in the literature. Therefore, this study aims to develop a mixture model to advance the knowledge of inhalation therapy. A heliox (78% helium and 22% oxygen) and a mixture of heliox and air are used to understand the flow behavior and particle TD in airways. The impact of different inhalation and Stokes numbers on the deposition efficiencies in the ideal and age-specific upper airways is studied. The study reports that less-dense heliox gas has lower turbulence intensity and results in lower deposition efficiency in the G3–G5 lung airways compared to air and mixture inhalations. Moreover, slightly higher deposition efficiencies during mixture inhalation as compared to air inhalation are found in the upper airways. The deposition patterns of different inhalations obtained in this study could help improve targeted drug delivery into the upper and deeper lung airways.
Fathalla, A, Salah, A, Bekhit, M, Eldesouky, E, Talha, A, Zenhom, A & Ali, A 2023, 'Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform', International Journal of Intelligent Systems, vol. 2023, pp. 1-11. View/Download from: Publisher's site View description>>
In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on fou...
FathollahZadeh Aghdam, R, Ahmad, N, Naveed, A & Berenjforoush Azar, B 2023, 'On the relationship between energy and development: A comprehensive note on causation and correlation', Energy Strategy Reviews, vol. 46, pp. 101034-101034. View/Download from: Publisher's site
Fathoni, AM, Putra, N & Mahlia, TMI 2023, 'A systematic review of battery thermal management systems based on heat pipes', Journal of Energy Storage, vol. 73, pp. 109081-109081. View/Download from: Publisher's site View description>>
The global economic increase is accompanied by an increase in energy use. As a result, there is an increase in emissions of greenhouse gases that can harm the environment and lead to global warming. Electric vehicles powered by lithium-ion batteries were developed to lower the transportation sector's contribution to greenhouse gas emissions. In order to work optimally, the battery must be maintained at its optimal temperature. Heat pipe-based thermal management systems of electric vehicles' batteries have been gaining interest recently due to their ability to dissipate heat to the environment quickly and work passively without any added energy. Heat pipes are anticipated to keep battery packs for electric vehicles at their ideal operating temperature, ensure temperature uniformity between battery cells, and minimize thermal runaway possibility. This paper mainly discusses the application of heat pipes in the thermal management system of the electric vehicle battery. Besides conventional heat pipes, hybrid thermal management systems for electric vehicle batteries based on heat pipes have also been reviewed and discussed. For the hybrid battery management system, heat pipes coupled with phase change materials, air cooling and liquid cooling have been analysed. Finally, this review study describes the limitations and future work opportunities in the research area of thermal management systems in electric vehicle batteries based on heat pipes.
Fattah, IMR, Farhan, ZA, Kontoleon, KJ, kianfar, E & Hadrawi, SK 2023, 'Hollow fiber membrane contactor based carbon dioxide absorption − stripping: a review', Macromolecular Research, vol. 31, no. 4, pp. 299-325. View/Download from: Publisher's site View description>>
Energy need is predicted to increase by 47% in the next 30 years. Global warming resulting from the continuously increasing atmospheric Carbon dioxide concentration is becoming a serious and pressing issue that needs to be controlled. Carbon dioxide capture and storage/use (CCS/CCU) provide a promising route to mitigate the environmental consequences of Carbon dioxide emission from fossil fuel combustion. In recent years, hollow fiber membrane contactors are regarded as an advanced technique with several competitive advantages over conventional technologies such as easy scale-up, independent control of flow rates, more operational flexibility, absence of flooding and foaming as well as high interfacial area per unit volume. However, many factors such as the membrane material selection, proper choice of solvent, and membrane module design are critical to success. In this regard, this paper aims at covering all areas related to hollow fiber membranes, including membrane material, membrane modification, membrane surface modification, shape, solvent characterization, operating parameters and costs, hybrid process, hydrophilicity and hydrophobicity of the absorption materials in the membranes, Advantages and Disadvantages of Membrane Contact Technology, membrane lifetime, and energy consumption as well as commercially available systems. Current progress, future potential, and development of pilot-scale applications and thermal fluid of this strategy are also assessed carefully. Furthermore, pore wetting as the main technical challenge in membrane contactor industrial implementation for post- and pre-combustion Carbon dioxide capture processes is investigated in detail. Graphical abstract: [Figure not available: see fulltext.].
Faust, O, De Michele, S, Koh, JEW, Jahmunah, V, Lih, OS, Kamath, AP, Barua, PD, Ciaccio, EJ, Lewis, SK, Green, PH, Bhagat, G & Acharya, UR 2023, 'Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images', Computer Methods and Programs in Biomedicine, vol. 230, pp. 107320-107320. View/Download from: Publisher's site
Fei, Y, Han, N, Shi, J, Tang, S, Zhuang, H, Wang, L, Ran, J, Gao, E, Habila, MA, Chen, Z, Tao, D, Ni, B-J & Jiang, M 2023, 'Red mud-derived iron carbon catalyst for the removal of organic pollutants in wastewater', Chemosphere, vol. 337, pp. 139211-139211. View/Download from: Publisher's site
Fei, Z, Wang, X, Wu, N, Huang, J & Zhang, JA 2023, 'Air-Ground Integrated Sensing and Communications: Opportunities and Challenges', IEEE Communications Magazine, vol. 61, no. 5, pp. 55-61. View/Download from: Publisher's site
Feng, A, Mao, S, Onggowarsito, C, Naidu, G, Li, W & Fu, Q 2023, 'Tillandsia-Inspired Composite Materials for Atmospheric Water Harvesting', ACS Sustainable Chemistry & Engineering, vol. 11, no. 15, pp. 5819-5825. View/Download from: Publisher's site View description>>
Atmospheric water harvesting (AWH) is a potentially promising small-scale approach to alleviate the water crisis in arid or semiarid regions. Inspired by the asymmetric structure of tillandsia leaves, a plant species native to semiarid regions, we report the development of a bioinspired composite (BiC) to draw moisture for AWH applications. With the advent of the post-COVID era, the nonwoven materials in used masks are discarded, landfilled, or incinerated along with the masks as medical waste, and the negative impact on the environment is inevitable. The nonwoven sheet has porosity, softness, and certain mechanical strength. We innovatively developed BiCs, immobilizing hygroscopic salt with a nonwoven mask for fast vapor liquefaction and using a polymer network to store water. The resulting BiC material manages to achieve a high-water adsorption capacity of 1.24 g g-1 under a low-moderate humidity environment and a high-water release ratio of ca. 90% without the use of photothermal materials, while maintaining high structural integrity in cyclic testing.
Feng, A, Onggowarsito, C, Mao, S, Qiao, GG & Fu, Q 2023, 'Divide and Conquer: A Novel Dual‐Layered Hydrogel for Atmospheric Moisture Harvesting', ChemSusChem, vol. 16, no. 14, p. e202300137. View/Download from: Publisher's site View description>>
AbstractAtmospheric water harvesting (AWH) has been recognized as a next‐generation technology to alleviate water shortages in arid areas. However, the current AWH materials suffer from insufficient water adsorption capacity and high‐water retention, which hinder the practical application of AWH materials. In this study, we developed a novel dual‐layered hydrogel (DLH) composed of a light‐to‐heat conversion layer (LHL) containing novel polydopamine‐manganese nanoparticles (PDA−Mn NPs) and a water adsorption layer (WAL) made of 2‐(acryloyloxyethyl) trimethylammonium chloride (AEtMA). The WAL has a strong ability to adsorb water molecules in the air and has a high‐water storage capacity, and the PDA−Mn NPs embedded in the LHL have excellent photothermal conversion efficiency, leading to light‐induced autonomous water release. As a result, the DLH displays a high‐water adsorption capacity of 7.73 g g−1 under optimal conditions and could near‐quantitatively release captured water within 4 h sunlight exposure. Coupled with its low cost, we believed that the DLH will be one of the promising AWH materials for practical applications.
Feng, K, Ji, JC & Ni, Q 2023, 'A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics', Structural Health Monitoring, vol. 22, no. 2, pp. 1027-1048. View/Download from: Publisher's site View description>>
The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.
Feng, K, Ji, JC & Ni, Q 2023, 'A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems', International Journal of Fatigue, vol. 168, pp. 107459-107459. View/Download from: Publisher's site View description>>
With the material degradation of gear over its service lifespan, the gearbox is prone to fatigue, especially under harsh working environments. The interaction between gear fatigue and gear dynamics often results in high complexity measurements. This poses significant challenges to developing effective vibration-based techniques to monitor the gear fatigue propagation and predict its remaining useful life (RUL). To address this issue, a novel transmission error-based indicator is proposed to assess the fatigue severity, and then it is utilized to predict the RUL of the gearbox. The effectiveness of the proposed prognostic methodology is validated using endurance tests.
Feng, K, Ji, JC, Ni, Q & Beer, M 2023, 'A review of vibration-based gear wear monitoring and prediction techniques', Mechanical Systems and Signal Processing, vol. 182, pp. 109605-109605. View/Download from: Publisher's site View description>>
Gearbox plays a vital role in a wide range of mechanical power transmission systems in many industrial applications, including wind turbines, vehicles, mining and material handling equipment, oil and gas processing equipment, offshore vessels, and aircraft. As an inevitable phenomenon during gear service life, gear wear affects the durability of gear tooth and reduces the remaining useful life of the gear transmission system. The propagation of gear wear can lead to severe gear failures such as gear root crack, tooth spall, and tooth breakage, which can further cause unexpected equipment shutdown or hazardous incidents. Therefore, it is necessary to monitor gear wear propagation progression in order to perform predictive maintenance. Vibration analysis is a widely used and effective technique to monitor the operating condition of rotating machinery, especially for the diagnosis of localized failures such as gear root crack and tooth surface spalling. However, vibration-based techniques for gear wear analysis and monitoring are very limited, mainly due to the difficulties in identifying the complex vibration characteristics induced by gear wear propagation. Understanding the effect of gear wear on vibration characteristics is essential to develop vibration-based techniques for monitoring and tracking gear wear evolution. However, no research work has been previously published to summarize the research progress in vibration-based gear wear monitoring and prediction. To fill the research gap, this review paper aims to conduct a state-of-the-art comprehensive review on vibration-based gear wear monitoring, including studying the gear surface features caused by different gear wear mechanisms, investigating the relationships between gear surface features and vibration characteristics, and summarizing the current research progress of vibration-based gear wear monitoring. This review also makes some recommendations for future research work in this area. It is e...
Feng, K, Ji, JC, Ni, Q, Li, Y, Mao, W & Liu, L 2023, 'A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system', Wear, vol. 522, pp. 204697-204697. View/Download from: Publisher's site View description>>
Gearbox has a compact structure, a stable transmission capability, and high transmission efficiency. Thus, it is widely applied and used as a critical transmission system in intelligent manufacturing systems, such as machine tools and robotics. The gearbox usually operates in harsh and non-stationary working environments, making the gear surface prone to wear. The progression of gear surface wear may lead to severe gear failures, such as gear tooth breakage and root crack, potentially damaging the whole gear transmission system. Therefore, it is essential to assess the gear surface wear progression and predict its remaining useful life (RUL) in order to ensure the reliable operation of the gear transmission system. To this end, this paper developed a novel gear wear prognostic scheme based on vibration analysis for gear health management. More specifically, a novel health indicator (HI) is first developed for gear wear monitoring in the proposed prognostic scheme. The novel HI, inferred from the cyclic correntropy and Wasserstein distance (WD), can accurately reflect the wear-induced cyclic correntropy spectra distribution change over time. Therefore, the novel HI can robustly evaluate the gear wear severity with high accuracy. With the developed HI, a network, namely the optimized gated recurrent unit (GRU), is applied for predicting the gear transmission system RUL during surface wear progression. As for the optimized GRU network, the genetic algorithm (GA) is applied to find the optimal hyperparameters adaptively, which can significantly improve the practicality of the developed prognostic scheme. To conclude, the developed prognostic scheme can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. A series of endurance tests are conducted to verify the effectiveness of the developed prognostic scheme for gear health management in surface wear progression.
Feng, K, Ji, JC, Ni, Q, Yun, H, Zheng, J & Liu, Z 2023, 'A novel vibration indicator to monitor gear natural fatigue pitting propagation', Structural Health Monitoring, vol. 22, no. 5, pp. 3126-3140. View/Download from: Publisher's site View description>>
Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshing process, there is a close relationship between the tribological features of fatigue pitting and gear vibration cyclostationarity. Based on the vibration cyclostationarity, this paper develops a novel second-order cyclostationary (CS2) fatigue pitting monitoring indicator, which can accurately assess the degradation status of the gear system and benefit subsequent health management. The advantage of the developed cyclostationary indicator in evaluating and monitoring the process of fatigue pitting propagation is demonstrated with the natural fatigue pitting progression test, through comparisons with other conventional indicators.
Feng, K, Ji, JC, Zhang, Y, Ni, Q, Liu, Z & Beer, M 2023, 'Digital twin-driven intelligent assessment of gear surface degradation', Mechanical Systems and Signal Processing, vol. 186, pp. 109896-109896. View/Download from: Publisher's site View description>>
Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
Feng, K, Ni, Q, Chen, Y, Ge, J & Liu, Z 2023, 'A cyclostationarity-based wear monitoring framework of spur gears in intelligent manufacturing systems', Structural Health Monitoring, vol. 22, no. 5, pp. 3092-3108. View/Download from: Publisher's site View description>>
The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth, and tooth breaking, all of which could damage the whole transmission system. Hence, it is essential to monitor and evaluate the gear surface wear propagation. The gear wear has been proven highly relevant with the vibration second-order cyclostationary (CS2) characteristics. Therefore, this paper develops a novel cyclostationarity-based framework to monitor and evaluate gear wear propagation. More specifically, the squared envelope (SE) of the residual signal, removing deterministic components, is utilized to identify the gear wear distribution and its propagation trends, validated using the measured gear surface morphology. Moreover, a new CS2-based indicator is proposed to assess the severity of gear surface wear, achieving a high correlation with measured surface roughness: [Formula: see text] is more than 0.9. The developed cyclostationarity-based framework can comprehensively evaluate the degradation status of the gear system caused by surface wear, significantly benefiting the health management of the gear transmission system, which is of great practical value for the health management of intelligent manufacturing systems. A series of endurance tests are conducted to verify the effectiveness and superiority of the developed framework for gear wear monitoring compared with the conventional indicators.
Feng, K, Xu, Y, Wang, Y, Li, S, Jiang, Q, Sun, B, Zheng, J & Ni, Q 2023, 'Digital Twin Enabled Domain Adversarial Graph Networks for Bearing Fault Diagnosis', IEEE Transactions on Industrial Cyber-Physical Systems, vol. 1, pp. 113-122. View/Download from: Publisher's site
Feng, S, Hao Ngo, H, Guo, W, Woong Chang, S, Duc Nguyen, D, Thanh Bui, X, Zhang, X, Ma, XY & Ngoc Hoang, B 2023, 'Biohydrogen production, storage, and delivery: A comprehensive overview of current strategies and limitations', Chemical Engineering Journal, vol. 471, pp. 144669-144669. View/Download from: Publisher's site View description>>
The development of biohydrogen is crucial for achieving a sustainable and eco-friendly society and reducing dependence on traditional fossil fuels. Biohydrogen production, storage, and delivery are three essential components of the biohydrogen economy. Strategies like dark fermentation and photo-fermentation have been widely studied for biohydrogen production. At the same time, hydrogen storage options have also been explored, including compressed, liquid, and material-based hydrogen. However, many of the technologies aimed at developing the biohydrogen economy are still immature, and the current biohydrogen economy is facing challenges like low biohydrogen production, high hydrogen storage costs, and unsatisfactory hydrogen delivery efficiency. Therefore, this review aims to present a comprehensive overview of the latest technologies for biohydrogen production, storage, and delivery, while highlighting their respective benefits and drawbacks. Furthermore, the review proposes perspectives on the challenges facing current biohydrogen production, storage, and delivery technologies, as well as suggesting further research directions to improve these technologies for widespread implementation of the biohydrogen economy.
Feng, S, Ngo, HH, Guo, W, Khan, MA, Zhang, S, Luo, G, Liu, Y, An, D & Zhang, X 2023, 'Fruit peel crude enzymes for enhancement of biohydrogen production from synthetic swine wastewater by improving biohydrogen-formation processes of dark fermentation', Bioresource Technology, vol. 372, pp. 128670-128670. View/Download from: Publisher's site
Feng, X, Chen, Z, Wang, S, Cen, L, Ni, B-J & Liu, Q 2023, 'Insights into the weathering behavior of pyrite in alkaline soil through electrochemical characterizations: Actual hazards or potentially benefits?', Journal of Hazardous Materials, vol. 451, pp. 131145-131145. View/Download from: Publisher's site View description>>
Pyrite is the most common metal sulfide mineral in the crust and readily weathers under natural circumstances to release H+ to acidify surrounding groundwater and soil, resulting in heavy metal ions in the surrounding environment (e.g., meadow and saline soils). Meadow and saline soils are two common, widely distributed alkaline soils and can affect pyrite weathering. Currently, the weathering behaviors of pyrite in saline and meadow soil solutions have not been systematically studied. Electrochemistry coupled with surface analysis methods were employed to study pyrite weathering behaviors in simulated saline and meadow soil solutions in this work. Experimental results suggest that saline soil and higher temperatures increase pyrite weathering rates due to the lower resistance and greater capacitance. Surface reactions and diffusion control the weathering kinetics, and the activation energies for the simulated meadow and saline soil solutions are 27.1 and 15.8 kJ mol-1, respectively. In-depth investigations reveal that pyrite is initially oxidized to Fe(OH)3 and S0, and Fe(OH)3 further transforms into goethite γ-FeOOH and hematite α-Fe2O3, while S0 ultimately converts into sulfate. When these iron compounds enter alkaline soils, the alkalinity of soil changes, and iron (hydr)oxides effectively reduce the bioavailability of heavy metals and benefit alkaline soils. Meanwhile, weathering of natural pyrite ores containing toxic elements (such as Cr, As, and Cd) makes these elements bioavailable and potentially degrades the surrounding environment.
Feng, X, Zhang, Y, Meng, MH, Li, Y, Joe, CE, Wang, Z & Bai, G 2023, 'Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph', ISA Transactions, vol. 141, pp. 10-19. View/Download from: Publisher's site
Feng, Y, Wang, Q, Chen, X, Wu, D & Gao, W 2023, 'Virtual modelling technique for geometric-material nonlinear dynamics of structures', Structural Safety, vol. 100, pp. 102284-102284. View/Download from: Publisher's site View description>>
This paper presents a virtual modelling technique for dynamic safety assessment of practical structures undergoing geometric and material blended nonlinearities. The variational inputs of systematic properties are treated within the 3D dynamic geometric-elastoplastic analyses. To circumvent numerical challenges in solving the coupled nonlinear problems, a freshly developed dynamic virtual modelling (DVM) technique is employed to determine the inherent relationship between the variational input data and the nonlinear structural response by using a new clustering based extended support vector regression (C-XSVR) algorithm with a novel T-spline polynomial kernel function. The virtual modelling models can be constructed at each time step within the Newmark's time integration procedure, which then can be used to predict deflection, force, and stress of the concerned structure at different periods. The DVM is capable of visibly forecasting potential large deformation nonlinear behaviours in an efficient manner, based on the explicit relationship functions. To demonstrate the accuracy and effectiveness of the proposed framework, nonlinear behaviours of two practical applications under future forecasted working conditions are predicted and validated in the numerical investigations.
Feng, Y, Wang, Q, Yu, Y, Zhang, T, Wu, D, Chen, X, Luo, Z & Gao, W 2023, 'Experimental-numerical-virtual (ENV) modelling technique for composite structure against low velocity impacts', Engineering Structures, vol. 278, pp. 115488-115488. View/Download from: Publisher's site
Feng, Y, Wu, D, Stewart, MG & Gao, W 2023, 'Past, current and future trends and challenges in non-deterministic fracture mechanics: A review', Computer Methods in Applied Mechanics and Engineering, vol. 412, pp. 116102-116102. View/Download from: Publisher's site
Fernandez, E, Hossain, MJ, Nawazish Ali, SM & Sharma, V 2023, 'An efficient P2P energy trading platform based on evolutionary games for prosumers in a community', Sustainable Energy, Grids and Networks, vol. 34, pp. 101074-101074. View/Download from: Publisher's site
Fonseka, C, Ryu, S, Choo, Y, Naidu, G, Kandasamy, J, Thiruvenkatachari, R, Foseid, L, Ratnaweera, H & Vigneswaran, S 2023, 'Selective recovery of europium from real acid mine drainage by using novel amine based modified SBA15 adsorbent and membrane distillation system', Journal of Water Process Engineering, vol. 56, pp. 104551-104551. View/Download from: Publisher's site
Fonseka, C, Ryu, S, Naidu, G, Kandasamy, J, Thiruvenkatachari, R & Vigneswaran, S 2023, 'Europium adsorption by granulated Cr-MIL-PMIDA metal−organic frameworks and dynamic fixed bed column modelling', Journal of Water Process Engineering, vol. 56, pp. 104475-104475. View/Download from: Publisher's site
Fraile Navarro, D, Kocaballi, AB, Dras, M & Berkovsky, S 2023, 'Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice', ACM Transactions on Computer-Human Interaction, vol. 30, no. 2, pp. 1-34. View/Download from: Publisher's site View description>>
General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.
Fu, J, Abharian, S, Sarfarazi, V, Haeri, H, Rasekh, H & Xu, L 2023, 'The rock fracturing in the jointed tunnel face ground with TBM: Experimental and numerical study', Theoretical and Applied Fracture Mechanics, vol. 125, pp. 103933-103933. View/Download from: Publisher's site
Gabryelczyk, A, Yadav, S, Swiderska-Mocek, A, Altaee, A & Lota, G 2023, 'From waste to energy storage: calcinating and carbonizing chicken eggshells into electrode materials for supercapacitors and lithium-ion batteries', RSC Advances, vol. 13, no. 34, pp. 24162-24173. View/Download from: Publisher's site View description>>
The study explores waste-derived sustainable materials from the eggshell's inner and outer layers. The materials work as an inert scaffold to reduce the carbon content in supercapacitors and as a Li-ion anode with a specific capacity of 280 mA h g−1.
Gadde, S, Kleynhans, A, Holien, JK, Bhadbhade, M, Nguyen, PLD, Mittra, R, Yu, TT, Carter, DR, Parker, MW, Marshall, GM, Cheung, BB & Kumar, N 2023, 'Pyrimido[1,2-a]benzimidazoles as inhibitors of oncoproteins ubiquitin specific protease 5 and MYCN in the childhood cancer neuroblastoma', Bioorganic Chemistry, vol. 136, pp. 106462-106462. View/Download from: Publisher's site
Galpathage, SG, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2023, 'Pull-out behaviour of simulated tree roots embedded in compacted soil', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 176, no. 1, pp. 54-64. View/Download from: Publisher's site View description>>
Vegetated ground is strengthened by the suction generated during evapotranspiration as well as physically and mechanically by the reinforcement effect induced by tree roots. When suction suddenly decreases during flooding or intense rainfall, the shear strength of the soil–plant system relies mainly on the physical root reinforcement. In this paper, the pull-out behaviour of simulated roots embedded in a compacted soil is investigated to assess how the soil–plant system is mechanically strengthened. An analytical framework is developed to estimate the pull-out force and it is validated using a series of pull-out tests. The tests are carried out on simulated roots embedded in a box of compacted soil having different water contents and equivalent dry unit weights. The results show that the pull-out capacity of this root system is mainly influenced by the initial (as-compacted) suction, and the length and diameter of the roots. The model predictions agree reasonably well with the experimental observations.
Ganbat, N, Hamdi, FM, Ibrar, I, Altaee, A, Alsaka, L, Samal, AK, Zhou, J & Hawari, AH 2023, 'Iron slag permeable reactive barrier for PFOA removal by the electrokinetic process', Journal of Hazardous Materials, vol. 460, pp. 132360-132360. View/Download from: Publisher's site View description>>
The efficacy of the Standalone Electrokinetic (EK) process in soil PFAS removal is negligible, primarily due to the intersecting mechanisms of electromigration and electroosmosis transportation. Consequently, the redistribution of PFAS across the soil matrix occurs, hampering effective remediation efforts. Permeable reactive barrier (PRB) has been used to capture contaminants and extract them at the end of the EK process. This study conducted laboratory-scale tests to evaluate the feasibility of the iron slag PRB enhanced-EK process in conjunction with Sodium Cholate (NaC) biosurfactant as a cost-effective and sustainable method for removing PFOA from the soil. A 2 cm iron slag-based PRB with a pH of 9.5, obtained from the steel-making industry, was strategically embedded in the middle of the EK reactors to capture PFOA within the soil. The main component of the slag, iron oxide, exhibited significant adsorption capacity for PFOA contamination. The laboratory-scale tests were conducted over two weeks, revealing a PFOA removal rate of more than 79% in the slag/activated carbon PRB-EK test with NaC enhancement and 70% PFOA removal in the slag/activated carbon PRB-EK without NaC. By extending the duration of the slag/AC PRB-EK test with NaC enhancement to three weeks, the PFOA removal rate increased to 94.09%, with the slag/AC PRB capturing over 87% of the initial PFOA concentration of 10 mg/L. The specific energy required for soil decontamination by the EK process was determined to be 0.15 kWh/kg. The outcomes of this study confirm the feasibility of utilizing iron slag waste in the EK process to capture PFOA contaminants, offering a sustainable approach to soil decontamination. Combining iron slag PRB and NaC biosurfactant provides a cost-effective and environmentally friendly method for efficient PFOA removal from soil.
Ganguly, D, Schmidt, MO, Coleman, M, Ngo, T-VC, Sorrelle, N, Dominguez, ATA, Murimwa, GZ, Toombs, JE, Lewis, C, Fang, YV, Valdes-Mora, F, Gallego-Ortega, D, Wellstein, A & Brekken, RA 2023, 'Pleiotrophin drives a prometastatic immune niche in breast cancer', Journal of Experimental Medicine, vol. 220, no. 5. View/Download from: Publisher's site View description>>
Metastatic cancer cells adapt to thrive in secondary organs. To investigate metastatic adaptation, we performed transcriptomic analysis of metastatic and non-metastatic murine breast cancer cells. We found that pleiotrophin (PTN), a neurotrophic cytokine, is a metastasis-associated factor that is expressed highly by aggressive breast cancers. Moreover, elevated PTN in plasma correlated significantly with metastasis and reduced survival of breast cancer patients. Mechanistically, we find that PTN activates NF-κB in cancer cells leading to altered cytokine production, subsequent neutrophil recruitment, and an immune suppressive microenvironment. Consequently, inhibition of PTN, pharmacologically or genetically, reduces the accumulation of tumor-associated neutrophils and reverts local immune suppression, resulting in increased T cell activation and attenuated metastasis. Furthermore, inhibition of PTN significantly enhanced the efficacy of immune checkpoint blockade and chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a prometastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer.
Gao, H, Dai, B, Miao, H, Yang, X, Barroso, RJD & Walayat, H 2023, 'A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 19, no. 1, pp. 1-22. View/Download from: Publisher's site View description>>
Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.
Gao, H, Fang, D, Xiao, J, Hussain, W & Kim, JY 2023, 'CAMRL: A Joint Method of Channel Attention and Multidimensional Regression Loss for 3D Object Detection in Automated Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8831-8845. View/Download from: Publisher's site View description>>
Fully automated vehicles collect information about their road environments to adjust their driving actions, such as braking and slowing down. The development of artificial intelligence (AI) and the Internet of Things (IoT) has improved the cognitive abilities of vehicles, allowing them to detect traffic signs, pedestrians, and obstacles for increasing the intelligence of these transportation systems. Three-dimensional (3D) object detection in front-view images taken by vehicle cameras is important for both object detection and depth estimation. In this paper, a joint channel attention and multidimensional regression loss method for 3D object detection in automated vehicles (called CAMRL) is proposed to improve the average precision of 3D object detection by focusing on the model’s ability to infer the locations and sizes of objects. First, channel attention is introduced to effectively learn the yaw angles from the road images captured by vehicle cameras. Second, a multidimensional regression loss algorithm is designed to further optimize the size and position parameters during the training process. Third, the intrinsic parameters of the camera and the depth estimate of the model are combined to reduce the object depth computation error, allowing us to calculate the distance between an object and the camera after the object’s size is confirmed. As a result, objects are detected, and their depth estimations are validated. Then, the vehicle can determine when and how to stop if an object is nearby. Finally, experiments conducted on the KITTI dataset demonstrate that our method is effective and performs better than other baseline methods, especially in terms of 3D object detection and bird’s-eye view (BEV) evaluation.
Gao, H, Hussain, W, Durán Barroso, RJ, Arshad, J & Yin, Y 2023, 'Guest Editorial: Machine learning applied to quality and security in software systems', IET Software, vol. 17, no. 4, pp. 345-347. View/Download from: Publisher's site
Gao, H, Luo, B, Barroso, RJD & Hussain, W 2023, 'Guest Editorial Special Issue on Computational Intelligence to Edge AI for Ubiquitous IoT Systems', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 1, pp. 36-38. View/Download from: Publisher's site
Gao, H, Qiu, B, Barroso, RJD, Hussain, W, Xu, Y & Wang, X 2023, 'TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder', IEEE Transactions on Network Science and Engineering, vol. 10, no. 5, pp. 2978-2990. View/Download from: Publisher's site View description>>
With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector.
Gao, L, Li, X, Li, M, Zamyadi, A & Wang, Q 2023, 'Recent research advances in aqueous pollutants and treatment approaches', Process Safety and Environmental Protection, vol. 171, pp. 132-135. View/Download from: Publisher's site View description>>
The water industry faces significant challenges under the impacts of climate change, population growth, and water resource scarcity. Great efforts and progress have been made to understand the presence and behaviors of emerging contaminants, their health and environmental impacts, and the energy-efficient and cost-effective treatment technologies. In this ‘Aqueous Emerging Pollutants and Treatment’ special issue, we collect 32 articles to demonstrate recent research progress in aqueous pollutants and treatment approaches. An overview of these 32 articles is provided. Six main trends for future research in aqueous pollutants and their treatment technologies have been provided.
Gao, L, Liu, P, Jiang, Y, Xie, W, Lei, J, Li, Y & Du, Q 2023, 'CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking', IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14. View/Download from: Publisher's site
Gao, S, Wang, R, Wang, X, Yu, S, Dong, Y, Yao, S & Zhou, W 2023, 'Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 5168-5181. View/Download from: Publisher's site
Gao, Y, Zhang, Z, Zhang, H, Zhao, M, Yang, Y & Wang, M 2023, 'Fast data-free model compression via dictionary-pair reconstruction', Knowledge and Information Systems, vol. 65, no. 8, pp. 3435-3461. View/Download from: Publisher's site
Gaur, M, Chaturvedi, K, Vishwakarma, DK, Ramasamy, S & Prasad, M 2023, 'Self-supervised ensembled learning for autism spectrum classification', Research in Autism Spectrum Disorders, vol. 107, pp. 102223-102223. View/Download from: Publisher's site
Gautam, K, Sharma, P, Dwivedi, S, Singh, A, Gaur, VK, Varjani, S, Srivastava, JK, Pandey, A, Chang, J-S & Ngo, HH 2023, 'A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil', Environmental Research, vol. 225, pp. 115592-115592. View/Download from: Publisher's site
Ge, M, Pineda, J & Sheng, D 2023, 'Competing effects of wetting and volume change on G0 in compacted loess', Géotechnique Letters, vol. 13, no. 4, pp. 182-190. View/Download from: Publisher's site View description>>
This paper explores the relative contributions of wetting (suction reduction) and its associated volume change on the small-strain shear stiffness, G0, in compacted loess from Xi’an, China. Results from one-dimensional compression tests with measurements of the shear wave velocity upon wetting and loading paths are presented. The experimental results show that the softening caused by wetting compete with the densification caused by plastic deformation and their effects on G0 are strongly controlled by stress level applied prior to wetting. Below the compaction stress, suction effects are dominant and G0 reduces irrespective of the magnitude of the collapse strain. With the increase in the stress level, the reduction in G0 caused by saturation is compensated by the plastic deformation triggered by soil collapse. This behaviour is clearly observed when the soil is first loaded to the compaction stress, where the maximum collapse strain is measured upon wetting. Volume change is dominant once the compaction stress is exceeded so that G0 tends to increase upon wetting. A wetting-induced stiffness factor D is defined to demonstrate that the change in G0 varies linearly with the stress level and this behaviour is independent of the compaction conditions.
Gedela, R, Indraratna, B, Medawela, S & Nguyen, TT 2023, 'Effects Of Fines Content On The Strength And Stiffness Of Biopolymer Treated Low-Plasticity Soils', Australian Geomechanics Journal, vol. 58, no. 1, pp. 33-41. View/Download from: Publisher's site View description>>
The use of biopolymers to enhance the engineering properties of soil has received increasing attention in recent years, however, the interactive role that biopolymers and the fines content of the soil play in governing the geotechnical parameters still requires insightful investigation, in relation to chemical soil treatment that can be ecologically detrimental. This paper examines the combined effects of Xanthan Gum (XG) derived from specific bacterial strains and the presence of clay fines content (kaolin) on the strength and stiffness of low plasticity soils, with special reference of cyclic traffic (road and rail) loading. In this study, fine sand is mixed with different contents of kaolin, whereby laboratory compression and tensile tests were conducted on natural (untreated) and XG-treated soil specimens. The results indicate that soil strength can be enhanced significantly when XG is added, however the effectiveness is a function of the kaolin content (KC). At an optimum XG content of 2% and a fines content increasing from 5% to 30%, split tensile strength increases from 230 to 750 kPa,while the unconfined compressive strength rises from 1.4 to 7.9 MPa, respectively. For XG content between 0.5% and 2%, the small strain stiffness of treated soil increases fourfold from 206 to 854 MPa.
Gedela, R, Indraratna, B, Nguyen, TT & Medawela, S 2023, 'The effect of biopolymer treatment on the potential instability of a soft soil under cyclic loading', Transportation Geotechnics, vol. 42, pp. 101102-101102. View/Download from: Publisher's site
Ghabussi, A, Mortazavi, M & Betha, R 2023, 'Seismic performance of a cold-formed and hot-rolled steel wall system equipped with curved steel dampers', Structures, vol. 53, pp. 296-316. View/Download from: Publisher's site
Ghadi, MJ, Mishra, DK, Azizivahed, A, Li, L & Zhang, J 2023, 'Mobile compressed air energy storage for active distribution systems', International Journal of Electrical Power & Energy Systems, vol. 154, pp. 109434-109434. View/Download from: Publisher's site
Ghalambaz, M, Mehryan, SAM, Ramezani, SR, Hajjar, A, El Kadri, M, Islam, MS, Younis, O & Ghodrat, M 2023, 'Phase change heat transfer in a vertical metal foam-phase change material thermal energy storage heat dissipator', Journal of Energy Storage, vol. 66, pp. 107370-107370. View/Download from: Publisher's site
Ghannam, S & Hussain, F 2023, 'Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study', Knowledge-Based Systems, vol. 275, pp. 110660-110660. View/Download from: Publisher's site
Ghezelbash, R, Maghsoudi, A, Shamekhi, M, Pradhan, B & Daviran, M 2023, 'Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity', Neural Computing and Applications, vol. 35, no. 1, pp. 719-733. View/Download from: Publisher's site
Gholami, H, Mohammadifar, A, Golzari, S, Song, Y & Pradhan, B 2023, 'Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion', Science of The Total Environment, vol. 904, pp. 166960-166960. View/Download from: Publisher's site View description>>
Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modell...
Gholami, K, Abbasi, M, Azizivahed, A & Li, L 2023, 'An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10695-10714. View/Download from: Publisher's site View description>>
AbstractTo overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.
Gholami, K, Azizivahed, A, Arefi, A & Li, L 2023, 'Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program', International Journal of Electrical Power & Energy Systems, vol. 146, pp. 108761-108761. View/Download from: Publisher's site
Ghorbanpour, SM, Richards, C, Pienaar, D, Sesperez, K, Aboulkheyr Es., H, Nikolic, VN, Karadzov Orlic, N, Mikovic, Z, Stefanovic, M, Cakic, Z, Alqudah, A, Cole, L, Gorrie, C, McGrath, K, Kavurma, MM, Ebrahimi Warkiani, M & McClements, L 2023, 'A placenta-on-a-chip model to determine the regulation of FKBPL and galectin-3 in preeclampsia', Cellular and Molecular Life Sciences, vol. 80, no. 2. View/Download from: Publisher's site View description>>
AbstractPreeclampsia is a pregnancy-specific cardiovascular disorder, involving significant maternal endothelial dysfunction. Although inappropriate placentation due to aberrant angiogenesis, inflammation and shallow trophoblast invasion are the root causes of preeclampsia, pathogenic mechanisms are poorly understood, particularly in early pregnancy. Here, we first confirm the abnormal expression of important vascular and inflammatory proteins, FK506-binding protein-like (FKBPL) and galectin-3 (Gal-3), in human plasma and placental tissues from women with preeclampsia and normotensive controls. We then employ a three-dimensional microfluidic placental model incorporating human umbilical vein endothelial cells (HUVECs) and a first trimester trophoblast cell line (ACH-3P) to investigate FKBPL and Gal-3 signaling in inflammatory conditions. In human samples, both circulating (n = 17 controls; n = 30 preeclampsia) and placental (n ≥ 6) FKBPL and Gal-3 levels were increased in preeclampsia compared to controls (plasma: FKBPL, p < 0.0001; Gal-3, p < 0.01; placenta: FKBPL, p < 0.05; Gal-3, p < 0.01), indicative of vascular dysfunction in preeclampsia. In our placenta-on-a-chip model, we show that endothelial cells are critical for trophoblast-mediated migration and that trophoblasts effectively remodel endothelial vascular networks. Inflammatory cytokine tumour necrosis factor-α (10 ng/mL) modulates both FKBPL and Gal-3 signaling in conjunction with trophoblast migration and impairs vascular network formation (p < 0.005). Our placenta-on-a-chip recapitulates aspects of inappropriate placental development and vascular dysfunction in preeclampsia.
Gill, AQ 2023, 'The digital ecosystem information framework: Insights from action design research', Journal of Information Science. View/Download from: Publisher's site View description>>
Digital ecosystem (DE) is a dynamic configuration of informational organisms, individual and organisational actors, which interact in the digitally networked and federated environment. Traditional approaches are challenged by the need for handling information in complex DE where information flows beyond the boundary of a single actor. This article presents the informational organism-interaction centric digital ecosystem information (DEi) framework for information operations, management, and governance. The DEi framework emerged based on the insights obtained through the application of well-known thematic network analysis and abstraction, reflection and learning techniques to 15 action design research projects across nine different industry partners in Australia. The DEi framework includes 27 topics that are organised into nine key knowledge and three focus areas. The DEi framework can be used by researchers and practitioners as a resource for designing digital information capabilities as appropriate to their context.
Gilmore, N, Britz, T, Maartensson, E, Orbegoso-Jordan, C, Schroder, S & Malerba, M 2023, 'Continental-scale assessment of micro-pumped hydro energy storage using agricultural reservoirs', Applied Energy, vol. 349, pp. 121715-121715. View/Download from: Publisher's site
Goldsmith, R, Miao, G, Daniel, S, Briozzo, P, Chai, H & Gardner, A 2023, 'Becoming an engineering education researcher through a kaleidoscope of practice theory perspectives', Australasian Journal of Engineering Education, vol. 28, no. 1, pp. 85-96. View/Download from: Publisher's site View description>>
There is a considerable body of literature on the challenges that are encountered in the transition from technical engineering research to engineering education research. These challenges include conceptual difficulties, shifts in identities and in paradigms, and changes of cultural and social capital. Many of the studies in this area emphasise the importance of having a network of engineering education researchers, but there is little research on what such a network would look like. Our research builds on this by investigating how the Centre for Research in Engineering & IT Education (CREITE) has established conditions which enable the development of engineering education research capabilities across several universities in NSW. Our novel research approach views six case studies of CREITE members through the lens of three practice theories: community of practice; Bourdieu’s theory of practice; and the theory of practice architecture. The findings reveal a kaleidoscopic understanding of what constrains and enables engineering educators to engage with the field of EER, and the pivotal role played by a research group such as CREITE.
Gomes, SDC, Nguyen, QD, Li, W & Castel, A 2023, 'Carbonation resistance of calcined clay-ground granulated blast furnace slag alkali-activated mortar', Construction and Building Materials, vol. 393, pp. 131811-131811. View/Download from: Publisher's site
Gong, S, Ball, J & Surawski, N 2023, 'A method of estimating imperviousness for the catchment modelling of urban environments', Journal of Hydroinformatics, vol. 25, no. 2, pp. 451-468. View/Download from: Publisher's site View description>>
AbstractUrban impervious surfaces, a symbol of urbanisation, have permanently changed urban hydrology behaviour and play a critical role in modelling rainfall-runoff process. The distribution pattern of impervious surfaces is intrinsically connected with functional land zoning schemes. However, estimating impervious fractions for catchment modelling is becoming increasingly difficult due to intricate land zoning categories and heterogeneous land use land cover (LULC) during urbanisation. This study demonstrates an integrated approach of deep learning (DL) and grid sampling method to overcome the challenges of LULC classification, sample standardisation and statistical sample extraction. The classified impervious features were extracted within the land zoning scope and translated into polynomial functions using a probability-fitting approach to measure the occurrence likelihood distribution of samples' impervious fraction. Then, we use the information entropy (IE) to evaluate prediction stability by quantifying the condition entropy and information gain (IG) from each functional land zones to the occurrence likelihood of different impervious fraction intervals. The DL model shows robust LULC prediction, while probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the land zoning categories. The IE stability test shows a robust approach that clarifies different confident ranges of imperviousness estimation based on land zoning information.
Gong, S, Ball, JE & Surawski, N 2023, 'An initial parameter estimation approach for urban catchment modelling', Urban Water Journal, vol. 20, no. 2, pp. 171-183. View/Download from: Publisher's site
Gong, S, Cui, L, Gu, B, Lyu, B, Hoang, DT & Niyato, D 2023, 'Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-Aided and Wireless-Powered Wireless Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8114-8127. View/Download from: Publisher's site View description>>
In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.
Gong, S, Guo, Z & Wen, S 2023, 'Finite-time synchronization of T-S fuzzy memristive neural networks with time delay', Fuzzy Sets and Systems, vol. 459, pp. 67-81. View/Download from: Publisher's site View description>>
This paper focuses on the study of synchronization problem for T-S fuzzy memristive neural networks with time delay. First, a delay-independent nonlinear fuzzy control is designed. Second, under the designed fuzzy control, two kinds of finite-time synchronization criteria are obtained by comparison method and Lyapunov function method, respectively. Furthermore, the settling time is estimated. Finally, a numerical simulation example is provided to demonstrate the effectiveness and feasibility of the theoretical results, and an application of the obtained theories is also given in the pseudorandom number generator (PRNG).
Gong, S, Wang, M, Gu, B, Zhang, W, Hoang, DT & Niyato, D 2023, 'Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10933-10948. View/Download from: Publisher's site View description>>
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.
Gong, Y, Li, Z, Liu, W, Lu, X, Liu, X, Tsang, IW & Yin, Y 2023, 'Missingness-Pattern-Adaptive Learning With Incomplete Data', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11053-11066. View/Download from: Publisher's site View description>>
Many real-world problems deal with collections of data with missing values, e.g., RNA sequential analytics, image completion, video processing, etc. Usually, such missing data is a serious impediment to a good learning achievement. Existing methods tend to use a universal model for all incomplete data, resulting in a suboptimal model for each missingness pattern. In this paper, we present a general model for learning with incomplete data. The proposed model can be appropriately adjusted with different missingness patterns, alleviating competitions between data. Our model is based on observable features only, so it does not incur errors from data imputation. We further introduce a low-rank constraint to promote the generalization ability of our model. Analysis of the generalization error justifies our idea theoretically. In additional, a subgradient method is proposed to optimize our model with a proven convergence rate. Experiments on different types of data show that our method compares favorably with typical imputation strategies and other state-of-the-art models for incomplete data. More importantly, our method can be seamlessly incorporated into the neural networks with the best results achieved. The source code is released at https://github.com/YS-GONG/missingness-patterns.
Gong, Y, Yin, J, Zhang, T, Yin, W, Sun, L, Liang, Q & Wang, Q 2023, 'Ferrous sulfide nanoparticles control mercury speciation and bioavailability to methylating bacteria in contaminated groundwater: Impacts of mercury species', Chemical Engineering Journal, vol. 455, pp. 140612-140612. View/Download from: Publisher's site View description>>
Mercury speciation in groundwater affects its removal effectiveness and methylation potential. Yet, most studies focus on the removal of inorganic dissolved Hg(II) and few studies explored the mercury methylation before and after the treatment. This study comprehensively explored the removal performance of three model mercury species, namely, dissolved inorganic divalent Hg (Hg(II), including free Hg2+ and Hg2+ complexes with Cl− and OH−), Hg2+ bound to dissolved organic matter (Hg-DOM), and HgS nanoparticles by FeS nanoparticles and further investigated the resultant impacts on the microbial methylation of Hg. Among three different stabilizers (starch, carboxymethyl cellulose (CMC), carboxymethyl starch (CMS)), CMC stabilized FeS nanoparticles (CMC-FeS) demonstrated best physical stability and highest mercury uptake. The CMC-FeS nanoparticles efficiently immobilized the three mercury species within 20 h. The sorption isotherm data of Hg(II) and Hg-DOM were well fitted by the dual-mode isotherm model and the maximum sorption capacities were 3358.28 and 2396.38 mg/g, respectively. Hg(II) and Hg-DOM were predominantly removed via ion exchange, chemical precipitation, and surface complexation whereas HgS was mainly immobilized through heteroaggregation. The simple treatment greatly reduced the bioavailable Hg species, thereby diminishing the net MeHg production by 70.2 %, 32.7 %, and 11.3 %, respectively. This study provides compelling evidence that FeS nanoparticles efficiently removed various mercury species in groundwater and remarkably inhibited the microbial methylation of mercury.
Gooch, LJ, Masia, MJ, Stewart, MG & Lam, CY 2023, 'Statistical assessment of tensile and shear properties of unreinforced clay brick masonry', Construction and Building Materials, vol. 386, pp. 131578-131578. View/Download from: Publisher's site
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A guide to current methodology and usage of reverse vaccinology towardsin silicovaccine discovery', FEMS Microbiology Reviews, vol. 47, no. 2, p. fuad004. View/Download from: Publisher's site View description>>
AbstractReverse vaccinology (RV) was described at its inception in 2000 as an in silico process that starts from the genomic sequence of the pathogen and ends with a list of potential protein and/or peptide candidates to be experimentally validated for vaccine development. Twenty-two years later, this process has evolved from a few steps entailing a handful of bioinformatics tools to a multitude of steps with a plethora of tools. Other in silico related processes with overlapping workflow steps have also emerged with terms such as subtractive proteomics, computational vaccinology, and immunoinformatics. From the perspective of a new RV practitioner, determining the appropriate workflow steps and bioinformatics tools can be a time consuming and overwhelming task, given the number of choices. This review presents the current understanding of RV and its usage in the research community as determined by a comprehensive survey of scientific papers published in the last seven years. We believe the current mainstream workflow steps and tools presented here will be a valuable guideline for all researchers wanting to apply an up-to-date in silico vaccine discovery process.
Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A state-of-the-art methodology for high-throughput in silico vaccine discovery against protozoan parasites and exemplified with discovered candidates for Toxoplasma gondii', Scientific Reports, vol. 13, no. 1, p. 8243. View/Download from: Publisher's site View description>>
AbstractVaccine discovery against eukaryotic parasites is not trivial as highlighted by the limited number of known vaccines compared to the number of protozoal diseases that need one. Only three of 17 priority diseases have commercial vaccines. Live and attenuated vaccines have proved to be more effective than subunit vaccines but adversely pose more unacceptable risks. One promising approach for subunit vaccines is in silico vaccine discovery, which predicts protein vaccine candidates given thousands of target organism protein sequences. This approach, nonetheless, is an overarching concept with no standardised guidebook on implementation. No known subunit vaccines against protozoan parasites exist as a result of this approach, and consequently none to emulate. The study goal was to combine current in silico discovery knowledge specific to protozoan parasites and develop a workflow representing a state-of-the-art approach. This approach reflectively integrates a parasite’s biology, a host's immune system defences, and importantly, bioinformatics programs needed to predict vaccine candidates. To demonstrate the workflow effectiveness, every Toxoplasma gondii protein was ranked in its capacity to provide long-term protective immunity. Although testing in animal models is required to validate these predictions, most of the top ranked candidates are supported by publications reinforcing our confidence in the approach.
Goss, DM, Vasilescu, SA, Sacks, G, Gardner, DK & Warkiani, ME 2023, 'Microfluidics facilitating the use of small extracellular vesicles in innovative approaches to male infertility', Nature Reviews Urology, vol. 20, no. 2, pp. 66-95. View/Download from: Publisher's site View description>>
Sperm are transcriptionally and translationally quiescent and, therefore, rely on the seminal plasma microenvironment for function, survival and fertilization of the oocyte in the oviduct. The male reproductive system influences sperm function via the binding and fusion of secreted epididymal (epididymosomes) and prostatic (prostasomes) small extracellular vesicles (S-EVs) that facilitate the transfer of proteins, lipids and nucleic acids to sperm. Seminal plasma S-EVs have important roles in sperm maturation, immune and oxidative stress protection, capacitation, fertilization and endometrial implantation and receptivity. Supplementing asthenozoospermic samples with normospermic-derived S-EVs can improve sperm motility and S-EV microRNAs can be used to predict non-obstructive azoospermia. Thus, S-EV influence on sperm physiology might have both therapeutic and diagnostic potential; however, the isolation of pure populations of S-EVs from bodily fluids with current conventional methods presents a substantial hurdle. Many conventional techniques lack accuracy, effectiveness, and practicality; yet microfluidic technology has the potential to simplify and improve S-EV isolation and detection.
Gowd, SC, Ganeshan, P, Vigneswaran, VS, Hossain, MS, Kumar, D, Rajendran, K, Ngo, HH & Pugazhendhi, A 2023, 'Economic perspectives and policy insights on carbon capture, storage, and utilization for sustainable development', Science of The Total Environment, vol. 883, pp. 163656-163656. View/Download from: Publisher's site
Grelewicz, P, Khuat, TT, Czeczot, J, Nowak, P, Klopot, T & Gabrys, B 2023, 'Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 7, pp. 4226-4238. View/Download from: Publisher's site
Grelewicz, P, Nowak, P, Khuat, TT, Czeczot, J, Klopot, T & Gabrys, B 2023, 'Practical implementation of computationally-efficient machine learning-based control performance assessment system for a class of closed loop systems', Applied Soft Computing, vol. 146, pp. 110690-110690. View/Download from: Publisher's site
Grochow, J & Qiao, Y 2023, 'On the Complexity of Isomorphism Problems for Tensors, Groups, and Polynomials I: Tensor Isomorphism-Completeness', SIAM Journal on Computing, vol. 52, no. 2, pp. 568-617. View/Download from: Publisher's site
Guan, J, Liu, Y, Kong, Q, Xiao, F, Zhu, Q, Tian, J & Wang, W 2023, 'Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection', EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, no. 1. View/Download from: Publisher's site View description>>
AbstractUnsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID-constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.
Guan, J, Pan, L, Wang, C, Yu, S, Gao, L & Zheng, X 2023, 'Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 17254-17264. View/Download from: Publisher's site
Guan, S, Lu, H, Zhu, L & Fang, G 2023, 'PoseGU: 3D human pose estimation with novel human pose generator and unbiased learning', Computer Vision and Image Understanding, vol. 233, pp. 103715-103715. View/Download from: Publisher's site
Guan, W, Song, X, Wang, K, Wen, H, Ni, H, Wang, Y & Chang, X 2023, 'Egocentric Early Action Prediction via Multimodal Transformer-Based Dual Action Prediction', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 9, pp. 4472-4483. View/Download from: Publisher's site
AbstractCollaborative robots (“cobots”) have attracted growing attention in academia and industry over the last years. Due to in-built safety features and easy programming, they allow for close human-cobot collaboration and support e.g. flexible manufacturing. However, the lack of a common understanding what a cobot is along with its traditional focus on arm-based cobots complicates further research and industry adoption. Thus, this paper analyses the variety of definitions in literature incl. standards and practice examples to derive a consistent and holistic definition and taxonomy of what a collaborative robot is. Aside from contributing a structured overview of various forms of human-robot collaboration, this builds an important foundation for future research as it systematically differentiates different cobot types. Companies and other organisations will benefit by a better understanding of what type of cobot they need and how to ensure safe collaboration.
Guertler, MR, Brackemann, T, Burden, A & Caldwell, G 2023, 'Mapping socio-technical dependencies to enable the successful adoption of collaborative robots in industry', Procedia CIRP, vol. 119, pp. 564-569. View/Download from: Publisher's site
Gul, M, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Mujtaba, MA, Harith, MH, Syahir, AZ, Ahmed, W & Farooq, AB 2023, 'Corrigendum to “Effect of TMP-based-cottonseed oil-biolubricant blends on tribological behavior of cylinder liner-piston ring combinations” [Fuel 278 (2020) 118242]', Fuel, vol. 331, pp. 125742-125742. View/Download from: Publisher's site
Gulied, M, Logade, K, Mutahir, H, Shaftah, S, Salauddin, S, Hameed, A, Zavahir, S, Elmakki, T, Shon, HK, Hong, S, Park, H & Han, DS 2023, 'A review of membrane-based dewatering technology for the concentration of liquid foods', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110583-110583. View/Download from: Publisher's site
Gunawan, Y, Firmansyah, AI, Supriatna, NK, al Irsyad, MI, Cendrawati, DG, Ahadi, K, Adilla, I & Silitonga, AS 2023, 'Comprehensive assessment using preheat crude palm oil on endurance test engine diesel: Technical and supply chain scheme', Industrial Crops and Products, vol. 204, pp. 117286-117286. View/Download from: Publisher's site
Guo, CA, Guo, YJ, Zhu, H, Ni, W & Yuan, J 2023, 'Optimization of Multibeam Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 215-224. View/Download from: Publisher's site View description>>
Currently, there is increasing interest in analog multibeam antennas whose beams can be flexibly steered to arbitrary directions. In a previous paper, we presented the theoretical framework for synthesizing individually steerable multiple beams using generalized joined coupler (GJC) matrices. The synthesis method was to optimize the array excitation vectors to approximate known distributions. In this article, we present a more robust optimization method to optimize the multibeams directly in order to control the half-power beamwidth, the sidelobe levels, and nulls for mitigating system interference. The effectiveness of the proposed method is demonstrated by numerical examples. We reveal how the quality of the multiple beams is inherently determined by the dimensions of the GJC matrix. Experimental results of a 3 10 Nolen-like GJC matrix are presented for the first time to validate the proposed method in realizing low sidelobe multibeams.
Guo, J, Lou, H, Yu, J, Li, R, Fang, W, Liu, J, Long, P, Ying, S & Ying, M 2023, 'isQ: An Integrated Software Stack for Quantum Programming', IEEE Transactions on Quantum Engineering, vol. 4, pp. 1-16. View/Download from: Publisher's site
Guo, K & Guo, Y 2023, 'Design and Optimization of Linear Rotary Drilling Motor', IEEE Transactions on Industrial Electronics, pp. 1-11. View/Download from: Publisher's site
Guo, K, Wu, M, Soo, Z, Yang, Y, Zhang, Y, Zhang, Q, Lin, H, Grosser, M, Venter, D, Zhang, G & Lu, J 2023, 'Artificial intelligence-driven biomedical genomics', Knowledge-Based Systems, vol. 279, pp. 110937-110937. View/Download from: Publisher's site
Guo, Y, Lei, B, Yu, L, Lin, X & Li, W 2023, 'Investigation on mechanical properties and failure criterion of multi-recycled aggregate concrete under triaxial compression', Procedia Structural Integrity, vol. 45, pp. 66-73. View/Download from: Publisher's site
Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Yin, W & Zhu, J 2023, 'Measurement and Modeling of Magnetic Materials under 3D Vectorial Magnetization for Electrical Machine Design and Analysis', Energies, vol. 16, no. 1, pp. 417-417. View/Download from: Publisher's site View description>>
The magnetic properties of magnetic cores are essential for the design of electrical machines, and consequently appropriate mathematical modeling is needed. Usually, the design and analysis of electrical machines consider only the one-dimensional (1D) magnetic properties of core materials, i.e., the relationship of magnetic flux density (B) versus magnetic field strength (H), and their associated power loss under 1D magnetization, in which the B and H are constrained in the same orientation. Some studies have also been performed with the two-dimensional (2D) magnetizations in which the B and H are vectorial, rotating on a plane, and they may not be in the same direction. It has been discovered that the 2D rotational property is very different from its 1D alternating counterpart. However, the magnetic fields in an electrical machine, in particular claw pole and transverse flux machines, are naturally three-dimensional (3D), and the B and H vectors are rotational and may not lie on the same plane. It can be expected that the 3D vectorial property might be different from its 2D or 1D counterpart, and hence it should be investigated for the interests of both academic research and engineering application. This paper targets at a general summary about the magnetic material characterization with 3D vectorial magnetization, and their application prospect in electrical machine design and analysis.
Guo, Y, Yu, H, Ma, L, Zeng, L & Luo, X 2023, 'THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection', Engineering Applications of Artificial Intelligence, vol. 123, pp. 106271-106271. View/Download from: Publisher's site
Guo, Z, Zhao, S, Halkon, BJ & Clemon, L 2023, 'Simulation of active vibration control of a moving stage', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A164-A164. View/Download from: Publisher's site View description>>
Active vibration control (AVC) has gained considerable interest due to its inherent adaptability and cost-effectiveness, with its ability to suppress vibrations in the controlled system across various applications. Existing studies focus on the AVC of non-moving systems and usually assume that the vibration signal to be controlled is known and can serve as the ideal reference signal in feedforward control systems. However, in many practical applications, the ideal reference signal is not accessible, and a sensor must be used to detect the reference signal. This can degrade the AVC performance, especially for a moving system. This study, therefore, aims to explore the potential application of piezoelectric stack actuators (PSA) to control the vibration of a moving stage driven by a stepper motor. The secondary path was estimated offline, and vibration data were collected at different moving speeds. The effects of the location of the reference sensor were initially investigated. Then, extensive simulations were conducted to evaluate the performance of different adaptive control algorithms regarding vibration reduction, convergence speed, computational complexities, etc. This research underlines the potential of integrating PSA within a moving system to effectively control its vibration.
Gupta, A, Kumar, D, Verma, H, Tanveer, M, Javier, AP, Lin, C-T & Prasad, M 2023, 'Recognition of multi-cognitive tasks from EEG signals using EMD methods', Neural Computing and Applications, vol. 35, no. 31, pp. 22989-23006. View/Download from: Publisher's site View description>>
AbstractMental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.
Gupta, BB, Prajapati, V, Nedjah, N, Vijayakumar, P, El-Latif, AAA & Chang, X 2023, 'Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)', Neural Computing and Applications, vol. 35, no. 7, pp. 5055-5080. View/Download from: Publisher's site View description>>
Telecare medical information system (TMIS) is used to connect patients and doctors who are at a different location from each other. The authentication of the user and system is very crucial as the medical data of the user is stored on the server. Many systems have been developed in order to achieve this goal. We show some vulnerabilities of existing systems in this paper. We then propose a secure authentication mechanism to achieve the same goal. Machine learning and the nonce-based system is used for authentication of the entity and to prove the freshness of transmitted messages. Smart card blocking mechanisms have been included in each phase of the proposed system to prevent unauthorized access of data. The proposed system has been evaluated formally with the AVISPA tool. Then the proposed model has also been checked against different attacks and evaluated for different functionalities. We provide relative analysis with some recently proposed models and show our proposed system is relatively more efficient and secure.
Hafiz, M, Alfahel, R, Altaee, A & Hawari, AH 2023, 'Techno-economic assessment of forward osmosis as a pretreatment process for mitigation of scaling in multi-stage flash seawater desalination process', Separation and Purification Technology, vol. 309, pp. 123007-123007. View/Download from: Publisher's site
Haghighitalab, A, Dominici, M, Matin, MM, Shekari, F, Ebrahimi Warkiani, M, Lim, R, Ahmadiankia, N, Mirahmadi, M, Bahrami, AR & Bidkhori, HR 2023, 'Extracellular vesicles and their cells of origin: Open issues in autoimmune diseases', Frontiers in Immunology, vol. 14, p. 1090416. View/Download from: Publisher's site View description>>
The conventional therapeutic approaches to treat autoimmune diseases through suppressing the immune system, such as steroidal and non-steroidal anti-inflammatory drugs, are not adequately practical. Moreover, these regimens are associated with considerable complications. Designing tolerogenic therapeutic strategies based on stem cells, immune cells, and their extracellular vesicles (EVs) seems to open a promising path to managing autoimmune diseases’ vast burden. Mesenchymal stem/stromal cells (MSCs), dendritic cells, and regulatory T cells (Tregs) are the main cell types applied to restore a tolerogenic immune status; MSCs play a more beneficial role due to their amenable properties and extensive cross-talks with different immune cells. With existing concerns about the employment of cells, new cell-free therapeutic paradigms, such as EV-based therapies, are gaining attention in this field. Additionally, EVs’ unique properties have made them to be known as smart immunomodulators and are considered as a potential substitute for cell therapy. This review provides an overview of the advantages and disadvantages of cell-based and EV-based methods for treating autoimmune diseases. The study also presents an outlook on the future of EVs to be implemented in clinics for autoimmune patients.
Hakim, WL, Fadhillah, MF, Park, S, Pradhan, B, Won, J-S & Lee, C-W 2023, 'InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression', Remote Sensing of Environment, vol. 287, pp. 113453-113453. View/Download from: Publisher's site
Halkon, BJ, Darwish, A, Rothberg, S, Mohammadi, M & Oberst, S 2023, 'Correction of scanning laser Doppler vibrometer measurements when subjected to six degree-of-freedom base motion', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A74-A74. View/Download from: Publisher's site View description>>
Scanning laser Doppler vibrometer (SLDV) measurements are affected by sensor head vibrations as though they are vibrations of the target surface itself. Previous work has established a fully general theoretical analysis which shows that the only measurement required for measurement correction is of the vibration velocity at the incident point on the final steering mirror in the direction of the outgoing laser beam. Two practical—but not quite perfect—options for measurement correction were presented (one more suitable to manufacturer implementation, one more applicable to the vibration engineer end user). In both cases, placement of the correction transducer is critical with correction working for totally arbitrary instrument vibration and scan angle. Experimental validation, employing frequency-domain based processing, has been completed for one degree-of-freedom, on-axis vibration. Simultaneously, advancements in the data processing approach have realised improved correction in practice, especially for lower frequencies and for transient, as opposed to statistically stationary, vibration. In this paper, extension of the experimental validation to six degree-of-freedom instrument vibration is presented for the first time. In combination with the latest data processing approaches, reductions in the measurement error of 29.4 and 28.2 dB for the frequency- and time-domain processing techniques, respectively, are realised.
Han, C, Li, W, Li, W, Yang, L & Huang, Z 2023, 'CoFeNi based trifunctional electrocatalysts featuring in-situ formed heterostructure', Inorganic Chemistry Communications, vol. 149, pp. 110402-110402. View/Download from: Publisher's site View description>>
All-in-one transition metal-based electrocatalysts with high activities towards different reactions in aqueous electrolytes are of critical importance as they can dramatically bring down the cost of relevant energy devices. Herein a facile and low-cost synthesis of CoFeNi nanoparticles encapsulated by an N-doped carbon layer has been developed by pyrolyzing Prussian blue (PB) precursors. The obtained catalyst features tri-catalytic activity towards OER, ORR, and HER reactions in alkaline and acidic condition, and show great potential as a catalyst for water splitting and anode material for Zinc-Air batteries. Moreover, compared with the single phase, the sample with the heterostructure composed of both fcc and bcc phases exhibited dramatic enhancement in multi-catalytic activity. The heterostructure originates from an in-situ phase separation induced by composition variation. This demonstrates the effectiveness of heterostructure engineering introduced by in-situ phase separation in boosting the multi-catalytic activity.
Han, C, Zheng, Z, Su, K, Yu, D, Yuan, Z, Gao, C, Sang, N & Yang, Y 2023, 'DMRNet++: Learning Discriminative Features With Decoupled Networks and Enriched Pairs for One-Step Person Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7319-7337. View/Download from: Publisher's site
Han, Z, Xu, C, Ma, S, Hu, Y, Zhao, G & Yu, S 2023, 'DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET', IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1061-1065. View/Download from: Publisher's site
Han, Z, Xu, C, Zhao, G, Wang, S, Cheng, K & Yu, S 2023, 'Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3440-3454. View/Download from: Publisher's site View description>>
With the characteristics of low-latency, seamless coverage and high bandwidth, the Low Earth Orbit (LEO) satellite network has been the promising technology for the sixth-generation mobile communication (6G) networks, especially, the inter-satellite link can improve the flexibility of inter-satellite networking and routing. However, in the existing works, since the impact of link attributes on the satellite topology has not been well investigated, it is difficult to avoid the loss of topology information for the transmission path selection, which may aggravate the unreliability of routing path. In this paper, we propose a novel time-varying topology model for the LEO satellite network, to improve the adaptability of the satellite routing. Firstly, the weighted time-space evolution graph based on the link attributes is established to construct the time-varying topology model of LEO satellite networks. Then, the utility function of the link attributes is modelled and the multi-attribute decision-making is introduced to calculate the weight of each link attribute for the quantification of the link utility. Finally, based on the topology model, the inter-satellite link utility-based dynamic routing algorithm is proposed to improve the adaptability of satellite routing. The simulation results demonstrate that the proposed routing algorithm outperforms the existing routing algorithms in terms of packet drop rate, end-to-end delay and throughput.
Hang, J, Wu, Y, Li, Y, Lai, T, Zhang, J & Li, Y 2023, 'A deep learning semantic segmentation network with attention mechanism for concrete crack detection', Structural Health Monitoring, vol. 22, no. 5, pp. 3006-3026. View/Download from: Publisher's site View description>>
In this research, an attention-based feature fusion network (AFFNet), with a backbone residual network (ResNet101) enhanced with two attention mechanism modules, is proposed for automatic pixel-level detection of concrete crack. In particular, the inclusion of attention mechanism modules, for example, the vertical and horizontal compression attention module (VH-CAM) and the efficient channel attention upsample module (ECAUM), is to enable selective concentration on the crack feature. The VH-CAM generates a feature map integrating pixel-level information in vertical and horizontal directions. The ECAUM applied on each decoder layer combines efficient channel attention (ECA) and feature fusion, which can provide rich contextual information as guidance to help low-level features recover crack localization. The proposed model is evaluated on the test dataset and the results reach 84.49% for mean intersection over union (MIoU). Comparison with other state-of-the-art models proves high efficiency and accuracy of the proposed method.
AbstractThis paper investigates soil fluidisation at the microscale using the discrete element method (DEM) in combination with the lattice Boltzmann method (LBM). Numerical simulations were carried out at varying hydraulic gradients across the granular assembly of soil. The development of local hydraulic gradients, the contact distribution, and the associated fabric changes were investigated. Microscale findings suggest that a critical hydromechanical state inducing fluid-like instability of a granular assembly can be defined by a substantial increase in grain slip associated with a rapid reduction in interparticle contacts. Based on these results, a new micromechanical criterion is proposed to characterise the transformation of granular soil from a hydromechanically stable to an unstable state. The constraint ratio (ratio of the number of constraints to the number of degrees of freedom) is introduced to portray the relative slippage between particles and the loss of interparticle contacts within the granular fabric. Its magnitude of unity corresponds to the condition of zero effective stress, representing the critical hydromechanical state. In practical terms, the results of this study reflect the phenomenon of subgrade mud pumping that occurs in railways when heavy-haul trains pass through at certain axle loads and speeds.
Internal instability means that finer particles pass through the constrictions of coarser particles at a hydraulic gradient well below that of heave or piping, rendering the soil ineffective for its intended purpose. The soil could make a transition from an internally stable state to an unstable state or vice versa due to shear-induced deformation. The discrete element method (DEM) is adopted in this study to examine and quantify soil behavior by simulating the quasi-static shear deformation of internally stable and unstable soils at the micro- and macroscales. The dense bimodal specimens were sheared under drained conditions following a constant mean stress path in order to investigate the influence of stress heterogeneity. At the macroscale, the peak deviatoric stress was found to be a function of the fines content and the initial void ratios of the specimens. The development of the average number of contacts per particle and the stress transfer to the finer fraction during shearing are discussed. The simulation results innovatively show that a dense specimen could undergo a transition from an internally stable to an unstable soil as it dilates during shear. These numerical results have significant implications on the importance of real-life situations, such as predicting mud pumping in railroad tracks.
Hasan, ASMM & Trianni, A 2023, 'Boosting the adoption of industrial energy efficiency measures through Industry 4.0 technologies to improve operational performance', Journal of Cleaner Production, vol. 425, pp. 138597-138597. View/Download from: Publisher's site
Hasan, HA, Hacheem, ZA, Almurshedi, AD & Khabbaz, H 2023, 'The Influence of Styrene Butadiene Latex on Sandy Soil Reinforced by Soil Mixed Columns under Raft Foundation', Mathematical Modelling of Engineering Problems, vol. 10, no. 3, pp. 733-739. View/Download from: Publisher's site
Hassan, M, Kennard, M, Yoshitake, S, Ishac, K, Takahashi, S, Kim, S, Matsui, T & Hirokawa, M 2023, 'Augmenting the Sense of Social Presence in Online Video Games Through the Sharing of Biosignals'. View/Download from: Publisher's site
Hassan, Z, Mahmood, M, Ahmed, N, Saeed, MH, Khan, R, Abbas, MM, Kalam, MA, Almomani, F & Abdelsalam, E 2023, 'Techno‐economic assessment of evacuated flat‐plate solar collector system for industrial process heat', Energy Science & Engineering, vol. 11, no. 6, pp. 2185-2201. View/Download from: Publisher's site View description>>
AbstractIn the industrial sector, hot water applications constitute a significant share of final energy consumption. This creates a wide demand‐supply energy gap that must be bridged by integrating renewable sources with conventional fuels. This paper presents the performance analysis of a solar water heating system based on an evacuated flat‐plate collector (EFPC) with a surface area of 4 m2. A water–glycol mixture was used as the heat transfer fluid (HTF) with mass flow rates of 0.03, 0.0336, and 0.0504 kg/s under a vacuum pressure of –0.8 bar created inside the collector. A detailed numerical model was developed in MATLAB for the proposed EFPC system, followed by experimental validation. A maximum root mean square error of 2.81 for the absorber temperature and a percentage error of 6.62 was observed for the thermal efficiency in model validation. This substantiates the model's capability to predict actual system performance with reasonable accuracy. The maximum thermal efficiency of the EFPC is 78% with a maximum fluid outlet temperature of 98°C in June and 69°C in January. The maximum useful energy extracted is 1300 W in January. Additionally, the effect of design parameters on system performance such as mass flow rates, collector areas, tube spacing, and different HTF mixtures is simulated. Lastly, an economic analysis of the EFPC was conducted for hot water demand in a textile industry. The results revealed a payback period of 7.4 years, which highlights the feasibility of this system.
Hassani, S, Mousavi, M & Dackermann, U 2023, 'Johansen cointegration of frequency response functions contaminated with nonstationary colored noise for structural damage detection', Journal of Sound and Vibration, vol. 552, pp. 117641-117641. View/Download from: Publisher's site
Hastings, C & Craig, L 2023, 'Accumulating Financial Vulnerability, Not Financial Security: Social Reproduction and Older Women’s Homelessness', Housing, Theory and Society, vol. 40, no. 3, pp. 356-376. View/Download from: Publisher's site
Hastings, C, Overgaard, C, Wilson, S, Ramia, G, Morris, A & Mitchell, E 2023, 'Crowded house: accommodation precarity and self-reported academic performance of international students', Compare: A Journal of Comparative and International Education, vol. ahead-of-print, no. ahead-of-print, pp. 1-20. View/Download from: Publisher's site View description>>
This article draws on two surveys of international students in Sydney and Melbourne, undertaken in 2019 and during the 2020 COVID-19 lockdowns. Using the concept of bounded agency, we identify how the challenges of living in one of the world’s most expensive rental housing markets impact students’ perceptions of their academic attainment. We find housing insecurity, unaffordability and condition, amplified by financial stress, contribute significantly to student anxiety about their studies. These relationships differ by student background and education. We argue students’ agency to meet their educational ambitions in Australia is constrained by the cost of housing and the housing choices they consequently make to mitigate financial stress. Our findings suggest the importance of ‘town’ or non-institutional aspects of the international student experience on their satisfaction and academic outcomes. We call for further research to explore these relationships in other global contexts.
Hastings, C, Ramia, G, Wilson, S, Mitchell, E & Morris, A 2023, 'Precarity Before and During the Pandemic: International Student Employment and Personal Finances in Australia', Journal of Studies in International Education, vol. 27, no. 1, pp. 39-63. View/Download from: Publisher's site View description>>
There is mounting evidence of increased international student financial and work precarity over the last decade in Australia. Yet, there has been a little scholarly analysis of which students are most affected by precarity and its sources. Drawing on two surveys of international students in Australia's two largest cities, conducted before and during the pandemic, we investigate the financial and work vulnerabilities of international students. We demonstrate that vulnerability is related to characteristics which describe particular cohorts of students: being from low-income countries, working class families, seeking a low-level qualification, enrolled in a non-university institution, and being without a scholarship. The concepts of “noncitizenship” and “work precarity” are used to explain how the mechanisms of each characteristic heighten vulnerability, thereby contributing to a broader evidence-base about the causality of international student precarity.
Hazeri, AH, Abouei Mehrizi, A, Mohseni, SS, Ebrahimi Warkiani, M & Razavi Bazaz, S 2023, 'A Novel Strategy for Square-Wave Micromixers: A Survey of RBC Lysis for Further Biological Analysis', Industrial & Engineering Chemistry Research, vol. 62, no. 40, pp. 16215-16224. View/Download from: Publisher's site
Hazrat, MA, Rasul, MG, Khan, MMK, Ashwath, N, Fattah, IMR, Ong, HC & Mahlia, TMI 2023, 'Biodiesel production from transesterification of Australian Brassica napus L. oil: optimisation and reaction kinetic model development', Environment, Development and Sustainability, vol. 25, no. 11, pp. 12247-12272. View/Download from: Publisher's site View description>>
AbstractEdible oil-based feedstocks based biodiesel is still leading the industry around the world. Canola oil (Brassica napus L.) contributes significantly to that race. Process optimisation and the development of reaction kinetic models of edible oil feedstocks are still required since the knowledge of kinetics is needed for designing industrial facilities and evaluating the performance of catalysts during transesterification or other related processes in a biorefinery. This research focuses on the transesterification process for biodiesel production because of its higher output efficiency, reactivity with feedstock, techno-economic feasibility in terms of FFA content, and environmental sustainability. The response surface method with the Box–Behnken model was used to optimise the process. Multivariate analysis of variance (ANOVA) was also performed to investigate the effectiveness of the regression model. The optimal process conditions were found to be 5.89 M methanol, 0.5% (w/w) KOH, 60 °C and 120 min. The predicted yield was 99.5% for a 95% confidence interval (99.1, 99.9). The experimental yield was 99.6% for these conditions. Two different kinetic models were also developed in this study. The activation energy was 16.9% higher for the pseudo-first-order irreversible reaction than for the pseudo-homogenous irreversible reaction. Such a comprehensive analysis will assist stakeholders in evaluating the technology for industrial development in biodiesel fuel commercialisation.
He, B, Armaghani, DJ & Lai, SH 2023, 'Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach', Tunnelling and Underground Space Technology, vol. 133, pp. 104979-104979. View/Download from: Publisher's site
He, H, Teng, J, Zhang, S & Sheng, D 2023, 'Determining frost heave classification by using ratio of frost heave to square root of time', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 45, no. 12, pp. 2519-2528. View/Download from: Publisher's site View description>>
The frost heave classification is the critical index for the design of foundation engineering in cold regions. At present, it is considered as a basic property of materials. Many criteria belong to empirical or semi-empirical methods and lack theoretical support. The frost heave tests are tedious and long time-consuming, and are not easily operated. To propose a rational and simple frost heave classification index, from the frost heave mechanism, an analytical model for unsaturated frozen soil is established and validated. Then a new frost heave classification index R (mm/h0.5), which is the ratio of frost heave to square root of time, is identified based on the proposed model. Through comparison with the large number of frost heave results, the value of R less than 0.21 indicates the low frost heave classification, that between 0.21 and 1.18 represents the medium heave classification, and that greater than 1.18 means the high frost heave classification. From a statistical probability perspective, the probability density distribution of the values of each classification index is analyzed, and their trends are also compared. It is found that the concentration and stability of the new index R are the highest during freezing process. The new index R has theoretical support and simultaneously couples the basic soil properties and freezing environmental factors. It breaks through the limitation of the existing indexes, and enriches the frost heave classification system, and provides theoretical support for the engineering design in cold regions.
He, J, Li, H, Mai, J, Ke, Y, Zhai, C, Li, JJ, Jiang, L, Shen, G & Ding, X 2023, 'Profiling extracellular vesicle surface proteins with 10 µL peripheral plasma within 4 h', Journal of Extracellular Vesicles, vol. 12, no. 9. View/Download from: Publisher's site View description>>
AbstractExtracellular vesicle (EV) surface proteins, expressed by primary tumours, are important biomarkers for early cancer diagnosis. However, the detection of these EV proteins is complicated by their low abundance and interference from non‐EV components in clinical samples. Herein, we present a MEmbrane‐Specific Separation and two‐step Cascade AmpLificatioN (MESS2CAN) strategy for direct detection of EV surface proteins within 4 h. MESS2CAN utilises novel lipid probes (long chains linked by PEG2K with biotin at one end, and DSPE at the other end) and streptavidin‐coated magnetic beads, permitting a 49.6% EV recovery rate within 1 h. A dual amplification strategy with a primer exchange reaction (PER) cascaded by the Cas12a system then allows sensitive detection of the target protein at 10 EV particles per microliter. Using 4 cell lines and 90 clinical test samples, we demonstrate MESS2CAN for analysing HER2, EpCAM and EGFR expression on EVs derived from cells and patient plasma. MESS2CAN reports the desired specificity and sensitivity of EGFR (AUC = 0.98) and of HER2 (AUC = 1) for discriminating between HER2‐positive breast cancer, triple‐negative breast cancer and healthy donors. MESS2CAN is a pioneering method for highly sensitive in vitro EV diagnostics, applicable to clinical samples with trace amounts of EVs.
He, L, Shi, K, Wang, D, Wang, X & Xu, G 2023, 'A topic‐controllable keywords‐to‐text generator with knowledge base network', CAAI Transactions on Intelligence Technology. View/Download from: Publisher's site View description>>
AbstractWith the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations.
He, L, Xu, G, Jameel, S, Wang, X & Chen, H 2023, 'Graph-Aware Deep Fusion Networks for Online Spam Review Detection', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2557-2565. View/Download from: Publisher's site
He, T, Wu, M, Aguilera, RP, Lu, DD-C, Liu, Q & Vazquez, S 2023, 'Low Computational Burden Model Predictive Control for Single-Phase Cascaded H-Bridge Converters Without Weighting Factor', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2396-2406. View/Download from: Publisher's site View description>>
In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 μ s). Fast response speed to variations in voltage reference and load resistance can be achieved t.
He, X, Xu, H & Sheng, D 2023, 'Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs', Acta Geotechnica, vol. 18, no. 4, pp. 1681-1698. View/Download from: Publisher's site View description>>
AbstractData-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of sample data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an example. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.
He, Y, Liu, P, Zhu, L & Yang, Y 2023, 'Filter Pruning by Switching to Neighboring CNNs With Good Attributes', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 8044-8056. View/Download from: Publisher's site View description>>
Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose meta-attribute-based filter pruning (MFP). First, to expand the existing magnitude information-based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.
He, Y, Wang, K, Zhang, W, Lin, X & Zhang, Y 2023, 'Scaling Up k-Clique Densest Subgraph Detection', Proceedings of the ACM on Management of Data, vol. 1, no. 1, pp. 1-26. View/Download from: Publisher's site View description>>
In this paper, we study the k-clique densest subgraph problem, which detects the subgraph that maximizes the ratio between the number of k-cliques and the number of vertices in it. The problem has been extensively studied in the literature and has many applications in a wide range of fields such as biology and finance. Existing solutions rely heavily on repeatedly computing all the k-cliques, which are not scalable to handle large k values on large-scale graphs. In this paper, by adapting the idea of 'pivoting', we propose the SCT*-Index to compactly organize the k-cliques. Based on the SCT*-Index, our SCTL algorithm can directly obtain the k-cliques from the index and efficiently achieve near-optimal approximation. To further improve SCTL, we propose SCTL* that includes novel graph reductions and batch-processing optimizations to reduce the search space and decrease the number of visited k-cliques, respectively. As evaluated in our experiments, SCTL* significantly outperform existing approaches by up to two orders of magnitude. In addition, we propose a sampling-based approximate algorithm that can provide reasonable approximations for any k value on billion-scale graphs. Extensive experiments on 12 real-world graphs validate both the efficiency and effectiveness of the proposed techniques.
He, Z, Fan, X, Jin, W, Gao, S, Yan, B, Chen, C, Ding, W, Yin, S, Zhou, X, Liu, H, Li, X & Wang, Q 2023, 'Chlorine-resistant bacteria in drinking water: Generation, identification and inactivation using ozone-based technologies', Journal of Water Process Engineering, vol. 53, pp. 103772-103772. View/Download from: Publisher's site
He, Z, Fan, X, Qu, L, Zhou, X, Jin, W, Hatshan, MR, Li, X, Liu, H, Jiang, G & Wang, Q 2023, 'Cultivation of Chlorella pyrenoidosa and Scenedesmus obliquus in swine wastewater: Nitrogen and phosphorus removal and microalgal growth', Process Safety and Environmental Protection, vol. 179, pp. 887-895. View/Download from: Publisher's site View description>>
Using microalgae to treat swine wastewater can effectively reduce the increasing pollution and save the cost of cultivating microalgae. In this study, the growth and denitrification and phosphorus removal effects of Scenedesmus obliquus and Chlorella pyrenoidosa at different dilutions in swine wastewater were investigated to solve the problem that microalgae could not be cultivated in the raw swine wastewater. After diluting the swine wastewater 8 and 12 times, the growth was optimized after 11 days of cultivation of Scenedesmus obliquus and 9 days of cultivation of Chlorella pyrenoidosa. Compared to Chlorella pyrenoidosa, the biomass and chlorophyll-a content were higher in Scenedesmus obliquus, at 1.48 g/L and 18.46 mg/L, respectively. The removal of nitrogen and phosphorus indicators was almost 100 %. Subsequently, Scenedesmus obliquus was cultured in an 8-fold dilution of swine and domestic wastewater, with dry weights of 0.83 g/L and 1.44 g/L, and lipid contents of 41.26 % and 25.11 %, respectively. Compared to Chlorella pyrenoidosa, Scenedesmus obliquus was more tolerant to nitrogen and phosphorus in swine wastewater, and at the same time, it had a higher growth rate, making it more suitable for treating swine wastewater and accumulating biomass.
He, Z, Zhou, X, Fan, X, Jin, W, Chen, C, Yan, B, Yin, S, Zhou, T, Li, X & Jiang, G 2023, 'Advanced oxidation-based combined conditioning technologies to improve sludge dewaterability: A mini review', Journal of Water Process Engineering, vol. 53, pp. 103773-103773. View/Download from: Publisher's site
Hertrampf, T, Oberst, S & Sepehrirahnama, S 2023, 'Recurrence rate spectrograms for impact localization in wood', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A142-A142. View/Download from: Publisher's site View description>>
Characteristics like cellular grain structure, inhomogeneous density, aging, and altering environmental conditions (moisture, temperature) give wood highly anisotropic viscoelastic properties and non-linear vibrational wave propagation properties. Nonlinearity limits the use of linear methods, such as modal analysis and parameter identification via transfer functions. Acoustic localization of natural damage to wood, like crack growth, is of general interest in structural health monitoring of timber structures. Time-difference of arrival or energy attenuation is commonly used for localization, which are prone to boundary reflections or require the frequency response function. Recent advancements in machine learning-based classification of non-linear signals can achieve a much higher accuracy when recurrence rate-based spectrograms are used compared relative to conventional short-time Fourier transforms, especially in the presence of noise. Hence, in this work, multi-sensor measurements of impulse induced vibration in wood beams are classified by their distance to the excitation, based on their time series, avoiding a priori knowledge of a transfer function for the localization. The machine learning model is trained across various widths and thicknesses of samples, giving a localization estimate independent of beam dimensions. This research will contribute to early detection of damage in the field of vibration-based structural health monitoring of wood.
Hieu, NQ, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Jamalipour, A 2023, 'Joint Power Allocation and Rate Control for Rate Splitting Multiple Access Networks With Covert Communications', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2274-2287. View/Download from: Publisher's site View description>>
Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.
Hieu, NQ, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach', IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1536-1548. View/Download from: Publisher's site View description>>
Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.
Hoang, AT, Balasubramanian, D, Venugopal, IP, Rajendran, V, Nguyen, DT, Lawrence, KR, Nguyen, XP & Kalam, MA 2023, 'A feasible and promising approach for diesel engine fuelled with a blend of biodiesel and low-viscosity Cinnamon oil: A comprehensive analysis of performance, combustion, and exergy', Journal of Cleaner Production, vol. 401, pp. 136682-136682. View/Download from: Publisher's site
Hoang, AT, Murugesan, P, PV, E, Balasubramanian, D, Parida, S, Priya Jayabal, C, Nachippan, M, Kalam, MA, Truong, TH, Cao, DN & Le, VV 2023, 'Strategic combination of waste plastic/tire pyrolysis oil with biodiesel for natural gas-enriched HCCI engine: Experimental analysis and machine learning model', Energy, vol. 280, pp. 128233-128233. View/Download from: Publisher's site
Hoang, T-D, Huang, X & Qin, P 2023, 'Gradient Descent-Based Direction-of-Arrival Estimation for Lens Antenna Array', IEEE Signal Processing Letters, vol. 30, pp. 838-842. View/Download from: Publisher's site View description>>
In this letter, we investigate a novel optimization approach to direction-of-arrival (DoA) estimation for a lens antenna array. Inspired by a property of the sinc function and ${\ell _{2}}$-norm optimization, we develop the gradient descent-based spatial spectrum reconstruction (GD-SSR) to estimate the DoAs based on the sum signal covariance vector (SSCV). Our proposed algorithm does not require a priori knowledge of signal number and has a lower complexity compared with existing techniques while achieving a better estimation performance, even in a low-SNR regime. In addition, the proposed model does not require any pretraining process as prior learning-based methods. The simulation results show that our scheme not only outperforms other techniques but also resolves the angular ambiguity problem.
Hoang, TM, Xu, C, Vahid, A, Tuan, HD, Duong, TQ & Hanzo, L 2023, 'Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13221-13234. View/Download from: Publisher's site
Hofer, OJ, Alsweiler, J, Tran, T & Crowther, CA 2023, 'Glycemic control in gestational diabetes and impact on biomarkers in women and infants', Pediatric Research, vol. 94, no. 2, pp. 466-476. View/Download from: Publisher's site View description>>
AbstractBackgroundGestational diabetes mellitus (GDM) is linked to the dysregulation of inflammatory markers in women with GDM compared to women without. It is unclear whether the intensity of glycemic control influences these biomarkers. We aimed to assess whether different glycemic targets for women with GDM and compliance influence maternal and infant biomarkers.MethodsMaternity hospitals caring for women with GDM were randomized in the TARGET Trial to tight or less tight glycemic targets. Maternal blood was collected at study entry, 36 weeks’ gestation, and 6 months postpartum, and cord plasma after birth. We assessed compliance to targets and concentrations of maternal serum and infant biomarkers.ResultsEighty-two women and infants were included in the study. Concentrations of maternal and infant biomarkers did not differ between women assigned to tighter and less tight glycemic targets; however, concentrations were altered in maternal serum leptin and CRP and infant cord C-peptide, leptin, and IGF in women who complied with tighter targets.ConclusionsUse of tighter glycemic targets in women with GDM does not change the concentrations of maternal and infant biomarkers compared to less tight targets. However, when compliance is achieved to tighter targets, maternal and infant biomarkers are altered.ImpactThe use of ti...
Hoke, JC, Ippoliti, M, Rosenberg, E, Abanin, D, Acharya, R, Andersen, TI, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Dau, AG, Debroy, DM, Del Toro Barba, A, Demura, S, Di Paolo, A, Drozdov, IK, Dunsworth, A, Eppens, D, Erickson, C, Farhi, E, Fatemi, R, Ferreira, VS, Burgos, LF, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Kechedzhi, K, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, A, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lensky, YD, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Martin, O, McClean, JR, McEwen, M, Miao, KC, Mieszala, A, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, R, Sterling, G, Strain, D, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Heidweiller, CV, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Neven, H, Babbush, R, Bacon, D, Boixo, S, Hilton, J, Lucero, E, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Mi, X, Khemani, V & Roushan, P 2023, 'Measurement-induced entanglement and teleportation on a noisy quantum processor', Nature, vol. 622, no. 7983, pp. 481-486. View/Download from: Publisher's site View description>>
AbstractMeasurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time3–10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11–13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15–17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
Holmes, NP, Roohani, I, Entezari, A, Guagliardo, P, Mirkhalaf, M, Lu, Z, Chen, Y-S, Yang, L, Dunstan, CR, Zreiqat, H & Cairney, JM 2023, 'Discovering an unknown territory using atom probe tomography: Elemental exchange at the bioceramic scaffold/bone tissue interface', Acta Biomaterialia, vol. 162, pp. 199-210. View/Download from: Publisher's site
Honda, T, Tran, T, Popplestone, S, Draper, CE, Yousafzai, AK, Romero, L & Fisher, J 2023, 'Parents’ mental health and the social-emotional development of their children aged between 24 and 59 months in low-and middle-income countries: A systematic review and meta-analyses', SSM - Mental Health, vol. 3, pp. 100197-100197. View/Download from: Publisher's site
Ho-Pham, LT, Nguyen, HG, Nguyen-Pham, SQ, Hoang, DK, Tran, TS & Nguyen, TV 2023, 'Longitudinal changes in bone mineral density during perimenopausal transition: the Vietnam Osteoporosis Study', Osteoporosis International, vol. 34, no. 8, pp. 1381-1387. View/Download from: Publisher's site
Hoque, M, Alam, M, Wang, S, Zaman, JU, Rahman, MS, Johir, MAH, Tian, L, Choi, J-G, Ahmed, MB & Yoon, M-H 2023, 'Interaction chemistry of functional groups for natural biopolymer-based hydrogel design', Materials Science and Engineering: R: Reports, vol. 156, pp. 100758-100758. View/Download from: Publisher's site
Horry, MJ, Chakraborty, S, Pradhan, B, Paul, M, Zhu, J, Barua, PD, Mir, HS, Chen, F, Zhou, J & Acharya, UR 2023, 'Full-Resolution Lung Nodule Localization From Chest X-Ray Images Using Residual Encoder-Decoder Networks', IEEE Access, vol. 11, pp. 143016-143036. View/Download from: Publisher's site
Horry, MJ, Chakraborty, S, Pradhan, B, Shulka, N & Almazroui, M 2023, 'Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches', Earth Systems and Environment, vol. 7, no. 2, pp. 525-540. View/Download from: Publisher's site View description>>
AbstractHigh-velocity data streams present a challenge to deep learning-based computer vision models due to the resources needed to retrain for new incremental data. This study presents a novel staggered training approach using an ensemble model comprising the following: (i) a resource-intensive high-accuracy vision transformer; and (ii) a fast training, but less accurate, low parameter-count convolutional neural network. The vision transformer provides a scalable and accurate base model. A convolutional neural network (CNN) quickly incorporates new data into the ensemble model. Incremental data are simulated by dividing the very large So2Sat LCZ42 satellite image dataset into four intervals. The CNN is trained every interval and the vision transformer trained every half interval. We call this combination of a complementary ensemble with staggered training a “two-speed” network. The novelty of this approach is in the use of a staggered training schedule that allows the ensemble model to efficiently incorporate new data by retraining the high-speed CNN in advance of the resource-intensive vision transformer, thereby allowing for stable continuous improvement of the ensemble. Additionally, the ensemble models for each data increment out-perform each of the component models, with best accuracy of 65% against a holdout test partition of the RGB version of the So2Sat dataset.
Hossain, A, Molla, MM, Kamrujjaman, M, Mohebujjaman, M & Saha, SC 2023, 'MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology', Energies, vol. 16, no. 11, pp. 4408-4408. View/Download from: Publisher's site View description>>
The numerical investigation of magneto-hydrodynamic (MHD) mixed convection flow and entropy formation of non-Newtonian Bingham fluid in a lid-driven wavy square cavity filled with nanofluid was investigated by the finite volume method (FVM). The numerical data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The physical model is a two-dimensional wavy square cavity with thermally adiabatic horizontal boundaries, while the right and left vertical walls maintain a temperature of TC and TH, respectively. The top wall has a steady speed of u=u0. Pertinent non-dimensional parameters such as Reynolds number (Re=10,100,200,400), Hartmann number (Ha=0,10,20), Bingham number (Bn=0,2,5,10,50,100,200), nanoparticle volume fraction (ϕ=0,0.02,0.04), and Prandtl number (Pr=6.2) have been simulated numerically. The Richardson number Ri is calculated by combining the values of Re with a fixed value of Gr, which is the governing factor for the mixed convective flow. Using the Response Surface Methodology (RSM) method, the correlation equations are obtained using the input parameters for the average Nusselt number (Nu¯), total entropy generation (Es)t, and Bejan number (Beavg). The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The sensitivity of the output response to the input parameters is also tested. According to the findings, the mean Nusselt numbers (Nu¯) drop when Ha and Bn are increased and grow when Re and ϕ are augmented. It is found that (Es)t is reduced by raising Ha, but (Es)t rises with the augmentation of ϕ and Re. It is also found that the ϕ and Re numbers have a positive sensitivity to the Nu¯, while the sensitivity of the Ha and Bn numbers is negative.
Hossain, E, Rana, R, Higgins, N, Soar, J, Barua, PD, Pisani, AR & Turner, K 2023, 'Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review', Computers in Biology and Medicine, vol. 155, pp. 106649-106649. View/Download from: Publisher's site
Hossain, MA, Islam, MR, Hossain, MA & Hossain, MJ 2023, 'Control strategy review for hydrogen-renewable energy power system', Journal of Energy Storage, vol. 72, pp. 108170-108170. View/Download from: Publisher's site
Hossain, SM, Yu, H, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'ZiF-8 induced carbon electrodes for selective lithium recovery from aqueous feed water by employing capacitive deionization system', Desalination, vol. 546, pp. 116201-116201. View/Download from: Publisher's site View description>>
The demand for lithium (Li) will grow from about 500,000 metric tons of lithium carbonate equivalent in 2021 to 3–4 million metric tons in 2030. To meet the Li demand, the separation of Li-mixed monovalent and divalent cations is critical for Li extraction from an aqueous medium. Capacitive deionization (CDI) and membrane capacitive deionization (MCDI) have recently emerged as viable water treatment technologies, yet ion-specific selective recovery using CDI systems is still under-investigated. In this study, the electrode surface of each system was modified to improve Li+ selectivity. Metal-organic frameworks (MOF), particularly zeolitic imidazolate framework-8 (ZiF-8), have shown substantial promise due to their tunable pore size and pore channel chemistry. Through an aqueous medium-based surface modification, we offer a simple technique of synthesizing ZiF-8 on carbon electrodes and underneath the cation exchange membrane (CEM). The bare CDI and MCDI systems initially showed poor selectivity towards Li+ in the mono and divalent ion incorporated simulated solutions. The relative selectivity (ρMLi; (M = metal ions)) in the CDI system was estimated as 0.73, 0.43, 0.67, and 0.58 for Na+, K+, Mg2+, and Ca2+, respectively, which was 0.93, 0.97, 0.39, and 0.30 in the MCDI system. In the case of bare activated carbon (AC) electrodes, the difference of hydration enthalpy played a critical role in Li+ selectivity towards other monovalent ions. However, despite having high hydration enthalpy, the Mg2+ and Ca2+ showed low Li+ selectivity due to the superior charge density of divalent ions. On the other hand, after the modification of AC electrodes with in-situ growth of ZiF-8 on the surface, the Li+ selectivity for monovalent Na+ and K+ was estimated at 3.08 and 1.12, respectively, which is 4.2 and 2.6 times higher than the bare AC electrode, respectively. Besides, compared to Na+, the trade-off between the low dehydration energy of K+ and the rapid ion transit ...
Hosseini, SAH, Rahmani, O, Hayati, H & Keshtkar, M 2023, 'An exact solution of dynamic response of DNS with a medium viscoelastic layer by moving load', Advances in Materials Research (South Korea), vol. 12, no. 3, pp. 193-210. View/Download from: Publisher's site View description>>
This paper aims to analyze the dynamic response of a double nanobeam system with a medium viscoelastic layer under a moving load. The governing equations are based on the Eringen nonlocal theory. A thin viscoelastic layer has coupled two nanobeams together. An exact solution is derived for each nanobeam, and the dynamic deflection is achieved. The effect of parameters such as nonlocal parameter, velocity of moving load, spring coefficient and the viscoelastic layer damping ratio was studied. The results showed that the effect of the nonlocal parameter is significantly important and the classical theories are not suitable for nano and microstructures.
Hosseinzadeh, A, Altaee, A, Li, X & Zhou, JL 2023, 'Machine learning-based modeling and analysis of perfluoroalkyl and polyfluoroalkyl substances controlling systems in protecting water resources', Current Opinion in Chemical Engineering, vol. 42, pp. 100983-100983. View/Download from: Publisher's site
Hu, S, Yuan, X, Ni, W, Wang, X & Jamalipour, A 2023, 'RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 20162-20173. View/Download from: Publisher's site
Hu, S, Yuan, X, Ni, W, Wang, X & Jamalipour, A 2023, 'Visual Camouflage and Online Trajectory Planning for Unmanned Aerial Vehicle-Based Disguised Video Surveillance: Recent Advances and a Case Study', IEEE Vehicular Technology Magazine, vol. 18, no. 3, pp. 48-57. View/Download from: Publisher's site
Hu, Y, Jin, P, Guo, Y, Lei, G & Zhu, J 2023, 'A New SVM Strategy to Suppress Total Harmonic Distortion and Current Stress in HFLMCs', IEEE Transactions on Industrial Electronics, pp. 1-11. View/Download from: Publisher's site
Huang, C-W, Huang, W-Y, Lin, C, Li, Y-L, Huang, T-P, Bui, X-T & Ngo, HH 2023, 'Ecological risk assessment and corrective actions for dioxin-polluted sediment in a chemical plant's brine water storage pond', Science of The Total Environment, vol. 859, pp. 160239-160239. View/Download from: Publisher's site
Huang, C-W, Li, Y-L, Lin, C, Bui, X-T, Vo, T-D-H & Ngo, HH 2023, 'Seasonal influence on pollution index and risk of multiple compositions of microplastics in an urban river', Science of The Total Environment, vol. 859, pp. 160021-160021. View/Download from: Publisher's site View description>>
Emerging contaminant microplastics (MPs) are getting worldwide attention for their ubiquitous occurrence and potential risk to the environment. However, the seasonal influence on freshwater MP pollution remains poorly understood. To better understand and evaluate the riverine MPs in different seasons, this study conducted the risk assessment of MPs in an urban river, Houjin River, during the different seasons. The present study found that the MPs (0.1-5 mm, mostly 0.1-2 mm) were more abundant in the dry season (183.33 ± 128.95 items/m3) compared with the wet season (102.08 ± 45.80 items/m3). Similarly, the mixture of different MPs polymers was more diverse in the dry season. The related pollution indices such as the contamination factor (CF) and pollution load index (PLI) showed that average CF and PLI were 5.15 and 2.10 in the dry season, which significantly decreased to 1.58 and 1.25, respectively, in the wet season (p < 0.05). Additionally, significant difference of the average risk quotient (RQ) was observed, which was 0.037 in the dry season and 0.021 in the wet season (p < 0.05). To sum up, the results of this study indicate the seasonal effects on the pollution and risk of multiple compositions of MPs in the urban river, suggesting higher impacts of riverine MPs pollution in the dry season, as well as the potential increase of MPs, may lead to environmental risk in the future.
Huang, C-W, Lin, C, Nguyen, MK, Hussain, A, Bui, X-T & Ngo, HH 2023, 'A review of biosensor for environmental monitoring: principle, application, and corresponding achievement of sustainable development goals', Bioengineered, vol. 14, no. 1, pp. 58-80. View/Download from: Publisher's site
Huang, H, Zhao, G, Bo, Y, Yu, J, Liang, L, Yang, Y & Ou, K 2023, 'Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene', Measurement, vol. 211, pp. 112602-112602. View/Download from: Publisher's site
Huang, J, Ma, B, Wang, M, Zhou, X, Yao, L, Wang, S, Qi, L & Chen, Y 2023, 'Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC', IEEE Transactions on Consumer Electronics, pp. 1-1. View/Download from: Publisher's site
Huang, J, Song, X, Xiao, F, Cao, Z & Lin, C-T 2023, 'Belief f-divergence for EEG complexity evaluation', Information Sciences, vol. 643, pp. 119189-119189. View/Download from: Publisher's site
Huang, M, Cao, C, Liu, L, Wei, W, Zhu, Q-L & Huang, Z 2023, 'Controlled synthesis of MOF-derived hollow and yolk–shell nanocages for improved water oxidation and selective ethylene glycol reformation', eScience, vol. 3, no. 5, pp. 100118-100118. View/Download from: Publisher's site View description>>
Delicately designed metal–organic framework (MOF)-derived nanostructured electrocatalysts are essential for improving the reaction kinetics of the oxygen evolution reaction and tuning the selectivity of small organic molecule oxidation reactions. Herein, novel oxalate-modified hollow CoFe-based layered double hydroxide nanocages (h-CoFe-LDH NCs) and yolk–shell ZIF@CoFe-LDH nanocages (ys-ZIF@CoFe-LDH NCs) are developed through an etching–doping reconstruction strategy from a Co-based MOF precursor (ZIF-67). The distinctive nanostructures, along with the incorporation of the secondary metal element and intercalated oxalate groups, enable h-CoFe-LDH NCs and ys-ZIF@CoFe-LDH NCs to expose more active sites with high intrinsic activity. The resultant h-CoFe-LDH NCs exhibit outstanding OER activity with an overpotential of only 278 mV to deliver a current density of 50 mA cm−2. Additionally, controlling the reconstruction degree enables the formation of ys-ZIF@CoFe-LDH NCs with a yolk–shell nanocage nanostructure, which show outstanding electrocatalytic performance for the selective ethylene glycol oxidation reaction (EGOR) toward formate, with a Faradaic efficiency of up to 91%. Consequently, a hybrid water electrolysis system integrating the EGOR and the hydrogen evolution reaction using Pt/C||ys-ZIF@CoFe-LDH NCs is explored for energy-saving hydrogen production, requiring a cell voltage 127 mV lower than water electrolysis to achieve a current density of 50 mA cm−2. This work demonstrates a feasible way to design advanced MOF-derived electrocatalysts toward enhanced electrocatalytic reactions.
Huang, M, Li, G, Liu, Z & Zhu, L 2023, 'Lightweight Distortion-Aware Network for Salient Object Detection in Omnidirectional Images', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 10, pp. 6191-6197. View/Download from: Publisher's site
Huang, M, Li, G, Liu, Z, Wu, Y, Gong, C, Zhu, L & Yang, Y 2023, 'Exploring viewport features for semi-supervised saliency prediction in omnidirectional images', Image and Vision Computing, vol. 129, pp. 104590-104590. View/Download from: Publisher's site
Huang, M, Zhou, S, Ma, D-D, Wei, W, Zhu, Q-L & Huang, Z 2023, 'MOF-derived MoC-Fe heterojunctions encapsulated in N-doped carbon nanotubes for water splitting', Chemical Engineering Journal, vol. 473, pp. 145170-145170. View/Download from: Publisher's site View description>>
Engineering the synergistic interfacial structures in nanostructured electrocatalysts is an effective yet challenging pursuit. Here we report porous nitrogen-doped carbon nanotubes (NCNTs) entrapping heterojunctions between carbide and transition metal nanoparticles (NPs) as excellent bifunctional catalyst for hydrogen and oxygen evolution reactions (HER and OER). Dual-phase MoC and Fe NPs confined in NCNTs (denoted as MoC-Fe@NCNTs) was fabricated by trapping [Fe(C2O4)3]3– into Zn/Mo-HZIF framework followed by pyrolysis. The resultant catalyst exhibited commendable bifunctional activities with small overpotentials at 50 mA cm−2 for the HER of 252 and OER of 304 mV, respectively. Theoretical calculations and experimental observation prove that the combination of Fe NPs generates synergistic heterointerfaces and improves OER activity of MoC, thus endowing outstanding bifunctional electrocatalytic performances. Moreover, the NCNTs, as the electronic communication amplifier, can facilitate electron transfer and inhibit the aggregation and corrosion of the active species. The controllable fabrication of MOF-derived heterostructures reported in this work provides a prospect for developing bifunctional MOF derivatives for water electrolysis.
Huang, Q-S, Chu, C, Li, Q, Liu, Q, Liu, X, Sun, J, Ni, B-J & Mao, S 2023, 'Three-Phase Interface Construction on Hydrophobic Carbonaceous Catalysts for Highly Active and Selective Photocatalytic CO2 Conversion', ACS Catalysis, vol. 13, no. 17, pp. 11232-11243. View/Download from: Publisher's site
Huang, S, Liu, Y, Tsang, IW, Xu, Z & Lv, J 2023, 'Multi-View Subspace Clustering by Joint Measuring of Consistency and Diversity', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 8, pp. 8270-8281. View/Download from: Publisher's site
Huang, S, Tegg, L, Qu, J, Yang, L, McCarroll, I, Burr, P & Cairney, JM 2023, 'Nanoscale Distribution of Alloying Elements in Optimized ZIRLO Using the Invizo 6000', Microscopy and Microanalysis, vol. 29, no. Supplement_1, pp. 614-615. View/Download from: Publisher's site
Huang, W-Y, Huang, C-W, Li, Y-L, Huang, T-P, Lin, C, Ngo, HH & Bui, X-T 2023, 'Reduced pollution level and ecological risk of mercury-polluted sediment in a alkali-chlorine factory’s brine water storage pond after corrective actions: A case study in Southern Taiwan', Environmental Technology & Innovation, vol. 29, pp. 103003-103003. View/Download from: Publisher's site
Huang, X, Mei, G & Zhang, J 2023, 'Cross-source point cloud registration: Challenges, progress and prospects', Neurocomputing, vol. 548, pp. 126383-126383. View/Download from: Publisher's site
Huang, X, Tuyen Le, A & Guo, YJ 2023, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2364-2378. View/Download from: Publisher's site View description>>
An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.
Huang, Y, Du, Z, Bao, G, Fang, G, Cappadona, M, McClements, L, Tuch, BE, Lu, H & Xu, X 2023, 'Smart Drug-Delivery System of Upconversion Nanoparticles Coated with Mesoporous Silica for Controlled Release', Pharmaceutics, vol. 15, no. 1, pp. 89-89. View/Download from: Publisher's site View description>>
Drug-delivery vehicles have garnered immense interest in recent years due to unparalleled progress made in material science and nanomedicine. However, the development of stimuli-responsive devices with controllable drug-release systems (DRSs) is still in its nascent stage. In this paper, we designed a two-way controlled drug-release system that can be promoted and prolonged, using the external stimulation of near-infrared light (NIR) and protein coating. A hierarchical nanostructure was fabricated using upconversion nanoparticles (UCNPs)—mesoporous silica as the core-shell structure with protein lysozyme coating. The mesoporous silica shell provides abundant pores for the loading of drug molecules and a specific type of photosensitive molecules. The morphology and the physical properties of the nanostructures were thoroughly characterized. The results exhibited the uniform core-shell nanostructures of ~four UCNPs encapsulated in one mesoporous silica nanoparticle. The core-shell nanoparticles were in the spherical shape with an average size of 200 nm, average surface area of 446.54 m2/g, and pore size of 4.6 nm. Using doxorubicin (DOX), a chemotherapy agent as the drug model, we demonstrated that a novel DRS with capacity of smart modulation to promote or inhibit the drug release under NIR light and protein coating, respectively. Further, we demonstrated the therapeutic effect of the designed DRSs using breast cancer cells. The reported novel controlled DRS with dual functionality could have a promising potential for chemotherapy treatment of solid cancers.
Huang, Y, Li, Y, Jourjon, G, Seneviratne, S, Thilakarathna, K, Cheng, A, Webb, D & Xu, RYD 2023, 'Calibrated reconstruction based adversarial autoencoder model for novelty detection', Pattern Recognition Letters, vol. 169, pp. 50-57. View/Download from: Publisher's site
Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2023, 'Fractal Belief Rényi Divergence with Its Applications in Pattern Classification', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16. View/Download from: Publisher's site
Huang, Y, Xiao, F, Cao, Z & Lin, C-T 2023, 'Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14709-14726. View/Download from: Publisher's site
Huang, Z, Shivakumara, P, Kaljahi, MA, Kumar, A, Pal, U, Lu, T & Blumenstein, M 2023, 'Writer age estimation through handwriting', Multimedia Tools and Applications, vol. 82, no. 11, pp. 16033-16055. View/Download from: Publisher's site View description>>
Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.
Huang, Z, Wen, J, Chen, S, Zhu, L & Zheng, N 2023, 'Discriminative Radial Domain Adaptation', IEEE Transactions on Image Processing, vol. 32, pp. 1419-1431. View/Download from: Publisher's site
Huo, P, Deng, R, Chen, X, Liu, Y, Yang, L, Wu, L, Wei, W & Ni, B-J 2023, 'Model-Based Evaluation of N2O Recovery as an Energy Source in Sulfur-Driven NO-Based Autotrophic Denitrification', Chemical Engineering Journal, vol. 453, pp. 139732-139732. View/Download from: Publisher's site View description>>
Instead of the conventional perception of nitrous oxide (N2O) as a potent greenhouse gas whose production should be minimized, this work aimed to assess N2O recovery as a potential energy source from nitric oxide (NO) in the form of Fe(II)EDTA-NO through element sulfur (S0) or thiosulfate (S2O32−)-driven NO-based autotrophic denitrification (SNADS0 or SNADS2O3). A mathematical model was proposed to describe substrate dynamics related to N2O production and reduction and was successfully calibrated and validated using batch experimental data from lab-scale SNADS0 and SNADS2O3 systems under different substrates conditions. The model was subsequently employed to assess the potential of N2O accumulation and recovery by altering the S/N mass ratio and the ratio of gas volume to liquid volume of the system. The simulation results suggested that with a S/N mass ratio of nearly 1.0, high-purity N2O could be more rapidly and efficiently recovered from Fe(II)EDTA-NO in the SNADS0 and SNADS2O3 systems with a higher ratio of gas volume to liquid volume (i.e., a N2O recovery efficiency of up to 80.2%−84.9% reached within 3.1 h−3.5 h under the studied conditions). Comparatively, the SNADS0 process showed an economic and viable advantage for practical applications to the efficient treatment and resource utilization of NO-containing flue gas.
Huo, P, Deng, R, Yang, L, Liu, Y, Wei, W, Ni, B-J & Chen, X 2023, 'Exposure of sulfur-driven autotrophic denitrification to hydroxylamine/hydrazine: Underlying mechanisms and implications for promoting partial denitrification and N2O recovery', Chemical Engineering Journal, vol. 477, pp. 146943-146943. View/Download from: Publisher's site
Huo, Y, Zheng, H, Jiang, Y, Chen, H, Cao, W, Mameda, N, Nghiem, LD, Zhang, X & Liu, Q 2023, 'Comparison and Characterization of Nitrogen/Sulfur-Doped Activated Carbon for Activating Peroxydisulfate to Degrade Acid Orange 7: An Experimental and Theoretical Study', Industrial & Engineering Chemistry Research, vol. 62, no. 30, pp. 11894-11904. View/Download from: Publisher's site
Huq, T, Ong, HC, Chew, BT, Kazi, SN, Zubir, MNM, Ong, ZC & Azlan, NBBM 2023, 'Graphene nanoplatelet nanofluids stabilised by hybridisation with graphene oxide: preparation, stability, and performance in flat plate solar thermal collector', Journal of Thermal Analysis and Calorimetry, vol. 148, no. 5, pp. 2105-2118. View/Download from: Publisher's site
Hussain, A, Wu, SC, Le, T-H, Huang, W-Y, Lin, C, Bui, X-T & Ngo, HH 2023, 'Enhanced biodegradation of endocrine disruptor bisphenol A by food waste composting without bioaugmentation: Analysis of bacterial communities and their relative abundances', Journal of Hazardous Materials, vol. 460, pp. 132345-132345. View/Download from: Publisher's site
Hussain, W & Merigo, JM 2023, 'Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 9, pp. 12875-12894. View/Download from: Publisher's site View description>>
AbstractThere has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.
Hussain, W, Merigó, JM, Gil-Lafuente, J & Gao, H 2023, 'Complex nonlinear neural network prediction with IOWA layer', Soft Computing, vol. 27, no. 8, pp. 4853-4863. View/Download from: Publisher's site View description>>
AbstractNeural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator.
Ibrahim, IA & Hossain, MJ 2023, 'Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models', Energy Conversion and Management, vol. 296, pp. 117663-117663. View/Download from: Publisher's site
Ibrar, I, Alsaka, L, Yadav, S, Altaee, A, Zhou, JL & Shon, HK 2023, 'Kappa carrageenan-vanillin composite hydrogel for landfill leachate wastewater treatment', Desalination, vol. 565, pp. 116826-116826. View/Download from: Publisher's site
Ibrar, I, Yadav, S, Altaee, A, Braytee, A, Samal, AK, Zaid, SMJ & Hawari, AH 2023, 'A machine learning approach for prediction of reverse solute flux in forward osmosis', Journal of Water Process Engineering, vol. 54, pp. 103956-103956. View/Download from: Publisher's site
Ijaz Malik, MA, Kalam, MA, Mujtaba, MA & Almomani, F 2023, 'A review of recent advances in the synthesis of environmentally friendly, sustainable, and nontoxic bio-lubricants: Recommendations for the future implementations', Environmental Technology & Innovation, vol. 32, pp. 103366-103366. View/Download from: Publisher's site
Ikram, MM, Saha, G & Saha, SC 2023, 'Unsteady conjugate heat transfer characteristics in hexagonal cavity equipped with a multi-blade dynamic modulator', International Journal of Heat and Mass Transfer, vol. 200, pp. 123527-123527. View/Download from: Publisher's site View description>>
Purpose: The transient analysis of the thermal response and frictional loss characteristics for flow-modulated conjugate heat transfer phenomena has been investigated in the present study. The flow domain is a partitioned cavity of a hexagonal structure equipped with a multi-blade flow modulator. The clockwise rotating blade is adiabatic and stirrers the internal flow along with the natural convection caused by the bottom heated floor of uniform heat flux. The conjugate behavior is introduced through the solid subdomains consisting of two brick-made partitions and one glass partition of uniform thickness. The material of the partition wall reflects the physical aspects of industrial applications. Approach: The two-dimensional unsteady continuity, momentum, and energy equations are expressed in a non-dimensional form where the buoyant force is modeled through the Boussinesq approximation. The Arbitrary Lagrangian Euler (ALE) finite element is adopted to solve the moving mesh problem by formulating a free triangular discretization scheme. Parametric computational investigations are carried out for air as the working fluid (Pr = 0.71) and 3 different configurations of the rotating modulator while varying the other parameters, i.e., Reynolds number (Re) and Rayleigh number (Ra) for a fixed Biot number (Bi = 104). This dynamic mesh problem encompasses a wide range of parameters, i.e., (100 ≤ Re ≤ 103), and (104 ≤ Ra ≤ 106) for Bi = 104 to evaluate the thermodynamic response of the present thermo-fluid system. Various thermo-fluid system responses are visualized through the spatially average Nusselt number evaluated on the heated surface, system effectiveness, average thermal storage capacity, and frictional power loss of the flow domain. The thermal response is fragmented into individual responses in terms of component signal frequency by the Fast Fourier Transform (FFT) analysis. Findings: According to the current analysis, increasing the number of blades i...
Imran, S, Gul, M, Kalam, MA, Zulkifli, NWM, Mujtaba, MA, Yusoff, MNAM & Awang, MSN 2023, 'Effect of various nanoparticle biodiesel blends on thermal efficiency and exhaust pollutants', International Journal of Energy and Environmental Engineering, vol. 14, no. 4, pp. 937-948. View/Download from: Publisher's site
Imran, S, Mujtaba, MA, Zafar, MM, Hussain, A, Mehmood, A, Farwa, UE, Korakianitis, T, Kalam, MA, Fayaz, H & Saleel, CA 2023, 'Assessing the potential of GHG emissions for the textile sector: A baseline study', Heliyon, vol. 9, no. 11, pp. e22404-e22404. View/Download from: Publisher's site
Inamdar, MA, Raghavendra, U, Gudigar, A, Bhandary, S, Salvi, M, Deo, RC, Barua, PD, Ciaccio, EJ, Molinari, F & Acharya, UR 2023, 'A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images', IEEE Access, vol. 11, pp. 108982-108994. View/Download from: Publisher's site
Inayat, A, Jamil, F, Ahmed, SF, Ayoub, M, Abdul, PM, Aslam, M, Mofijur, M, Khan, Z & Mustafa, A 2023, 'Thermal degradation characteristics, kinetic and thermodynamic analyses of date palm surface fibers at different heating rates', Fuel, vol. 335, pp. 127076-127076. View/Download from: Publisher's site View description>>
The potential of the least-exploited date pam waste was presented as feedstock for bio-oil production. The surface fibers of the date palm are widely available as waste material in the Gulf region, the Middle East, and Africa. Chemical composition analysis and physiochemical characterization showed that surface fibers are valuable feedstock for energy production. Surface fibers were analyzed thermogravimetrically at different heating rates (10, 20, and 30 °C /min) in an inert atmosphere. Decomposition was carried out in three stages: dehydration, devolatilization, and solid combustion. Kinetic analysis was performed on the devolatilization region using the Coats–Redfern model–fitting method using twenty–one reaction mechanisms from four different solid-state reaction mechanisms. Two diffusion models: one–way transport (g(x) = α2) and Valensi equation (g(x) = α+(1-α) × ln(1-α)) showed the highest regression coefficient (R2) with the experimental data. The activation energy (Ea) and the pre-exponential factor (A) was estimated to be 91.40 kJ/mol and 1.59 × 103 –29.39 × 103 min−1, respectively. The kinetic parameters were found to be dependent on the heating rate. The surface fibers' thermodynamic parameters ΔH, ΔG, and ΔS were 80–97, 151–164, and −0.17- −0.18 kJ/mol, respectively. This indicates that the pyrolysis of surface fibers is endothermal and not spontaneous. Since there is not much experimental work on the pyrolysis of surface fibers available in the literature, the reported results are crucial for designing the pyrolysis process.
Inbanaathan, PV, Balasubramanian, D, Nguyen, VN, Le, VV, Wae-Hayee, M, R, R, Veza, I, Yukesh, N, Kalam, MA, Sonthalia, A & Varuvel, EG 2023, 'Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine', International Journal of Hydrogen Energy, vol. 48, no. 99, pp. 39531-39552. View/Download from: Publisher's site
Indraratna, B, Armaghani, DJ, Gomes Correia, A, Hunt, H & Ngo, T 2023, 'Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques', Transportation Geotechnics, vol. 38, pp. 100895-100895. View/Download from: Publisher's site View description>>
The resilient modulus (MR) of ballast is one of the key output parameters in any rail design project because it controls the elastic magnitude of track deformation under cyclic loading. This study investigates the response of MR under cyclic conditions as a function of four key parameters, i.e., the loading magnitude, the number of loading cycles, the loading frequency, and the confining pressure. To do so, two non-linear predictive models, namely, the artificial neural network (ANN), and the adaptive neuro-fuzzy inference system (ANFIS), are used to predict the MR values under different loading conditions. To evaluate and predict MR, an experimental database with 196 data samples is considered in this study. A series of sensitivity analyses is carried out to investigate the most effective parameters in each non-linear model and also predict the highest performance model. Although the results from the primary validation phase are satisfactory for the ANN and ANFIS models, ANFIS proves better (i.e., the coefficient of determination = 0.709) at estimating the MR during the secondary validation phase, using an independent dataset. Hence, it can be used as a powerful and practical model for predicting the magnitude of MR. On the basis of the ANFIS model, this study also offers some design considerations in terms of MR of ballast under a practical range of cyclic loading parameters.
Isfeld, AC, Stewart, MG & Masia, MJ 2023, 'Structural reliability and partial safety factor assessment of unreinforced masonry in vertical bending', Australian Journal of Structural Engineering, vol. 24, no. 3, pp. 191-205. View/Download from: Publisher's site
Islam Rony, Z, Mofijur, M, Hasan, MM, Rasul, MG, Jahirul, MI, Forruque Ahmed, S, Kalam, MA, Anjum Badruddin, I, Yunus Khan, TM & Show, P-L 2023, 'Alternative fuels to reduce greenhouse gas emissions from marine transport and promote UN sustainable development goals', Fuel, vol. 338, pp. 127220-127220. View/Download from: Publisher's site View description>>
The International Maritime Organization (IMO) has placed stricter controls on several aspects of global maritime transport operations to protect the environment. In light of this, the goal of this study is to examine and assess the different prospective paths and technologies that will assist the shipping industry in decarbonizing its operations. We consider how the utilisation of various alternative energy sources reduces greenhouse gas (GHG) emissions from marine transportation and contributes to the promotion of the United Nations Sustainable Development Goals (SGDs). The complexities associated with maritime industry operations using alternative energy sources are also explored. Biofuel as an alternative energy source, including biomethanol and biodiesel, can reduce greenhouse gas emissions in the shipping industry by 25% to 100%. However, the current supply of biofuels can only meet about 15% of the total demand which is not sufficient to sustainably power the entire marine fleet. There are several issues associated with these biofuels, including oxidation, ecological consequences, feedstock availability, technical and operational constraints, and economic factors that must be addressed before their full potential may be achieved.
Islam, MM, Ramezani, F, Lu, HY & Naderpour, M 2023, 'Optimal placement of applications in the fog environment: A systematic literature review', Journal of Parallel and Distributed Computing, vol. 174, pp. 46-69. View/Download from: Publisher's site
Islam, MS, Molley, TG, Hung, T-T, Sathish, CI, Putra, VDL, Jalandhra, GK, Ireland, J, Li, Y, Yi, J, Kruzic, JJ & Kilian, KA 2023, 'Magnetic Nanofibrous Hydrogels for Dynamic Control of Stem Cell Differentiation', ACS Applied Materials & Interfaces, vol. 15, no. 44, pp. 50663-50678. View/Download from: Publisher's site
Islam, MS, Rahman, MM, Larpruenrudee, P, Arsalanloo, A, Beni, HM, Islam, MA, Gu, Y & Sauret, E 2023, 'How microplastics are transported and deposited in realistic upper airways?', Physics of Fluids, vol. 35, no. 6. View/Download from: Publisher's site View description>>
Microplastics are tiny plastic debris in the environment from industrial processes, various consumer items, and the breakdown of industrial waste. Recently, microplastics have been found for the first time in the airways, which increases the concern about long-term exposure and corresponding impacts on respiratory health. To date, a precise understanding of the microplastic transport to the airways is missing in the literature. Therefore, this first-ever study aims to analyze the microplastic transport and deposition within the upper lung airways. A computational fluid dynamics-discrete phase model approach is used to analyze the fluid flow and microplastic transport in airways. The sphericity concept and shape factor values are used to define the non-spherical microplastics. An accurate mesh test is performed for the computational mesh. The numerical results report that the highly asymmetric and complex morphology of the upper airway influences the flow fields and microplastic motion along with the flow rate and microplastic shape. The nasal cavity, mouth-throat, and trachea have high pressure, while a high flow velocity is observed at the area after passing the trachea. The flow rates, shape, and size of microplastics influence the overall deposition pattern. A higher flow rate leads to a lower deposition efficiency for all microplastic shapes. The nasal cavity has a high deposition rate compared to other regions. The microplastic deposition hot spot is calculated for shape and size-specific microplastic at various flow conditions. The findings of this study and more case-specific analysis will improve the knowledge of microplastic transport in airways and benefit future therapeutics development. The future study will be focused on the effect of various microplastic shapes on the human lung airways under the healthy and diseased airways conditions.
Islam, MT & Hossain, MJ 2023, 'Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review', Energies, vol. 16, no. 4, pp. 1864-1864. View/Download from: Publisher's site View description>>
Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints.
Izadi, R, Assarian, D, Altaee, A & Mahinroosta, M 2023, 'Investigation of methods for fuel desulfurization wastewater treatment', Chemical Engineering Research and Design, vol. 190, pp. 198-219. View/Download from: Publisher's site
Jacob, A, Ashok, B, Ong, HC & Le, PTK 2023, 'Scaling-up heterotrophic cultures of C. Pyrenoidosa microalgae for sustainable synthesis of low-density biodiesel mixtures and predict CI engine behavior at optimal proportions', Environment, Development and Sustainability, vol. 25, no. 1, pp. 400-422. View/Download from: Publisher's site
Jafari, M, Shoeibi, A, Khodatars, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Khosravi, A, Ling, SH, Delfan, N, Zhang, Y, Wang, S-H, Górriz, JM, Alinejad-Rokny, H & Acharya, UR 2023, 'Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.', Comput. Biol. Medicine, vol. 160, pp. 106998-106998. View/Download from: Publisher's site
Jafarizadeh, S & Veitch, D 2023, 'Robust Weighted-Average Continuous-Time Consensus With Communication Time Delay', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2074-2086. View/Download from: Publisher's site
Jahandari, S, Tao, Z, Alim, MA & Li, W 2023, 'Integral waterproof concrete: A comprehensive review', Journal of Building Engineering, vol. 78, pp. 107718-107718. View/Download from: Publisher's site
Jahmunah, V, En Wei Koh, J, Sudarshan, VK, Raghavendra, U, Gudigar, A, Lih Oh, S, Wen Loh, H, Faust, O, Datta Barua, P, Ciaccio, EJ & Rajendra Acharya, U 2023, 'Endoscopy, video capsule endoscopy, and biopsy for automated celiac disease detection: A review', Biocybernetics and Biomedical Engineering, vol. 43, no. 1, pp. 82-108. View/Download from: Publisher's site
Jain, K, Pradhan, B & Mishra, V 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 304, pp. v-vi.
Jakubowski, K, Chacon, A, Tran, LT, Stopic, A, Garbe, U, Bevitt, J, Olsen, S, Franklin, DR, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2023, 'A Monte Carlo model of the Dingo thermal neutron imaging beamline', Scientific Reports, vol. 13, no. 1, p. 17415. View/Download from: Publisher's site View description>>
AbstractIn this study, we present a validated Geant4 Monte Carlo simulation model of the Dingo thermal neutron imaging beamline at the Australian Centre for Neutron Scattering. The model, constructed using CAD drawings of the entire beam transport path and shielding structures, is designed to precisely predict the in-beam neutron field at the position at the sample irradiation stage. The model’s performance was assessed by comparing simulation results to various experimental measurements, including planar thermal neutron distribution obtained in-beam using gold foil activation and $$^{10}$$10B$$_{4}$$4C-coated microdosimeters and the out-of-beam neutron spectra measured with Bonner spheres. The simulation results demonstrated that the predicted neutron fluence at the field’s centre is within 8.1% and 2.1% of the gold foil and $$^{10}$$10B$$_{4}$$Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 118, no. 1-2, pp. 74-78. View/Download from: Publisher's site View description>>
AbstractNeben der zeitökonomischen Gestaltung gewinnt die ergonomische Optimierung von Arbeitssystemen zunehmend an Bedeutung. Hohe Personalaufwände zur Erstellung bewegungsökonomischer Analysen sind jedoch Hemmnisse in deren industriellen Umsetzung. Markerloses Motion Capturing bietet Potenzial zur aufwandsreduzierten Erstellung entsprechender Analysen auf Basis des Prozessbausteinsystems MTM-Human Work Design. Der Beitrag beschreibt, wie Maschinelles Lernen unter Nutzung abstrahierter Videodaten zur Bewegungsanalyse eingesetzt werden kann.
Jaradat, Y, Far, H & Mortazavi, M 2023, 'A Mathematical Approach for Predicting Sufficient Separation Gap between Adjacent Buildings to Avoid Earthquake-Induced Pounding', Civil Engineering Journal, vol. 9, no. 10, pp. 2370-2398. View/Download from: Publisher's site View description>>
Studies on earthquake-related damage underscore that buildings are vulnerable to significant harm or even collapse during moderate to strong ground motions. Of particular concern is seismic-induced pounding, observed in numerous past and recent earthquakes, often resulting from inadequate separation gaps between neighboring structures. This study conducted an experimental and numerical investigation to develop a mathematical equation to calculate a sufficient separation gap in order to avoid the collision between adjacent mid-rise steel-frame buildings during seismic excitation. In this study, the coupled configuration of 15-storey & 10-storey, 15-storey & 5-storey, and 10-storey & 5-storey steel frame structures was considered in the investigation. The investigation concluded with a large number of data outputs. The outputs were used to predict structural behavior during earthquakes. The obtained data were categorized into three main categories according to the earthquake's Peak Ground Acceleration (PGA) levels. Also, the derived equations were divided into three different equations to estimate the required seismic gap between neighboring buildings accordingly. The derived equations are distilled to empower engineers to rigorously evaluate non-irregular mid-rise steel frame buildings. Doi: 10.28991/CEJ-2023-09-10-02 Full Text: PDF
Jaradat, Y, Far, H & Mortazavi, M 2023, 'Experimental Evaluation of Theoretical Impact Models for Seismic Pounding', Journal of Earthquake Engineering, vol. 27, no. 12, pp. 3269-3289. View/Download from: Publisher's site
Jathar, LD, Ganesan, S, Awasarmol, U, Nikam, K, Shahapurkar, K, Soudagar, MEM, Fayaz, H, El-Shafay, AS, Kalam, MA, Bouadila, S, Baddadi, S, Tirth, V, Nizami, AS, Lam, SS & Rehan, M 2023, 'Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle', Environmental Pollution, vol. 326, pp. 121474-121474. View/Download from: Publisher's site
Jauregi Unanue, I, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161. View/Download from: Publisher's site
Jena, R, Pradhan, B, Almazroui, M, Assiri, M & Park, H-J 2023, 'Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques', Geoscience Frontiers, vol. 14, no. 1, pp. 101460-101460. View/Download from: Publisher's site
Jena, R, Pradhan, B, Gite, S, Alamri, A & Park, H-J 2023, 'A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model', Gondwana Research, vol. 123, pp. 54-67. View/Download from: Publisher's site View description>>
Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.
Jena, R, Shanableh, A, Al-Ruzouq, R, Pradhan, B, Gibril, MBA, Khalil, MA, Ghorbanzadeh, O & Ghamisi, P 2023, 'Earthquake spatial probability and hazard estimation using various explainable AI (XAI) models at the Arabian peninsula', Remote Sensing Applications: Society and Environment, vol. 31, pp. 101004-101004. View/Download from: Publisher's site
Jia, C, Luo, M, Yan, C, Zhu, L, Chang, X & Zheng, Q 2023, 'Collaborative Contrastive Refining for Weakly Supervised Person Search', IEEE Transactions on Image Processing, vol. 32, pp. 4951-4963. View/Download from: Publisher's site
Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science Perspective of Graph Convolutional Networks: A Survey', IEEE Access, vol. 11, pp. 39083-39122. View/Download from: Publisher's site
Jia, Z, Liu, Q, He, Y, Wu, Q, Liu, RP & Sun, Y 2023, 'Efficient end-to-end failure probing matrix construction in data center networks', Journal of Communications and Networks, vol. 25, no. 4, pp. 532-543. View/Download from: Publisher's site
Jia, Z, Xu, X, Zhu, D & Zheng, Y 2023, 'Design, printing, and engineering of regenerative biomaterials for personalized bone healthcare', Progress in Materials Science, vol. 134, pp. 101072-101072. View/Download from: Publisher's site
Jiang, J, Dorji, P, Badeti, U, Sohn, W, Freguia, S, Phuntsho, S, El Saliby, I & Shon, HK 2023, 'Potential nutrient recovery from source-separated urine through hybrid membrane bioreactor and membrane capacitive deionisation', Desalination, vol. 566, pp. 116924-116924. View/Download from: Publisher's site
Jiang, L, Li, F, Chen, Z, Zhu, B, Yi, C, Li, Y, Zhang, T, Peng, Y, Si, Y, Cao, Z, Chen, A, Yao, D, Chen, X & Xu, P 2023, 'Information transmission velocity-based dynamic hierarchical brain networks', NeuroImage, vol. 270, pp. 119997-119997. View/Download from: Publisher's site
Jiang, P, Yu, Y & Li, K 2023, 'Hydrophilic TiO 2 @MWCNT/PVDF membrane for enhanced photodegradation of methyl orange in water', Fullerenes, Nanotubes and Carbon Nanostructures, vol. 31, no. 12, pp. 1185-1191. View/Download from: Publisher's site
Jiang, W, Tao, J, Luo, J, Xie, W, Zhou, X, Cheng, B, Guo, G, Ngo, HH, Guo, W, Cai, H, Ye, Y, Chen, Y & Pozdnyakov, IP 2023, 'Pilot-scale two-phase anaerobic digestion of deoiled food waste and waste activated sludge: Effects of mixing ratios and functional analysis', Chemosphere, vol. 329, pp. 138653-138653. View/Download from: Publisher's site
Jiang, Y, Li, C, Wu, C, Rabczuk, T & Fang, J 2023, 'A double-phase field method for mixed mode crack modelling in 3D elasto-plastic solids with crack-direction-based strain energy decomposition', Computer Methods in Applied Mechanics and Engineering, vol. 405, pp. 115886-115886. View/Download from: Publisher's site
Jiang, Z, Li, C, Chang, X, Chen, L, Zhu, J & Yang, Y 2023, 'Dynamic Slimmable Denoising Network', IEEE Transactions on Image Processing, vol. 32, pp. 1583-1598. View/Download from: Publisher's site
Jiao, S, Goel, V, Navasardyan, S, Yang, Z, Khachatryan, L, Yang, Y, Wei, Y, Zhao, Y & Shi, H 2023, 'Collaborative Content-Dependent Modeling: A Return to the Roots of Salient Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 4237-4246. View/Download from: Publisher's site
Jin, P, Zhang, J, Lu, Y, Guo, Y, Lei, G & Zhu, J 2023, 'Variable Frequency Isolated Bidirectional CLLC Resonant Converter With Voltage Controlled Variable Capacitors', IEEE Transactions on Industrial Electronics, vol. 70, no. 9, pp. 8907-8917. View/Download from: Publisher's site
Jin, W, Zhao, B, Yu, H, Tao, X, Yin, R & Liu, G 2023, 'Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 255-288. View/Download from: Publisher's site
Jin, W, Zhao, B, Zhang, L, Liu, C & Yu, H 2023, 'Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis', Information Processing & Management, vol. 60, no. 3, pp. 103260-103260. View/Download from: Publisher's site
Joseph, S, Dhandapani, Y, Geddes, DA, Zhao, Z, Bishnoi, S, Vieira, M, Martirena, F, Castel, A, Kanavaris, F, Bansal, T & Riding, KA 2023, 'Mechanical properties of concrete made with calcined clay: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 4. View/Download from: Publisher's site
Joshi, A, Pradhan, B, Chakraborty, S & Behera, MD 2023, 'Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm', Ecological Informatics, vol. 77, pp. 102194-102194. View/Download from: Publisher's site
Jouneghani, HG, Fanaie, N, Kalaleh, MT & Mortazavi, M 2023, 'Determining elastic lateral stiffness of steel moment frame equipped with elliptic brace', Steel and Composite Structures, vol. 46, no. 3, pp. 293-318. View/Download from: Publisher's site View description>>
This study aims to examine the elastic stiffness properties of Elliptic-Braced Moment Resisting Frame (EBMRF) subjected to lateral loads. Installing the elliptic brace in the middle span of the frames in the facade of a building, as a new lateral bracing system not only it can improve the structural behavior, but it provides sufficient space to consider opening it needed. In this regard, for the first time, an accurate theoretical formulation has been developed in order that the elastic stiffness is investigated in a two-dimensional single-story single-span EBMRF. The concept of strain energy and Castigliano’s theorem were employed to perform the analysis. All influential factors were considered, including axial and shearing loads in addition to the bending moment in the elliptic brace. At the end of the analysis, the elastic lateral stiffness could be calculated using an improved relation through strain energy method based on geometric properties of the employed sections as well as specifications of the utilized materials. For the ease of finite element (FE) modeling and its use in linear design, an equivalent element was developed for the elliptic brace. The proposed relation was verified by different examples using OpenSees software. It was found that there is a negligible difference between elastic stiffness values derived by the developed equations and those of numerical analysis using FE method.
Jui, SJJ, Deo, RC, Barua, PD, Devi, A, Soar, J & Acharya, UR 2023, 'Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012–2022)', IEEE Access, vol. 11, pp. 71905-71924. View/Download from: Publisher's site
Kabir, MM, Akter, MM, Huang, Z, Tijing, L & Shon, HK 2023, 'Hydrogen production from water industries for a circular economy', Desalination, vol. 554, pp. 116448-116448. View/Download from: Publisher's site
Kabir, MM, Nahar, N, Akter, MM, Alam, F, Gilroyed, BH, Misu, MM, Didar-ul-Alam, M, Hakim, M, Tijing, L & Shon, HK 2023, 'Agro-waste-based functionalized and economic adsorbents for the effective treatment of toxic contaminants from tannery effluent', Journal of Water Process Engineering, vol. 52, pp. 103578-103578. View/Download from: Publisher's site View description>>
The present investigation explored two novel adsorbents, i.e., hydroxyapatite (HAP) and moringa-modified activated carbon (MMAC) from eggshell and moringa (Moringa oleifera) seeds, respectively, for the treatment of tannery effluents (TE). Particular emphasis was given to Cr(VI) adsorption when varying equilibration time, effluent pH, initial concentration of Cr(VI) and temperature. The adsorbents' characteristics suggested significant Cr(VI) accumulation onto the adsorbent's surface, whereas adsorption modelling recommended pseudo-second-order (PSO) and Langmuir models fitted well with the experimental data based on the regression coefficient (R2) values with minimum errors. The surface complexation model (SCM) indicated that speciation of Cr(VI) sorbed complexes formed an inner-sphere compound dominated by acidic pH, validated by pHpzc. The maximum adsorption capacities (qmax) of Cr(VI) were accounted to be 295 and 280 mg/g for HAP and MMAC, correspondingly. Interestingly, both adsorbents effectively removed other metallic ions; Fe, Pd, Cu and Zn removal was 85 %, while Cd, Ni, and Mn removal was 70 %. The Cr(VI) adsorption processes followed chemisorption mechanisms dominated by the surface complexation phenomenon. The performance of a 100 L packed-bed reactor was evaluated, and the breakthrough time of Cr(VI) adsorption for both adsorbents was 15 min. The adsorbents had splendid regeneration capacities and could be re-used numerous times. In essence, the present study concludes that both adsorbents are highly effective at removing Cr(VI) and other contaminants. The adsorbents are innovative and economical and can be one of the breakthrough feasible options for treating toxic contaminants in a large-scale TE.
Kabir, MM, Roy, SK, Alam, F, Nam, SY, Im, KS, Tijing, L & Shon, HK 2023, 'Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy', Desalination, vol. 567, pp. 116992-116992. View/Download from: Publisher's site View description>>
Currently, there exists a significant number of green hydrogen production (GHP) technologies based on scaling-up issues (SCUI). Optimal prediction and process optimization could be one of the most substantial SCUI of GHP. Machine learning (ML)-based prediction and optimization of GHP technologies from water industries for a circular economy (CRE) could be a plausible solution for these SCUI. We studied a detailed techno-economic and environmental feasibility study, which recommended proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environment-friendly technologies for GHP. Thus, the present investigation aims to apply different ML models to predict and optimize the GHP of DF and PEM technologies to solve the SCUI. The results revealed K-nearest neighbor and random forest are the best-fitted models to predict GHP for DF and PEM, correspondingly based on the regression co-efficient (R2), root mean squared error (RMSE) and mean absolute error (MEA). The permutation variable index (PVI) recommended that chemical oxygen demand (COD), butyrate, temperature, pH and acetate/butyrate ratio are the most influential process parameters in decreasing order for DF, while temperature, cell areas, cell pressure, cell voltage and catalysts loadings are the most effective process parameters for PEM in reducing order. The partial dependency analysis (PDA) demonstrated GHP increases with increasing COD values up to 10 mg/L, and the optimal temperature range in the DF process is between 25 and 30 °C. On the other hand, cell temperature up to 35 °C should be considered optimum for PEM, and 40–70 cm2 cell areas could produce a significant GHP. In summary, the present study underscores the potential of machine learning (ML) and artificial intelligence (AI) as promising techniques for optimizing GHP, ultimately addressing scaling-up challenges in large-scale industrial GHP production and ensuring a sustainable hydrogen economy (HE).
Kaim, V, Singh, N, Kanaujia, BK, Matekovits, L, Esselle, KP & Rambabu, K 2023, 'Multi-Channel Implantable Cubic Rectenna MIMO System With CP Diversity in Orthogonal Space for Enhanced Wireless Power Transfer in Biotelemetry', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 200-214. View/Download from: Publisher's site
Kalhori, H, Rafiee, R, Ye, L, Halkon, B & Bahmanpour, M 2023, 'Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel', International Journal of Impact Engineering, vol. 176, pp. 104576-104576. View/Download from: Publisher's site
Kanavaris, F, Vieira, M, Bishnoi, S, Zhao, Z, Wilson, W, Tagnit Hamou, A, Avet, F, Castel, A, Zunino, F, Visalaksh, T, Martirena, F, Bernal, SA, Juenger, MCG & Riding, K 2023, 'Correction: Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 10. View/Download from: Publisher's site
Kanavaris, F, Vieira, M, Bishnoi, S, Zhao, Z, Wilson, W, Tagnit Hamou, A, Avet, F, Castel, A, Zunino, F, Visalakshi, T, Martirena, F, Bernal, SA, Juenger, MCG & Riding, K 2023, 'Standardisation of low clinker cements containing calcined clay and limestone: a review by RILEM TC-282 CCL', Materials and Structures, vol. 56, no. 9. View/Download from: Publisher's site
Kang, J, Jia, W & He, X 2023, 'Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision', Neurocomputing, vol. 562, pp. 126882-126882. View/Download from: Publisher's site
Kang, K, Li, L & Sohaib, O 2023, 'Graduates’ intention to develop live commerce: The educational background perspective using multi-group analysis', Entrepreneurial Business and Economics Review, vol. 11, no. 1, pp. 113-126. View/Download from: Publisher's site
Kang, X, Li, C, Ding, W, Ma, Y, Zhou, X, Gao, S, Chen, C, Liu, W, He, Z, Li, X & Jiang, G 2023, 'Optimization of biological enzymes combined with Fe2+-activated advanced oxidation process for waste activated sludge conditioning using the response surface method', Journal of Water Process Engineering, vol. 53, pp. 103634-103634. View/Download from: Publisher's site
Kapellos, TS, Baßler, K, Fujii, W, Nalkurthi, C, Schaar, AC, Bonaguro, L, Pecht, T, Galvao, I, Agrawal, S, Saglam, A, Dudkin, E, Frishberg, A, de Domenico, E, Horne, A, Donovan, C, Kim, RY, Gallego-Ortega, D, Gillett, TE, Ansari, M, Schulte-Schrepping, J, Offermann, N, Antignano, I, Sivri, B, Lu, W, Eapen, MS, van Uelft, M, Osei-Sarpong, C, van den Berge, M, Donker, HC, Groen, HJM, Sohal, SS, Klein, J, Schreiber, T, Feißt, A, Yildirim, AÖ, Schiller, HB, Nawijn, MC, Becker, M, Händler, K, Beyer, M, Capasso, M, Ulas, T, Hasenauer, J, Pizarro, C, Theis, FJ, Hansbro, PM, Skowasch, D & Schultze, JL 2023, 'Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease', Cell Reports, vol. 42, no. 6, pp. 112525-112525. View/Download from: Publisher's site View description>>
Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.
Kaplan, E, Baygin, M, Barua, PD, Dogan, S, Tuncer, T, Altunisik, E, Palmer, EE & Acharya, UR 2023, 'ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images', Medical Engineering & Physics, vol. 115, pp. 103971-103971. View/Download from: Publisher's site
Kaplan, E, Chan, WY, Altinsoy, HB, Baygin, M, Barua, PD, Chakraborty, S, Dogan, S, Tuncer, T & Acharya, UR 2023, 'PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI', Journal of Digital Imaging, vol. 36, no. 6, pp. 2441-2460. View/Download from: Publisher's site
Karbassiyazdi, E, Altaee, A, Ibrar, I, Razmjou, A, Alsaka, L, Ganbat, N, Malekizadeh, A, Ghobadi, R & Khabbaz, H 2023, 'Fabrication of carbon-based hydrogel membrane for landfill leachate wastewater treatment', Desalination, vol. 564, pp. 116783-116783. View/Download from: Publisher's site View description>>
The challenge of effectively managing the discharge of metal ions into aquatic environments, which poses a significant risk to both human health and ecosystems, persists despite the availability of various analytical tools and techniques. There are limitations of existing separation technologies and the inefficacy of hydrogel materials in removing low molecular weight contaminants, such as metal ions, in aqueous solutions. This study added carbon powder to the hydrogel membrane to reduce the low-mechanical strength and drying problems and increase its capacity for adsorbing ionic and non-ionic substances. The study introduced a novel carbon-based aluminium hydroxide hydrogel for wastewater filtration. CG was characterized using various analytical techniques, including examining surface morphology, elemental analysis, surface functional groups, and surface charge. These analytical tools provided a comprehensive understanding of the properties and performance of the CG. The effects of different carbon-based hydrogel (CG) concentrations on water flux and ion rejection were evaluated in a gravity filtration setup. Experiments investigated the influence of different ion concentrations, activated carbon (AC) concentration, centrifugation, water flux, and rejection on removing heavy metals from synthetic and natural wastewater. The pure water flux of the hydrogel membrane was 120 LMH. The results indicated that an AC concentration of 4 g/L in the aqueous solution is optimal for heavy metals removal, with 99.9 % removal for Pb2+ and Cu2+, 84 % rejection for Ca2+, and 85 % rejection for Mg2+ in 10 mg/L of synthetic water. Besides, the 4 g/L AC hydrogel membrane removed 90 % of Ni, Zn, Pb, As, and Cu ions and 53 % of the total organic carbon from leachate wastewater.
Karbassiyazdi, E, Altaee, A, Razmjou, A, Samal, AK & Khabbaz, H 2023, 'Gravity-driven composite cellulose acetate/activated carbon aluminium-based hydrogel membrane for landfill wastewater treatment', Chemical Engineering Research and Design, vol. 200, pp. 682-692. View/Download from: Publisher's site
Karbassiyazdi, E, Kasula, M, Modak, S, Pala, J, Kalantari, M, Altaee, A, Esfahani, MR & Razmjou, A 2023, 'A juxtaposed review on adsorptive removal of PFAS by metal-organic frameworks (MOFs) with carbon-based materials, ion exchange resins, and polymer adsorbents', Chemosphere, vol. 311, pp. 136933-136933. View/Download from: Publisher's site
Karimi, M & Maxit, L 2023, 'Acoustic source localisation using vibroacoustic beamforming', Mechanical Systems and Signal Processing, vol. 199, pp. 110454-110454. View/Download from: Publisher's site
Karmaker, AK, Behrens, S, Hossain, MJ & Pota, H 2023, 'Multi-stakeholder perspectives for transport electrification: A review on placement and scheduling of electric vehicle charging infrastructure', Journal of Cleaner Production, vol. 427, pp. 139145-139145. View/Download from: Publisher's site
Katzmarek, DA, Yang, Y, Ghasemian, MB, Kalantar-Zadeh, K, Ziolkowski, RW & Iacopi, F 2023, 'Characteristics of Epitaxial Graphene on SiC/Si Substrates in the Radio Frequency Spectrum', IEEE Electron Device Letters, vol. 44, no. 2, pp. 297-300. View/Download from: Publisher's site
Kaur, P, Bohidar, HB, Nisbet, DR, Pfeffer, FM, Rifai, A, Williams, R & Agrawal, R 2023, 'Waste to high-value products: The performance and potential of carboxymethylcellulose hydrogels via the circular economy', Cellulose, vol. 30, no. 5, pp. 2713-2730. View/Download from: Publisher's site
KB, H, S, V, G, N, R, P, Alwetaishi, M, Alahmadi, AA, Alzaed, AN, MA, K & Shahapurkar, K 2023, 'Effects of machining parameters on H13 die steel using CNC drilling machine', Composites and Advanced Materials, vol. 32. View/Download from: Publisher's site View description>>
In order to enhance the fitness of the product and in order to improve productivity in turning operations, greater amount of challenges have been faced. In this paper, we have made a comparative analysis of HSS and carbide coated HSS drills while machining with H13 steel plates. For the drilling operation, process parameters were analysed using the Taguchi design of experiments. The response performance characteristics of surface roughness of H13 die steel plates for the drilling settings, cutting speed (rpm), and feed rate (mm/min) is optimized. The design of the experiment was conducted using the Taguchi technique for the L18 orthogonal array, and an analysis of variance was observed. The effect of drilling settings on the quality of drilled holes is examined; variation in surface roughness for various levels of speed and feed and the different combinations of these levels will form an L18 orthogonal array design of experiment by Taguchi analysis. A total of 36 cutting tests were performed with two different drill bits; here three different cutting speeds of 300, 600, and 900 rpm were taken with a feed rate of 0.02, 0.04, and 0.06 mm/rev combinations. The response of SN ratio for surface roughness of HSS and carbide tool has been found out for different levels of speed and feed. From this Taguchi analysis, it is identified that the optimal parameter. As a result, the factors are analysed, and optimized parameters have been concluded for H13 material using HSS, and carbide tools were examined both statistically and experimentally. The carbide coated drill bit gives 60% better surface roughness value based on experimental data obtained. The surface roughness value based on experimentation for HSS tool was found to be 34.16% and carbide coated drill bit was 23.40%.
Ke, G, Duanxiong, K, Zhang, X, Tang, Z, Yang, R & Li, W 2023, 'Synthesis of Quaternary Hydrotalcite-Carbon Nanotube Composite and Its Sulfate Adsorption Performance in Cement Paste', Journal of Materials in Civil Engineering, vol. 35, no. 11. View/Download from: Publisher's site
Ke, Y, Shi, FL, Zhang, SS, Nie, XF & Li, WG 2023, 'Strength Model for Debonding Failure in RC Beams Flexurally Strengthened with NSM FRP and Anchored with FRP U-Jackets', Journal of Composites for Construction, vol. 27, no. 5. View/Download from: Publisher's site View description>>
The flexural performance of reinforced concrete (RC) beams could be effectively improved by applying a near-surface mounted (NSM) fiber-reinforced polymer (FRP) at the beam soffit. However, such NSM FRP flexurally-strengthened beams frequently failed due to FRP debonding, which limited the full utilization of the FRP strength. In some experimental studies, FRP U-jackets have been used as the anchorage to mitigate or prevent debonding failures in NSM FRP flexurally-strengthened beams. These studies showed excellent anchoring performance of the FRP U-jackets. The authors recently developed a finite-element (FE) approach that could accurately predict the behavior of RC beams that had been flexurally strengthened with NSM FRP (NSM-strengthened beams), which were anchored with FRP U-jackets. Based on a parametric study that was undertaken, which used the simplified version of the FE approach, this paper proposed a strength model for the most common debonding failure mode in NSM-strengthened beams with FRP U-jackets. The proposed strength model consisted of an equation for the maximum NSM FRP strain (Ef) at debonding failure. Once the maximum FRP strain was known, the load-carrying capacity of the strengthened beam could be obtained through a section analysis. Comparing the predictions made by the proposed strength model with the test results showed that the proposed strength model could provide close predictions.
Kedziora, DJ, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2023, 'Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality', Machine Learning: Science and Technology, vol. 4, no. 4, pp. 045042-045042. View/Download from: Publisher's site View description>>
AbstractNovel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.
Keles, T, Yildiz, AM, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Demir, CF, Ciaccio, EJ & Acharya, UR 2023, 'A new one-dimensional testosterone pattern-based EEG sentence classification method', Engineering Applications of Artificial Intelligence, vol. 119, pp. 105722-105722. View/Download from: Publisher's site
Keshavarz, R, Shariati, N & Miri, M-A 2023, 'Real-Time Discrete Fractional Fourier Transform Using Metamaterial Coupled Lines Network', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 8, pp. 3414-3423. View/Download from: Publisher's site
Kha, J, Karimi, M & Maxit, L 2023, 'Acoustic radiation from a baffled finite shell in an underwater waveguide', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A55-A55. View/Download from: Publisher's site View description>>
Analytical modelling of vibroacoustic systems can help us to understand the physical phenomena involved in more complex problems, and it provides a benchmark solution and reference upon which more complex systems can be built. In the present work, the system of interest consists of a finite elastic cylindrical shell inserted in infinitely rigid cylindrical baffles and immersed in an underwater acoustic waveguide. The latter consists of a finite fluid layer bounded by an upper free surface and a lower rigid floor. In such a fluid domain, the acoustic waves radiated from the excited shell will exhibit reflections off the boundaries. This phenomenon is modelled by the image-source theory and embedded in the fluid loading term, which intervenes in the shell equations. Investigations into the influence on the finiteness of the elastic shell, types of supports (i.e., simply supported, clamped, free, and combinations of these), and depth of the waveguide on the shell’s acoustic radiation are presented.
Kha, J, Karimi, M, Maxit, L, Skvortsov, A & Kirby, R 2023, 'Forced vibroacoustic response of a cylindrical shell in an underwater acoustic waveguide', Ocean Engineering, vol. 273, pp. 113899-113899. View/Download from: Publisher's site
Khabbaz, H, Rujikiatkamjorn, C & Parsa, A 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 325 LNCE, pp. v-vi.
Khade, S, Gite, S, D. Thepade, S, Pradhan, B & Alamri, A 2023, 'Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers', Computer Modeling in Engineering & Sciences, vol. 136, no. 1, pp. 323-345. View/Download from: Publisher's site
Khademi, P, Mousavi, M, Dackermann, U & Gandomi, AH 2023, 'Time–frequency analysis of ultrasonic signals for quality assessment of bonded concrete', Construction and Building Materials, vol. 403, pp. 133062-133062. View/Download from: Publisher's site
Khan, A, Ibrar, I, Mirdad, A, Al-Juboori, RA, Deka, P, Subbiah, S & Altaee, A 2023, 'Novel Approach to Landfill Wastewater Treatment Fouling Mitigation: Air Gap Membrane Distillation with Tin Sulfide-Coated PTFE Membrane', Membranes, vol. 13, no. 5, pp. 483-483. View/Download from: Publisher's site View description>>
This study addressed the fouling issue in membrane distillation (M.D.) technology, a promising method for water purification and wastewater reclamation. To enhance the anti-fouling properties of the M.D. membrane, a tin sulfide (TS) coating onto polytetrafluoroethylene (PTFE) was proposed and evaluated with air gap membrane distillation (AGMD) using landfill leachate wastewater at high recovery rates (80% and 90%). The presence of TS on the membrane surface was confirmed using various techniques, such as Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FT-IR), Energy Dispersive Spectroscopy (EDS), contact angle measurement, and porosity analysis. The results indicated the TS-PTFE membrane exhibited better anti-fouling properties than the pristine PTFE membrane, and its fouling factors (FFs) were 10.4–13.1% compared to 14.4–16.5% for the PTFE membrane. The fouling was attributed to pore blockage and cake formation of carbonous and nitrogenous compounds. The study also found that physical cleaning with deionized (DI) water effectively restored the water flux, with more than 97% recovered for the TS-PTFE membrane. Additionally, the TS-PTFE membrane showed better water flux and product quality at 55 °C and excellent stability in maintaining the contact angle over time compared to the PTFE membrane.
Khan, AUH, Liu, Y, Fang, C, Naidu, R, Shon, HK, Rogers, Z & Dharmarajan, R 2023, 'A comprehensive physicochemical characterization of zinc oxide nanoparticles extracted from sunscreens and wastewaters', Environmental Advances, vol. 12, pp. 100381-100381. View/Download from: Publisher's site
Khan, AUH, Naidu, R, Dharmarajan, R, Fang, C, Shon, H, Dong, Z & Liu, Y 2023, 'The interaction mechanisms of co-existing polybrominated diphenyl ethers and engineered nanoparticles in environmental waters: A critical review', Journal of Environmental Sciences, vol. 124, pp. 227-252. View/Download from: Publisher's site
Khan, NA, Hussain Khoja, A, Ahmed, N, Riaz, F, Mahmood, M, Ali, M, Kalam, MA & Mujtaba, MA 2023, 'Solar-assisted hybrid oil heating system for heavy refinery products storage', Case Studies in Thermal Engineering, vol. 49, pp. 103276-103276. View/Download from: Publisher's site
Khare, SK, March, S, Barua, PD, Gadre, VM & Acharya, UR 2023, 'Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade', Information Fusion, vol. 99, pp. 101898-101898. View/Download from: Publisher's site
Khatri, I, Choudhry, A, Rao, A, Tyagi, A, Vishwakarma, DK & Prasad, M 2023, 'Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm', Applied Soft Computing, vol. 149, pp. 111037-111037. View/Download from: Publisher's site
Khavari Kashani, MR, Wang, Q, Khatebasreh, M, Li, X, Sheikh Asadi, AM, Boczkaj, G & Ghanbari, F 2023, 'Sequential treatment of landfill leachate by electrocoagulation/aeration, PMS/ZVI/UV and electro-Fenton: Performance, biodegradability and toxicity studies', Journal of Environmental Management, vol. 338, pp. 117781-117781. View/Download from: Publisher's site View description>>
This study presents a systematic study on sequential treatment of highly resistant landfill leachate by electrocoagulation (EC)/aeration, sulfate radical advanced oxidation process (SR-AOP) and electro-Fenton (EF). In case of SR-AOP, peroxymonosulfate (PMS) catalyzed by zero valent iron (ZVI) and ultraviolet irradiation (UV) system was developed. Treatment process was optimized in respect to COD removal. Analysis of results revealed that sequential application of EC/aeration, PMS/ZVI/UV, and EF processes provide an extraordinary performance and meet the environmental regulations. The source of iron for EF process was provided from previous process reducing the cost of sequential process. Separately, EC/aeration (inlet COD = 4040 mg/L), PMS/ZVI/UV (inlet COD = 1560 mg/L), and EF (inlet COD = 471 mg/L) removed 61, 69 and 82% of COD respectively. Overall, sequential processes of EC/aeration, PMS/ZVI/UV and EF could remove the COD, TOC and ammonia of the landfill leachate around 98%, 93% and 94%, respectively. The comparison of different sequences of following processes indicated that current configuration (EC/aeration-PMS/ZVI/UV-EF) could meet the discharge standards. Furthermore, humification degree was significantly improved after oxidative processes. Biodegradability study was also performed by means of BOD/COD, average oxidation state (AOS), and Zahn-Wellens test, and the best results associated with these indices were obtained 0.56, 2.37, and over 98%, respectively. Phytotoxicity of leachate was remarkably reduced and the final effluent can be considered as a non-phytotoxic wastewater.
Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2023, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578-8589. View/Download from: Publisher's site View description>>
Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning “knowledge” among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.
Khoo, WH, Jackson, K, Phetsouphanh, C, Zaunders, JJ, Alquicira-Hernandez, J, Yazar, S, Ruiz-Diaz, S, Singh, M, Dhenni, R, Kyaw, W, Tea, F, Merheb, V, Lee, FXZ, Burrell, R, Howard-Jones, A, Koirala, A, Zhou, L, Yuksel, A, Catchpoole, DR, Lai, CL, Vitagliano, TL, Rouet, R, Christ, D, Tang, B, West, NP, George, S, Gerrard, J, Croucher, PI, Kelleher, AD, Goodnow, CG, Sprent, JD, Powell, JE, Brilot, F, Nanan, R, Hsu, PS, Deenick, EK, Britton, PN & Phan, TG 2023, 'Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19', Clinical Immunology, vol. 246, pp. 109209-109209. View/Download from: Publisher's site
Khounani, Z, Abdul Razak, NN, Hosseinzadeh-Bandbafha, H, Madadi, M, Sun, F, Fattah, IMR, Karimi, K, Gupta, VK, Aghbashlo, M & Tabatabaei, M 2023, 'Assessing the environmental impacts of furfural production in a poplar wood biorefinery: A study on the role of mannitol concentration and catalyst type', Industrial Crops and Products, vol. 203, pp. 117230-117230. View/Download from: Publisher's site
Khuat, TT & Gabrys, B 2023, 'An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data', Applied Soft Computing, vol. 137, pp. 110152-110152. View/Download from: Publisher's site
Khuat, TT & Gabrys, B 2023, 'hyperbox-brain: A Python toolbox for hyperbox-based machine learning algorithms', SoftwareX, vol. 23, pp. 101425-101425. View/Download from: Publisher's site
This article proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks (FMNNs), popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.
Khuat, TT, Kedziora, DJ & Gabrys, B 2023, 'The Roles and Modes of Human Interactions with Automated Machine Learning Systems: A Critical Review and Perspectives', Foundations and Trends® in Human–Computer Interaction, vol. 17, no. 3-4, pp. 195-387. View/Download from: Publisher's site
Kim, B-J, Shon, HK, Han, DS & Park, H 2023, 'In-situ desalination-coupled electrolysis with concurrent one-step-synthesis of value-added chemicals', Desalination, vol. 551, pp. 116431-116431. View/Download from: Publisher's site
Kim, J, Tijing, L, Shon, HK & Hong, S 2023, 'Electrically conductive membrane distillation via an alternating current operation for zero liquid discharge', Water Research, vol. 244, pp. 120510-120510. View/Download from: Publisher's site
Kirik, S, Dogan, S, Baygin, M, Barua, PD, Demir, CF, Keles, T, Yildiz, AM, Baygin, N, Tuncer, I, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'FGPat18: Feynman graph pattern-based language detection model using EEG signals', Biomedical Signal Processing and Control, vol. 85, pp. 104927-104927. View/Download from: Publisher's site
Kivrak, T, Nayak, J, Gelen, MA, Barua, PD, Baygin, M, Pamukcu, HE, Dogan, S, Tuncer, T & Acharya, UR 2023, 'EfDenseNet: Automated Pulmonary Hypertension Detection Model Based on EfficientNetb0 and DenseNet201 Using CT Images', IEEE Access, vol. 11, pp. 142711-142724. View/Download from: Publisher's site
Kołodziejczak-Radzimska, A, Bielejewski, M, Zembrzuska, J, Ciesielczyk, F, Jesionowski, T & Nghiem, LD 2023, 'Exploring the functionality of an active ZrF-laccase biocatalyst towards tartrazine decolorization', Environmental Technology & Innovation, vol. 31, pp. 103201-103201. View/Download from: Publisher's site
Kong, Y, Liu, L, Qiao, M, Wang, Z & Tao, D 2023, 'Trust-Region Adaptive Frequency for Online Continual Learning', International Journal of Computer Vision, vol. 131, no. 7, pp. 1825-1839. View/Download from: Publisher's site View description>>
AbstractIn the paradigm of online continual learning, one neural network is exposed to a sequence of tasks, where the data arrive in an online fashion and previously seen data are not accessible. Such online fashion causes insufficient learning and severe forgetting on past tasks issues, preventing a good stability-plasticity trade-off, where ideally the network is expected to have high plasticity to adapt to new tasks well and have the stability to prevent forgetting on old tasks simultaneously. To solve these issues, we propose a trust-region adaptive frequency approach, which alternates between standard-process and intra-process updates. Specifically, the standard-process replays data stored in a coreset and interleaves the data with current data, and the intra-process updates the network parameters based on the coreset. Furthermore, to improve the unsatisfactory performance stemming from online fashion, the frequency of the intra-process is adjusted based on a trust region, which is measured by the confidence score of current data. During the intra-process, we distill the dark knowledge to retain useful learned knowledge. Moreover, to store more representative data in the coreset, a confidence-based coreset selection is presented in an online manner. The experimental results on standard benchmarks show that the proposed method significantly outperforms state-of-art continual learning algorithms.
Past studies have shown that shallow subgrade soil can transform to a slurry (i.e., fluidization) under unfavourable cyclic loading. However, the depth-dependent behaviour of soil parameters during this process has not been properly understood. The current study utilised a large-scale cylindrical test rig, where instrumentation was installed to observe the soil behaviour along the depth of the test specimens under cyclic loading, to examine and quantify the onset of soil fluidization. The results show that excess pore water pressure tends to rise more at the upper layers causing zero-effective stress, while void ratio expands rapidly within the deteriorated soil fabric, making the water content approach the liquid limit of soil when internal moisture migration occurs from the bottom to the top of the specimen. The larger the cyclic load, the deeper the fluidized zone and the faster the fluidization. The study also suggests that the zero-effective stress condition alone cannot interpret the inception of soil fluidization; hence, the change in void ratio and the liquidity index during the application of cyclic loading should also be considered in tandem.
Kozanoglu, DC, Daim, TU & Contreras-Cruz, A 2023, 'Unraveling the Dynamics of Immigrant Engineers’ Full-Utilization in Australia', IEEE Transactions on Engineering Management, vol. 70, no. 11, pp. 3776-3791. View/Download from: Publisher's site View description>>
The study aims to improve our understanding of the full-utilization of immigrant engineers by answering three research questions: (1) what are the economic and social costs of the under-utilization of immigrant engineers, (2) what factors determine immigrant engineers’ employment, and (3) what might be potential solutions to tackle with their under-utilization? We adopt the intersectionality theory to observe a rich set of social factors influential in immigrant engineers’ under-utilization by using 188 surveys and 14 interviews of immigrant engineers living in Australia. The paper concludes with the findings’ theoretical and policy implications, followed by suggestions for future studies.
Krishankumar, R, Mishra, AR, Ravichandran, KS, Kar, S, Gandomi, AH & Bausys, R 2023, 'An integrated personalized decision approach with probabilistic linguistic context for grading restaurants in India', Applied Soft Computing, vol. 136, pp. 110089-110089. View/Download from: Publisher's site
Kronowetter, F, Pretsch, L, Chiang, YK, Melnikov, A, Sepehrirahnama, S, Oberst, S, Powell, DA & Marburg, S 2023, 'Sound attenuation enhancement of acoustic meta-atoms via coupling', The Journal of the Acoustical Society of America, vol. 154, no. 2, pp. 842-851. View/Download from: Publisher's site View description>>
Arrangements of acoustic meta-atoms, better known as acoustic metamaterials, are commonly applied in acoustic cloaking, for the attenuation of acoustic fields or for acoustic focusing. A precise design of single meta-atoms is required for these purposes. Understanding the details of their interaction allows improvement of the collective performance of the meta-atoms as a system, for example, in sound attenuation. Destructive interference of their scattered fields, for example, can be mitigated by adjusting the coupling or tuning of individual meta-atoms. Comprehensive numerical studies of various configurations of a resonator pair show that the coupling can lead to degenerate modes at periodic distances between the resonators. We show how the resonators' separation and relative orientation influence the coupling and thereby tunes the sound attenuation. The simulation results are supported by experiments using a two-dimensional parallel-plate waveguide. It is shown that coupling parameters like distance, orientation, detuning, and radiation loss provide additional degrees of freedom for efficient acoustic meta-atom tuning to achieve unprecedented interactions with excellent sound attenuation properties.
Kulandaivelu, J, Chen, Y, Choi, PM, Li, X, Rebosura, M, Song, Y, Yuan, Z, Mueller, JF & Jiang, G 2023, 'Fate of micropollutants in a lab-scale urban wastewater system: Impact of iron-rich drinking water treatment sludge', Journal of Hazardous Materials Advances, vol. 12, pp. 100360-100360. View/Download from: Publisher's site
Kumar, A, Naidu, G, Fukuda, H, Du, F, Vigneswaran, S, Drioli, E & Lienhard, JH 2023, 'Correction to “Metals Recovery from Seawater Desalination Brines: Technologies, Opportunities, and Challenges”', ACS Sustainable Chemistry & Engineering, vol. 11, no. 1, pp. 464-465. View/Download from: Publisher's site
Kumari, P, Bahadur, N, Conlan, XA, Zeng, X, Kong, L, O'Dell, LA, Sadek, A, Merenda, A & Dumée, LF 2023, 'Stimuli-responsive heterojunctions based photo-electrocatalytic membrane reactors for reactive filtration of persistent organic pollutants', Chemical Engineering Journal, vol. 452, pp. 139374-139374. View/Download from: Publisher's site View description>>
The design of semiconducting metal oxide heterojunctions is promising to overcome conventional limitations associated to photocatalysis or electrocatalysis, such as fast recombination of electron-hole pairs and poor long-term stability leading to low catalytic performance. A route to tackle this issue is to design catalysts at the atomic levels by arranging order and controlling nanoscale interfaces to yield catalytic materials with greater response rates and stability to dissolution or corrosion. The present study focuses on the formation of such nanoscale heterojunctions between TiO2 and ZnO via atomic layer deposition across conductive and porous stainless-steel substrates to develop enhanced photo-electro-responsive catalytic membrane reactors. The heterojunction nano-sheet based structures produced higher density of electron and hole pairs and offered efficient separation of charges, longer lifetime of photo-generated electrons compared to single metal oxides, resulting in enhanced photocurrent efficiency. The tailoring of both the nanoscale dimensions of the metal oxide layers and the stacking of these inorganic nano-sheets led to the development of multi-heterojunctions, of a few tens of nanometres, deposited across conductive porous substrates. The high electron mobility across the heterojunction nano-sheets increased the oxygen evolution potential from 1.4 to 1.7 eV, leading to enhanced electrochemical reactions, as well as offered photocurrent densities 2–3 times higher than pristine single metal oxide membranes. The formation of type II heterojunction structures between TiO2 and ZnO leads to band alignment at the interface, yielding an efficient charge separation mechanism and high catalytic performance. A prototype of novel cross-flow filtration module was designed in this study to support the coupling of photo-electrocatalysis on the membrane surface and simultaneous pressure driven membrane processes. The designed 3D printed modules demonstrated hig...
Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
Lal Mohammadi, E, Khaksar Najafi, E, Zanganeh Ranjbar, P, Payan, M, Jamshidi Chenari, R & Fatahi, B 2023, 'Recycling industrial alkaline solutions for soil stabilization by low-concentrated fly ash-based alkali cements', Construction and Building Materials, vol. 393, pp. 132083-132083. View/Download from: Publisher's site
Lalsangi, S, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Ağbulut, Ü & Kalam, MA 2023, 'Analysis of CRDI diesel engine characteristics operated on dual fuel mode fueled with biodiesel-hydrogen enriched producer gas under the single and multi-injection scheme', International Journal of Hydrogen Energy, vol. 48, no. 74, pp. 28927-28944. View/Download from: Publisher's site
Larpruenrudee, P, Bennett, NS, Luo, Z, Fitch, R, Sauret, E & Islam, MS 2023, 'A novel design for faster hydrogen storage: A combined semi-cylindrical and central return tube heat exchanger', Journal of Energy Storage, vol. 71, pp. 108018-108018. View/Download from: Publisher's site
Larpruenrudee, P, Do, DK, Bennett, NS, Saha, SC, Ghalambaz, M & Islam, MS 2023, 'Computational Fluid Dynamics Analysis of Spray Cooling in Australia', Energies, vol. 16, no. 14, pp. 5317-5317. View/Download from: Publisher's site View description>>
Spray cooling technology offers high levels of uniform heat removal with very low fluid volumes and has found recent application in relatively small-scale use cases. Since it is a complex process, research can enable spray cooling to be applied more widely and at larger scales, such as in HVAC, as a means to operate more efficiently. Weather conditions are one of the main parameters that directly affect the effectiveness of spray cooling. This study investigates the spray cooling performance for temperature and humidity conditions in six Australian cities. ANSYS Fluent (2021 R1) software is applied for the numerical simulation. The numerical model is first validated with the available literature before a numerical simulation is conducted to assess each city throughout the year. These include Adelaide, Brisbane, Darwin, Melbourne, Perth, and Sydney. The spray cooling pattern, temperature, and humidity distribution, as well as the evaporation effect on different regions in Australia, is simulated and analysed based on the CFD technique. The results from this study indicate that weather conditions influence spray cooling for all cities, especially in summer. Along the wind tunnel, the temperature significantly drops at the spray cooling area, while the humidity increases. Due to the effect of spray cooling inside the wind tunnel, the temperature at the outlet is still lower than the inlet for all cases. However, the humidity at the outlet is higher than the inlet for all cases.
Laval, H, Holmes, A, Marcus, MA, Watts, B, Bonfante, G, Schmutz, M, Deniau, E, Szymanski, R, Lartigau‐Dagron, C, Xu, X, Cairney, JM, Hirakawa, K, Awai, F, Kubo, T, Wantz, G, Bousquet, A, Holmes, NP & Chambon, S 2023, 'Toward High Efficiency Water Processed Organic Photovoltaics: Controlling the Nanoparticle Morphology with Surface Energies', Advanced Energy Materials, vol. 13, no. 26. View/Download from: Publisher's site View description>>
AbstractHere efficient organic photovoltaic devices fabricated from water‐based colloidal dispersions with donor:acceptor composite nanoparticles achieving up to 9.98% power conversion efficiency (PCE) are reported. This high efficiency for water processed organic solar cells is attributed to morphology control by surface energy matching between the donor and the acceptor materials. Indeed, due to a low interfacial energy between donor and the acceptor, no large phase separation occurs during the nanoparticle formation process as well as upon thermal annealing. Indeed, synchrotron‐based scanning transmission X‐ray microscopy reveals that the internal morphology of composite nanoparticles is intermixed as well as the active layer morphology after thermal treatment. The PCE of this system reaches 85% that of devices prepared from chlorinated solvent. The gap between water‐based inks and organic solvent‐based inks gets narrower, which is promising for the development of eco‐friendly processing and fabrication of organic photovoltaics.
Law, D, Patrisia, Y, Gunasekara, C, Castel, A, Nguyen, QD & Wardhono, A 2023, 'Durability Assessment of Alkali-Activated Concrete Exposed to a Marine Environment', Journal of Materials in Civil Engineering, vol. 35, no. 9. View/Download from: Publisher's site
Le, L-T, Nghiem, LD, Bui, X-T & Jahng, D 2023, 'Improve nitrogen removal of the biofilm single-stage PN/A process by optimizing the intermittent aeration strategy', Environmental Technology & Innovation, vol. 30, pp. 103078-103078. View/Download from: Publisher's site
Le, T, Desa, S & Khabbaz, H 2023, 'The Influence Of Bagasse Fly Ash Particle Size In Controlling Expansive Soils In Combination With Hydrated Lime', Australian Geomechanics Journal, vol. 58, no. 1, pp. 47-57. View/Download from: Publisher's site View description>>
Sugarcane is the second largest export crop in Australia. Industrial production of sugar, from sugarcane, results in bagasse fly ash (BFA), a by-product from the cogeneration in sugar milling operations that generate electricity by steam. The chemical and physical properties of BFA highlight its potential as a promising pozzolan for the stabilization of expansive soils, due primarily to a high content and surface area of the amorphous silicate found in BFA. Silicate in bagasse fly ash reacts extensively with calcium hydrate in lime to produce hydrated products via pozzolanic reactions, this results in a hardening of the material to which BFA and lime have been added. This reaction has been studied to be a function of the size of BFA particles and conditions of the curing process. This study explored the variables that influence the reaction and evaluated shrinkage and compressive strength of the mixtures to which bagasse fly ash, in the form of different particle size distributions, and hydrated lime are added. The maximum BFA particles sizes considered within this study include 75, 150 and 425 μm; curing times of 7 and 28 days are also explored. A suite of testing, including Atterberg limits, linear shrinkage (LS), and unconfined compressive strength (UCS) tests were completed on the prepared mixtures. The findings indicate that bagasse fly ash with a maximum size of 425 μm yields a higher UCS and lower LS, compared to finer BFA particle mixtures. The ash with a maximum particle size of 425-μm also improves the ductility of treated soils and accelerates their strength gain, compared to soil- lime stabilized samples. The results of the study build towards a better understanding of BFA, and the ways in which such a material maybe engineered to replace concrete in road work projects and other applications involving expansive soils.
Lee, SS, Cao, S, Barzegarkhoo, R, Farhangi, M & Siwakoti, YP 2023, 'Single-Phase 5-Level Split-Midpoint Cross-Clamped (5L-SMCC) Inverter: An Alternative to the Two-Stage ANPC Topology', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 1995-2003. View/Download from: Publisher's site
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 2023, 'Five-Level Unity-Gain Active Neutral-Point-Clamped Inverters Designed Using Half-Bridges', IEEE Transactions on Industry Applications, vol. 59, no. 3, pp. 3520-3529. View/Download from: Publisher's site
Lee, T & Shraibman, A 2023, 'Around the log-rank conjecture', Israel Journal of Mathematics, vol. 256, no. 2, pp. 441-477. View/Download from: Publisher's site
Lei, B, Xiong, Q, Zhao, H, Dong, W, Tam, VWY, Sun, Z & Li, W 2023, 'Performance of asphalt mortar with recycled concrete powder under different filler-to-asphalt weight ratios', Case Studies in Construction Materials, vol. 18, pp. e01834-e01834. View/Download from: Publisher's site View description>>
The feasibility of using recycled concrete powder (RCP) as fillers in asphalt mortar is investigated in this study, to achieve a sustainable and economical asphalt production. The differences in physical properties between limestone powder (LSP) and RCP are analyzed and compared. Moreover, the interactions of LSP and RCP with asphalt are studied as well. The properties of asphalt mortar studied include ductility, softening point, penetration, viscous and elastic behaviors of asphalt mortar. Compared to LSP, the RCP presented a rougher surface, additional pores, more complex pore structures, larger Brunauer, Emmett and Teller (BET) surface areas, and smaller particle size. At 0.6 and 0.9 of filler-to-asphalt weight ratio (F/A), the RCP is more effective for the performance enhancement of asphalt mortar compared with the LSP. When the F/A is 0.9 and LSP is completely replaced by RCP, the 15 °C penetration index (PI) and ductility of asphalt mortar decrease by 9.3% and 29.2% respectively. The softening point increases by 5.4%. By contrast, the RCP causes a considerable decrease in PI, equivalent brittle point (T1.2) and ductility when F/A ratio is 1.2. After RCP completely replace LSP, the PI, T1.2, and ductility of asphalt mortar decrease by 47.1%, 44.0%, and 29.0%, respectively. However, at F/A of 0.6, the asphalt mortar with 100% RCP replacement ratio presented both acceptable ductility and plasticity. Under the same temperature and F/A, the complex shear modulus G* and rutting resistance factor G* /sinδ of asphalt mortar raise with the increase of RCP replacement, which indicates that the RCP can better enhance the high-temperature rutting resistance of asphalt mortar than the counterpart LSP. It also implies that the modification of LSP and RCP in asphalt mortar mainly depends on the physical interactions rather than the chemical reactions.
Lei, B, Yu, H, Guo, Y, Dong, W, Liang, R, Wang, X, Lin, X, Wang, K & Li, W 2023, 'Fracture behaviours of sustainable multi-recycled aggregate concrete under combined compression-shear loading', Journal of Building Engineering, vol. 72, pp. 106382-106382. View/Download from: Publisher's site
Lei, B, Yu, H, Guo, Y, Zhao, H, Wang, K & Li, W 2023, 'Mechanical properties of multi-recycled aggregate concrete under combined compression-shear loading', Engineering Failure Analysis, vol. 143, pp. 106910-106910. View/Download from: Publisher's site View description>>
The mechanical properties and strength failure criteria of multi-recycled aggregate concrete (multi-RAC) under combined compression and shear loading states are investigated in this paper. The peak shear strength, peak shear displacement, and failure patterns are compared under different regeneration cycles and normal compressive stress ratios. The results reveal that both the peak shear strength and peak shear displacement increase with the increased normal stress ratio. The shear failure pattern with higher severity corresponds to more spalling powder and debris deposited on the shear fracture surface. When the regeneration cycles of coarse aggregate increase, the peak shear strength decreases and the descending trend become more evident with the higher vertical compressive stress ratio. Under the normal compressive stress, contact friction strength is the dominant component of peak shear strength among the cohesive strength, contact friction strength, and shear dilation strength. Based on different stress expressions, three compression-shear failure criterion models considering the regeneration cycles of coarse aggregate under planar stress state were established for RAC. The stress invariance failure criterion model and octahedral stress failure criterion model in quadratic parabolic functional form can provide high prediction accuracies.
Lei, B, Yu, L, Guo, Y, Mahmood, AH, Qu, F, Wang, X & Li, W 2023, 'Failure behaviour and damage evolution of multi-recycled aggregate concrete under triaxial compression', Engineering Failure Analysis, vol. 153, pp. 107572-107572. View/Download from: Publisher's site
Lei, Y, Ye, D, Shen, S, Sui, Y, Zhu, T & Zhou, W 2023, 'New challenges in reinforcement learning: a survey of security and privacy', Artificial Intelligence Review, vol. 56, no. 7, pp. 7195-7236. View/Download from: Publisher's site View description>>
Reinforcement learning is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.
Leng, D, Wang, R, Yang, Y, Li, Y & Liu, G 2023, 'Study on a three-dimensional variable-stiffness TMD for mitigating bi-directional vibration of monopile offshore wind turbines', Ocean Engineering, vol. 281, pp. 114791-114791. View/Download from: Publisher's site
Leong, D, Do, TT-T & Lin, C-T 2023, 'Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4862-4870. View/Download from: Publisher's site
Li, A, Yang, B, Huo, H, Chen, H, Xu, G & Wang, Z 2023, 'Hyperbolic Neural Collaborative Recommender', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9114-9127. View/Download from: Publisher's site
Li, B, Cui, W, Zhang, L, Zhu, C, Wang, W, Tsang, IW & Zhou, JT 2023, 'DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13586-13598. View/Download from: Publisher's site View description>>
Time series analysis is essential to many far-reaching applications of data science and statistics including economic and financial forecasting, surveillance, and automated business processing. Though being greatly successful of Transformer in computer vision and natural language processing, the potential of employing it as the general backbone in analyzing the ubiquitous times series data has not been fully released yet. Prior Transformer variants on time series highly rely on task-dependent designs and pre-assumed 'pattern biases', revealing its insufficiency in representing nuanced seasonal, cyclic, and outlier patterns which are highly prevalent in time series. As a consequence, they can not generalize well to different time series analysis tasks. To tackle the challenges, we propose DifFormer, an effective and efficient Transformer architecture that can serve as a workhorse for a variety of time-series analysis tasks. DifFormer incorporates a novel multi-resolutional differencing mechanism, which is able to progressively and adaptively make nuanced yet meaningful changes prominent, meanwhile, the periodic or cyclic patterns can be dynamically captured with flexible lagging and dynamic ranging operations. Extensive experiments demonstrate DifFormer significantly outperforms state-of-the-art models on three essential time-series analysis tasks, including classification, regression, and forecasting. In addition to its superior performances, DifFormer also excels in efficiency - a linear time/memory complexity with empirically lower time consumption.
Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 2023, 'Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway Systems', IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14182-14196. View/Download from: Publisher's site
Li, B, Wen, S, Yan, Z, Wen, G & Huang, T 2023, 'A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems', IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 3, pp. 584-602. View/Download from: Publisher's site
Li, B, Zhu, JG, Liu, CC, Lei, G & Li, YJ 2023, 'Design and optimization of dual-stator FSPMM for integrated compressor', Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 27, no. 1, pp. 101-109. View/Download from: Publisher's site View description>>
An integrated flux-switching permanent magnet machine (FSPMM) was proposed based on the 6 / 4 dual stator structure for the issue of sizing and cooling in axial flow compressor. Firstly, the analytical expression of cogging torque considering the rotor skew was derived and the model of flow trajectory is built. An E-core and C-core dual-stator FSPMM were designed and finite element model was built by the three-dimensional finite-element method. The electromagnetic performance was calculated and compared, including the distribution of magnetic density, no-load back-EMF, cogging torque, electromagnetic torque and torque ripple. The results show that the performance of E-core FSPMM is obviously better than that of C-core FSPMM. The rotor of E-core FSPMM is optimized considering electromagnetic and hydrodynamic performance. The results show that the torque ripple of optimized 6 / 4 dual E-core stator FSPMM reduces by 15. 25% and the outlet velocity is pushed up to 113. 27 m / s for driving multistage axial-flow compressors.
Li, C, Fang, J, Wan, Y, Qiu, N, Steven, G & Li, Q 2023, 'Phase field fracture model for additively manufactured metallic materials', International Journal of Mechanical Sciences, vol. 251, pp. 108324-108324. View/Download from: Publisher's site
Li, C, Zhou, J, Du, K, Armaghani, DJ & Huang, S 2023, 'Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression', Natural Resources Research, vol. 32, no. 6, pp. 2995-3023. View/Download from: Publisher's site
Li, D, Ma, XY, Zhang, S, Wang, YK, Han, Y, Chen, R, Wang, XC & Ngo, HH 2023, 'Aquatic photolysis of high-risk chemicals of emerging concern from secondary effluent mediated by sunlight irradiation for ecological safety and the enhanced methods', Water Research, vol. 238, pp. 120002-120002. View/Download from: Publisher's site
Li, D, Ou, T, Fu, Q, Li, D-S, Liu, Z & Sun, Y 2023, 'A Novel Thin Film Composite Membrane for Osmotic Energy Generation', Industrial & Engineering Chemistry Research, vol. 62, no. 14, pp. 5889-5897. View/Download from: Publisher's site
Li, D, Ou, T, Fu, Q, Li, D-S, Liu, Z & Sun, Y 2023, 'A Novel Thin Film Composite Membrane for Osmotic Energy Generation', INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, vol. 62, no. 14, pp. 5889-5897. View/Download from: Publisher's site
Li, D, Wang, G, Li, J, Yan, L, Liu, H, Jiu, J, Li, X, Li, JJ & Wang, B 2023, 'Biomaterials for Tissue-Engineered Treatment of Tendinopathy in Animal Models: A Systematic Review', Tissue Engineering Part B: Reviews, vol. 29, no. 4, pp. 387-413. View/Download from: Publisher's site
Li, F, Zheng, J, Zhang, Y-F, Jia, W, Wei, Q & He, X 2023, 'Cross-domain learning for underwater image enhancement', Signal Processing: Image Communication, vol. 110, pp. 116890-116890. View/Download from: Publisher's site View description>>
The poor quality of underwater images has become a widely-known cause affecting the performance of the underwater development projects, including mineral exploitation, driving photography, and navigation for autonomous underwater vehicles. In recent years, deep learning-based techniques have achieved remarkable successes in image restoration and enhancement tasks. However, the limited availability of paired training data (underwater images and their corresponding clear images) and the requirement for vivid color correction remain challenging for underwater image enhancement, as almost all learning-based methods require paired data for training. In this study, instead of creating the time-consuming paired data, we explore the unsupervised training strategy. Specifically, we introduce a universal cross-domain GAN-based framework to generate high-quality images to address the dependence on paired training data. To ensure the vivid colorfulness, the color loss is designed to constrain the training process. Also, a feature fusion module (FFM) is proposed to increase the capacity of the whole model as well as the dual discriminator channel adopted in the architecture. Extensive quantitative and perceptual experiments show that our approach overcomes the limitation of paired data and obtains superior performance over the state-of-the-art on several underwater benchmarks in terms of both accuracy and model deployment.
Li, G, Wu, Y, Wang, C, Peng, S, Niu, J & Yu, S 2023, 'The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things', IEEE Intelligent Systems, vol. 38, no. 5, pp. 45-55. View/Download from: Publisher's site
Li, H, Feng, C-M, Xu, Y, Zhou, T, Yao, L & Chang, X 2023, 'Zero-Shot Camouflaged Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 5126-5137. View/Download from: Publisher's site
Li, J, Jiang, M, Qin, Y, Zhang, R & Ling, SH 2023, 'Intelligent depression detection with asynchronous federated optimization', Complex & Intelligent Systems, vol. 9, no. 1, pp. 115-131. View/Download from: Publisher's site View description>>
AbstractThe growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users.
Li, J, Li, X, Liu, H, Gao, L, Wang, W, Wang, Z, Zhou, T & Wang, Q 2023, 'Climate change impacts on wastewater infrastructure: A systematic review and typological adaptation strategy', Water Research, vol. 242, pp. 120282-120282. View/Download from: Publisher's site View description>>
Wastewater infrastructures play an indispensable role in society's functioning, human production activities, and sanitation safety. However, climate change has posed a serious threat to wastewater infrastructures. To date, a comprehensive summary with rigorous evidence evaluation for the impact of climate change on wastewater infrastructure is lacking. We conducted a systematic review for scientific literature, grey literature, and news. In total, 61,649 documents were retrieved, and 96 of them were deemed relevant and subjected to detailed analysis. We developed a typological adaptation strategy for city-level decision-making for cities in all-income contexts to cope with climate change for wastewater structures. 84% and 60% of present studies focused on the higher-income countries and sewer systems, respectively. Overflow, breakage, and corrosion were the primary challenge for sewer systems, while inundation and fluctuation of treatment performance were the major issues for wastewater treatment plants. In order to adapt to the climate change impact, typological adaptation strategy was developed to provide a simple guideline to rapidly select the adaptation measures for vulnerable wastewater facilities for cities with various income levels. Future studies are encouraged to focus more on the model-related improvement/prediction, the impact of climate change on other wastewater facilities besides sewers, and countries with low or lower-middle incomes. This review provided insight to comprehensively understand the climate change impact on wastewater facilities and facilitate the policymaking in coping with climate change.
Li, J, Pan, Y, Lyu, Y, Yao, Y, Sui, Y & Tsang, IW 2023, 'Earning Extra Performance from Restrictive Feedbacks', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-13. View/Download from: Publisher's site
Li, J, Tang, S, Zhu, L, Zhang, W, Yang, Y, Chua, T-S, Wu, F & Zhuang, Y 2023, 'Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-16. View/Download from: Publisher's site
Li, K, Chen, J, Sun, X, Lei, G, Cai, Y & Chen, L 2023, 'Application of wireless energy transmission technology in electric vehicles', Renewable and Sustainable Energy Reviews, vol. 184, pp. 113569-113569. View/Download from: Publisher's site
Li, K, Cui, Y, Li, W, Lv, T, Yuan, X, Li, S, Ni, W, Simsek, M & Dressler, F 2023, 'When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4148-4173. View/Download from: Publisher's site View description>>
In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.
Li, K, Lau, BPL, Yuan, X, Ni, W, Guizani, M & Yuen, C 2023, 'Toward Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities', IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21855-21872. View/Download from: Publisher's site
Li, K, Li, X, Chen, Q & Nimbalkar, S 2023, 'Laboratory Analyses of Noncoaxiality and Anisotropy of Spherical Granular Media under True Triaxial State', International Journal of Geomechanics, vol. 23, no. 9. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Multi-Source Domain Adaptation with Incomplete Source Label Spaces', Procedia Computer Science, vol. 225, pp. 2343-2350. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 31, no. 12, pp. 4180-4194. View/Download from: Publisher's site
Li, K, Niu, Z, Shi, K & Qiu, P 2023, 'Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding', Data Analysis and Knowledge Discovery, vol. 7, no. 5, pp. 48-59. View/Download from: Publisher's site View description>>
[Objective] This paper proposes a new model that integrates multiple features to provide accurate paper recommendation services for researchers. [Methods] First, we designed a feature extraction framework to extract and fuse entity relation features and topic features from the knowledge graph and the content of academic papers, respectively. Then, we proposed a paper recommendation method based on the knowledge embedding-based encoding-decoding model, which improved the learning effect of high-dimensional fusion features. [Results] We examined our new model on the DBLP-v11 dataset. The proposed method improved the Recall and MRR scores by 8.9% and 2.9%, respectively, compared with the suboptimal model. [Limitations] The proposed graph feature learning method does not consider the weight of entities in the real environment. [Conclusions] The new paper recommendation method could effectively learn high-dimensional features, which provide guidance for subsequent research.
Li, L & kang, K 2023, 'Factors Affecting Chinese Students Promote Distance Learning On the Telecommunication Platform during the COVID-19 Pandemic', INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCH, vol. 2, no. 07, pp. 536-542. View/Download from: Publisher's site View description>>
Influenced by the situation of COVID-19, all Chinese students have to follow a self-isolation policy and accept distance education from home. Among telecommunication platforms, the DingTalk platform is the most popular one in China and already has more than 130 million student users. Although DingTalk provides teachers and schools with comprehensive teaching functions, there are numerous negative feedbacks from Chinese students. To analyse related problems and promote distance education successfully, this paper establishes the research model based on the COM-B Behaviour Changing theory and the Hofstede cultural theory, and it analyses specific factors affecting Chinese students’ motivation to promote distance learning on telecommunication platform under the situation of COVID-19. The research results will be beneficial to improve the distance learning system, which can attract more and more young students to accept a high-quality distance education during and after the COVID-19 pandemic.
Li, L & Kang, K 2023, 'Why ethnic minority groups’ online-startups are booming in China’s tight cultural ecosystem?', Journal of Entrepreneurship in Emerging Economies, vol. 15, no. 2, pp. 278-300. View/Download from: Publisher's site View description>>
PurposeAlthough most Chinese ethnic minority groups (EMGs) hold conservative thinking to online-startups, the new entrepreneurial model is booming on live streaming platforms. In China’s tight cultural ecosystem, the tight cultural control would lead EMG entrepreneurs to keep conservative thinking and avoid challenging careers. Still, it would be helpful for Chinese Governments to issue systematical entrepreneurial policies and improve online-startup environment for EMGs. To discover the relationships among influencing factors and EMGs’ online-startup motivation, this paper aims to draw on the tight and loose cultural theory and the capability-opportunity-motivation-behaviour (COM-B) behaviour changing theory and establishes the research model based on China’s tight cultural ecosystem.Design/methodology/approachThrough analysing 617 questionnaires from 37 EMGs based on the partial least squares path modelling and variance-based structural equation modelling method, the study proves that environmental opportunity factors and personal capability factors have positive impacts on EMGs’ online-startup motivation and EMGs’ conservative thinking negatively moderates the relationship between their online-startup motivation and entrepreneurial development behaviour. In addition to testing the hypotheses, the paper also measures the importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for EMG entrepreneurs and related departments.FindingsRegarding the environmental opportunity unit, both policy support and platform support significantly impact Chinese EMGs’ motivation to promote online-startups. For the personal cap...
Li, L, Guo, R, You, P, Bai, J, Qin, P-Y & Liu, Y 2023, 'Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming', IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 6, pp. 1271-1275. View/Download from: Publisher's site View description>>
A new method called alternating sequential quadratic programming is proposed to synthesize pattern-reconfigurable sparse linear arrays with minimum element spacing control. The method can find the common element positions and multiple sets of excitations for generating multiple reconfigurable patterns which accurately meet their given upper and lower pattern bounds. In addition, by introducing auxiliary weighting coefficients and collective excitation coefficient vectors and choosing them as optimization variables alternately, the proposed method can appropriately incorporate the minimum element spacing constraint into the pattern synthesis. Synthesized results show that the proposed method can give satisfactory reconfigurable pattern performance but save much more elements compared with some existing methods.
Li, L, Ju, N & Sheng, D 2023, 'Seismic performance and failure mechanism of interbedded slopes with steep rock layers', Engineering Geology, vol. 326, pp. 107312-107312. View/Download from: Publisher's site View description>>
Numerous interbedded rock (IR) slopes fail during the Wenchuan earthquake in the mountainous region of western China. Landslides are also triggered in IR slopes with a 60° layer inclination, which are generally stable in gravity-dominant environments. This study examines the effect of seismic motion on the response characteristics and failure patterns of IR slopes with steep layers to develop a landslide hazard assessment tool for earthquake-prone regions. First, we use a centrifuge shaking table test to model the failure process and acceleration responses of two IR slope models with stratigraphic dips of 60° and 80°, respectively, under different seismic intensities. Next, we adopt the Particle Flow Code to examine the crack propagation features and peak ground acceleration amplification effects for the IR slopes. We find that the seismic failure pattern of IR slopes depends largely on rock layer inclination: buckling failure is triggered when rock layers are parallel or nearly parallel to the slope surface, while toppling failure is triggered when the rock layer inclination is significantly higher than that of the slope surface. Following seismic excitation, the damage is mainly observed in the weak rock layers, creating lateral stress on adjacent strong rocks, which undergoes deformation and ultimate macroscopic failure. Further, displacement of the IR slope is negatively correlated to rock layer inclination. Rock layer thickness has a major influence on the damaged area inside the slope mass, while rock layer stiffness mainly affects the deformation distribution near the slope shoulder.
Li, L, Kang, K & Sohaib, O 2023, 'Investigating factors affecting Chinese tertiary students’ online-startup motivation based on the COM-B behaviour changing theory', Journal of Entrepreneurship in Emerging Economies, vol. 15, no. 3, pp. 566-588. View/Download from: Publisher's site View description>>
PurposeThis study aims to present the Chinese entrepreneurial environment and explore Chinese tertiary students’ online-startup motivation on live streaming platforms. Based on the COM-B behaviour changing theory, this paper discovers various influencing factors from environmental opportunity and personal capability aspects. It analyses their effects under the cooperative system established among official departments, industries and universities. Meanwhile, considering social and cultural control, it also refers to the uncertainty-avoidance dimension from the Hofstede cultural theory and re-evaluates its influence on Chinese tertiary students’ online-startup motivation.Design/methodology/approachThe authors analyse 474 responses from online questionnaires through partial least squares path modelling and variance-based structural equation modelling. The paper claims that environmental opportunity and personal capability factors positively affect students’ online-startup motivation, but uncertainty-avoidance thinking plays a negative role. The study also measures the importance-performance map analysis to explore additional findings and discuss managerial implications.FindingsBoth platform support and official department support positively impact Chinese tertiary students’ online-startup motivation and entrepreneurial skills learned from universities are beneficial for them to build online-startup confidence. Meanwhile, influenced by the cooperative system implemented among official departments, industries and universities, official department support positively affects platform support and entrepreneurial skills. Conversely, influenced by Chinese traditional Confucian culture...
Li, L, Kang, K, Zhao, A & Feng, Y 2023, 'The impact of social presence and facilitation factors on online consumers' impulse buying in live shopping – celebrity endorsement as a moderating factor', Information Technology & People, vol. 36, no. 6, pp. 2611-2631. View/Download from: Publisher's site View description>>
PurposeAlthough prior studies have studied the relationship between online consumers' attitudes and buying behaviour, the research focussing on online consumers' impulse buying behaviours and exploring the role of celebrity endorsement is limited. Drawing on the social presence and the social facilitation theory, this paper establishes a research model based on the stimuli–organism–response (S–O–R) model and the motivation theory. It explores how live streamers impact online consumers' impulse buying behaviours under specific social and cultural backgrounds, with celebrity endorsement as a moderating variable.Design/methodology/approachTo test the research model, the online questionnaire method has been conducted in this study. This paper utilises Chinese online consumers as samples and promotes an online survey. Using the variance-based structural equation modelling and partial least squares path modelling (SEM-PLS), 433 valid questionnaires have been analysed on SmartPLS.FindingsFirst, live streamers' attractive appearance positively correlates with online consumers' hedonic attitude and positively impacts their utilitarian attitude to live shopping. Second, live streamers' real-time interaction positively affects consumers' utilitarian attitudes because of their professional marketing and communication skills. Third, their hedonic and utilitarian attitudes positively influence online consumers' impulse buying behaviours. Finally, this paper presents that celebrity endorsement negatively moderates the relationship between online consumers' hedonic attitudes and impulse buying during live shopping.Originalit...
Li, M & Yang, Y 2023, 'Single- and Multiple-Material Additively Manufactured Electronics: A Further Step From the Microwave-to-Terahertz Regimes', IEEE Microwave Magazine, vol. 24, no. 1, pp. 30-45. View/Download from: Publisher's site
Li, M, Chen, S-L, Liu, Y & Guo, YJ 2023, 'Wide-Angle Beam Scanning Phased Array Antennas: A Review', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 695-712. View/Download from: Publisher's site
Li, M, Liu, R, Wang, F, Chang, X & Liang, X 2023, 'Auxiliary signal-guided knowledge encoder-decoder for medical report generation', World Wide Web, vol. 26, no. 1, pp. 253-270. View/Download from: Publisher's site View description>>
AbstractMedical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists’ working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.
Li, M, Liu, Y, Bao, Z, Chen, L, Hu, J & Guo, YJ 2023, 'Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3719-3724. View/Download from: Publisher's site
Li, P, Li, W, Wang, K, Zhao, H & Shah, SP 2023, 'Hydration and microstructure of cement paste mixed with seawater – An advanced investigation by SEM-EDS method', Construction and Building Materials, vol. 392, pp. 131925-131925. View/Download from: Publisher's site
Li, P, Li, W, Wang, K, Zhou, JL, Castel, A, Zhang, S & Shah, SP 2023, 'Hydration of Portland cement with seawater toward concrete sustainability: Phase evolution and thermodynamic modelling', Cement and Concrete Composites, vol. 138, pp. 105007-105007. View/Download from: Publisher's site View description>>
To mitigate the shortage of freshwater resource in the island and coastal regions, using seawater (SW) for concrete mix can provide significant economic and environmental benefits. To achieve a safe and reliable application, in-depth investigation is needed on hydration of Portland cement in SW. The composition of solid and liquid phases in hydrated Portland cement was quantitively determined and analysed in this study. The use of SW not only significantly increases the hydration rate of clinker but also affects the evolution of phase assemblage. Both the thermodynamic calculations and experimental determinations indicates the formation of Friedel's salt (FS) instead of sulfo-AFm in hydrated cement by SW, implying sulfate ions cannot compete with chloride ions to combine with AFm phases. The characteristic reaction in SW leads to higher sulfate concentration, thus indirectly hindering ettringite (AFt) conversion at the late stage. Through the experimental quantification of thermogravimetric analysis and X-ray diffraction analysis, the kinetic model of clinker dissolution was modified to be more suitable for the hydration of Portland cement in SW. The calculation from coupled models exhibits a novel method to evaluate the evolution of phases in cement hydration. Through model calculations, 3.70% higher solid volume and 12.2% lower liquid volume were obtained in the cement-SW paste at the end of the hydration, which may cause the mechanical properties to be more sensitive under environmental humidity and the temperature.
Li, P, Yu, H, Luo, X & Wu, J 2023, 'LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection', IEEE Transactions on Big Data, vol. 9, no. 4, pp. 1116-1127. View/Download from: Publisher's site
Li, Q, Wang, X, Wang, Z & Xu, G 2023, 'Be Causal: De-Biasing Social Network Confounding in Recommendation', ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, pp. 1-23. View/Download from: Publisher's site View description>>
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC ( De-Bias Network Confounding in Recommendation ), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.
Li, Q, Wang, Z, Liu, S, Li, G & Xu, G 2023, 'Causal Optimal Transport for Treatment Effect Estimation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 4083-4095. View/Download from: Publisher's site View description>>
Treatment effect estimation helps answer questions, such as whether a specific treatment affects the outcome of interest. One fundamental issue in this research is to alleviate the treatment assignment bias among those treated units and controlled units. Classical causal inference methods resort to the propensity score estimation, which unfortunately tends to be misspecified when only limited overlapping exists between the treated and the controlled units. Moreover, existing supervised methods mainly consider the treatment assignment information underlying the factual space, and thus, their performance of counterfactual inference may be degraded due to overfitting of the factual results. To alleviate those issues, we build on the optimal transport theory and propose a novel causal optimal transport (CausalOT) model to estimate an individual treatment effect (ITE). With the proposed propensity measure, CausalOT can infer the counterfactual outcome by solving a novel regularized optimal transport problem, which allows the utilization of global information on observational covariates to alleviate the issue of limited overlapping. In addition, a novel counterfactual loss is designed for CausalOT to align the factual outcome distribution with the counterfactual outcome distribution. Most importantly, we prove the theoretical generalization bound for the counterfactual error of CausalOT. Empirical studies on benchmark datasets confirm that the proposed CausalOT outperforms state-of-the-art causal inference methods.
Li, Q, Wu, D, Gao, W & Hui, D 2023, 'Nonlinear dynamic stability analysis of axial impact loaded structures via the nonlocal strain gradient theory', Applied Mathematical Modelling, vol. 115, pp. 259-278. View/Download from: Publisher's site View description>>
In engineering applications, there has been an overwhelming tendency towards portability, miniaturization, and integration in recent years. To link the intrinsic size dependency feature of the small-scale structure with its structural stability, the nonlocal strain gradient theory, which captures the size effect in a more general size-dependent continuum-based model, is introduced to explore the nonlinear dynamic stability behaviour of nanoplates. Four types of axial impact loading configurations, namely, sinusoidal, exponential, rectangular, and damping, are considered. Some practical factors, such as Winkler-Pasternak elastic foundation and damping, are taken into account in the analysis. The equations of motion for the size-dependent initially imperfect plate are derived in the framework of the first-order shear deformation plate theory in conjunction with the Von Kármán nonlinear terms. Then the Airy stress function corresponding to simply supported nanoplate is introduced; then, by applying the Galerkin method, the obtained differential equations are addressed by the fourth-order Runge-Kutta algorithm. Subsequently, the specific value of the critical dynamic buckling load is determined by the Volmir criterion. Organic solar cells (OSCs), a type of emerging solar-to-electrical energy conversion nanodevice, are used as an illustrative example within the existing framework. The effects of size dependency in conjunction with the pulse load configuration, the initial imperfection, the elastic foundation, as well as the damping ratio on the nonlinear dynamic buckling behaviour of the OSC are thoroughly investigated.
Li, R, Millist, L, Foster, E, Yuan, X, Guvenc, U, Radfar, M, Marendy, P, Ni, W, O’Brien, TJ & Casillas-Espinosa, PM 2023, 'Spike and wave discharges detection in genetic absence epilepsy rat from Strasbourg and patients with genetic generalized epilepsy', Epilepsy Research, vol. 194, pp. 107181-107181. View/Download from: Publisher's site
Li, R, Yuan, X, Radfar, M, Marendy, P, Ni, W, O'Brien, TJ & Casillas-Espinosa, P 2023, 'Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review', IEEE Reviews in Biomedical Engineering, vol. 16, pp. 109-135. View/Download from: Publisher's site
Li, S, Ji, JC, Xu, Y, Sun, X, Feng, K, Sun, B, Wang, Y, Gu, F, Zhang, K & Ni, Q 2023, 'IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions', Reliability Engineering & System Safety, vol. 237, pp. 109387-109387. View/Download from: Publisher's site View description>>
Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to the entire industrial production. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status information. However, the complex working conditions of rolling bearings often make the periodic impulsive characteristics related to fault information easily buried in noise interferences. Therefore, it is challenging for existing approaches to learning discriminative fault-related features in these scenarios. To address this issue, a novel multibranch CNN named IFD-MDCN is developed in this paper, which represents multibranch denoising convolutional networks (MDCN) with an improved flow direction (IFD) strategy. The main contributions of this work include: (1) designing a multiscale denoising branch to extract multi-level information and reduce noise impact. More specifically, the multiscale denoising branch adopts a Gaussian multi-level noise reduction procedure to represent vibration signals at multiple levels and filter out the noise components, and then it uses a multiscale convolutional module to extract abundant features from these denoised signal representations; (2) establishing an improved flow direction strategy-based adaptive resonance branch to learn periodic impulsive features associated with fault information from vibration signals. Extensive experimental results reveal that the IFD-MDCN outperforms five state-of-the-art approaches, especially in strong noise scenarios.
Li, S, Jiang, Q, Xu, Y, Feng, K, Wang, Y, Sun, B, Yan, X, Sheng, X, Zhang, K & Ni, Q 2023, 'Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis', Reliability Engineering & System Safety, vol. 240, pp. 109590-109590. View/Download from: Publisher's site
Li, T, Lian, S, Zhao, S, Lu, J & Burnett, IS 2023, 'Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1449-1463. View/Download from: Publisher's site View description>>
Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered-x least mean square (FxLMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion FxLMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion FxLMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.
Li, T, Rao, L, Zhao, S, Duan, H, Lu, J & Burnett, IS 2023, 'An augmented diffusion algorithm with bidirectional communication for a distributed active noise control system', The Journal of the Acoustical Society of America, vol. 154, no. 6, pp. 3568-3579. View/Download from: Publisher's site View description>>
Recent studies on diffusion adaptation for distributed active noise control (DANC) systems have attracted significant research interest due to their balance between computational burden and stability compared to conventional centralized and decentralized adaptation schemes. The conventional multitask diffusion FxLMS algorithm assumes that the converged solutions of all control filters are consistent to each other, which is unrealistic in practice hence results in inferior performance in noise reduction. An augmented diffusion FxLMS algorithm has been proposed to overcome this problem, which adopts a neighborhood-wide adaptation and node-based combination approach to mitigate the bias in the converged solution of the multitask diffusion algorithms. However, the improvement comes at the expense of a higher computational burden and communication cost. All existing DANC systems, including the multitask and augmented diffusion algorithms, assume one-way communication between nodes. By contrast, this paper proposes a bidirectional communication scheme for the augmented diffusion algorithm to further reduce the memory requirement, computational burden, and communication cost. Simulation results in the free field and with measured room impulse responses both demonstrate that the proposed augmented diffusion algorithm with bidirectional communication can achieve a faster convergence speed than that based on one-way communication with a lower memory, computation, and communication burden.
Li, W, Hu, Y, Jiang, C, Wu, S, Bai, Q & Lai, E 2023, 'ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networks', Applied Soft Computing, vol. 136, pp. 110062-110062. View/Download from: Publisher's site
Li, W, Ji, J, Huang, L & Cai, Z 2023, 'Periodic orbit analysis for a delayed model of malicious signal transmission in wireless sensor networks with discontinuous control', Mathematical Methods in the Applied Sciences, vol. 46, no. 5, pp. 5267-5285. View/Download from: Publisher's site View description>>
This paper employs a discontinuous temporary immunity control to obtain the periodic orbit for a class of delayed malicious signal transmission model in wireless sensor networks under the framework of differential inclusion. The positivity and boundedness of the solution for the discontinuous system is proved first. Then, by using the Kakutani's fixed point theorem of set‐valued maps, the existence of a periodic orbit is obtained under some assumptions and constraints. Furthermore, the globally exponentially stable ‐periodic orbit is investigated using the Lyapunov functional method. The obtained results can help us better understand the dynamic characteristics of discontinuous delayed systems and have direct applications to the wireless sensor networks for guaranteeing fast response to malicious signals. Finally, the numerical simulations of three examples are given to validate the correctness of the theoretical results.
Li, W, Ji, J, Huang, L & Zhang, L 2023, 'Global dynamics and control of malicious signal transmission in wireless sensor networks', Nonlinear Analysis: Hybrid Systems, vol. 48, pp. 101324-101324. View/Download from: Publisher's site View description>>
This paper studies the global dynamics of a discontinuous delayed model of malicious signal transmission in wireless sensor networks under the framework of differential inclusion. The local stability of two types of steady states are investigated for the discontinuous system by studying the corresponding characteristic equation. The sufficient conditions for the existence of two types of globally asymptotically stable steady states are obtained for the discontinuous system by using the comparison arguments method. Furthermore, the optimal control of the discontinuous system is investigated by using Pontryagin's maximum principle. Numerical simulations of two examples are carried out to illustrate the main theoretical results. The obtained results can help us to better control and predict the spread of malicious signal transmission in wireless sensor networks.
Li, W, Ji, J, Huang, L & Zhang, Y 2023, 'Complex dynamics and impulsive control of a chemostat model under the ratio threshold policy', Chaos, Solitons & Fractals, vol. 167, pp. 113077-113077. View/Download from: Publisher's site View description>>
In this paper, we study the periodic solution and global stability of a chemostat model under impulsive control. First, we investigate the positivity and boundedness of the solution of the controlled system. Second, we find the periodic solution of the controlled system by employing the Poincare map and Brouwer's fixed-point theorem. Furthermore, we obtain a sufficient condition which allows the existence of orbitally stable order-k periodic solutions (k=1,2) by using the comparison method and the vector field analysis. We find that the controlled system exists a unique positive equilibrium point that is globally asymptotically stable (GAS) under some conditions. Finally, we provide two numerical examples to verify the correctness of the theoretical results.
Li, W, Jiang, C, Xiao, J, Xu, C & Deng, M 2023, 'Assessment of Thermal Damage in Polymethyl Methacrylate Using Quasi-static Components of Ultrasonic Waves', Journal of Nondestructive Evaluation, vol. 42, no. 1. View/Download from: Publisher's site
Li, W, Li, X, Han, C, Gao, L, Wu, H & Li, M 2023, 'A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter', Science of The Total Environment, vol. 855, pp. 158963-158963. View/Download from: Publisher's site View description>>
Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D EEMs) has been extensively used for dissolved organic matter (DOM) characterization. However, the application of 3D EEMs is constantly limited by issues such as contradictory component identification, confusing interpretation of spectral indicators, and inability to establish biodegradability. In this study, some improvements were proposed by investigating the 3D EEMs, spectral indicators, and degradability of the standard and representative DOM. To overcome the unclear identification of DOM components, it was recommended to partition 3D EEMs into three subareas: aromatic protein (New-I), humic-like (New-II), and soluble microbial by-product-like (New-III). Significant strong positive correlations (ρ = 0.727, P < 0.001) were observed between fluorescence index (FI) and biological index (BIX), and (R = 0.809, P < 0.001) humification index (HIX) and specific ultraviolet absorbance of 254 nm (SUVA254). Except for FI (R = -0.483, P = 0.023), no other spectral indicators (P > 0.05) were found to be significantly correlated with molecular weight. As thence results, the FI and HIX were the most suitable indicators for evaluating DOM. The half-life (20 < 21 < 26 < 29 < 46 days) revealed that the degradability of individual DOM components was in the order of tyrosine > tryptophan > fulvic acid > protein > humic acid. The degradation dynamics were governed by first-order decay kinetics (R2 = 0.91-0.99). This study clarified the fluorescence properties and degradability of DOM, as well as the reliability of spectral indicators. The degradation performance of individual DOM components engaged in the carbon cycling process was revealed, paving the path for further applications of 3D EEMs in DOM research.
Li, W, Lv, T, Cao, Y, Ni, W & Peng, M 2023, 'Multi-Carrier NOMA-Empowered Wireless Federated Learning With Optimal Power and Bandwidth Allocation', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 9762-9777. View/Download from: Publisher's site
Li, W, Zhang, Y, Ji, J & Huang, L 2023, 'Dynamics of a diffusion epidemic SIRI system in heterogeneous environment', Zeitschrift für angewandte Mathematik und Physik, vol. 74, no. 3, p. 104. View/Download from: Publisher's site View description>>
This paper studies the dynamical behaviors of a diffusion epidemic SIRI system with distinct dispersal rates. The overall solution of the system is derived by using L p theory and the Young's inequality. The uniformly boundedness of the solution is obtained for the system. The asymptotic smoothness of the semi-flow and the existence of the global attractor are discussed. Moreover, the basic reproduction number is defined in a spatially uniform environment and the threshold dynamical behaviors are obtained for extinction or continuous persistence of disease. When the spread rate of the susceptible individuals or the infected individuals is close to zero, the asymptotic profiles of the system are studied. This can help us to better understand the dynamic characteristics of the model in a bounded space domain with zero flux boundary conditions.
Li, X, Cui, Y, Zhang, JA, Liu, F, Zhang, D & Hanzo, L 2023, 'Integrated Human Activity Sensing and Communications', IEEE Communications Magazine, vol. 61, no. 5, pp. 90-96. View/Download from: Publisher's site
Li, X, Li, D, Li, J, Wang, G, Yan, L, Liu, H, Jiu, J, Li, JJ & Wang, B 2023, 'Preclinical Studies and Clinical Trials on Cell-Based Treatments for Meniscus Regeneration', Tissue Engineering Part B: Reviews, vol. 29, no. 6, pp. 634-670. View/Download from: Publisher's site
Li, X, Li, X, Jia, J, Li, L, Yuan, J, Gao, Y & Yu, S 2023, 'A High Accuracy and Adaptive Anomaly Detection Model With Dual-Domain Graph Convolutional Network for Insider Threat Detection', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1638-1652. View/Download from: Publisher's site
Li, X, Liu, H, Gao, L, Sherchan, SP, Zhou, T, Khan, SJ, van Loosdrecht, MCM & Wang, Q 2023, 'Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties', Nature Communications, vol. 14, no. 1, p. 4548. View/Download from: Publisher's site View description>>
AbstractAlthough the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.
Li, X, Liu, H, Zhang, Z, Zhou, T & Wang, Q 2023, 'Sulfite pretreatment enhances the medium-chain fatty acids production from waste activated sludge anaerobic fermentation', Science of The Total Environment, vol. 871, pp. 162080-162080. View/Download from: Publisher's site View description>>
Production of high-value medium chain fatty acids (MCFAs) from anaerobic fermentation of waste activated sludge (WAS) has been considered as a promising alternative for renewable energy resources. However, the low biodegradability of WAS greatly limits the anaerobic fermentation performance. This study proposed and demonstrated a novel approach, sulfite pretreatment, to efficiently produce MCFAs through anaerobic fermentation of WAS. Pretreatment of WAS at a sulfite concentration of 100-500 mg S/L for 24 h effectively improved the MCFAs production and MCFAs selectivity and the promotion effect was positively correlated with the sulfite concentration used in pretreatment (Pearson's R > 0.9). The maximum MCFAs production of 6.84 g COD/L and MCFAs selectivity of 39.1 % were both achieved under 500 mg S/L sulfite pretreatment, which accounts for 2.6 times and 2.4 times of the control, respectively (MCFAs production of 2.62 g COD/L and MCFAs selectivity of 16.4 % in the control). Sulfite pretreatment also enhanced the WAS degradation from 25 ± 2 % in the control to a maximum of 39 ± 2 % under 500 mg S/L sulfite pretreatment. The electron transfer efficiency and COD flows from the substrate to products were enhanced by up to 25 % due to the sulfite pretreatment, which supports the enhanced WAS degradation. Sulfite pretreatment also promoted the solubilization, hydrolysis, and acidification processes during the anaerobic fermentation by up to 200 %, 60 %, and 45 %, respectively, which subsequently makes more substrates available for MCFAs production. The findings from this study provide a potential solution of using industrial sulfite-laden wastes for WAS pretreatment, to enhance the MCFAs production at a minimized cost.
Li, X, Liu, Q, Wu, S, Cao, Z & Bai, Q 2023, 'Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems', Journal of Industrial Information Integration, vol. 31, pp. 100426-100426. View/Download from: Publisher's site
Li, X, Peng, Y & Xu, M 2023, 'Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning', Biomedical Signal Processing and Control, vol. 80, pp. 104239-104239. View/Download from: Publisher's site
Li, X, Yan, L, Li, D, Fan, Z, Liu, H, Wang, G, Jiu, J, Yang, Z, Li, JJ & Wang, B 2023, 'Failure modes after anterior cruciate ligament reconstruction: a systematic review and meta-analysis', International Orthopaedics, vol. 47, no. 3, pp. 719-734. View/Download from: Publisher's site View description>>
PURPOSE: The reason for graft failure after anterior cruciate ligament reconstruction (ACLR) is multifactorial. Controversies remain regarding the predominant factor and incidence of failure aetiology in the literature. This review aimed to provide a meta-analysis of the literature to evaluate the relative proportion of various failure modes among patients with ACLR failure. METHODS: The PubMed, Embase, Cochrane Library, Web of Science, and EBSCO databases were searched for literature on ACLR failure or revision from 1975 to 2021. Data related to causes for ACLR surgical failure were extracted, and a random effects model was used to pool the results, which incorporates potential heterogeneity. Failure modes were compared between different populations, research methods, graft types, femoral portal techniques, and fixation methods by subgroup analysis or linear regression. Funnel plots were used to identify publication bias and small-study effects. RESULTS: A total of 39 studies were analyzed, including 33 cohort studies and six registry-based studies reporting 6578 failures. The results showed that among patients with ACLR failure or revision, traumatic reinjury was the most common failure mode with a rate of 40% (95% CI: 35-44%), followed by technical error (34%, 95% CI: 28-42%) and biological failure (11%, 95% CI: 7-15%). Femoral tunnel malposition was the most common cause of the technical error (29%, 95% CI: 18-41%), with more than two times higher occurrence than tibial tunnel malposition (11%, 95% CI: 6-16%). Traumatic reinjury was the most common factor for ACLR failure in European populations and in recent studies, while technical errors were more common in Asian populations, earlier studies, and surgery performed using the transtibial (TT) portal technique. Biological factors were more likely to result in ACLR failure in hamstring (HT) autografts compared to bone-patellar tendon-bone (BPTB) autografts. CONCLUSION: Trauma is the most important fa...
Li, X, Zhang, S, Sherchan, S, Orive, G, Lertxundi, U, Haramoto, E, Honda, R, Kumar, M, Arora, S, Kitajima, M & Jiang, G 2023, 'Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis', Journal of Hazardous Materials, vol. 441, pp. 129848-129848. View/Download from: Publisher's site View description>>
Wastewater-based epidemiology (WBE) has been considered as a promising approach for population-wide surveillance of coronavirus disease 2019 (COVID-19). Many studies have successfully quantified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater (CRNA). However, the correlation between the CRNA and the COVID-19 clinically confirmed cases in the corresponding wastewater catchments varies and the impacts of environmental and other factors remain unclear. A systematic review and meta-analysis were conducted to identify the correlation between CRNA and various types of clinically confirmed case numbers, including prevalence and incidence rates. The impacts of environmental factors, WBE sampling design, and epidemiological conditions on the correlation were assessed for the same datasets. The systematic review identified 133 correlation coefficients, ranging from -0.38 to 0.99. The correlation between CRNA and new cases (either daily new, weekly new, or future cases) was stronger than that of active cases and cumulative cases. These correlation coefficients were potentially affected by environmental and epidemiological conditions and WBE sampling design. Larger variations of air temperature and clinical testing coverage, and the increase of catchment size showed strong negative impacts on the correlation between CRNA and COVID-19 case numbers. Interestingly, the sampling technique had negligible impact although increasing the sampling frequency improved the correlation. These findings highlight the importance of viral shedding dynamics, in-sewer decay, WBE sampling design and clinical testing on the accurate back-estimation of COVID-19 case numbers through the WBE approach.
Li, Y, Chen, H, Li, Y, Li, L, Yu, PS & Xu, G 2023, 'Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11801-11814. View/Download from: Publisher's site
Li, Y, Chen, Z, Sun, X, Gao, C, Liu, X & Guo, Y 2023, 'Back propagation neural network‐based torque ripple reduction strategy for high frequency square‐wave voltage injection‐based interior permanent magnet synchronous motor sensorless control', IET Electric Power Applications, vol. 17, no. 2, pp. 195-205. View/Download from: Publisher's site View description>>
In interior permanent magnet synchronous motor (IPMSM) position-sensorless drives, the high-frequency (HF) square-wave voltage injection method is often used to estimate the rotor position and speed in low-speed range by tracking the salient polarity of the motor. In order to reduce the torque ripple caused by HF signal injection, a strategy to update the magnitude of the injected signal online by back propagation neural network is proposed in this paper. With the proposed method, the neural network can update the magnitude of the injected signal online according to the d-axis current and the position error information. It can not only ensure the accuracy of position extraction but also effectively reduce the current harmonics caused by the injected signal, and then the torque ripple can be reduced. In addition, the proposed method is easy to implement, resulting in low computation burden. Finally, the experiments are implemented on a 1-kW IPMSM drive. The experimental results show that compared with the conventional fixed magnitude injection, the peak-to-peak value of the torque ripple is reduced by nearly half along with the decrease of the injected magnitude.
Li, Y, Ma, B, Zheng, J, Zhu, J & Lei, G 2023, 'Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints', IEEE Transactions on Industrial Electronics, vol. 70, no. 12, pp. 12048-12059. View/Download from: Publisher's site
Li, Y, Shen, J & Cetindamar, D 2023, 'Think Tank Innovation-Driven Knowledge Service Ecosystems: A Conceptual Framework and Case Study Application', Sustainability, vol. 15, no. 10, pp. 8355-8355. View/Download from: Publisher's site View description>>
By drawing on ecosystem and innovation-driven development theories, the aim of this paper is to increase our understanding of their application to think tanks. The composition, structure, and features of the knowledge service ecosystem of think tanks are conceptualized via a literature review. The model developed from this was validated by analyzing the data collected from 25 think tanks in the United States (US). The model constructed provides a reference for the sustainable and healthy development of knowledge services in think tanks and an innovation-driven development perspective for researchers interested in their innovation ecosystem dynamics. The intake of talent forms a necessary part of think tank construction, but, more importantly, this continuous intake is a crucial driving force for their sustainable development. This paper suggests that an increasing focus on talents in knowledge service ecosystems can lead to and assist in establishing innovative think tanks in many countries.
Li, Y, Wang, X, Zheng, J, Feng, K & Ji, JC 2023, 'Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system', Measurement Science and Technology, vol. 34, no. 6, pp. 065011-065011. View/Download from: Publisher's site View description>>
AbstractMultiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.
Li, Y, Yin, J & Chen, L 2023, 'Informative pseudo-labeling for graph neural networks with few labels', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 228-254. View/Download from: Publisher's site View description>>
AbstractGraph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity problem. It is the process of augmenting the training set with pseudo-labeled unlabeled nodes to retrain a model in a self-training cycle. However, the existing pseudo-labeling approaches often suffer from two major drawbacks. First, these methods conservatively expand the label set by selecting only high-confidence unlabeled nodes without assessing their informativeness. Second, these methods incorporate pseudo-labels to the same loss function with genuine labels, ignoring their distinct contributions to the classification task. In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels. Our key idea is to pseudo-label the most informative nodes that can maximally represent the local neighborhoods via mutual information maximization. To mitigate the potential label noise and class-imbalance problem arising from pseudo-labeling, we also carefully devise a generalized cross entropy with a class-balanced regularization to incorporate pseudo-labels into model retraining. Extensive experiments on six real-world graph datasets validate that our proposed approach significantly outperforms state-of-the-art baselines and competitive self-supervised methods on graphs.
Li, Y, Zeng, D, Gu, L, Zhu, A, Chen, Q & Yu, S 2023, 'PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8234-8238. View/Download from: Publisher's site
Li, Y, Zhang, X, Ngo, HH, Guo, W, Long, T, Wen, H & Zhang, D 2023, 'Combination of magnetic biochar beads and peroxymonosulfate pretreatment process for mitigating ultrafiltration membrane fouling caused by typical natural organic matters in water', Journal of Membrane Science, vol. 670, pp. 121383-121383. View/Download from: Publisher's site
Li, Z, Gao, W, Kessissoglou, N, Oberst, S, Wang, MY & Luo, Z 2023, 'Multifunctional mechanical metamaterials with tunable double-negative isotropic properties', Materials & Design, vol. 232, pp. 112146-112146. View/Download from: Publisher's site
Li, Z, Gao, W, Yu Wang, M, Wang, CH & Luo, Z 2023, 'Three-dimensional metamaterials exhibiting extreme isotropy and negative Poisson's ratio', International Journal of Mechanical Sciences, vol. 259, pp. 108617-108617. View/Download from: Publisher's site
Li, Z, Xu, P, Chang, X, Yang, L, Zhang, Y, Yao, L & Chen, X 2023, 'When Object Detection Meets Knowledge Distillation: A Survey', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10555-10579. View/Download from: Publisher's site
Lian, J-W, Ansari, M, Hu, P, Guo, YJ & Ding, D 2023, 'Wideband and High-Efficiency Parallel-Plate Luneburg Lens Employing All-Metal Metamaterial for Multibeam Antenna Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3193-3203. View/Download from: Publisher's site
Liang, C, Wang, W, Zhou, T, Miao, J, Luo, Y & Yang, Y 2023, 'Local-Global Context Aware Transformer for Language-Guided Video Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10055-10069. View/Download from: Publisher's site
Liang, C, Yang, Z, Zhu, L & Yang, Y 2023, 'Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition', IEEE Transactions on Image Processing, vol. 32, pp. 2508-2519. View/Download from: Publisher's site
Liao, W, Zhang, Q, Yuan, B, Zhang, G & Lu, J 2023, 'Heterogeneous Multidomain Recommender System Through Adversarial Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8965-8977. View/Download from: Publisher's site View description>>
To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation. In this article, we propose a novel multidomain recommender system (called HMRec) to deal with two challenging issues: 1) how to exploit valuable information from multiple source domains when no single source domain is sufficient and 2) how to ensure positive transfer from heterogeneous data in source domains with different feature spaces. In HMRec, domain-shared and domain-specific features are extracted to enable the knowledge transfer between multiple heterogeneous source and target domains. To ensure positive transfer, the domain-shared subspaces from multiple domains are maximally matched by a multiclass domain discriminator in an adversarial learning process. The recommendation in the target domain is completed by a matrix factorization module with aligned latent features from both the user and the item side. Extensive experiments on four cross-domain recommendation tasks with real-world datasets demonstrate that HMRec can effectively transfer knowledge from multiple heterogeneous domains collaboratively to increase the rating prediction accuracy in the target domain and significantly outperforms six state-of-the-art non-transfer or cross-domain baselines.
Lih, OS, Jahmunah, V, Palmer, EE, Barua, PD, Dogan, S, Tuncer, T, García, S, Molinari, F & Acharya, UR 2023, 'EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population', Computers in Biology and Medicine, vol. 164, pp. 107312-107312. View/Download from: Publisher's site
Lim, L-A, Atif, A, Heggart, K & Sutton, N 2023, 'In Search of Alignment between Learning Analytics and Learning Design: A Multiple Case Study in a Higher Education Institution', Education Sciences, vol. 13, no. 11, pp. 1114-1114. View/Download from: Publisher's site View description>>
Learning design (LD) has increasingly been recognized as a significant contextual element for the interpretation and adoption of learning analytics (LA). Yet, few studies have explored how instructors integrate LA feedback into their learning designs, especially within open automated feedback (AF) systems. This research presents a multiple-case study at one higher education institution to unveil instructors’ pilot efforts in using an open AF system to align LA and LD within their unique contexts, with the goal of delivering personalized feedback and tailored support. A notable finding from these cases is that instructors successfully aligned LA with LD for personalized feedback through checkpoint analytics in highly structured courses. Moreover, they relied on checkpoint analytics as an evaluation mechanism for evaluating impact. Importantly, students perceived a stronger sense of instructors’ support, reinforcing previous findings on the effectiveness of personalized feedback. This study contributes essential empirical insights to the intersection of learning analytics and learning design, shedding light on practical ways educators align LA and LD for personalized feedback and support.
Lin, B-L, Lee, D-J, Mannina, G & Guo, W 2023, 'Advanced biological technologies for removal and recovery of reactive nitrogen (Nr) from wastewaters', Bioresource Technology, vol. 368, pp. 128327-128327. View/Download from: Publisher's site
Lin, C-T, Liu, J, Fang, C-N, Hsiao, S-Y, Chang, Y-C & Wang, Y-K 2023, 'Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 261-271. View/Download from: Publisher's site
Lin, D, Ji, J, Yu, C, Wang, X & Xu, N 2023, 'A non-linear model of screen panel for dynamics analysis of a flip-flow vibrating screen', Powder Technology, vol. 418, pp. 118312-118312. View/Download from: Publisher's site View description>>
By taking advantage of periodic high-frequency flexure deformation of screen panels, flip-flow vibrating screens (FFVSs) can achieve outstanding sieving performance. As the amplitude of the relative displacement between the main frame and the floating frame of a FFVS exceeds the relaxing length of screen panel, the tensile stress generated from the deformation of screen panel can considerably affect the dynamics and the screening performance of the FFVS. However, there is a research gap in understanding the mechanical properties (especially the stiffness and damping) of screen panels. To address this research issue, the dynamic tests are first conducted to investigate the dynamic behavior of screen panels under harmonic excitations. Then the Kelvin-Voigt (KV) model is adopted to represent the hysteresis feature of the tension force. Furthermore, to characterize the mechanical properties of the screen panels under different stretching lengths, a nonlinear mechanical model is introduced and incorporated into the dynamic model of the FFVS. The effects of the stiffness, damping and relaxing length of screen panel, the shear springs and the eccentric mass moment on the vibration characteristics of the FFVS are numerically studied using a genetic algorithm and Newmark-β algorithm. The obtained results show that the panel tension force can induce the hardening nonlinearity in the relative displacement response of FFVS and the soft type of nonlinearity in the displacement response of the main screen frame in a certain frequency region. Furthermore, at the second-order resonance peak, a small change in frequency can cause a substantial increase in the vibration amplitude of the main frame and a significant decrease in the relative amplitude. This nonlinear phenomenon would induce a large alternating stress on the main frame structure and thus reduce the service life of the FFVS.
Lin, D, Ji, JC, Wang, X, Wang, Y, Xu, N, Ni, Q, Zhao, G & Feng, K 2023, 'A rigid-flexible coupled dynamic model of a flip-flow vibrating screen considering the effects of processed materials', Powder Technology, vol. 427, pp. 118753-118753. View/Download from: Publisher's site View description>>
Flip-flow vibrating screens (FFVSs) are the critical screening equipment for classifying and dewatering wet materials in mining processing industry. During the screening process, the FFVSs can be regarded as a complex rigid-flexible coupled multi-body system where the screening operation and the dynamics of two screen frames interact. However, there exists no mechanical model that can describe the dynamics of FFVSs during the screening process. The lack of such a dynamic model causes the amplitudes of the main and the floating screen frames unpredictable after the processed materials are loaded on FFVSs, which affects the screening performance and the service life of FFVSs. To bridge this research gap, the loaded dynamic model of a FFVS is established in this paper. First, dynamic tests are performed to investigate the equivalent stiffness and the equivalent damping of the force along the screen surface which is induced by the processed materials. Then, the proposed model of the FFVS is verified qualitatively by existing experimental results, and the effects of the processed materials on the dynamics of the FFVS are explored by comparing the non-load dynamics of the FFVS. Finally, the sensitivities of the main parameters on the dynamic response are investigated based on Sobol's method of global sensitivity analysis. It is shown that the proposed rigid-flexible coupled multi-body dynamic model of the FFVS can not only effectively reveal the dynamic response of FFVS in the screening process, but can also provide a reference for modelling the dynamics of the screening process of other screening equipment.
Lin, G, Khan, JU, Zhand, S, Liu, Y & Jin, D 2023, 'Modular DNAzymes-Hydrogel Membrane Carriers for Highly Sensitive Isothermal Cross-Cascade Detection of Pathogenic Bacteria Nucleic Acids', Analytical Chemistry, vol. 95, no. 35, pp. 13353-13360. View/Download from: Publisher's site View description>>
The increasing prevalence of antimicrobial resistance has called for improved diagnostic testing of pathogenic bacteria. However, the development of rapid, cost-effective, and easy-to-use tests for bacterial infections remains a constant challenge. Here, we report a class of modular hydrogel membrane carriers incorporated with composite DNAzymes, which enable rapid and highly sensitive detection of pathogenic bacteria gene target analytes. We apply free radical polymerization to incorporate composite DNAzymes, consisting of an RNA substrate component and a DNAzyme component (e.g., 10-23 or 8-17 DNAzymes), into polyethylene glycol diacrylate polymer networks. Initiated by a nucleic acid target acting as an assembly facilitator, multicomponent DNAzymes are combined to cleave the RNA substrate component in the hydrogel carriers, which releases the DNAzyme component to cleave RNA reporter probes to generate fluorescence. We modulate the morphology, composition, and microporous structures of the DNAzyme carriers to achieve quantitative assay performance. We demonstrate a rapid and high-sensitivity detection of C. trachomatis gene target analytes as low as 50 fM in a short assay time of 25 min. The work represents a crucial step forward in the development of a generic, isothermal, and protein enzyme-free pathogenic bacteria testing platform technology.
Lin, J, Hui, D, Kumar, A, Yu, Z & Huang, Y 2023, 'Editorial: Climate change and/or pollution on the carbon cycle in terrestrial ecosystems', Frontiers in Environmental Science, vol. 11. View/Download from: Publisher's site
Lin, J-Y, Yang, Y, Wong, S-W & Chen, R-S 2023, 'In-Band Full-Duplex Filtering Antenna Arrays Using High-Order Mode Cavity Resonators', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 4, pp. 1630-1639. View/Download from: Publisher's site View description>>
In this article, a series of narrowband and wideband in-band full-duplex (IBFD) filtering antenna arrays (FAAs) using cavity-based high-order modes are investigated. It is found that the pairs of degenerate high-order modes in a single cavity resonator, TM$_{1n0}$ and TM$_{n10}$ ($n$ is even), are suitable for the IBFD FAA designs due to their advantages: 1) in-phase and same amplitude for each magnetic loop, which helps to enhance the gain, and 2) the modal orthogonality, which guarantees the isolation and cross-polarization level between two channels. The higher order response can be achieved by cascading more high-order mode resonators with the required external quality factor ($Q_{e})$ and coupling coefficient ($K)$. For proof of concept, two types of full-duplex waveguide arrays are implemented and tested. First, a second-order IBFD FAA with 6 $\times$ 5 elements, using a pair of degenerate modes TM$_{610}$ and TM$_{160}$, is designed with 1% overlapped 10-dB bandwidth and 18.4-dBi realized gain within the passband. The method to improve realized gain and isolation level without increasing any circuit volume is also presented. Second, a fifth-order IBFD FAA using TM$_{410}$ and TM$_{140}$ modes is implemented with an overlapped 10-d...
Lin, J-Y, Yang, Y, Wong, S-W, Li, X, Wang, L & Dutkiewicz, E 2023, 'Two-Way Waveguide Diplexer and Its Application to Diplexing In-Band Full-Duplex Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1171-1179. View/Download from: Publisher's site View description>>
A design of a diplexing in-band full-duplex (IBFD) slot antenna based on the quadruple-mode resonator (QMR) is presented for the first time. First, a two-way waveguide diplexer integration using QMR is designed. Four waveguide modes, namely, TE$_{011}$, TE$_{101}$, TM$_{\mathrm{210,}}$ and TM$_{120}$, are primarily used. These four modes are modal orthogonal to each other in a single QMR. Each of the quadruple modes can be independently manipulated with low mutual interference with other modes. Taking advantage of this characteristic, a two-way waveguide diplexer integration can be implemented with four independent frequency channels. In each diplexer, the downlink channel is dominated by fundamental mode TE$_{011}$/TE$_{101}$, while the uplink channel is dominated by high-order mode TM$_{210}$/TM$_{120}$. By cascading QMRs, higher order frequency responses are achieved with expected coupling coefficient ($K)$ and external quality factor ($Q_{{\text{e}}})$. Based on the proposed two-way diplexer concept, a diplexing IBFD antenna with a turnstile junction is designed by replacing the inputs with a cross-coupled radiating slot. It integrates the filtering, diplexing, orthomode transducing, and radiating functions into a single element. Fou...
Lin, Q, Pang, L, Ngo, HH, Guo, W, Zhao, S, Liu, L, Chen, L & Li, F 2023, 'Occurrence of microplastics in three types of household cleaning products and their estimated emissions into the aquatic environment', Science of The Total Environment, vol. 902, pp. 165903-165903. View/Download from: Publisher's site
Lin, S, Kong, X, Wang, J & Liu, A 2023, 'Helix-HPSO approach for UAV path planning in a multi-building environment', Journal of Reliable Intelligent Environments, vol. 9, no. 4, pp. 371-384. View/Download from: Publisher's site View description>>
Regular inspection of historic buildings is essential, while path planning of the building inspection is challenging because it requires comprehensive coverage at a low cost. Most of the previous research does not consider the multiple buildings’ environment. In this paper, a three-dimensional path planning approach is proposed to provide the inspection for multiple buildings. The proposed Helix-HPSO approach generates the helix-shaped path for each building and uses HPSO for path planning between buildings. The computational experiment validates the proposed approach. The helix-shaped path costs less than the traditional back-and-forth path for building inspection. HPSO is compared with other bio-inspired algorithms for optimization problems and PSO for path planning.
Lin, S, Liu, A, Wang, J & Kong, X 2023, 'An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse', Journal of Computational Science, vol. 67, pp. 101938-101938. View/Download from: Publisher's site View description>>
Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.
Lin, W, Gong, C, Chen, R, He, X, Nan, J, Li, G, Hao Ngo, H & Ding, A 2023, 'In-situ utilization of EPS improves the directional oxidation ability of Fe(III)/H2O2 and enhances sludge dewaterability', Chemical Engineering Journal, vol. 475, pp. 146123-146123. View/Download from: Publisher's site
Lin, W, Guo, J, Zeng, J, Chen, R, Ngo, HH, Nan, J, Li, G, Ma, J & Ding, A 2023, 'Enhanced sludge dewaterability by ferrate/ferric chloride: The key role of Fe(IV) on the changes of EPS properties', Science of The Total Environment, vol. 858, pp. 159562-159562. View/Download from: Publisher's site
Lin, X, Li, W, Castel, A, Kim, T, Huang, Y & Wang, K 2023, 'A comprehensive review on self-healing cementitious composites with crystalline admixtures: Design, performance and application', Construction and Building Materials, vol. 409, pp. 134108-134108. View/Download from: Publisher's site View description>>
Crystalline admixture (CA) has garnered attention as a promising self-healing agent for cementitious composites. This paper aims to provide a compressive review on the effects of CA on the self-healing behaviours and durability properties of cementitious composites. CA is in powder form, consisting of Portland cement and special chemicals as self-healing stimulants. Since the powder-form CA was directly mixed with the cementitious mixture, CA addition has no significant impact on the properties of fresh concrete but enhances the compressive strength of CA-cementitious composites. Furthermore, self-healing is activated by moisture, resulting in the production of calcium-based self-healing products. In terms of crack closure efficacy, CA-cementitious specimens cured under wet/dry cycle demonstrated a higher crack closure ratio than those cured under water immersion or air exposure. Specimens cured in chloride solution exhibited the best healing recovery. However, reduced mechanical recoveries are observed in specimens exposed to freeze–thaw cycles and those in chloride solution, while better mechanical recoveries are found in specimens exposed to wet/dry cycles. Overall, CA can reduce the sorptivity, permeability, chloride penetration, and the depth of sodium ions penetration, offering favourable protection for cementitious composites. Although some durability properties of CA-cementitious composites have been explored, further studies are required to investigate potential effects on shrinkage, ingress of aggressive ions, carbonation, and alkali-silica reaction (ASR). The application of CA in cementitious composites could be considered as a cost-effective approach for inducing self-healing capability, given its affordable and straightforward construction process.
Lin, X, Li, W, Guo, Y, Dong, W, Castel, A & Wang, K 2023, 'Biochar-cement concrete toward decarbonisation and sustainability for construction: Characteristic, performance and perspective', Journal of Cleaner Production, vol. 419, pp. 138219-138219. View/Download from: Publisher's site
Lin, Y, Chen, Y, Chen, J, Chen, J, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Efficient Chloroquine Removal by Electro-Fenton with FeS2-Modified Cathode: Performance, Influencing Factors, Pathway Contributions, and Degradation Mechanisms', ACS ES&T Water, vol. 3, no. 8, pp. 2786-2796. View/Download from: Publisher's site
Litvinov, A, Gardner, A, Pradhan, S & Childers, J 2023, 'The role and understanding of empathy in entrepreneurial engineering: a systematic literature review', Australasian Journal of Engineering Education, vol. 28, no. 2, pp. 148-165. View/Download from: Publisher's site
Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9627-9641. View/Download from: Publisher's site
Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3594-3599. View/Download from: Publisher's site
Liu, C & Zowghi, D 2023, 'Citizen involvement in digital transformation: a systematic review and a framework', Online Information Review, vol. 47, no. 4, pp. 644-660. View/Download from: Publisher's site View description>>
PurposeThe purpose of this paper is to improve the understanding of the factors influencing the success of digital transformation (DT) and problems/challenges in DT as well as the communication methods used to involve citizens, based on a systematic literature review of research articles about citizen involvement in DT published between January 2010 and May 2021.Design/methodology/approachAfter establishing inclusion and exclusion criteria, a systematic review of relevant studies was conducted. Out of a total of 547 articles, 33 met the paper selection criteria.FindingsThe analysis of the included 33 empirical studies reveals that the factors influencing the success of DT can be described as the opposite side from challenges and problems in DT. These factors and challenges/problems all influence DT and they can be grouped into organisational values, management capabilities, organisational infrastructure, and workforce capabilities. The communication methods for citizen involvement in DT include: (1) communication mediated by human, (2) communication mediated by computers, and (3) mixed communication methods.Originality/valueThe study identified specific factors that influence DT supported by citizen involvement, at a more fine-grained level. The findings concerning communication methods extend related studies for citizen involvement by adding town hall meetings and communication methods mediated by computers. Furthermore, this study links the research findings to develop a framework for citizen involvement in DT, assisting in better selecting communication methods to involve citizens for ...
Liu, CL, Yagi, Y, Kamiya, T, Blumenstein, M, Lu, H, Yang, W & Cho, SB 2023, 'Preface for ACPR 2023 Proceedings', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14406 LNCS, pp. v-vi.
Liu, D, Li, W, Duan, L, Tsang, IW & Yang, G 2023, 'Noisy Label Learning With Provable Consistency for a Wider Family of Losses', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13536-13552. View/Download from: Publisher's site View description>>
Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.
LIU, F, WU, Q, LI, C, CHEN, F & XU, Y 2023, 'Polar Coding Aided by Adaptive Channel Equalization for Underwater Acoustic Communication', IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E106.A, no. 1, pp. 83-87. View/Download from: Publisher's site
Liu, H, Li, X, Zhang, Z, Nghiem, LD, Gao, L, Batstone, DJ & Wang, Q 2023, 'Achieving expanded sludge treatment capacity with additional benefits for an anaerobic digester using free ammonia pretreatment', Chemical Engineering Journal, vol. 465, pp. 142846-142846. View/Download from: Publisher's site View description>>
Population growth rapidly increased waste activated sludge (WAS) production in wastewater treatment plants (WWTPs), making the expansion of sludge treatment capacity urgent. Free ammonia (FA) pretreatment is experimentally applied to expand the treatment capacity of an anaerobic digester through reducing sludge retention time (SRT) for the first time. Two semi-continuous flow mesophilic (37 °C) anaerobic digestion systems, control system with a uniform SRT of 12 d and the experimental systems with progressively reduced SRTs (from 12 d to 10 d and then 8 d), were operated for>7 months. The volatile solids (VS) destruction in the experimental system at a SRT of 8 d was comparable to the control system (30.0 ± 1.4 % vs 30.5 ± 1.7 %) but increased by 16.2 % (35.1 ± 1.5 % vs 30.2 ± 1.4 %) under an SRT of 10 d, which was supported by methane production and total chemical oxygen demand (COD) removal. The biomass-specific hydrolysis rate was significantly increased by up to 80 % (from 0.05 ± 0.01 g COD/g VS/d to 0.09 ± 0.01 g COD/g VS/d), which may contribute to the expanded capacity. The volatile fatty acids (VFAs)/alkalinity of systems maintained a reasonable range (0.01 – 0.06), suggesting the stability of digesters. FA pretreatment played a dominant role in the changes in the bacterial microbial community (52.80 % in PC1) and archaeal community (94.25 % in PC1). FA pretreatment improved the removal of pathogen by 1.3–2.0 log and antibiotic resistance genes by 34–86 %. This study first demonstrated that FA pretreatment expands the treatment capacity of an anaerobic digester by up to 50 % with economic and environmental benefits, promoting FA pretreatment to be a wider and pragmatic implementation for WWTPs.
Liu, H, Li, X, Zhou, T, Zhang, Z, Nghiem, LD, Gao, L & Wang, Q 2023, 'Long-term effect of free ammonia pretreatment on the semi-continuous anaerobic primary sludge digester for enhancing performance: Towards sustainable sludge treatment', Chemical Engineering Journal, vol. 465, pp. 142780-142780. View/Download from: Publisher's site View description>>
Primary sludge (PS) is one of the major sludge sources for anaerobic digesters in wastewater treatment plants. Although the impact of free ammonia (FA) pretreatment on methane production from anaerobic PS digestion was previously investigated using batch biochemical methane potential tests, these tests could not fully represent the continuous/semi-continuous anaerobic digestion that is currently used in practice. This study comprehensively evaluated the impact of FA pretreatment on the performance of anaerobic PS digestion for the first time using semi-continuous systems that run for over 120 days. FA pretreatment (560 mg NH3-N/L, 24 h) improved the volatile solids (VS) removal of PS by 12.2 % from 60.5 % to 67.9 %, with a similar improvement in total chemical oxygen demand removal of 14.9 % and methane production of 16.1 %. FA pretreatment increased the biomass-specific hydrolysis rate of digesters by 23.5 %. Model-based analysis revealed that the enhanced anaerobic digestion performance may be due to both the increased apparent hydrolysis rate (increased by 26.7 %) and the enhanced degradability extent (increased by 9.5 %) of PS, caused by FA pretreatment. The dewaterability of digested sludge was enhanced by 14.0 % due to FA pretreatment, which is also supported by the reduced capillary suction time from 15.1 s to 10.9 s. Removals of Fecal Coliform and E. Coli were enhanced by 0.6 and 1.4 log Most Probable Number/g vS by FA pretreatment. This study firstly manifested that FA pretreatment is a favourable approach to improve the performance of anaerobic PS digestion with extra benefits in pathogen removal and dewaterability.
Liu, H, Wang, C, Sohn, W, Wang, Q, Shon, HK & Sun, P 2023, 'Source-separated urine treatment based on forward osmosis technology: Performance, applications and future prospects', Desalination, vol. 565, pp. 116872-116872. View/Download from: Publisher's site
Liu, H, Yan, X, Jiu, J, Li, JJ, Zhang, Y, Wang, G, Li, D, Yan, L, Du, Y, Zhao, B & Wang, B 2023, 'Self-assembly of gelatin microcarrier-based MSC microtissues for spinal cord injury repair', Chemical Engineering Journal, vol. 451, pp. 138806-138806. View/Download from: Publisher's site View description>>
Current approaches for treating spinal cord injury (SCI) are mainly based on cell transplantation. Mesenchymal stem cells (MSCs) can help slow the progression of SCI due to their trophic function. However, SCI creates a complex microenvironment that reduces cell activity and hence cellular function, ultimately resulting in poor therapeutic outcomes. To help maintain function in transplanted cells, we produced functional tissue constructs by self-assembly of MSC microtissues comprising of porous gelatin microcarriers (GM) and MSCs. These microtissues maintained cellular activity without incurring an excessive amount of apoptosis and delayed senescence in vitro. The paracrine function of MSCs also improved within microtissues, shown by the increased secretion of nerve regeneration-related factors. Microtissues were transplanted in a rat model of complete spinal cord transection, and therapeutic effects were evaluated through behavioral measurements, imaging, histology, and western blot analysis. RNA-seq of spinal cord tissues using Gene Ontology analysis further revealed that the microtissues may have induced repair in SCI through mechanisms related to neurotrophin-3 (NT-3) regulation of response mediator protein 2 (CRMP2) phosphorylation, and inhibition of inflammatory response through interleukin-17 (IL-17), Chemokine C-X-C motif Ligand 1 (CXCL1) axis. The gelatin microcarrier-based MSC microtissues we developed may be effective in providing a new treatment strategy for SCI.
Liu, J, He, Z, Liu, P, Wei, J, Li, J & Wu, C 2023, 'High-velocity projectile impact resistance of reinforced concrete slabs with ultra-high performance concrete strengthening - A numerical study', Structures, vol. 52, pp. 422-436. View/Download from: Publisher's site
Liu, K, Liu, J, Li, J, Tao, M & Wu, C 2023, 'Experimental investigation of heating–cooling effects on the mechanical properties of geopolymer-based high performance concrete heated to elevated temperatures', Structures, vol. 47, pp. 735-747. View/Download from: Publisher's site View description>>
In this study, geopolymer-based high performance concrete (G-HPC) reinforced with steel fibres was utilized to investigate its dynamic behaviour after heating–cooling treatment. A furnace with a heating capacity of 1100 °C was adopted to heat the specimens. The P-wave velocity and quasi-static uniaxial compressive strength of G-HPC after the heating–cooling treatment were obtained and compared with those without the heating–cooling treatment. The experimental findings indicated that the G-HPC specimens suffered different degrees of thermal damage under 250–1000 °C high temperature, while it still remained a good explosive spalling resistance. Moreover, the water cooling regime would cause more serious damage to the G-HPC specimens than the natural cooling. Further, a 50 mm-diameter Split Hopkinson Pressure Bar (SHPB) apparatus was applied to characterize the dynamic behaviour of G-HPC after the heating–cooling treatment, and a high-speed camera was employed to record the failure process. Upon increasing the temperature, the dynamic compressive strength and elastic modulus of G-HPC were deteriorated especially in the temperature range between 250 °C and 750 °C, thereby leading to partial loss of its ability to resist impact loads. However, even after being heated to 1000 °C, the specimens still demonstrated a significant strain rate effect. Besides, the high cooling rate under water was observed to induce a thermal shock, resulting in the secondary damage for the heated specimens.
Liu, K, Lyu, S, Shivakumara, P, Blumenstein, M & Lu, Y 2023, 'A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation', IEEE Signal Processing Letters, vol. 30, pp. 1607-1611. View/Download from: Publisher's site
Liu, L, Ba, X, Guo, Y, Lei, G, Sun, X & Zhu, J 2023, 'Improved Iron Loss Prediction Models for Interior PMSMs Considering Coupling Effects of Multiphysics Factors', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 416-427. View/Download from: Publisher's site View description>>
This paper presents improved iron loss analytical prediction models for interior permanent magnet synchronous motors (IPMSMs) used in electric vehicles. The effects of slotting harmonics, pulse-width modulation (PWM) carrier harmonics, temperature rise and mechanical stress are considered in the proposed models. Specifically, by investigating the stator flux density as piecewise linear with trapezoidal waveform, the iron losses in the teeth and yoke regions are calculated separately, considering the different magnetic field distributions and waveforms. To deliberate the PWM harmonic influence, a correction coefficient is added to the hysteresis loss models, while the eddy current loss models are updated by summing all the eddy current losses caused by the power supplying current harmonics. Moreover, the coupling interaction effects of magnetic, thermal, and stress fields on the empirical coefficients of hysteresis and eddy current losses are analyzed in detail and also implemented in the iron loss prediction process. The feasibility and superiority of the proposed models are verified by numerical and experimental case studies on an IPMSM prototype.
Liu, Q, Geng, X, Huang, H, Qin, T, Lu, J & Jiang, D 2023, 'MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 5, pp. 2594-2605. View/Download from: Publisher's site
Liu, Q, Tang, H, Chen, K, Sun, C, Li, W, Jiao, S & Tam, VWY 2023, 'Improving industrial drying process of recycled fine aggregates as a means of carbonation to improve the mechanical properties and plastic shrinkage of self-leveling mortar', Construction and Building Materials, vol. 403, pp. 133001-133001. View/Download from: Publisher's site
Liu, Q-F, Cai, Y, Peng, H, Meng, Z, Mundra, S & Castel, A 2023, 'A numerical study on chloride transport in alkali-activated fly ash/slag concretes', Cement and Concrete Research, vol. 166, pp. 107094-107094. View/Download from: Publisher's site
Liu, S, Cheng, J, You, H, Chong, W, Zheng, M, Wei, Q, Liu, W, Chen, H, Li, X & Liu, H 2023, 'Spatial distribution of ammonia oxidizers in marine sediments of the Bohai, Yellow and East China Seas', Journal of Water Process Engineering, vol. 53, pp. 103867-103867. View/Download from: Publisher's site
Liu, S, Xu, M, Zheng, M, Liu, H, Kuang, S, Chen, H & Li, X 2023, 'Abundance, diversity, and community structure of comammox cladeA in sediments of China's offshore continental shelf', Science of The Total Environment, vol. 889, pp. 164290-164290. View/Download from: Publisher's site
Liu, S-H, Ke, J-R, Ong, HC & Lin, C-W 2023, 'Isopropanol and styrene removal from aqueous solutions and simultaneous power generation using microbial fuel cells with encapsulated deoxygenated anodes', Journal of Water Process Engineering, vol. 53, pp. 103729-103729. View/Download from: Publisher's site
Liu, T, Xia, J, Ling, Z, Fu, X, Yu, S & Chen, M 2023, 'Efficient Federated Learning for AIoT Applications Using Knowledge Distillation', IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7229-7243. View/Download from: Publisher's site View description>>
As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead.
Liu, W, Cao, D, Wang, Y, Xu, Z, Li, G, Nghiem, LD & Luo, W 2023, 'Occurrence and transformation of heavy metals during swine waste treatment: A full scale study', Science of The Total Environment, vol. 895, pp. 164947-164947. View/Download from: Publisher's site
Liu, W, Song, X, Ding, X, Xia, R, Lin, X, Li, G, Nghiem, LD & Luo, W 2023, 'Antibiotic removal from swine farming wastewater by anaerobic membrane bioreactor: Role of hydraulic retention time', Journal of Membrane Science, vol. 677, pp. 121629-121629. View/Download from: Publisher's site
Liu, W, Wang, Y, Xia, R, Ding, X, Xu, Z, Li, G, Nghiem, LD & Luo, W 2023, 'Occurrence and fate of antibiotics in swine waste treatment: An industrial case', Environmental Pollution, vol. 331, pp. 121945-121945. View/Download from: Publisher's site
Liu, X, Chen, Z, Lu, S, Xu, B, Cheng, D, Wei, W, Shen, Y & Ni, B-J 2023, 'Heterogeneous photocatalytic conversion of biomass to biofuels: A review', Chemical Engineering Journal, vol. 476, pp. 146794-146794. View/Download from: Publisher's site
Liu, X, Gong, K, Duan, X, Wei, W, Wang, T, Chen, Z, Zhang, L & Ni, B-J 2023, 'Photo-Induced Bismuth Single Atoms on TiO2 for Highly Efficient Photocatalytic Defluorination of Perfluorooctanoic Acid: Ionization of the C–F Bond', ACS ES&T Engineering, vol. 3, no. 10, pp. 1626-1636. View/Download from: Publisher's site
Liu, X, Shi, K, Yan, H, Cheng, J & Wen, S 2023, 'Integral-based event-triggering switched LFC scheme for power system under deception attack', Expert Systems with Applications, vol. 234, pp. 121075-121075. View/Download from: Publisher's site
Liu, X, Tian, K, Chen, Z, Wei, W, Xu, B & Ni, B-J 2023, 'Online TG-FTIR-MS analysis of the catalytic pyrolysis of polyethylene and polyvinyl chloride microplastics', Journal of Hazardous Materials, vol. 441, pp. 129881-129881. View/Download from: Publisher's site View description>>
Microplastics (MPs) are frequently detected in urban waters, which would pose a threat to human health through the food chain. Thus, efficient approaches to the elimination of MPs are urgently required. Pyrolysis is a powerful technique for the potential treatment of MPs. The online thermogravimetry-Fourier transform infrared reflection-Mass spectrometry (TG-FTIR-MS) is applied for tracking the pyrolysis process of representative polyethylene (PE) and polyvinyl chloride (PVC) MPs in urban waters, together with or without the FeAlOx catalyst. TG could quantitatively determine the decomposition behavior and kinetics of MPs while FTIR and MS spectra would be capable of characterizing the pyrolysis products. The results revealed that FeAlOx is an excellent carbon support, and the deposited carbon can be gasified to CO at higher pyrolysis temperatures. Moreover, more aromatic compounds were generated from the pyrolysis of PE MPs with the catalyzation of FeAlOx. Large quantities of benzene were also produced in the PVC MPs pyrolysis with or without FeAlOx. Also, FeAlOx largely decreased the concentrations of chlorine-containing compounds in the liquid products of PVC MPs pyrolysis. This study provides a efficient technique for the online observation of the MPs' catalytic pyrolysis process, which would guide future upcycling of MPs into value-added products.
Liu, X, Xu, Q, Du, M, Yang, J, Lu, Q, Pan, M, Zhong, H, Wang, D & Ni, B-J 2023, 'Calcium peroxide mediated sustainable microalgal-bacterial consortium system: Role and significance of configured anaerobic fermentation', Chemical Engineering Journal, vol. 476, pp. 146807-146807. View/Download from: Publisher's site
Liu, Y & Piccardi, M 2023, 'Topic-Based Unsupervised and Supervised Dictionary Induction', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 3, pp. 1-21. View/Download from: Publisher's site View description>>
Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.
Liu, Y, Feng, Y, Wu, D, Chen, X & Gao, W 2023, 'Virtual modelling integrated phase field method for dynamic fracture analysis', International Journal of Mechanical Sciences, vol. 252, pp. 108372-108372. View/Download from: Publisher's site
Liu, Y, Guo, Q, Fu, L, Ke, Z, Xu, K, Feng, W, Tsang, IW & Lau, RWH 2023, 'Structure-Informed Shadow Removal Networks', IEEE Transactions on Image Processing, vol. 32, pp. 5823-5836. View/Download from: Publisher's site
Liu, Y, Huang, X, Zhang, X, Ngo, HH, Fu, X, Wen, H & Jin, C 2023, 'The peroxidase-like cleaning strategy for organic fouling of water treatment membranes based on MoS2 functional layers', Journal of Water Process Engineering, vol. 54, pp. 103955-103955. View/Download from: Publisher's site
Liu, Y, Huang, Y, Wang, S, Lu, W & Wu, H 2023, 'Modality Coupling for Privacy Image Classification', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4843-4853. View/Download from: Publisher's site View description>>
Privacy image classification (PIC) has attracted increasing attention as it can help people make appropriate privacy decisions when sharing images. Most recently, some pioneer research efforts have been made to utilize multimodal information for PIC, since multi-modality can provide richer information than single modality. Those research efforts on multimodal PIC are under the assumption of independently identically distribution. However, connections between different modalities commonly exist in real-world cases. Taking the modalities of scene and object as example, in the scene of 'library/indoor', the object 'book jacket' resides with high probabilities. To this end, in this paper, a novel PIC approach, called CoupledPIC, is proposed to bridge this gap by comprehensively capturing the coupling relations between different modalities. In CoupledPIC, two submodules are designed to capture explicit and implicit coupling relations between different modalities respectively. The explicit modality coupling is learned with a tensor fusion networks based submodule, via the direct interaction of features. For the implicit modality coupling, a graph convolutional networks based submodule is proposed to learn on both the initial graphs and attention guided graphs, via information aggregation on graphs. Extensive experiments on the public benchmark, PicAlert, demonstrate the effectiveness of the proposed CoupledPIC, yielding significant improvement by modeling inter-modality coupling information.
Liu, Y, Lee, T-U, Koronaki, A, Pietroni, N & Xie, YM 2023, 'Reducing the number of different nodes in space frame structures through clustering and optimization', Engineering Structures, vol. 284, pp. 116016-116016. View/Download from: Publisher's site
Liu, Y, Wen, S, Wang, F, Zuo, C, Chen, C, Zhou, J & Jin, D 2023, 'Population Control of Upconversion Energy Transfer for Stimulation Emission Depletion Nanoscopy', Advanced Science, vol. 10, no. 20. View/Download from: Publisher's site View description>>
AbstractUpconverting stimulated emission depletion microscopy (U‐STED) is emerging as an effective approach for super‐resolution imaging due to its significantly low depletion power and its ability to surpass the limitations of the square‐root law and achieve higher resolution. Though the compelling performance, a trade‐off between the spatial resolution and imaging quality in U‐STED has been recognized in restricting the usability due to the low excitation power drove high depletion efficiency. Moreover, it is a burden to search for the right power relying on trial and error as the underpinning mechanism is unknown. Here, a method is proposed that can easily predict the ideal excitation power for high depletion efficiency with the assistance of the non‐saturate excitation based on the dynamic cross‐relaxation (CR) energy transfer of upconversion nanoparticles. This allows the authors to employ the rate equation model to simulate the populations of each relevant energy state of lanthanides and predict the ideal excitation power for high depletion efficiency. The authors demonstrate that the resolution of STED with the assistance of nonsaturated confocal super‐resolution results can easily achieve the highest resolution of sub‐40 nm, 1/24th of the excitation wavelengths. The finding on the CR effect provides opportunities for population control in realizing low‐power high‐resolution nanoscopy.
Liu, Y, Zhang, W, Zhang, X, Yang, L, Huang, Z, Fang, F, Sun, W, Gao, M & Pan, H 2023, 'Nanostructured light metal hydride: Fabrication strategies and hydrogen storage performance', Renewable and Sustainable Energy Reviews, vol. 184, pp. 113560-113560. View/Download from: Publisher's site View description>>
Hydrogen can play an important role in the development of a sustainable energy system. However, storing hydrogen in a safe, efficient and economical manner remains a huge challenge. Light metal hydrides have attracted considerable attention for hydrogen storage owing to their high gravimetric and volumetric hydrogen densities. However, the strong covalent and/or ionic bonds between metal atoms and hydrogen result in slow kinetics, poor reversibility, and temperatures too high for dehydrogenation, hence delaying their practical large–scale applications. Considerable efforts have been toward tailoring the thermodynamic and kinetic properties of light metal hydride–based hydrogen storage materials for performance improvement, with the fabrication of nanoscale particles being a key and effective strategy. This review covers the preparation methods and hydrogen storage performance of nanostructured light metal hydrides. The physical and chemical properties and hydrogen storage behaviors of reversible light metal hydrides are first summarized, including MgH2, borohydrides, aluminum hydrides, amide–hydride systems, and hydride composites. The second section focuses on the research progress in nanostructuring for enhancing the reversible hydrogen storage properties of these hydrides. Finally, the main challenges and the future research prospects are discussed. The combination of nanostructuring and nanocatalysis can significantly enhance the performance of these hydrides and make them practical hydrogen carriers.
Liu, Y, Zhang, X, Xu, Y, Liu, Q, Ngo, HH & Cao, W 2023, 'Transport behaviors of biochar particles in saturated porous media under DC electric field', Science of The Total Environment, vol. 856, pp. 159084-159084. View/Download from: Publisher's site
Liu, Y, Zhang, X, Zhao, Y, He, Y, Yu, S & Zhu, K 2023, 'Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3889-3903. View/Download from: Publisher's site View description>>
Due to the limited resources and data privacy issue, last decade witnesses the fast development of Distributed Machine Learning (DML) at network edges. Among all the existing DML paradigms, Federated Learning (FL) would be a promising one, since in FL, each client trains its local model without sharing the raw data with others. A community of clients with the same interest can join together to derive a high-performance model by periodically synchronizing the parameters of their local models under the help of a coordination server. However, FL will encounter the straggler problem at network edges, and hence the synchronization among clients becomes inefficient. It slows down the convergence speed of learning process. To alleviate the straggler problem, we propose a method named Chronos that accelerates FL with training volume tuning in this paper. More specifically, Chronos is a resource aware method that adaptively adjusts the amount of data used by each client for training (i.e. training volume) in each iteration in order to eliminate the synchronization waiting time caused by the heterogeneous and dynamical computing and communication resources. In addition, we theoretically analyze the convergence of Chronos in a non-convex setting and utilize the results for the algorithm design of Chronos in return to guarantee the convergence. Extensive experiments show that compared with the benchmark algorithms (i.e BSP and SSP), Chronos significantly improves convergence speed by up to 6.4×.
Liu, Z, Sun, G, Chen, Z, Ma, Y, Qiu, K, Li, M & Ni, B-J 2023, 'Anchoring Cu-N active sites on functionalized polyacrylonitrile fibers for highly selective H2S/CO2 separation', Journal of Hazardous Materials, vol. 450, pp. 131084-131084. View/Download from: Publisher's site View description>>
As an essential part of clean energy, natural gas is often mixed with varying degrees of H2S and CO2, which poses a serious environmental hazard and reduces the fuel's calorific value. However, technology for selective H2S removal from CO2-containing gas streams is still not fully established. Herein, we synthesized functional polyacrylonitrile fibers with Cu-N coordination structure (PANFEDA-Cu) by an amination-ligand reaction. The results showed that PANFEDA-Cu exhibited a remarkable adsorption capacity (143 mg/g) for H2S at ambient temperature, even in the presence of water vapor, and showed a good separation of H2S/CO2. X-ray absorption spectroscopy results confirmed the Cu-N active sites in as-prepared PANFEDA-Cu and the formed S-Cu-N coordination structures after H2S adsorption. The active Cu-N sites on the fiber surface and the strong interaction between highly reactive Cu atoms and S are the main reasons for the selective removal of H2S. Additionally, a possible mechanism for the selective adsorption/removal of H2S is proposed based on experimental and characterization results. This work will pave the way for the design of highly efficient and low-cost materials for gas separation.
Liu, Z, Yin, X, Ni, B, Chen, X, Xie, F, Guo, Z, Li, D, Liu, W, Yue, X & Zhou, A 2023, 'Synchronous vivianite and hydrogen recovery from waste activated sludge fermentation liquid via electro-fermentation mediated by iron anode', Chemical Engineering Journal, vol. 474, pp. 145442-145442. View/Download from: Publisher's site
Liyanaarachchi, H, Thambiliyagodage, C, Lokuge, H & Vigneswaran, S 2023, 'Kinetics and Thermodynamics Study of Methylene Blue Adsorption to Sucrose- and Urea-Derived Nitrogen-Enriched, Hierarchically Porous Carbon Activated by KOH and H3PO4', ACS Omega, vol. 8, no. 18, pp. 16158-16173. View/Download from: Publisher's site
Lo, Y-C, Blamires, SJ, Liao, C-P & Tso, I-M 2023, 'Nocturnal and diurnal predator and prey interactions with crab spider color polymorphs', Behavioral Ecology and Sociobiology, vol. 77, no. 2. View/Download from: Publisher's site
Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2023, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment, vol. 4, no. 4, pp. 377-385. View/Download from: Publisher's site
Logan, J, Kennedy, PJ & Catchpoole, D 2023, 'A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future', Scientific Data, vol. 10, no. 1, p. 595. View/Download from: Publisher's site View description>>
AbstractThe increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.
Loh, HW, Ooi, CP, Oh, SL, Barua, PD, Tan, YR, Acharya, UR & Fung, DSS 2023, 'ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique', Cognitive Neurodynamics. View/Download from: Publisher's site View description>>
AbstractIn this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD. Hence, this study proposes an electroencephalogram (EEG)-based deep learning system known as ADHD/CD-NET that is capable of objectively distinguishing ADHD, ADHD + CD, and CD. The 12-channel EEG signals were first segmented and converted into channel-wise continuous wavelet transform (CWT) correlation matrices. The resulting matrices were then used to train the convolutional neural network (CNN) model, and the model’s performance was evaluated using 10-fold cross-validation. Gradient-weighted class activation mapping (Grad-CAM) was also used to provide explanations for the prediction result made by the ‘black box’ CNN model. Internal private dataset (45 ADHD, 62 ADHD + CD and 16 CD) and external public dataset (61 ADHD and 60 healthy controls) were used to evaluate ADHD/CD-NET. As a result, ADHD/CD-NET achieved classification accuracy, sensitivity, specificity, and precision of 93.70%, 90.83%, 95.35% and 91.85% for the internal evaluation, and 98.19%, 98.36%, 98.03% and 98.06% for the external evaluation. Grad-CAM also identified significant channels that contributed to the diagnosis outcome. Therefore, ADHD/CD-NET can perform temporal localization and choose significant EEG channels for diagnosis, thus providing objective analysis for mental health professionals and clinicians to consider when making a diagnosis.
Loh, HW, Ooi, CP, Oh, SL, Barua, PD, Tan, YR, Molinari, F, March, S, Acharya, UR & Fung, DSS 2023, 'Deep neural network technique for automated detection of ADHD and CD using ECG signal', Computer Methods and Programs in Biomedicine, vol. 241, pp. 107775-107775. View/Download from: Publisher's site
Long, G, Xie, M, Shen, T, Zhou, T, Wang, X & Jiang, J 2023, 'Multi-center federated learning: clients clustering for better personalization', World Wide Web, vol. 26, no. 1, pp. 481-500. View/Download from: Publisher's site View description>>
AbstractPersonalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: https://github.com/mingxuts/multi-center-fed-learning).
Long, H, Ci, J, Guo, Z, Wen, S & Huang, T 2023, 'Synchronization of coupled switched neural networks subject to hybrid stochastic disturbances', Neural Networks, vol. 166, pp. 459-470. View/Download from: Publisher's site
Long, S, Yang, J, Hao, Z, Shi, Z, Liu, X, Xu, Q, Wang, Y, Wang, D & Ni, B-J 2023, 'Multiple roles of humic substances in anaerobic digestion systems: A review', Journal of Cleaner Production, vol. 418, pp. 138066-138066. View/Download from: Publisher's site
Lu, J, Gama, J, Yao, X & Minku, L 2023, 'Guest Editorial: Special Issue on Stream Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6683-6685. View/Download from: Publisher's site
Lu, J, Guo, Z, Li, M, He, M, Zhen, J, Ni, B-J & Zhang, J 2023, 'Manganese ore enhanced polycyclic aromatic hydrocarbons removal in constructed wetlands: Insights into the key removal mechanism and main driving factor', Chemical Engineering Journal, vol. 467, pp. 143430-143430. View/Download from: Publisher's site
Lu, K, Yang, K, Sun, H, Zhang, Q, Zheng, Q, Xu, K, Chen, J & Zhou, X 2023, 'SympGAN: A systematic knowledge integration system for symptom–gene associations network', Knowledge-Based Systems, vol. 276, pp. 110752-110752. View/Download from: Publisher's site
Lu, Q, Zhu, L, Xu, X, Whittle, J, Zowghi, D & Jacquet, A 2023, 'Operationalizing Responsible AI at Scale: CSIRO Data61's Pattern-Oriented Responsible AI Engineering Approach', Communications of the ACM, vol. 66, no. 7, pp. 64-66. View/Download from: Publisher's site
Lu, S & Oberst, S 2023, 'Recurrence-based reconstruction of dynamic pricing attractors', Nonlinear Dynamics, vol. 111, no. 16, pp. 15263-15278. View/Download from: Publisher's site View description>>
AbstractDynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems.
Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'Degradation Mode Knowledge Transfer Method for LFP Batteries', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1142-1152. View/Download from: Publisher's site
Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning', IEEE Transactions on Transportation Electrification, vol. 9, no. 3, pp. 4692-4703. View/Download from: Publisher's site
Lu, X, Xiao, L, Li, P, Ji, X, Xu, C, Yu, S & Zhuang, W 2023, 'Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 425-466. View/Download from: Publisher's site
Lu, Y, Xiao, W & Lu, DD-C 2023, 'Improved Voltage Regulation of PV System With Current-Sensorless Active Damping Technique', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 60-69. View/Download from: Publisher's site
Lu, Y, Yu, H, Ni, W & Song, L 2023, '3D real-time human reconstruction with a single RGBD camera', Applied Intelligence, vol. 53, no. 8, pp. 8735-8745. View/Download from: Publisher's site
Luo, L, Li, B, Fan, X, Wang, Y, Koprinska, I & Chen, F 2023, 'Dynamic customer segmentation via hierarchical fragmentation-coagulation processes', Machine Learning, vol. 112, no. 1, pp. 281-310. View/Download from: Publisher's site View description>>
Understanding customer behavior is necessary to develop efficient marketing strategies or launch tailored programs with social value for the public. Customer segmentation is a critical task for understanding diverse and dynamic customer behavior. However, as the popularity of different products varies, building dynamic customer behavior models for products with few customers may overfit the data. In this paper, we propose a new Bayesian nonparametric model for dynamic customer segmentation—Hierarchical Fragmentation-Coagulation Processes (HFCP), which allows sharing behavior patterns across multiple products. We conduct comprehensive empirical evaluations using two real-world purchase datasets. Our results show that HFCP can: (i) determine the number of groups required to model diverse customer behavior automatically; (ii) capture the changes such as split and merge of customer groups over time; (iii) discover behavior patterns shared among products and identify products with similar or different purchase behavior impacted by promotion, brand choice and change of seasons; and (iv) overcome overfitting problems and outperform previous customer segmentation models on estimating behavior for unseen customers. Hence, HFCP is a flexible and accurate segmentation model that can be used by stakeholders to understand dynamic customer behavior and compare the purchase behavior for different products.
Luo, L, Yang, C, Jiang, X, Guo, W, Ngo, HH & Wang, XC 2023, 'Impacts of fulvic acid and Cr(VI) on metabolism and chromium removal pathways of green microalgae', Journal of Hazardous Materials, vol. 459, pp. 132171-132171. View/Download from: Publisher's site
Luo, T, Dai, X, Chen, Z, Wu, L, Wei, W, Xu, Q & Ni, B-J 2023, 'Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow', Water Research, vol. 228, no. Pt A, pp. 119356-119356. View/Download from: Publisher's site View description>>
Both microplastics (MPs) and antibiotic resistance genes (ARGs) are intensively detected in waste activated sludge (WAS). However, the distinctive impacts of different MPs on ARGs emergence, dissemination, and its potential mechanisms remain unclear. In this study, long-term semi-continuous digesters were performed to examine the profiles of ARGs and antibiotic-resistant bacteria (ARB) in response to two different typical MPs (polyethylene (PE) and polyvinyl chloride (PVC)) in anaerobic sludge digestion. Metagenomic results show that PE- and PVC-MPs increase ARGs abundance by 14.8% and 23.6% in digester, respectively. ARB are also enriched by PE- and PVC-MPs, Acinetobacter sp. and Salmonella sp. are the dominant ARB. Further exploration reveals that PVC-MPs stimulates the acquisition of ARGs by human pathogen bacteria (HPB) and functional microorganisms (FMs), but PE-MPs doesn't. Network analysis shows that more ARGs tend to co-occur with HBP and FMs after MPs exposure, and more importantly, new bacteria are observed to acquire ARGs possibly via horizontal gene flow (HGF) in MPs-stressed digester. The genes involved in the HGF process, including reactive oxygen species (ROS) production, cell membrane permeability, extracellular polymeric substances (EPS) secretion, and ATP synthesis, are also enhanced by MPs, thereby attributing to the promoted ARGs dissemination. These findings offer advanced insights into the distinctive contribution of MPs to fate, host, dissemination of ARGs in anaerobic sludge digestion.
Luo, T, Dai, X, Wei, W, Xu, Q & Ni, B-J 2023, 'Microplastics Enhance the Prevalence of Antibiotic Resistance Genes in Anaerobic Sludge Digestion by Enriching Antibiotic-Resistant Bacteria in Surface Biofilm and Facilitating the Vertical and Horizontal Gene Transfer', Environmental Science & Technology, vol. 57, no. 39, pp. 14611-14621. View/Download from: Publisher's site
Luo, T, Wei, W & Ni, B-J 2023, 'Reply for comment on “Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow [Water Research 228 (2023), 119356]”', Water Research, vol. 236, pp. 119928-119928. View/Download from: Publisher's site
Luong, NT & Hoang, D 2023, 'BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks', International Journal of Information Security, vol. 22, no. 6, pp. 1547-1566. View/Download from: Publisher's site
Luu, HM, Mai, HS, Pham, XL, Le, QA, Le, QK, Walsum, TV, Le, NH, Franklin, DR, Le, HV, Moelker, A, Duc, TC & Trung, NL 2023, 'Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration.', Comput. Methods Programs Biomed., vol. 233, pp. 107453-107453. View/Download from: Publisher's site
Lv, M, Wang, J, Niu, X & Lu, H 2023, 'A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 8271-8290. View/Download from: Publisher's site
Lv, X, Xiao, Z, Fang, J, Li, Q, Lei, F & Sun, G 2023, 'On safety design of vehicle for protection of vulnerable road users: A review', Thin-Walled Structures, vol. 182, pp. 109990-109990. View/Download from: Publisher's site
Lyu, B, Hamdi, M, Yang, Y, Cao, Y, Yan, Z, Li, K, Wen, S & Huang, T 2023, 'Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 415-425. View/Download from: Publisher's site View description>>
Graph Neural Networks (GNNs) have attracted increasing research interest for their remarkable capability to model graph-structured knowledge. However, GNNs suffer from intensive data exchange and poor data locality, which will cause critical performance and energy bottlenecks under conventional complementary metal oxide semiconductor (CMOS)-based von-Neumann computing architectures (graphics processing unit (GPU), central processing unit (CPU)) for the “Memory Wall” issue. Fortunately, memristive crossbar-based computation has emerged as one of the most promising neuromorphic computing architectures, which has been widely studied as the computing platform for convolutional neural network (CNNs), recurrent neural network (RNNs), spiking neural network (SNNs), etc. This paper proposes the deployment of spectral graph convolutional networks (GCNs) on memristive crossbars. Further, based on the structure of GCNs (extremely high sparsity and unbalanced non-zero data distribution) and the neuromorphic characteristics of memristive crossbar circuit, we propose the acceleration method that consists of Sparse Laplace Matrix Reordering and Diagonal Block Matrix Multiplication. The simulated experiment on memristor crossbars achieves 90.3% overall accuracy on the supervised learning graph dataset (QM7), and compared with the original computation, the proposed acceleration computing framework (with half-size diagonal blocks) achieves a 27.3% reduction of memristor number. Additionally, on the unsupervised learning dataset (Karate club), our method shows no loss of accuracy with half-size diagonal block mapping and reaches a 32.2% reduction of memristor number.
Lyu, B, Wen, S, Shi, K & Huang, T 2023, 'Multiobjective Reinforcement Learning-Based Neural Architecture Search for Efficient Portrait Parsing', IEEE Transactions on Cybernetics, vol. 53, no. 2, pp. 1158-1169. View/Download from: Publisher's site View description>>
This article dedicates to automatically explore efficient portrait parsing models that are easily deployed in edge computing or terminal devices. In the interest of the tradeoff between the resource cost and performance, we design the multiobjective reinforcement learning (RL)-based neural architecture search (NAS) scheme, which comprehensively balances the accuracy, parameters, FLOPs, and inference latency. Finally, under varying hyperparameter configurations, the search procedure emits a bunch of excellent objective-oriented architectures. The combination of two-stage training with precomputing and memory-resident feature maps effectively reduces the time consumption of the RL-based NAS method, so that we complete approximately 1000 search iterations in two GPU days. To accelerate the convergence of the lightweight candidate architecture, we incorporate knowledge distillation into the training of the search process. This also provides a reasonable evaluation signal to the RL controller that enables it to converge well. In the end, we conduct full training with outstanding Pareto-optimal architectures, so that a series of excellent portrait parsing models (with only approximately 0.3M parameters) is received. Furthermore, we directly transfer the architectures searched on CelebAMask-HQ (Portrait Parsing) to other portrait and face segmentation tasks. Finally, we achieve the state-of-the-art performance of 96.5% MIOU on EG1800 (portrait segmentation) and 91.6% overall F1-score on HELEN (face labeling). That is, our models significantly surpass the artificial network on the accuracy, but with lower resource consumption and higher real-time performance.
Lyu, B, Zhou, C, Gong, S, Hoang, DT & Liang, Y-C 2023, 'Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8766-8780. View/Download from: Publisher's site View description>>
In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.
Lyu, C, Shi, Y, Sun, L & Lin, C-T 2023, 'Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble', IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 728-742. View/Download from: Publisher's site View description>>
Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this paper, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm which can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. Firstly, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multi-task optimization problem and employ an evolutionary multi-task optimization algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Secondly, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.
M.B., B, Rhakho, N, Jena, SR, Yadav, S, Altaee, A, Saxena, M & Samal, AK 2023, 'Detection of PFAS via surface-enhanced Raman scattering: Challenges and future perspectives', Sustainable Chemistry for the Environment, vol. 3, pp. 100031-100031. View/Download from: Publisher's site
Ma, C, Li, J, Ding, M, Liu, B, Wei, K, Weng, J & Poor, HV 2023, 'RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 4838-4852. View/Download from: Publisher's site
Ma, C, Li, J, Wei, K, Liu, B, Ding, M, Yuan, L, Han, Z & Vincent Poor, H 2023, 'Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning', Proceedings of the IEEE, vol. 111, no. 9, pp. 1097-1132. View/Download from: Publisher's site
Ma, C, Xu, Z, Hua, B, Zhang, Y, Shi, Q, Chu, L, Braun, R & Shi, J 2023, 'Random Body Movement Interference Mitigation in Radar Breath Detection Based on L1 Norm', IEEE Sensors Letters, vol. 7, no. 12, pp. 1-4. View/Download from: Publisher's site
Ma, C-Q, Han, N, Zhang, R-Z, Lin, S-N, Chen, Z, Liu, H, Yu, S, Dong, R-Z, Wang, Y-B, Ni, B-J & Xing, L-B 2023, 'Construction of artificial light-harvesting system based on host-guest interactions of sulfobutylether-β-cyclodextrin and its application in photocatalysis', Environmental Surfaces and Interfaces, vol. 1, pp. 3-9. View/Download from: Publisher's site
Ma, JT, Xie, WY, Lei, J, Fang, LY & Li, YS 2023, 'End-to-End Spectral-Spatial Cooperative Autoencoding Density Estimation Model', Tien Tzu Hsueh Pao/Acta Electronica Sinica, vol. 51, no. 4, pp. 1006-1020. View/Download from: Publisher's site View description>>
Hyperspectral image (HSI) is widely used in anomaly detection because of its rich spectral and spatial in⁃ formation, and plays an important role in the earth observation and deep space exploration. However, the existing hyper⁃ spectral anomaly detection (HAD) methods based on density estimation have the following problems. First, there is no joint optimization of the two different objective functions of probability density estimation and feature representation, which results in the deep neural network being unable to learn more accurate probability density function and low-dimen⁃ sional representation containing inherent information of HSI; the other is the lack of adaptive fusion of high-level spatial semantic information and low-dimensional epidemic spectral information. In addition, with the development of spectral imaging technology, the volume of HSI acquired by satellites or unmanned aerial vehicles is increasing. In the context of remote sensing big data, it becomes very difficult for traditional frameworks to process HSI, posing a great challenge to HAD. In this paper, based on the above problems, an end-to-end spectral-spatial cooperative autoencoding density estima⁃ tion (E2E-SSCADE) model is proposed. The HSI spatial features are extracted based on two-dimensional convolution, and the spectral features and spatial features of hyperspectral images are combined with the low-dimensional representation and reconstruction error representation. The end-to-end optimization is carried out by combining the density estimation net⁃ work, and the anomaly detection of large hyperspectral images is realized by distributed learning. Experiments show that the proposed E2E-SSCADE can excavate the low-dimensional representation of HSI intrinsic information from three per⁃ spectives of spectral vector, spatial dimension and reconstructed space, and construct a more accurate background model. With distributed training, fast and accurate anomaly detection of hyp...
Ma, M, Tam, VW, Le, KN, Butera, A, Li, W & Wang, X 2023, 'COMPARATIVE ANALYSIS ON INTERNATIONAL CONSTRUCTION AND DEMOLITION WASTE MANAGEMENT POLICIES AND LAWS FOR POLICY MAKERS IN CHINA', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 29, no. 2, pp. 107-130. View/Download from: Publisher's site View description>>
In the current age of enhanced environmental awareness, transformation to sustainable management in the construction sector is needed. China currently produces the largest amount of construction and demolition (C&D) waste around the world, but the average recovery rate of the waste was only about 5% in 2017. In order to investigate problems in current C&D waste management in China, a cross-national comparative analysis is conducted among China and seven selected countries (Japan, South Korea, Germany, Italy, Austria, the Netherlands and the United Kingdom), to compare legal texts of national policies and laws which relate to C&D waste management and are currently being used. Through the comparison, problems in management of C&D waste in China are investigated. The problems could be concluded to: (a) inadequate guidance on recycling, (b) lack of market incentives in utilising recycled materials, (c) incomplete knowledge of stakeholders’ responsibilities, (d) lack of penalty for other stakeholders, and (e) inefficient supervision system. By understanding these problems, this paper further provides recommendations to enhance the performance of C&D waste management in China.
Ma, X, Xu, H, Gao, H, Bian, M & Hussain, W 2023, 'Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2129-2139. View/Download from: Publisher's site
Ma, Y, Wu, N, Wu, K & Zhang, JA 2023, 'VAMP-Based Iterative Equalization for Index-Modulated Multicarrier FTN Signaling', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2304-2316. View/Download from: Publisher's site
Mahmoudi, A, Khezri, R, Bidram, A, Khooban, M, Aki, H, Khalilpour, K, Abdeltawab, H & Muyeen, SM 2023, 'Guest editorial: Application of cloud energy storage systems in power systems', IET Generation, Transmission & Distribution, vol. 17, no. 8, pp. 1687-1689. View/Download from: Publisher's site
Mai, C, Mojiri, A, Palanisami, S, Altaee, A, Huang, Y & Zhou, JL 2023, 'Wastewater Hydroponics for Pollutant Removal and Food Production: Principles, Progress and Future Outlook', Water, vol. 15, no. 14, pp. 2614-2614. View/Download from: Publisher's site View description>>
As the global population reaches eight billion, large quantities of wastewater (domestic, industrial, livestock) need to be treated in an efficient, green, and environmentally friendly manner. Wastewater hydroponics technology (HP) can efficiently remove various pollutants (conventional and emerging pollutants, heavy metals, and microorganisms) and create economic benefits. This paper aims to systematically review the principles, applications, and limitations of wastewater hydroponics technology in the context of pollution and nutrient removal. Unlike constructed wetlands, wastewater hydroponics has been proven to be effective in removing pollutants through small-scale in situ restoration. For instance, the average removal of COD, total nitrogen (TN), total phosphorus (TP), copper (Cu), and zinc (Zn) was more than 70%, 60%, 80%, 64.2%, and 49.5%, respectively. However, HP technology still has the disadvantages of high energy consumption, complex control parameters, and low public acceptance of using wastewater for planting crops. Therefore, further research is needed to reduce system energy consumption. In addition, hybrid technologies, such as two-stage hydroponics that use aquatic plants (algae or aquatic floating weeds) to recycle pollutant-containing wastewater nutrients for hydroponics, should be further developed.
Maidi, AM, Kalam, MA & Begum, F 2023, 'Photonic crystal fibre for blood components sensing', Sensing and Bio-Sensing Research, vol. 41, pp. 100565-100565. View/Download from: Publisher's site
Maidi, AM, Kalam, MA & Begum, F 2023, 'Unsafe food additive sensing through octagonal-core photonic crystal fibre sensor', Physica Scripta, vol. 98, no. 6, pp. 065528-065528. View/Download from: Publisher's site View description>>
AbstractTo detect food additives, a simple photonic crystal fibre design based on an octagonal hole and hollow circular cladding holes in two layers has been introduced. The numerical study of the design is conducted by simulation in the COMSOL Multiphysics software with the infiltrated test analytes: saccharin, sorbitol, and butyl acetate, operating in the wavelength variation from 1.6 to 4.0 μm. The performance of the proposed sensor is determined by analysing the principal optical parameters: effective refractive index, power fraction, relative sensitivity, confinement loss, chromatic dispersion, propagation constant, V-parameter, spot size, and beam divergence. At the optimal wavelength of 2.0 μm, the sensor design depicts high relative sensitivities of 98.06% for saccharin, 97.05% for sorbitol, 95.81% for butyl acetate, and 3.82 × 10−23 dBm−1 for saccharin, 3.44 × 10−22 dBm−1 for sorbitol, and 1.81 × 10−21 dBm−1 for butyl acetate for confinement loss, which is extremely low. Hence, the proposed food additive sensor is suitable for actual sensing applications based on these obtained results.
Understanding and quantifying the long-term deformation behaviour of granular materials under repeated loads is imperative for ensuring the longevity of railway tracks. One of the most relevant characteristics of granular materials under repeated cycles of loading and unloading is their ability to achieve a relatively stable state (shakedown) after being subjected to initial compression. The shakedown response of blended rubber–granular waste mixtures under triaxial test conditions has been investigated in past studies highlighting the influence of the rubber content, confining stress and cyclic loading amplitude. However, a clear methodology for estimating shakedown yield limits of these granular mixtures has not been discussed in detail. The current study highlights the influence of the peak shear strength of these mixtures under static loading on their shakedown response in cyclic loading conditions. It is observed that the variation of static shear strength with rubber contents and confining stresses is found to affect the shakedown response. A unified method of estimating the shakedown limit is proposed by analysing permanent axial strains with normalised cyclic stress ratio at different loading cycles. The proposed method is validated through two independent sets of drained cyclic triaxial test data on coal wash–rubber crumb mixtures and rail ballast.
Mannina, G, Ni, B-J, Makinia, J, Harmand, J, Alliet, M, Brepols, C, Ruano, MV, Robles, A, Heran, M, Gulhan, H, Rodriguez-Roda, I & Comas, J 2023, 'Biological processes modelling for MBR systems: A review of the state-of-the-art focusing on SMP and EPS', Water Research, vol. 242, pp. 120275-120275. View/Download from: Publisher's site
Mao, S, Feng, A, Zhang, S, Onggowarsito, C, Chen, Q, Su, D & Fu, Q 2023, 'Investigation of structure–property–application relationships of a hydrogel-based solar vapor generator', Journal of Materials Chemistry A, vol. 11, no. 42, pp. 23062-23070. View/Download from: Publisher's site View description>>
We correlated the hydration ability of different hydrophilic groups to the varying performance of their corresponding hydrogels in solar vapor generation (SVG), establishing the relationships between the chemical structure, hydration property, and applications.
Mao, X, Zhou, X, Fan, X, Jin, W, Xi, J, Tu, R, Naushad, M, Li, X, Liu, H & Wang, Q 2023, 'Proteomic analysis reveals mechanisms of mixed wastewater with different N/P ratios affecting the growth and biochemical characteristics of Chlorella pyrenoidosa', Bioresource Technology, vol. 381, pp. 129141-129141. View/Download from: Publisher's site
Mao, Y, Wan, Z, Dai, Y & Yu, X 2023, 'Deep Idempotent Network for Efficient Single Image Blind Deblurring', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 1, pp. 172-185. View/Download from: Publisher's site View description>>
In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multiview clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and infers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.
Mao, Z, Zhao, L, Huang, S, Jin, T, Fan, Y & Lee, AP 2023, 'Complete region of interest reconstruction by fusing multiview deformable three‐dimensional transesophageal echocardiography images', Medical Physics, vol. 50, no. 1, pp. 61-73. View/Download from: Publisher's site View description>>
AbstractBackgroundWhile three‐dimensional transesophageal echocardiography (3D TEE) has been increasingly used for assessing cardiac anatomy and function, it still suffers from a limited field of view (FoV) of the ultrasound transducer. Therefore, it is difficult to examine a complete region of interest without moving the transducer. Existing methods extend the FoV of 3D TEE images by mosaicing multiview static images, which requires synchronization between 3D TEE images and electrocardiogram (ECG) signal to avoid deformations in the images and can only get the widened image at a specific phase.PurposeThis work aims to develop a novel multiview nonrigid registration and fusion method to extend the FoV of 3D TEE images at different cardiac phases, avoiding the bias toward the specifically chosen phase.MethodsA multiview nonrigid registration and fusion method is proposed to enlarge the FoV of 3D TEE images by fusing dynamic images captured from different viewpoints sequentially. The deformation field for registering images is defined by a collection of affine transformations organized in a graph structure and is estimated by a direct (intensity‐based) method. The accuracy of the proposed method is evaluated by comparing it with two B‐spline–based methods, two Demons‐based methods, and one learning‐based method VoxelMorph. Twenty‐nine sequences of in vivo 3D TEE images captured from four patients are used for the comparative experiments. Four performance metrics including checkerboard volumes, signed distance, mean absolute distance (MAD), and Dice similarity coefficient (DSC) are used jointly to evaluate the accuracy of the results. Additionally, paired t‐tests are performed to examine the significance of the results....
Marjanovic, O, Patmore, G & Balnave, N 2023, 'Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams', Information Systems Frontiers, vol. 25, no. 4, pp. 1571-1588. View/Download from: Publisher's site View description>>
AbstractToday’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.
Martins, D, Karimi, M, Maxit, L & Kirby, R 2023, 'Non-negative intensity for a heavy fluid-loaded stiffened plate', Journal of Sound and Vibration, vol. 566, pp. 117891-117891. View/Download from: Publisher's site View description>>
Localisation of sound sources on vibrating structures is a critical part of the design in many engineering applications. In structures with stiffeners, so-called Bloch–Floquet waves are generated due to the interaction between the flexural waves in the host structure and the flexural/torsional waves of the stiffeners. It is known that the Bloch–Floquet waves have a significant contribution to the radiated sound. However, it is not understood which area of the vibrating stiffened structures contributes significantly to the radiation in the far-field. Non-negative intensity (NNI) is a powerful tool developed recently to locate the surface regions on structures that can contribute to the radiated sound power. Although NNI has been used for several distinct structures under different excitations, it has not been considered for analysing structures with stiffeners. In this work, NNI is evaluated for an infinite fluid-loaded stiffened plate subjected to a point force to localise the sources of sound and to shed light on the mechanism involved in the far-field radiation. An analytical model formulated in the wavenumber domain is presented to carry out fast calculations of the plate vibroacoustic responses and the NNI maps. A parametric study is then performed by comparing the vibroacoustic responses with the NNI maps to highlight the capability of NNI for sound source localisation. This is achieved by analysing the results for stiffened/unstiffened structures with excitation on/between the stiffeners, and at frequencies either in a passband or in a stopband. Moreover, the NNI maps are further explained and interpreted using identified Bloch–Floquet radiating bands.
Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2023, 'Corrigendum to ‘Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of technological forecasting and social change’ [Technol. Forecast. Soc. Change 165 (2021) 120487]', Technological Forecasting and Social Change, vol. 186, pp. 122157-122157. View/Download from: Publisher's site
Matin, A, Islam, MR, Wang, X, Huo, H & Xu, G 2023, 'AIoT for sustainable manufacturing: Overview, challenges, and opportunities', Internet of Things, vol. 24, pp. 100901-100901. View/Download from: Publisher's site
Mazaheri, H, Ong, HC, Masjuki, HH, Arslan, A, Chong, WT & Amini, Z 2023, 'Friction and wear characteristics of rice bran oil based biodiesel using calcium oxide catalyst derived from Chicoreus Brunneus shell', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 4, pp. 11015-11023. View/Download from: Publisher's site View description>>
McCarthy, PX, Gong, X, Braesemann, F, Stephany, F, Rizoiu, M-A & Kern, ML 2023, 'The impact of founder personalities on startup success', Scientific Reports, vol. 13, no. 1, p. 17200. View/Download from: Publisher's site View description>>
AbstractStartup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.
Medawela, S, Armaghani, DJ, Indraratna, B, Kerry Rowe, R & Thamwattana, N 2023, 'Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain', Computers and Geotechnics, vol. 161, pp. 105557-105557. View/Download from: Publisher's site
Medawela, S, Indraratna, B & Rowe, RK 2023, 'The reduction in porosity of permeable reactive barriers due to bio-geochemical clogging caused by acidic groundwater flow', Canadian Geotechnical Journal, vol. 60, no. 2, pp. 151-165. View/Download from: Publisher's site View description>>
This study demonstrates the change in porosity of permeable reactive barrier (PRB) material when it reacts with acidic flow. The laboratory column test data obtained over 9 months prove that the porosity of a granular limestone assembly decreases significantly due to bio-geochemical clogging caused by a continuous flow of acidic groundwater. The variations in pH, the pressure measurements, ion concentrations, and the results from X-ray diffraction suggest that clogging at the outlet of the column is much less than at the inlet. About 57% of the total reduction in porosity of the column is attributed to chemical clogging, while the remainder is mainly due to biological clogging. In this paper, a mathematical approach is proposed to estimate the reduction of reactive surface area based on changes in the pore volume. These proposed equations suggest that at the end of experimentation, the dissolution of calcite and bio-geochemical clogging can reduce the total surface area of limestone aggregates by more than 70%. The rigorous approach presented in this paper to determine the dominant clogging component within a granular filter at a given time is vital in estimating the longevity of a PRB and for planning its maintenance.
Mehta, M, Bui, TA, Yang, X, Aksoy, Y, Goldys, EM & Deng, W 2023, 'Lipid-Based Nanoparticles for Drug/Gene Delivery: An Overview of the Production Techniques and Difficulties Encountered in Their Industrial Development', ACS Materials Au, vol. 3, no. 6, pp. 600-619. View/Download from: Publisher's site
Mei, F, Li, JJ, Li, J, Dong, S, Li, Z & Xing, D 2023, 'Global Cluster Analysis and Network Visualization in Musculoskeletal Pain Management: A Scientometric Mapping', Orthopaedic Surgery, vol. 15, no. 1, pp. 301-314. View/Download from: Publisher's site View description>>
ObjectiveMusculoskeletal pain is the most prominent clinical manifestation of more than 150 musculoskeletal disease conditions, and its effective long‐term management poses a great challenge to healthcare systems globally. For this, it is important to understand current research progress on musculoskeletal pain management. The purpose of the present study is to provide a comprehensive insight into the current state of research and global trends in musculoskeletal pain management.MethodsPublications on musculoskeletal pain management from 1972 to 2021 were retrieved from the Science Citation Index‐Expanded (SCIE) database. Included articles were any article type related to aspects of musculoskeletal pain management, including etiology, mechanisms, epidemiology, treatment, outcomes, side effects, and patient compliance. Publication data were analyzed using bibliometric methods. The software VOSviewer was employed to perform bibliographic coupling, co‐authorship, co‐citation, and co‐occurrence analysis, and to visualize publication tendencies in musculoskeletal pain management.ResultsA total of 5475 articles were included in this study. The number of global publications on musculoskeletal pain management has escalated annually. Based on the number of publications and citations from the published literature, as well as the H‐index, the United States led global contributions in this area. The institutions making the highest contributions were the League of European Research Universities (LERU), the University of Sydney, and Harvard University. The journal BMC Musculoskeletal Disorders published the highest number of articles in this area. The published studies fall under six groups: “Prevention and rehabilitation,” “Etiology and diagnosis,...
Mei, F, Li, JJ, Lin, J, Zhou, D & Xing, D 2023, 'Constrained Condylar Prostheses for the Treatment of Charcot Arthropathy: A Case Report and Literature Review', Orthopaedic Surgery, vol. 15, no. 5, pp. 1423-1430. View/Download from: Publisher's site View description>>
BackgroundNeuroarthropathy of the knee or Charcot knee, leading to chronic joint destruction, is a rare disease that is difficult to diagnose. The treatment of this condition is difficult and controversial.Case PresentationA 74‐year‐old Asian woman has had bilateral knee pain for 22 years and deformity for 10 years, which has been aggravating for 2 months. Physical examination showed bilateral knee varus deformity greater than 15°, and −20 to 90° range of motion. X‐ray revealed bilateral varus deformity with massive free body hyperplasia. Combined with medical history as syringomyelia, the patient was diagnosed with bilateral Charcot knees and bilateral joint replacements were performed using Legacy Constrained Condylar Knee prostheses (LCCK; Zimmer, USA). The patient reported satisfactory treatment outcomes, pain relief, and improved range of motion in both knees, without postoperative complications or prosthesis loosening at 2 year after operation.ConclusionsTotal knee arthroplasty (TKA) may be considered a viable option for treating the Charcot knee. The use of constrained condylar prostheses can produce satisfactory results. Attention should be given to survival risks, complications, and other potential determining factors associated with TKA when devising a treatment strategy for the Charcot knee.
Meilianda, E, Mauluddin, S, Pradhan, B & Sugianto, S 2023, 'Decadal shoreline changes and effectiveness of coastal protection measures post-tsunami on 26 December 2004', Applied Geomatics, vol. 15, no. 3, pp. 743-758. View/Download from: Publisher's site
Meng, L, Jiang, X, Huang, J, Zeng, Z, Yu, S, Jung, T-P, Lin, C-T, Chavarriaga, R & Wu, D 2023, 'EEG-Based Brain–Computer Interfaces are Vulnerable to Backdoor Attacks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2224-2234. View/Download from: Publisher's site
Meng, X, Li, X, Charteris, A, Wang, Z, Naushad, M, Nghiem, LD, Liu, H & Wang, Q 2023, 'Impacts of site real-time adaptive control of water-sensitive urban designs on the stormwater trunk drainage system', Journal of Water Process Engineering, vol. 53, pp. 103656-103656. View/Download from: Publisher's site View description>>
Increased rainfall intensity due to climate change is expected to exacerbate flood inundation in urban areas. Water sensitive urban design (WSUD) provides a variety of benefits in stormwater quantity management, ranging from stormwater harvesting to flood mitigation. Currently, however, developed areas lack any system that can improve the management of existing stormwater harvesting facilities to increase stormwater storage capacity without enlarging the stormwater drainage system. This study modelled a new method, Site Real-Time Adaptive Control (SRAC), that combined existing stormwater harvesting infrastructure at both regional and site levels with the existing stormwater drainage system (SWDS) through a cloud computing platform to increase stormwater storage capacity and reduce runoff water to the surface. The research found that: (1) the SRAC can manage runoff water dynamically and reduce flood inundation. The proposed impact factor Mt could help designers to measure the recovery capacity between two continuous rainfall events; (2) the SRAC model could postpone the peak flow in the trunk drainage system by 8–10 min; (3) the SRAC model could remove most of the excess water during very frequent rainfall events, decreasing over 98 % excess flow in design events 1h1EY (14,650 m3) and 2h1EY (11,272 m3); (4) the SRAC model showed a 36–50 % reduction in total outfall volume in the 1 h rainfall events, a 42–50 % reduction in the 2 h rainfall events; (5) the SRAC model could increase the capacity of downstream water treatment plants and save 43 % of the stormwater trunk drainage demand.
Meng, X, Li, X, Nghiem, LD, Hatshan, MR, Lam, KL & Wang, Q 2023, 'Assessing the effectiveness of site real-time adaptive control for stormwater quality control', Journal of Water Process Engineering, vol. 56, pp. 104324-104324. View/Download from: Publisher's site
Merenda, A, Orr, SA, Liu, Y, Hernández Garcia, B, Osatiashtiani, A, Morales, G, Paniagua, M, Melero, JA, Lee, AF & Wilson, K 2023, 'Continuous flow (Sulfated) Zirconia Catalysed Cascade Conversion of Levulinic Acid to γ‐Valerolactone', ChemCatChem, vol. 15, no. 3. View/Download from: Publisher's site View description>>
Abstractγ‐Valerolactone (GVL) is a renewable and versatile platform chemical derived from sustainable carbon feedstocks. The cascade conversion of levulinic acid into GVL requires Brønsted and Lewis acid catalysed reactions. Here, a dual‐catalyst bed configuration is demonstrated that promotes synergy between Brønsted acid sites in sulfated zirconia (SZ) and Lewis acid sites in ZrO2/SBA‐15 for the liquid phase, continuous flow esterification and subsequent catalytic transfer hydrogenation (CTH) of levulinic acid to GVL. A saturated surface sulfate monolayer, possessing a high density of strong Brønsted acid sites, was optimal for levulinic acid esterification to isopropyl levulinate over SZ (>80 % conversion). A conformal ZrO2 bilayer, deposited over a SBA‐15 mesoporous silica and possessing mixed Brønsted:Lewis acidity, catalysed CTH of the levulinate ester and subsequent dealcoholisation/cyclisation to GVL (>60 % selectivity). Maximum stable productivity for the dual‐bed was 2.2 mmolGVL.gcat.h−1 at 150 °C, significantly outperforming either catalyst alone or a physical mixture of both. Flow chemistry is a versatile approach to achieve spatial control over cascade transformations involving distinct catalytically active sites.
Miao, J, Wei, Y, Wang, X & Yang, Y 2023, 'Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11297-11308. View/Download from: Publisher's site
Miao, MZ, Su, QP, Cui, Y, Bahnson, EM, Li, G, Wang, M, Yang, Y, Collins, JA, Wu, D, Gu, Q, Chubinskaya, S, Diekman, BO, Yamada, KM & Loeser, RF 2023, 'Redox-active endosomes mediate α5β1 integrin signaling and promote chondrocyte matrix metalloproteinase production in osteoarthritis', Science Signaling, vol. 16, no. 809. View/Download from: Publisher's site View description>>
Mechanical cues sensed by integrins induce cells to produce proteases to remodel the extracellular matrix. Excessive protease production occurs in many degenerative diseases, including osteoarthritis, in which articular cartilage degradation is associated with the genesis of matrix protein fragments that can activate integrins. We investigated the mechanisms by which integrin signals may promote protease production in response to matrix changes in osteoarthritis. Using a fragment of the matrix protein fibronectin (FN) to activate the α5β1 integrin in primary human chondrocytes, we found that endocytosis of the integrin and FN fragment complex drove the production of the matrix metalloproteinase MMP-13. Activation of α5β1 by the FN fragment, but not by intact FN, was accompanied by reactive oxygen species (ROS) production initially at the cell surface, then in early endosomes. These ROS-producing endosomes (called redoxosomes) contained the integrin-FN fragment complex, the ROS-producing enzyme NADPH oxidase 2 (NOX2), and SRC, a redox-regulated kinase that promotes MMP-13 production. In contrast, intact FN was endocytosed and trafficked to recycling endosomes without inducing ROS production. Articular cartilage from patients with osteoarthritis showed increased amounts of SRC and the NOX2 complex component p67 phox . Furthermore, we observed enhanced localization of SRC and p67 phox at early endosomes, suggesting that redoxosomes could transmit and sustain integrin signaling in response to matrix damage. This signaling mechanism not only amplifies the production of matrix-degrading proteases but also establishes a self-perpetuating cycle that contributes to the ongoing degradation of cartilage matrix in osteoarthritis.
Mineo, A, Cosenza, A, Ni, B-J & Mannina, G 2023, 'Enhancing the production of volatile fatty acids by potassium permanganate from wasted sewage sludge: A batch test experiment', Heliyon, vol. 9, no. 11, pp. e21957-e21957. View/Download from: Publisher's site
Minh, H-L, Sang-To, T, Khatir, S, Wahab, MA, Gandomi, AH & Cuong-Le, T 2023, 'Augmented deep neural network architecture for assessing damage severity in 3D concrete buildings under temperature fluctuations based on K-means optimization', Structures, vol. 57, pp. 105278-105278. View/Download from: Publisher's site
Mirakhorli, F, Razavi Bazaz, S, Warkiani, ME & Ralph, PJ 2023, 'Ultra-high throughput microfluidic concentrator for harvesting of Tetraselmis sp. (Chlorodendrophyceae, Chlorophyta)', Algal Research, vol. 72, pp. 103145-103145. View/Download from: Publisher's site
Mirdad, A, Hussain, FK & Hussain, OK 2023, 'A systematic literature review on pharmaceutical supply chain: research gaps and future opportunities', International Journal of Web and Grid Services, vol. 19, no. 2, pp. 233-258. View/Download from: Publisher's site
Mirzaeipoueinak, M, Mordechai, HS, Bangar, SS, Sharabi, M, Tipper, JL & Tavakoli, J 2023, 'Structure-function characterization of the transition zone in the intervertebral disc', Acta Biomaterialia, vol. 160, pp. 164-175. View/Download from: Publisher's site
Mishra, AK, Singh, O, Kumar, A, Puthal, D, Sharma, PK & Pradhan, B 2023, 'Hybrid Mode of Operation Schemes for P2P Communication to Analyze End-Point Individual Behaviour in IoT', ACM Transactions on Sensor Networks, vol. 19, no. 2, pp. 1-23. View/Download from: Publisher's site View description>>
The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of individuals using huge amounts of data collected from their activities. The behavioural data collection process from an individual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD.
Mishra, PN, Zhang, Y & Scheuermann, A 2023, 'Ventilated Well Method for Efficient Dewatering of Soft Soils: Dimensional Analysis and Validation through Numerical Modeling', Journal of Geotechnical and Geoenvironmental Engineering, vol. 149, no. 9. View/Download from: Publisher's site
Mishra, R, Ong, HC & Lin, C-W 2023, 'Progress on co-processing of biomass and plastic waste for hydrogen production', Energy Conversion and Management, vol. 284, pp. 116983-116983. View/Download from: Publisher's site
Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'Corrigendum to “New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies” [Environ. Res. 219 (2023) 115112]', Environmental Research, vol. 227, pp. 115693-115693. View/Download from: Publisher's site
Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies', Environmental Research, vol. 219, pp. 115112-115112. View/Download from: Publisher's site
Mofijur, M, Ahmed, SF, Rony, ZI, Khoo, KS, Chowdhury, AA, Kalam, MA, Le, VG, Badruddin, IA & Khan, TMY 2023, 'Screening of non-edible (second-generation) feedstocks for the production of sustainable aviation fuel', Fuel, vol. 331, pp. 125879-125879. View/Download from: Publisher's site View description>>
This paper examines the potential of suitable second-generation feedstocks for sustainable aviation fuel production, theoretically based on fatty acid-based fuel properties. The fatty acid composition of 38 s-generation feedstocks was collected from the literature. The fuel properties of these feedstocks were then calculated using empirical formula and assessed according to international fuel standards including American and European standards. The selected feedstocks were assessed and ranked using a multi-criteria decision analysis (MCDA) tool, i.e., PROMETHEE GAIA, to identify the suitability of the sources based on kinematic viscosity (KV), density (D), higher heating value (HHV), cetane number (CN), iodine value (IV), oxidation stability (OS), and cold filter plugging point (CFPP). It was found that 20 of the 38 feedstocks meet international fuel standards. The utilisation of the MCDA tool indicates that Ricinus communis is the highest-ranked feedstock for sustainable aviation fuel production, followed by the Azadirachta indica feedstock, with Sterculia feotida L. the lowest-ranked feedstock. The assessment of the properties of ranked feedstock against aviation fuel standards, including Jet A and Jet A1, reveals that the kinematic viscosity of all the feedstocks meets both these standards. However, fatty acid-based fuel properties could not satisfy the international aviation fuel standards for D, HHV, and freezing points. Further experimental work is recommended, including improvements in the processing and modification of biofuel produced from second-generation feedstocks. It is recommended that a comprehensive action plan is required to facilitate the introduction of sustainable biofuel from non-edible sources for the aviation industry, such as the adjustment of the current jet fuel standards.
Mofijur, M, Hasan, MM, Sultana, S, Kabir, Z, Djavanroodi, F, Ahmed, SF, Jahirul, MI, Badruddin, IA & Khan, TMY 2023, 'Advancements in algal membrane bioreactors: Overcoming obstacles and harnessing potential for eliminating hazardous pollutants from wastewater', Chemosphere, vol. 336, pp. 139291-139291. View/Download from: Publisher's site
Mohanty, N, Behera, BK & Ferrie, C 2023, 'Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels', IEEE Transactions on Quantum Engineering, vol. 4, pp. 1-14. View/Download from: Publisher's site
Moles, RJ, Perry, L, Naylor, JM, Center, J, Ebeling, P, Duque, G, Major, G, White, C, Yates, C, Jennings, M, Kotowicz, M, Tran, T, Bliuc, D, Si, L, Gibson, K, Basger, BJ, Bolton, P, Barnett, S, Hassett, G, Kelly, A, Bazarnik, B, Ezz, W, Luckie, K & Carter, SR 2023, 'Safer medicines To reduce falls and refractures for OsteoPorosis (#STOP): a study protocol for a randomised controlled trial of medical specialist-initiated pharmacist-led medication management reviews in primary care', BMJ Open, vol. 13, no. 8, pp. e072050-e072050. View/Download from: Publisher's site View description>>
IntroductionMinimal trauma fractures (MTFs) often occur in older patients with osteoporosis and may be precipitated by falls risk-increasing drugs. One category of falls risk-increasing drugs of concern are those with sedative/anticholinergic properties. Collaborative medication management services such as Australia’s Home Medicine Review (HMR) can reduce patients’ intake of sedative/anticholinergics and improve continuity of care. This paper describes a protocol for an randomised controlled trial to determine the efficacy of an HMR service for patients who have sustained MTF.Method and analysisEligible participants are as follows: ≥65 years of age, using ≥5 medicines including at least one falls risk-increasing drug, who have sustained an MTF and under treatment in one of eight Osteoporosis Refracture Prevention clinics in Australia. Consenting participants will be randomised to control (standard care) or intervention groups. For the intervention group, medical specialists will refer to a pharmacist for HMR focused on reducing falls risk predominately through making recommendations to reduce falls risk medicines, and adherence to antiosteoporosis medicines. Twelve months from treatment allocation, comparisons between groups will be made. The main outcome measure is participants’ cumulative exposure to sedative and anticholinergics, using the Drug Burden Index. Secondary outcomes include medication adherence, emergency department visits, hospitalisations, falls and mortality. Economic evaluation will compare the intervention strategy with standard care.Ethics and disseminationApproval was obtained via the New South Wales Research Ethics and Governance Information System (approval number: 2021/ETH12003) with site-specific approvals granted through Human Research Ethics Committees for each...
Moradi, F, Biloria, N & Prasad, M 2023, 'Analyzing the age-friendliness of the urban environment using computer vision methods', Environment and Planning B: Urban Analytics and City Science, vol. 50, no. 8, pp. 2294-2308. View/Download from: Publisher's site View description>>
The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.
Morris, A, Wilson, S, Mitchell, E, Ramia, G & Hastings, C 2023, 'International students struggling in the private rental sector in Australia prior to and during the pandemic', Housing Studies, vol. 38, no. 8, pp. 1589-1610. View/Download from: Publisher's site
Morshedi Rad, D, Hansen, WP, Zhand, S, Cranfield, C & Ebrahimi Warkiani, M 2023, 'A hybridized mechano-electroporation technique for efficient immune cell engineering', Journal of Advanced Research. View/Download from: Publisher's site
Motahari, R, Alavifar, Z, Zareh Andaryan, A, Chipulu, M & Saberi, M 2023, 'A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems', Computers & Operations Research, vol. 151, pp. 106090-106090. View/Download from: Publisher's site
Mousavi, M, Taskhiri, MS & Gandomi, AH 2023, 'Standing tree health assessment using contact–ultrasonic testing and machine learning', Computers and Electronics in Agriculture, vol. 209, pp. 107816-107816. View/Download from: Publisher's site
MS, K, Johnson, I, Ngo, H-H, Guo, W & Kumar, M 2023, 'Application of Chlorella vulgaris for nutrient removal from synthetic wastewater and MBR-treated bio-park secondary effluent: growth kinetics, effects of carbon and phosphate concentrations', Environmental Monitoring and Assessment, vol. 195, no. 3, p. 415. View/Download from: Publisher's site View description>>
Application of Chlorella vulgaris for polishing secondary effluent of a wastewater treatment (containing C, N and P) was investigated. As a first step, batch experiments were conducted in Bold's Basal Media (BBM) to quantify the effects of orthophosphates (0.1-107 mg/L), organic carbon (0-500 mg/L as acetate) and N/P ratio on the growth of Chlorella vulgaris. The results revealed that the orthophosphate concentration was found to control the removal rates of nitrates and phosphates; however, both were effectively removed (> 90%) when the initial orthophosphate concentration was 4-12 mg/L. The maximum nitrate and orthophosphate removals were observed at an N:P ratio of ~ 11. However, the specific growth rate (µ) was significantly increased (from 0.226 to 0.336 g/g/day) when the initial orthophosphate concentration was 0.1-4.3 mg/L. On the other hand, the presence of acetate had significantly improved the specific growth and specific nitrate removal rates of Chlorella vulgaris. The specific growth rate increased from 0.34 g/g/day in a purely autotrophic culture to 0.70 g/g/day in the presence of acetate. Subsequently, the Chlorella vulgaris (grown in BBM) was acclimated and grown in the membrane bioreactor (MBR)-treated real-time secondary effluent. Under the optimised conditions, 92% nitrate and 98% phosphate removals (with a growth rate of 0.192 g/g/day) were observed in the bio-park MBR effluent. Overall, the results indicate that coupling Chlorella vulgaris as a polishing treatment in existing wastewater treatment units could be beneficial for highest level of water reuse and energy recovery goals.
Muhit, IB, Masia, MJ & Stewart, MG 2023, 'Failure analysis and structural reliability of unreinforced masonry veneer walls: Influence of wall tie corrosion', Engineering Failure Analysis, vol. 151, pp. 107354-107354. View/Download from: Publisher's site
Munasinghe, N, Romeijn, T & Paul, G 2023, 'Voxel-based sensor placement for additive manufacturing applications', Journal of Intelligent Manufacturing, vol. 34, no. 2, pp. 739-751. View/Download from: Publisher's site
Muniappan, A, Jarin, T, Sabitha, R, Ghfar, AA, Fattah, IMR, Bowa, CK & Mwanza, M 2023, 'Bi-LSTM and partial mutual information selection-based forecasting groundwater salinization levels', Water Reuse, vol. 13, no. 4, pp. 525-544. View/Download from: Publisher's site View description>>
AbstractFresh-saline groundwater is distributed in a highly heterogeneous way throughout the world. Groundwater salinization is a serious environmental issue that harms ecosystems and public health in coastal regions worldwide. Because of the complexities of groundwater salinization processes and the variables that influence them, it is challenging to predict groundwater salinity concentrations precisely. It compares cutting-edge machine learning (ML) algorithms for predicting groundwater salinity and identifying contributing factors. It employs bi-directional long short-term memory (BiLSTM) to indicate groundwater salinity. The input variable selection problem has attracted attention in the time series modeling community because it has been shown that information-theoretic input variable selection algorithms provide a more accurate representation of the modeled process than linear alternatives. To generate sample combinations for training multiple BiLSTM models, PMIS-selected predictors are used, and the predicted values from various BiLSTM models are also used to calculate the degree of prediction uncertainty for groundwater levels. The findings give policymakers insights for recommending groundwater salinity remediation and management strategies in the context of excessive groundwater exploitation in coastal lowland regions. To ensure sustainable groundwater management in coastal areas, it is essential to recognize the significant impact of human-caused factors on groundwater salinization.
Munot S, S, Bray, JE, Redfern, J, Bauman, A, Marschner, S, Semsarian, C, Denniss, AR, Coggins, AR, Middleton, PM, Jennings, G, Angell, B, Kumar, S, Kovoor, P, Lai, K, Vukasovic, M, Nelson, M, Oppermann, I & Chow, CK 2023, 'Sex-related Disparity in Bystander Response and Survival Outcomes for Out-of-hospital Cardiac Arrest (OHCA) in New South Wales (NSW), Australia', Heart, Lung and Circulation, vol. 32, pp. S128-S129. View/Download from: Publisher's site
Nama, S, Saha, AK, Chakraborty, S, Gandomi, AH & Abualigah, L 2023, 'Boosting particle swarm optimization by backtracking search algorithm for optimization problems', Swarm and Evolutionary Computation, vol. 79, pp. 101304-101304. View/Download from: Publisher's site
Namisango, F, Kang, K & Rehman, J 2023, 'Examining the relationship between sociomaterial practices enacted in the organizational use of social media and the emerging role of organizational generativity', International Journal of Information Management, vol. 71, pp. 102643-102643. View/Download from: Publisher's site
Navidpour, AH, Hosseinzadeh, A, Zhou, JL & Huang, Z 2023, 'Progress in the application of surface engineering methods in immobilizing TiO2 and ZnO coatings for environmental photocatalysis', Catalysis Reviews, vol. 65, no. 3, pp. 822-873. View/Download from: Publisher's site View description>>
Photocatalysis is widely used for the degradation of organic pollutants, with TiO2 and ZnO as the best candidates with unique properties. However, agglomeration and recycling are major challenges in practical photocatalysis applications. Advanced deposition processes can provide nanotubular or hierarchical structures that are more promising than suspended particles. More importantly, higher efficiency of photoelectrocatalysis than photocatalysis for the degradation of persistent organic pollutants including perfluorooctanoic acid (PFOA) necessitates catalyst immobilization. Photoelectrocatalysis exhibited remarkably higher efficiency (56.1%) than direct photolysis (15.1%), electrocatalysis (5.0%) and photocatalysis (18.1%) for PFOA degradation. This paper aims to review the progress in the application of anodizing and thermal spraying as two major industrial surface engineering processes to bridge the gap between laboratorial and practical photocatalysis technology. Overall, thermal spraying is considered as one of the most efficient methods for the deposition of TiO2 and ZnO photocatalytic films.
Nazari, H, Shrestha, J, Naei, VY, Bazaz, SR, Sabbagh, M, Thiery, JP & Warkiani, ME 2023, 'Advances in TEER measurements of biological barriers in microphysiological systems', Biosensors and Bioelectronics, vol. 234, pp. 115355-115355. View/Download from: Publisher's site
Nejad, BJ & Alvandi, S 2023, 'On the performance of projects under uncertainty: an agent-based simulation modelling', International Journal of Project Organisation and Management, vol. 15, no. 2, pp. 129-157. View/Download from: Publisher's site
Neshat, M, Nezhad, MM, Mirjalili, S, Garcia, DA, Dahlquist, E & Gandomi, AH 2023, 'Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy', Energy, vol. 278, pp. 127701-127701. View/Download from: Publisher's site
Newsom, ET, Sadeghpour, A, Entezari, A, Vinzons, JLU, Stanford, RE, Mirkhalaf, M, Chon, D, Dunstan, CR & Zreiqat, H 2023, 'Design and evaluation of 3D-printed Sr-HT-Gahnite bioceramic for FDA regulatory submission: A Good Laboratory Practice sheep study', Acta Biomaterialia, vol. 156, pp. 214-221. View/Download from: Publisher's site
This letter presents a novel approach for spectrum sensing in cognitive satellite-terrestrial networks. The approach uses multi-agent deep reinforcement learning (DRL) and reconfigurable intelligent surface to address the problem of under-utilization of terrestrial network spectrum in remote areas. Unlike previous studies that rely only on current sensing data, this approach utilizes historical data to improve spectrum detection accuracy and post-decision state to accelerate agent learning speed. Simulation results show that it outperforms existing DRL methods in terms of faster agent learning convergence and more effective detection of primary network spectrum occupancy.
Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2023, 'Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks', IEEE Transactions on Green Communications and Networking, vol. 7, no. 4, pp. 1920-1931. View/Download from: Publisher's site View description>>
This paper presents a comprehensive analysis of the physical layer security performance of a cache-enabled satellite communication network that incorporates intelligent reflecting surfaces (IRS) in the presence of a passive eavesdropper. In the proposed system, content caches are deployed at both the ground station and the satellite, which can improve system performance by reducing latency and transmission overhead. Moreover, the use of IRS provides an additional layer of security by enabling the manipulation of the reflected signals to impede eavesdropping. Practical channel models are used to derive connection probability and secrecy probability for both the ground station-IRS-user and the satellite-IRS-user links. The obtained results are then used to evaluate the system’s secure transmission probability, which is maximized subject to the caching probabilities and transmission rate constraints. The paper presents numerical results to demonstrate the accuracy of the analysis and the effectiveness of deploying IRS and caching to support secure content delivery. The findings provide valuable insights into the potential benefits of utilizing IRS and caching technologies in satellite communication networks for improved physical layer security.
Nguyen, CC, Thai, MT, Hoang, TT, Davies, J, Phan, PT, Zhu, K, Wu, L, Brodie, MA, Tsai, D, Ha, QP, Phan, H-P, Lovell, NH & Nho Do, T 2023, 'Development of a soft robotic catheter for vascular intervention surgery', Sensors and Actuators A: Physical, vol. 357, pp. 114380-114380. View/Download from: Publisher's site
Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Pham, H-A, Dutkiewicz, E & Tuong, NH 2023, 'FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems', IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2642-2656. View/Download from: Publisher's site View description>>
In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain’s security and performance.
Nguyen, CT, Nguyen, DN, Hoang, DT, Phan, KT, Niyato, D, Pham, H-A & Dutkiewicz, E 2023, 'Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2636-2649. View/Download from: Publisher's site View description>>
Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.
Nguyen, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L & Yu, S 2023, 'Toward IoT Node Authentication Mechanism in Next Generation Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13333-13341. View/Download from: Publisher's site
Nguyen, DT, Ho-Le, TP, Pham, L, Ho-Van, VP, Hoang, TD, Tran, TS, Frost, S & Nguyen, TV 2023, 'BONEcheck: A digital tool for personalized bone health assessment', Osteoporosis and Sarcopenia, vol. 9, no. 3, pp. 79-87. View/Download from: Publisher's site
Nguyen, HAD & Ha, QP 2023, 'Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey', Robotica, vol. 41, no. 2, pp. 486-510. View/Download from: Publisher's site View description>>
AbstractThere has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.
Nguyen, HAD, Ha, QP, Duc, H, Azzi, M, Jiang, N, Barthelemy, X & Riley, M 2023, 'Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast', IEEE Access, vol. 11, pp. 35710-35725. View/Download from: Publisher's site
Nguyen, LN, Vu, MT, Vu, HP, Johir, MAH, Labeeuw, L, Ralph, PJ, Mahlia, TMI, Pandey, A, Sirohi, R & Nghiem, LD 2023, 'Microalgae-based carbon capture and utilization: A critical review on current system developments and biomass utilization', Critical Reviews in Environmental Science and Technology, vol. 53, no. 2, pp. 216-238. View/Download from: Publisher's site View description>>
Carbon capture and utilization (CCU) is an emerging technology with commercial potential to convert atmospheric carbon dioxide (CO2) into net zero or negative emission products. In microalgae-based CCU, microalgae utilize CO2 and sunlight to generate biomass for commercial applications. This paper reviews the current state of microalgal culture development for CCU and highlights its potential contribution to addressing climate change challenges. Current microalgal culture systems have not been designed for high throughput biomass growth and carbon capture. Raceways, high-rate algal ponds, and photobioreactors are the most widely used for microalgal cultivation at a large-scale. The limitations of these systems are related to microalgal growth requirements. Ponds are operated at narrow depth to ensure sufficient light distribution and thus need a large land surface. CO2 gas needs to be in a dissolved form for efficient utilization by microalgae. Innovative system designs to achieve optimized distribution of light, nutrient, and CO2 utilization for enhanced biomass production are crucial to achieve large-scale CO2 capture by microalgae. Data corroborated in this review highlights several innovative techniques to deliver CO2 effectively and enhance light illumination to microalgal cells. Submerged and internal illuminations can enhance light distribution without compromising culture volume and land requirements. CO2 delivery technique selections mainly depend on CO2 sources. The carbonation column appears to be the best option regarding efficiency, easy operation, and simple design. The downstream processes of microalgal culture (i.e. harvesting, biomass utilization, and water reuse) are important to make microalgae-based CCU a significant contribution to global carbon mitigation solutions.
Nguyen, M, Zhu, H, Sun, H, Nguyen, V, Jin, C & Lin, C-T 2023, 'An evaluation of various spatial audio rendering and presentation techniques to enhance active navigation with sensory augmentation', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A196-A196. View/Download from: Publisher's site View description>>
Active navigation is essential in everyday life and refers to the combination of cognition (spatial mapping, path planning, and decision making) and motor-sensory execution (moving and sensing environment). For people who are blind or have low-vision, auditory and tactile sensory augmentation is critical to active navigation. In assistive technologies, binaural spatial audio rendering is widely adopted. However, the most effective methods to support fluent spatial navigation are still being studied. For example, in a previous study, we demonstrated the feasibility of using spatialized earcons to support a shorelining task. In this work, we use the same shorelining task to explore various forms of spatial earcon presentation with a focus on standardization and effectiveness. We also explore the development of an intuitive auditory grammar for spatial and contextual cues. We conduct psychophysical experiments and present experimental measures such as performance time and accuracy, heart-rate variability, and the NASA task load index.
Nguyen, MK, Lin, C, Hoang, HG, Bui, XT, Ngo, HH, Le, VG & Tran, H-T 2023, 'Investigation of biochar amendments on odor reduction and their characteristics during food waste co-composting', Science of The Total Environment, vol. 865, pp. 161128-161128. View/Download from: Publisher's site
Nguyen, QD & Castel, A 2023, 'Long-term durability of underground reinforced concrete pipes in natural chloride and carbonation environments', Construction and Building Materials, vol. 394, pp. 132230-132230. View/Download from: Publisher's site
Nguyen, TAH, Bui, TH, Guo, WS & Ngo, HH 2023, 'Valorization of the aqueous phase from hydrothermal carbonization of different feedstocks: Challenges and perspectives', Chemical Engineering Journal, vol. 472, pp. 144802-144802. View/Download from: Publisher's site
Nguyen, TT & Indraratna, B 2023, 'Influence of varying water content on permanent deformation of mud-fouled ballast', Transportation Geotechnics, vol. 38, pp. 100919-100919. View/Download from: Publisher's site View description>>
The contamination of ballast by mud pumping is known to cause considerable reduction in the shear resistance as well as increased settlement of railroad foundations. However, how varying water content (w) of fouled ballast can affect this deterioration has not been properly understood. The current study thus adopts a large-scale cyclic triaxial test to examine permanent (plastic) deformation of mud-fouled ballast collected from a site with a history of mud pumping with a consideration of different water contents. In these tests, fouling content varies from 5 to 30 %, while the water content changes from 0 to 40 %. The results show that while increasing content of fines causes larger permanent settlement of ballast, varying water content of fines can influence this behaviour significantly. A salient finding of this study is the critical threshold of water content near to the liquid limit (LL) of fine soil (finer than 0.425 mm) that can cause a swift increase in ballast settlement. The results show that the peak permanent strain can increase by about 26 % compared to the dry state when w of fines reaches the LL. On the other hand, permanent strain of fouled ballast can decrease at the optimum water content of fines, if a sufficient mass of fines (>20 % by weight) is provided to reinforce the granular assembly. An empirical method is provided to estimate the ultimate settlement of mud-fouled tracks considering the moisture state that would be most beneficial in real-life applications.
Nguyen, TTH, Nguyen, XC, Nguyen, DLT, Nguyen, DD, Vo, TYB, Vo, QN, Nguyen, TD, Ly, QV, Ngo, HH, Vo, D-VN, Nguyen, TP, Kim, IT & Van Le, Q 2023, 'Converting biomass of agrowastes and invasive plant into alternative materials for water remediation', Biomass Conversion and Biorefinery, vol. 13, no. 6, pp. 5391-5406. View/Download from: Publisher's site View description>>
Three types of biomass of invasive plants and agrowastes, namely, the wattle bark of Acacia auriculiformis (BA), mimosa (BM), and coffee husks (BC), were converted into biochars through slow pyrolysis and investigated for their ability to remove dyes in water. The properties of the materials were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analysis. The BET surface area (total pore volume) of BC was 2.62 m2/g (0.007 cm3/g), far below those of BA and BM with 393.15 cm2/g (0.195 m3/g) and 285.53 cm2/g (0.153 m3/g), respectively. The optimal adsorption doses for the removal of methylene blue (MB) were found to be 2, 5, and 5 g/L for BC, BA, and BM, respectively. The suitable pH ranges for MB removal were 6–12 for BA, 7–12 for BC, and 2–10 for BM. The majority of MB (over 83%) was removed in the initial 30 min, followed by a more quasisteady state condition after the removal rate exceeded 90%. The experimental data were fitted with the kinetic models (PFO, PSO, Bangham, IDP), indicating that physicochemical adsorption, pore diffusion process, and multiple stages are the dominant mechanisms for the MB adsorption onto biochars. Finally, BA and BM showed similar adsorption efficiencies, while BC may not be favorable for use as an adsorbent due to its low surface area and low pore volume.
Ni, B-J, Thomas, KV & Kim, E-J 2023, 'Microplastics and nanoplastics in urban waters', Water Research, vol. 229, pp. 119473-119473. View/Download from: Publisher's site
Ni, Q, Ji, JC & Feng, K 2023, 'Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1301-1311. View/Download from: Publisher's site View description>>
The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.
Ni, Q, Ji, JC, Halkon, B, Feng, K & Nandi, AK 2023, 'Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics', Mechanical Systems and Signal Processing, vol. 200, pp. 110544-110544. View/Download from: Publisher's site View description>>
Various deep learning methodologies have recently been developed for machine condition monitoring recently, and they have achieved impressive success in bearing fault diagnostics. Despite the capability of effectively diagnosing bearing faults, most deep learning methods are tremendously data-dependent, which is not always available in industrial applications. In practical engineering, bearings are usually installed in rotating machinery where speed and load variations frequently occur, resulting in difficulty in collecting large training datasets under all operating conditions. Additionally, physical information is usually ignored in most deep learning algorithms, which sometimes leads to the generated results of low compliance with the physical law. To tackle these challenges, a novel Physics-Informed Residual Network (PIResNet) is proposed for learning the underlying physics that is embedded in both training and testing data, thus providing a physical consistent solution for imperfect data. In the proposed method, a physical modal-property-dominant-generated layer is adopted at first to generate the modal-property-dominant feature. Then, a domain-conversion layer is constructed to enable the feasibility of extracting the discriminative bearing fault features under varying operating speed conditions. Lastly, a parallel bi-channel residual learning architecture that can automatically extract the bearing fault signatures is meticulously established to incorporate the bearing fault characteristics. Experimental datasets under variable operating speeds and loads, and time-varying operating speeds are utilized to demonstrate the superiority of the PIResNet under non-stationary operating conditions.
Ni, Z, Zhang, JA & Liu, RP 2023, 'Waveform Optimizations Using Virtual Arrays in Broadband Radar Communications', IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 912-916. View/Download from: Publisher's site
Ni, Z, Zhang, JA, Wu, K & Liu, RP 2023, 'Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks', IEEE Transactions on Signal Processing, vol. 71, pp. 2699-2712. View/Download from: Publisher's site
Ni, Z, Zhang, JA, Wu, K, Yang, K & Liu, RP 2023, 'Receiver Design in Full-Duplex Joint Radar-Communication Systems', IEEE Transactions on Communications, vol. 71, no. 7, pp. 4234-4246. View/Download from: Publisher's site
Nichol, AJ, Hastings, C & Elder-Vass, D 2023, 'Putting philosophy to work: developing the conceptual architecture of research projects', Journal of Critical Realism, vol. 22, no. 3, pp. 364-383. View/Download from: Publisher's site
Nie, X, Chai, B, Wang, L, Liao, Q & Xu, M 2023, 'Learning enhanced features and inferring twice for fine-grained image classification', Multimedia Tools and Applications, vol. 82, no. 10, pp. 14799-14813. View/Download from: Publisher's site View description>>
AbstractFine-Grained Visual Categorization (FGVC) aims to distinguish between extremely similar subordinate-level categories within the same basic-level category. Existing research has proven the great importance of the discriminative features in FGVC but ignored the contributions for correct classification from other features, and the extracted features always contain more information about the obvious regions but less about subtle regions. In this paper, firstly, a novel module named forcing module is proposed to force the network to extract more diverse features for FGVC, which generates a suppression mask based on the class activation maps to suppress the most distinguishable regions, so as to force the network to extract other secondary distinguishable features as the final features. The forcing module consists of the original branch and the forcing branch. The original branch focuses on the primary discriminative regions while the forcing branch focuses on secondary discriminative regions. Secondly, in order to solve the problem that information of small-scale distinguishable features is lost seriously after multi-layer down-sampling, according to the class activation maps of the first prediction, the object is cropped and scaled as the second input. To reduce the prediction error, the first and second prediction probabilities are fused as the final prediction result. Experimental results indicate that the proposed method not only outperforms the baseline model by a large margin (3.7%, 5.9%, 3.1% respectively) on CUB-200-2011, Stanford-Cars, and FGVC-Aircraft, but also achieves state-of-the-art performance on FGVC-Aircraft.
Nie, X, Liu, L, He, L, Zhao, L, Lu, H, Lou, S, Xiong, R & Wang, Y 2023, 'Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3270-3281. View/Download from: Publisher's site
Nimbalkar, S & Basack, S 2023, 'Pile group in clay under cyclic lateral loading with emphasis on bending moment: Numerical modelling', Marine Georesources & Geotechnology, vol. 41, no. 3, pp. 269-284. View/Download from: Publisher's site View description>>
Pile foundations supporting major structures are often founded in soft compressible clays. Apart from usual super-structural loading, these piles are subjected to cyclic lateral loads originating from actions of waves, ship impacts, winds or moving vehicles. Such repetitive loading induces stress reversal in adjacent soft clay initiating progressive degradation in soil strength and stiffness. This not only deteriorates the pile capacity with unacceptable displacements, the bending moments also increase. Although past studies investigated the response of single pile under lateral cyclic loading, a detailed study on pile group in clay under cyclic lateral loading with emphasis on bending moment is of immense practical interest. This paper focuses on detailed study of the response of pile group in clay under cyclic lateral loading, with emphasis on bending moment, through numerical modelling via a three-dimensional dynamic finite element (FE) approach and simplified boundary element modelling (BEM). Comparisons of computed results with available test data imply that the results obtained by 3 D dynamic FE model are better than the BEM. Extensive parametric studies with field data indicate that pile bending moment has been significantly influenced by cyclic loading parameters (number of cycles, frequency and amplitude). Relevant conclusions are drawn from the entire study.
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions', Knowledge-Based Systems, vol. 271, pp. 110552-110552. View/Download from: Publisher's site
Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'Interpreting the antecedents of a predicted output by capturing the interdependencies among the system features and their evolution over time', Engineering Applications of Artificial Intelligence, vol. 117, pp. 105596-105596. View/Download from: Publisher's site
Nirbhav, Malik, A, Maheshwar, Jan, T & Prasad, M 2023, 'Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods', Journal of the Indian Society of Remote Sensing, vol. 51, no. 4, pp. 771-786. View/Download from: Publisher's site View description>>
Landslide hazards give rise to considerable demolition and losses to lives in hilly areas. To reduce the destruction in these endangered regions, the prediction of landslide incidents with good accuracy remains a key challenge. Over the years, machine learning models have been used to increase the accuracy and precision of landslide predictions. These machine learning models are sensitive to the data on which they are applied. Feature selection is a crucial task in applying machine learning as meticulously selected features can significantly improve the performance of the machine learning model. These selected features decrease the learning time of the model and increase comprehensibility. In this paper, we have considered three feature selection methods namely chi-squared, extra tree classifier and heat map. The paper substantiates that feature selection can significantly increase the performance of the model. The study was carried out on the landslide data of the Kullu to Rohtang Pass transport corridor in Himachal Pradesh, India. The classification score and receiver operating characteristics (ROC) curves were used to evaluate the model performance. Results exhibited that eliminating one or more features using different feature selection methods increased the comprehensibility of the model by reducing the dimensionality of the dataset. The model achieved an accuracy of 90.74% and an area under the ROC curve (AUROC) value of 0.979. Furthermore, it can be deduced that with a reduced number of features model learns faster without affecting the actual result.
Nirbhav, Malik, A, Maheshwar, Prasad, M, Saini, A & Long, NT 2023, 'A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, India', Environmental Earth Sciences, vol. 82, no. 7, p. 167. View/Download from: Publisher's site View description>>
Landslide susceptibility prediction can be considered a crucial step in landslide risk assessment. This prediction helps in planning the land use properly. The primary aim of the study is to investigate different machine learning methods and develop anatomy to train and validate the landslide susceptibility prediction models with the help of various statistical techniques. The Kullu–Rohtang pass transport corridor has been selected as the study area. Initially, a landslide inventory was prepared using different sources and nine landslide triggering features were used for further study. All landslide locations in the study area were arbitrarily divided into a ratio of 67:33 to train and test various landslide susceptibility prediction models. The best-triggering features were chosen with the help of the information gain ratio (IGR) defining the predictive capability of different triggering features. Afterwards, five landslide susceptibility prediction models were constructed using a decision tree, K-nearest neighbour (KNN), Gaussian Naïve Bayes, support vector machine (SVM) and multilayer perceptron (MLP). The comparison and validation study of different resulting models was done by applying the receiver operating characteristic (ROC) curve, the kappa index and other statistical methods. Results show that the different models have the outstanding predictive capability with the decision tree model (100%), the Gaussian Naïve Bayes model (100%), the SVM model (100%), and the MLP model (100%) and the KNN model (99.9%). The result indicates statistical differences among various models. The validation results demonstrate the perfect agreement between the expected and predicted landslides along the transport corridor.
Niu, K, Lu, G, Peng, X, Zhou, Y, Zeng, J & Zhang, K 2023, 'CNN autoencoders and LSTM-based reduced order model for student dropout prediction', Neural Computing and Applications, vol. 35, no. 30, pp. 22341-22357. View/Download from: Publisher's site View description>>
In recent years, Massive Open Online Courses (MOOCs) have become the main online learning method for students all over the world, but their development has been affected by the high dropout rate for a long time. Therefore, dropout prediction is a vital task for early teaching intervention and user retention. The students’ learning records are stored in MOOCs, which contain high-dimensional time series features. However, these features are hard to process, and the nonlinear relationship between the features is difficult to learn. These limitations have become obstacles to improve the performance in dropout prediction. In this paper, we propose a new neural dimension-reduced dropout prediction model based on neural network model to address the limitations. The proposed model, called CNNAE-LSTM, is constructed by convolutional neural network autoencoder (CNNAE) and long short-term memory neural network (LSTM). Specifically, CNNAE-LSTM compresses the students’ learning features into a low-dimensional latent space for reconstruction through CNNAE, then projects the latent space, retains the representative features in the learning records, and finally minimizes the reconstruction error to obtain the nonlinear relationship between features and dropout. The introduced LSTM neural network can obtain the time evolution of its latent vector. Our experiments on the KDD CUP 2015 dataset and the real-world dataset XuetangX demonstrate that the proposed model exhibits better predictive performance compared to the state-of-the-art baseline methods.
Niu, K, Pei, S, Peng, X, Zeng, J & Zhang, K 2023, 'Intensive Care Unit readmission prediction with correlation enhanced multi-task learning', Computers and Electrical Engineering, vol. 110, pp. 108780-108780. View/Download from: Publisher's site View description>>
Prediction for Intensive Care Unit (ICU) readmission is conducive to assisting doctors in treatment-related decision making and reducing the risk of relapse after discharge. Recently, existing ICU readmission prediction approaches train each sub-task independently, which prevents the models from using complementary information between these sub-tasks. In this paper, we propose correlation enhanced Multi-Task learning with Pearson and RNN-based Neural Ordinary Differential Equations Model (MP-ROM). In order to enhance the learning of general features and avoid the local optima in single-task training, we construct the Shared-Bottom structure of multi-task learning, which enables multiple tasks to share model structure and parameters. Besides, we add the task correlation score calculated by Pearson correlation calculation, enhancing the association between sub-tasks. Experiment results on MIMIC-III dataset show that MP-ROM achieves the highest average precision and demonstrates that task association enhanced can further improve the predictive performance of ICU readmission risk.
Niwa, K, Ueda, N, Sawada, H, Fujino, A, Takeda, S, Zhang, G & Kleijn, WB 2023, 'CoordiNet: Constrained Dynamics Learning for State Coordination Over Graph', IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 242-257. View/Download from: Publisher's site
Nosouhi, MR, Yu, S, Sood, K, Grobler, M, Jurdak, R, Dorri, A & Shen, S 2023, 'UCoin: An Efficient Privacy Preserving Scheme for Cryptocurrencies', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 242-255. View/Download from: Publisher's site View description>>
In cryptocurrencies, privacy of users is preserved using pseudonymity. However, it has been shown that pseudonymity does not result in anonymity if a users transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted thirdparty entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this paper, we propose Unlinkable Coin (UCoin), a secure mixbased approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDCnet, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDCnet protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted thirdparty, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.
Nsiah-Baafi, E, Andrews, A, Ramakokovhu, MM & Olubambi, PA 2023, 'Field-Assisted Sintering on Microstructural Evolution and Properties of TiAl Intermetallic Alloys', Transactions of the Indian Institute of Metals, vol. 76, no. 10, pp. 2625-2633. View/Download from: Publisher's site
O’Brien, TE, Anselmetti, G, Gkritsis, F, Elfving, VE, Polla, S, Huggins, WJ, Oumarou, O, Kechedzhi, K, Abanin, D, Acharya, R, Aleiner, I, Allen, R, Andersen, TI, Anderson, K, Ansmann, M, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, B, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Campero, J, Chen, Z, Chiaro, B, Chik, D, Cogan, J, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Demura, S, Drozdov, I, Dunsworth, A, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Ferreira, VS, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Gosula, R, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Hansen, M, Harrigan, MP, Harrington, SD, Heu, P, Hoffmann, MR, Hong, S, Huang, T, Huff, A, Ioffe, LB, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, K, Lester, BJ, Lill, AT, Liu, W, Livingston, WP, Locharla, A, Malone, FD, Mandrà, S, Martin, O, Martin, S, McClean, JR, McCourt, T, McEwen, M, Mi, X, Mieszala, A, Miao, KC, Mohseni, M, Montazeri, S, Morvan, A, Movassagh, R, Mruczkiewicz, W, Naaman, O, Neeley, M, Neill, C, Nersisyan, A, Newman, M, Ng, JH, Nguyen, A, Nguyen, M, Niu, MY, Omonije, S, Opremcak, A, Petukhov, A, Potter, R, Pryadko, LP, Quintana, C, Rocque, C, Roushan, P, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shutty, N, Shvarts, V, Skruzny, J, Smith, WC, Somma, RD, Sterling, G, Strain, D, Szalay, M, Thor, D, Torres, A, Vidal, G, Villalonga, B, Vollgraff Heidweiller, C, White, T, Woo, BWK, Xing, C, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zalcman, A, Zhang, Y, Zhu, N, Zobrist, N, Bacon, D, Boixo, S, Chen, Y, Hilton, J, Kelly, J, Lucero, E, Megrant, A, Neven, H, Smelyanskiy, V, Gogolin, C, Babbush, R & Rubin, NC 2023, 'Purification-based quantum error mitigation of pair-correlated electron simulations', Nature Physics, vol. 19, no. 12, pp. 1787-1792. View/Download from: Publisher's site View description>>
AbstractAn important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Before fault-tolerant quantum computing, robust error-mitigation strategies were necessary to continue this growth. Here, we validate recently introduced error-mitigation strategies that exploit the expectation that the ideal output of a quantum algorithm would be a pure state. We consider the task of simulating electron systems in the seniority-zero subspace where all electrons are paired with their opposite spin. This affords a computational stepping stone to a fully correlated model. We compare the performance of error mitigations on the basis of doubling quantum resources in time or in space on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques such as postselection. We study how the gain from error mitigation scales with the system size and observe a polynomial suppression of error with increased resources. Extrapolation of our results indicates that substantial hardware improvements will be required for classically intractable variational chemistry simulations.
Oberst, S & Sepehrirahnama, S 2023, 'A case study on generative learning approaches in a studio and flipped class-room setting for increased learning outcomes', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A59-A59. View/Download from: Publisher's site View description>>
Teaching and learning during Covid-19 have been strongly affected by lockdowns, isolated online learning, and the sudden requirement to alternatively assess students while considering the effect internet-based information sources. Here, we present outcomes on the learning outcome of students returning from distance learning into the face-to-face mode, studying the subject “Embedded Mechatronic Systems” employing increasingly methods of Generative Learning Theory (GLT) in a flipped classroom environment, using studio and project-based learning approaches. By introducing a group project component, the formerly disconnected laboratory components become strongly connected with students being exposed to practical aspects and teamwork, generating reflected reports and videos of their practical work. To overcome the effects of Covi-d19, tighter assessments, and in-person engagements is emphasised. Viva-voces have been introduced and AI invigilated final exams have been altered to in-class room quizzes, while monitoring the cohort’s performance over 3 sessions. Our data indicate that face-to-face learning and hands-on practice with peers using self-testing and self-explaining strategies enacts higher outcomes, opposed to remote modes of teaching. Our results exemplify on how to move back into face-to-face teaching with future steps to increase learning outcomes using the flipped classroom, GLT, and a studio setting being discussed.
Ojelade, OA, Zaman, SF & Ni, B-J 2023, 'Green ammonia production technologies: A review of practical progress', Journal of Environmental Management, vol. 342, pp. 118348-118348. View/Download from: Publisher's site
Ortega, JS, Corrales-Orovio, R, Ralph, P, Egaña, JT & Gentile, C 2023, 'Photosynthetic microorganisms for the oxygenation of advanced 3D bioprinted tissues', Acta Biomaterialia, vol. 165, pp. 180-196. View/Download from: Publisher's site View description>>
3D bioprinting technology has emerged as a tool that promises to revolutionize the biomedical field, including tissue engineering and regeneration. Despite major technological advancements, several challenges remain to be solved before 3D bioprinted tissues could be fully translated from the bench to the bedside. As oxygen plays a key role in aerobic metabolism, which allows energy production in the mitochondria; as a consequence, the lack of tissue oxygenation is one of the main limitations of current bioprinted tissues and organs. In order to improve tissue oxygenation, recent approaches have been established for a broad range of clinical applications, with some already applied using 3D bioprinting technologies. Among them, the incorporation of photosynthetic microorganisms, such as microalgae and cyanobacteria, is a promising approach that has been recently explored to generate chimerical plant-animal tissues where, upon light exposure, oxygen can be produced and released in a localized and controlled manner. This review will briefly summarize the state-of-the-art approaches to improve tissue oxygenation, as well as studies describing the use of photosynthetic microorganisms in 3D bioprinting technologies. STATEMENT OF SIGNIFICANCE: 3D bioprinting technology has emerged as a tool for the generation of viable and functional tissues for direct in vitro and in vivo applications, including disease modeling, drug discovery and regenerative medicine. Despite the latest advancements in this field, suboptimal oxygen delivery to cells before, during and after the bioprinting process limits their viability within 3D bioprinted tissues. This review article first highlights state-of-the-art approaches used to improve oxygen delivery in bioengineered tissues to overcome this challenge. Then, it focuses on the emerging roles played by photosynthetic organisms as novel biomaterials for bioink generation. Finally, it provides considerations around current challenges...
Ostermeier, FF, Jaehnert, J & Deuse, J 2023, 'Joint modelling of the order-dependent parts supply strategies sequencing, kitting and batch supply for assembly lines: insights from industrial practice', Production & Manufacturing Research, vol. 11, no. 1. View/Download from: Publisher's site
Otavio Mendes, J, Merenda, A, Wilson, K, Fraser Lee, A, Della Gaspera, E & van Embden, J 2023, 'Substrate Morphology Directs (001) Sb2Se3 Thin Film Growth by Crystallographic Orientation Filtering', Small, p. e2302721. View/Download from: Publisher's site View description>>
AbstractAntimony chalcogenide, Sb2X3 (X = S, Se), applications greatly benefit from efficient charge transport along covalently bonded (001) oriented (Sb4X6)n ribbons, making thin film orientation control highly desirable – although particularly hard to achieve experimentally. Here, it is shown for the first time that substrate nanostructure plays a key role in driving the growth of (001) oriented antimony chalcogenide thin films. Vapor Transport Deposition of Sb2Se3 thin films is conducted on ZnO substrates whose morphology is tuned between highly nanostructured and flat. The extent of Sb2Se3 (001) orientation is directly correlated to the degree of substrate nanostructure. These data showcase that nanostructuring a substrate is an effective tool to control the orientation and morphology of Sb2Se3 films. The optimized samples demonstrate high (001) crystallographic orientation. A growth mechanism for these films is proposed, wherein the substrate physically restricts the development of undesirable crystallographic orientations. It is shown that the surface chemistry of the nanostructured substrates can be altered and still drive the growth of (001) Sb2Se3 thin films – not limiting this phenomenon to a particular substrate type. Insights from this work are expected to guide the rational design of Sb2X3 thin film devices and other low‐dimensional crystal‐structured materials wherein performance is intrinsically linked to morphology and orientation.
Ou, K, Liu, Z, Liu, Z, Fu, Q, Cao, Y, Liu, Q & Sun, Y 2023, 'Ultra-thin flame retardant polymer nanocomposite coating based on synergistic effect of graphene and glass sheets', Materials Research Bulletin, vol. 164, pp. 112247-112247. View/Download from: Publisher's site
Ou, Y, Zhou, JL, Jia, Y, Liang, M, Hu, H & Ren, L 2023, 'Complete genome of Mycolicibacterium phocaicum RL-HY01, a PAEs-degrading marine bacterial strain isolated from Zhanjiang Bay, China', Marine Genomics, vol. 69, pp. 101019-101019. View/Download from: Publisher's site View description>>
Mycolicibacterium phocaicum RL-HY01, a marine bacterial strain with the capability to degrade phthalic acid esters (PAEs), was isolated from Zhanjiang Bay, China. Here, the complete genome sequence of strain RL-HY01 was presented. The genome of strain RL-HY01 contains one circular chromosome of 6,064,759 bp with a G + C content of 66.93 mol%. The genome contains 5681 predicted protein-encoding genes, 57 tRNA genes, and 6 rRNA genes. Genes and gene clusters potentially involved in the metabolism of PAEs were further identified. The genome Mycolicibacterium phocaicum RL-HY01 will be helpful for advancing our understanding of the fate of PAEs in marine ecosystem.
Ouyang, D, Wen, D, Qin, L, Chang, L, Lin, X & Zhang, Y 2023, 'When hierarchy meets 2-hop-labeling: efficient shortest distance and path queries on road networks.', VLDB J., vol. 32, no. 6, pp. 1263-1287. View/Download from: Publisher's site
Ouyang, P, Rao, P, Wu, J, Cui, J, Nimbalkar, S & Chen, Q 2023, 'Hydromechanical Modeling of High-Voltage Electropulse-Assisted Fluid Injection for Rock Fracturing', Rock Mechanics and Rock Engineering, vol. 56, no. 6, pp. 3861-3886. View/Download from: Publisher's site
Owen, B, Kechagidis, K, Bazaz, SR, Enjalbert, R, Essmann, E, Mallorie, C, Mirghaderi, F, Schaaf, C, Thota, K, Vernekar, R, Zhou, Q, Warkiani, ME, Stark, H & Krüger, T 2023, 'Lattice-Boltzmann modelling for inertial particle microfluidics applications - a tutorial review', Advances in Physics: X, vol. 8, no. 1. View/Download from: Publisher's site
Pacholak, A, Żur-Pińska, J, Piński, A, Nguyen, QA, Ligaj, M, Luczak, M, Nghiem, LD & Kaczorek, E 2023, 'Potential negative effect of long-term exposure to nitrofurans on bacteria isolated from wastewater', Science of The Total Environment, vol. 872, pp. 162199-162199. View/Download from: Publisher's site
Pan, Y, Li, J, Zong, Z, Wu, C & Qian, H 2023, 'Experimental and numerical study on ground shock propagation in calcareous sand', International Journal of Impact Engineering, vol. 180, pp. 104724-104724. View/Download from: Publisher's site
Pang, S, Du, A, Orgun, MA, Wang, Y, Sheng, QZ, Wang, S, Huang, X & Yu, Z 2023, 'Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation', IEEE Transactions on Cybernetics, vol. 53, no. 11, pp. 6776-6787. View/Download from: Publisher's site
Park, E, Wong, RK, Kwon, J & Chu, VW 2023, 'A Stable Model for Maximizing the Number of Significant Features', International Journal of Data Science and Analytics.
Park, MJ, Pathak, NB, Wang, C, Tran, VH, Han, D-S, Hong, S, Phuntsho, S & Shon, HK 2023, 'Fouling of reverse osmosis membrane: Autopsy results from a wastewater treatment facility at central park, Sydney', Desalination, vol. 565, pp. 116848-116848. View/Download from: Publisher's site
Parsa, SM, Norouzpour, F, Shoeibi, S, Shahsavar, A, Aberoumand, S, Said, Z, Guo, W, Ngo, HH, Ni, B-J, Afrand, M & Karimi, N 2023, 'A comprehensive study to find the optimal fraction of nanoparticle coated at the interface of solar desalination absorbers: 5E and GHGs analysis in different seasons', Solar Energy Materials and Solar Cells, vol. 256, pp. 112308-112308. View/Download from: Publisher's site View description>>
In recent years utilizing nanoparticles in black paint (nano-paint) of solar desalination absorbers has become a topic growing interest. However, in most of studies, only the effect of using different types of nanoparticles brought into the spotlight, while in those limited studies that discussed on optimum concentration of nanoparticles, the results were controversial and not conclusive. Herein, an experimental study to find the optimum concentration of nanoparticles (silver, 1–5%) in solar absorbers in summer, spring and autumn with/without reflectors was conducted. To find the optimum concentration, performance of the systems from different viewpoints including energetic, exergetic, economic, productivity, exergoeconomic, energy-matrices, and environmental (amount of CO2/SO2/NO emission/reduction) analysis for each season and thorough its lifetime was examined thoroughly. Ascribe the highly variable of parameters; two Matlab codes have been developed to precisely calculate the economic and exergoeconomic parameters for different scenarios. The results showed that increasing the nanoparticle concentration to 5% from an economic viewpoint was reasonable just in summer, while for autumn and spring, 2.5% was optimum. The same was valid for productivity and exergy analysis. Importantly, the embodied energy of nanoparticles for the first time considered in calculation to obtain more accurate results in terms of environmental analysis and energy-matrices. Finally, it was concluded that even though using 5% nanoparticle in limited cases led to better results, the 2.5% concentration with reflector was optimum for all seasons and through the lifetime. This work would be a cornerstone for future researches in the context of using nano-paint in solar absorbers.
Parsaei, K, Keshavarz, R, Boroujeni, RM & Shariati, N 2023, 'Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 12, pp. 5265-5274. View/Download from: Publisher's site
Patibanda, R, Hill, C, Saini, A, Li, X, Chen, Y, Matviienko, A, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Auto-Paizo Games: Towards Understanding the Design of Games That Aim to Unify a Player’s Physical Body and the Virtual World', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 893-918. View/Download from: Publisher's site View description>>
Most digital bodily games focus on the body as they use movement as input. However, they also draw the player’s focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach – the 'Body as a Play Material' – where a player uses their body as both input and output to unify the physical body and the virtual world. To showcase this approach, we designed three games where a player uses one of their hands (input) to play against the other hand (output) by loaning control over its movements to an Electrical Muscle Stimulation (EMS) system. We conducted a thematic analysis on the data obtained from a field study with 12 participants to articulate four player experience themes. We discuss our results about how participants appreciated the engagement with the variety of bodily movements for play and the ambiguity of using their body as a play material. Ultimately, our work aims to unify the physical body and the virtual world.
Patibanda, R, Saini, A, Overdevest, N, Montoya, MF, Li, X, Chen, Y, Nisal, S, Andres, J, Knibbe, J, van den Hoven, E & Mueller, FF 2023, 'Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players', Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CHI PLAY, pp. 769-802. View/Download from: Publisher's site View description>>
Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called 'Fused Spectatorship', where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences.
Patil, G, Shivakumara, P, Gornale, SS, Pal, U & Blumenstein, M 2023, 'A new robust approach for altered handwritten text detection', Multimedia Tools and Applications, vol. 82, no. 14, pp. 20925-20949. View/Download from: Publisher's site
Patmore, G, Balnave, N & Marjanovic, O 2023, 'Business Co-operatives in Australia: “Unlikely Soil for a Co-operative Movement”', Enterprise & Society, vol. 24, no. 1, pp. 149-173. View/Download from: Publisher's site View description>>
While co-operatives are traditionally associated with workers, consumers, and farmers, the business model, with its emphasis on democracy and community, has also been adopted by small business owners, the self-employed, and professionals. These business co-operatives are distinct phenomenon, because they primarily consist of independent organizational entities that are not co-operatives and are generally in direct competition with one another. They are unique in that they bring together separate organizations that seek to combat market threats while adopting a philosophy based on co-operative principles. This article begins with an overview of the Australian co-operative landscape. It then defines the concept of business co-operatives and then draws upon the Visual Atlas of Australian Co-operatives History Project, which has developed a large database of Australian co-operatives over time and space, to examine the development of business co-operatives in Australia. It looks at where business co-operatives formed in the economy, the motivation underlying their formation, their average life spans, and their relationships with the broader co-operative movement. The article highlights the value of business co-operatives in introducing the values of participatory democracy and working for the common good into unanticipated markets and reinforcing the co-operative movement.
Peellage, WH, Fatahi, B & Rasekh, H 2023, 'Assessment of cyclic deformation and critical stress amplitude of jointed rocks via cyclic triaxial testing', Journal of Rock Mechanics and Geotechnical Engineering, vol. 15, no. 6, pp. 1370-1390. View/Download from: Publisher's site
Pei, C, Qiu, Y, Li, F, Huang, X, Si, Y, Li, Y, Zhang, X, Chen, C, Liu, Q, Cao, Z, Ding, N, Gao, S, Alho, K, Yao, D & Xu, P 2023, 'The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS', Cerebral Cortex, vol. 33, no. 8, pp. 4740-4751. View/Download from: Publisher's site View description>>
AbstractHuman language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.
Peng, L, Qiu, H, Li, S, Xu, Y, Liang, C, Wang, N, Liu, Y & Ni, B-J 2023, 'The mitigation effect of free ammonia and free nitrous acid on nitrous oxide production from the full-nitrification and partial-nitritation systems', Bioresource Technology, vol. 372, pp. 128564-128564. View/Download from: Publisher's site View description>>
The potentials of using endogenous free ammonia (FA) and free nitrous acid (FNA) as nitrous oxide (N2O) mitigators were investigated in treatment of both mainstream and sidestream wastewater. Although the N2O emission factor of a sidestream partial-nitritation (PN) reactor (averaged 1.70 % ± 0.39 %, n = 30) was about 2.4 times higher than a mainstream full-nitrification (FN) reactor (averaged 0.72 % ± 0.24 %, n = 30) (P < 0.01), one-hour exposure of PN sludge to 1.5 mg HNO2-N/L FNA could virtually abolish N2O emission. As for FN sludge, both 45 mg NH3-N/L FA and 0.015 mg HNO2-N/L FNA successfully mitigated N2O production at varying dissolved oxygen (DO) levels (50 % vs 61 %), while 1.5 mg HNO2-N/L FNA not only reduced more N2O (92 %) but also altered the N2O dependency on DO. Both FNA and FA sludge treatment were effective N2O mitigation strategies with FNA toward the end of carbon neutrality and FA being more economically appealing (2 % cost saving).
Peng, P, Sun, H, Marcireau, A, Nguyen, M, Zhu, H, Lin, C-T & Jin, C 2023, 'Auditory sensory augmentation to support table tennis games for people with vision loss', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A197-A197. View/Download from: Publisher's site View description>>
People with vision loss often face limitations in regular sports games with standard rules and equipment. For example, in current blind table tennis, conventional rules are modified so that the ball rolls along the table instead of bouncing. In this work, we propose an auditory sensory augmentation system to support traditional table tennis in three dimensions. We capture the trajectory of the table tennis ball using two neuromorphic event cameras and sonify the path of the ball using loudspeakers mounted near the left and right edges of the playing table. The two event cameras capture rapid changes in brightness allowing fast and precise ball tracking. The ball's 3D trajectory is then sonified using four lines of loudspeakers mounted at two different heights near the left and right edges of the playing table. We present a preliminary implementation and investigation of the proposed sensory augmentation system with a focus on the technical and perceptual challenges.
Peng, Y, Song, A, Ciesielski, V, Fayek, HM & Chang, X 2023, 'PRE-NAS: Evolutionary Neural Architecture Search With Predictor', IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 26-36. View/Download from: Publisher's site View description>>
Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space. Prediction of the performance of a network can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, predictor-assisted evolutionary NAS (PRE-NAS) which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous. This circumvents bias and leads to more accurate predictions. Extensive experiments on the NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, a competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet, respectively.
Pérez-Romero, ME, Alfaro-García, VG, Merigó, JM & Flores-Romero, MB 2023, 'Covariance Logarithmic Aggregation Operators in Decision-Making Processes', Cybernetics and Systems, vol. 54, no. 2, pp. 220-238. View/Download from: Publisher's site
Perrin, R, Halkon, BJ & Guo, Z 2023, '(Re-)exploring the normal modes of axisymmetric structures: An English church bell case study', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A74-A74. View/Download from: Publisher's site View description>>
The normal modes of axisymmetric structures are of interest to structural engineers, physicists and musical acousticians. Previously, some of the present authors have made studies of church bells, hand bells, elephant bells, various gongs and rings. Group theoretical arguments have been used, with considerable success, in classifying the normal modes of these structures and in understanding how “beats” arise from split degenerate doublets. It is now pointed out that further information can be obtained from group theory using a variety of additional arguments. In particular, it infers a basic distinction between “in-extensional” and “extensional” types of modes. In the present work, we concentrate on the case of an English church bell, as an example axisymmetric structure, whose normal modes were previously measured in a frequency range of up to about 10 kHz. In that earlier work, the results were analyzed with what was then considered a state-of-the-art finite-element package. We now repeat this exercise with a modern finite-element package to explore the differences between the types of modes and validate the Group theory observations. The agreement with experiment is much improved and some new level of understanding of the spectrum of the bell is achieved.
Petrov, K, Sutcliffe, S, Truscott, H, Kutay, C, Eisemberg, CC, Spencer, RJ, Lawler, I, Bower, DS, Van Dyke, JU & Georges, A 2023, 'Turtles in trouble. Conservation ecology and priorities for Australian freshwater turtles', Austral Ecology, vol. 48, no. 8, pp. 1603-1656. View/Download from: Publisher's site View description>>
AbstractThe Australian freshwater turtle fauna is dominated by species in the family Chelidae. The extant fauna comprises a series of distinct lineages, each of considerable antiquity, relicts of a more extensive and perhaps diverse fauna that existed when wetter climes prevailed. Several phylogenetically distinctive species are restricted to single, often small, drainage basins, which presents challenges for their conservation. Specific threats include water resource development, which alters the magnitude, frequency, and timing of flows and converts lentic to lotic habitat via dams and weirs, fragmentation of habitat, sedimentation, nutrification, and a reduction in the frequency and extent of floodplain flooding. Drainage of wetlands and altered land use are of particular concern for some species that are now very restricted in range and critically endangered. The introduced European red fox is a devastatingly efficient predator of turtle nests and can have a major impact on recruitment. In the north, species such as the northern snake‐necked turtle are heavily depredated by feral pigs. Other invasive animals and aquatic weeds dramatically alter freshwater habitats, with consequential impacts on freshwater turtles. Novel pathogens such as viruses have brought at least one species to the brink of extinction. Species that routinely migrate across land are impacted by structural simplification of habitat, reduction in availability of terrestrial refugia, fencing (including conservation fencing), and in some areas, by high levels of road mortality. We report on the listing process and challenges for listing freshwater turtles under the Australian Environment Protection and Biodiversity Conservation Act, summarize the state of knowledge relevant to listing decisions, identify the key threatening processes impacting turtles, and identify key knowledge gaps that impede the setting of priorities. We also focus on how to best ...
Phillips, L, Oberst, S & Sepehrirahnama, S 2023, 'Sensor fusion for simultaneous measurement of micro-vibrations', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A141-A142. View/Download from: Publisher's site View description>>
Low-amplitude micro-vibrations are common in nature and engineering, throughout structural applications, and biology. The ability to accurately measure and analyse these vibrations in the presence of noise (unwanted signal content) has far reaching consequences across many fields of acoustics. Methodologies for the enhancement and improvement of such signals are, therefore, sought. We explore the capability of sensor fusion in combination with Kalman filtering (KF), using pairs of accelerometers to improve measurement of low-amplitude micro-vibrations. Prior research of pairing sensor fusion with machine learning approaches like KF, support vector machines, or coherence analysis reported up to 90% reductions in “ghost” detections. This research attempts to extend this success to micro-vibrations where broadband noise, and external perturbations can have dramatic impacts on measurability. A pair of accelerometers has been placed both parallel and perpendicular to the axis of an excitation in pine timber planks of varying dimensions and cuts. Simultaneous measurement of ∼5 N excitations with an automated hammer at varying distances are recorded and patterns observed in the time domain through preliminary analysis in MATLAB. Features identifiable from this data become clearer as compared to conventional approaches and have potential applications in non-invasive early predictive analysis of structures for timber pest control.
Pillay, A, Yeola, A, Tea, F, Denkova, M, Houston, S, Burrell, R, Merheb, V, Lee, FXZ, Lopez, JA, Moran, L, Jadhav, A, Sterling, K, Lai, CL, Vitagliano, TL, Aggarwal, A, Catchpoole, D, Wood, N, Phan, TG, Nanan, R, Hsu, P, Turville, SG, Britton, PN & Brilot, F 2023, 'Infection and Vaccine Induced Spike Antibody Responses Against SARS-CoV-2 Variants of Concern in COVID-19-Naïve Children and Adults', Journal of Clinical Immunology, vol. 43, no. 8, pp. 1706-1723. View/Download from: Publisher's site View description>>
AbstractAlthough a more efficient adaptive humoral immune response has been proposed to underlie the usually favorable outcome of pediatric COVID-19, the breadth of viral and vaccine cross-reactivity toward the ever-mutating Spike protein among variants of concern (VOCs) has not yet been compared between children and adults. We assessed antibodies to conformational Spike in COVID-19-naïve children and adults vaccinated by BNT162b2 and ChAdOx1, and naturally infected with SARS-CoV-2 Early Clade, Delta, and Omicron. Sera were analyzed against Spike including naturally occurring VOCs Alpha, Beta, Gamma, Delta, and Omicron BA.1, BA.2, BA.5, BQ.1.1, BA2.75.2, and XBB.1, and variants of interest Epsilon, Kappa, Eta, D.2, and artificial mutant Spikes. There was no notable difference between breadth and longevity of antibody against VOCs in children and adults. Vaccinated individuals displayed similar immunoreactivity profiles across variants compared with naturally infected individuals. Delta-infected patients had an enhanced cross-reactivity toward Delta and earlier VOCs compared to patients infected by Early Clade SARS-CoV-2. Although Omicron BA.1, BA.2, BA.5, BQ.1.1, BA2.75.2, and XBB.1 antibody titers were generated after Omicron infection, cross-reactive binding against Omicron subvariants was reduced across all infection, immunization, and age groups. Some mutations, such as 498R and 501Y, epistatically combined to enhance cross-reactive binding, but could not fully compensate for antibody-evasive mutations within the Omicron subvariants tested. Our results reveal important molecular features central to the generation of high antibody titers and broad immunoreactivity that should be considered in future vaccine design and global serosurveillance in the context of limited vaccine boosters available to the pediatric population.
Ping, J, Zhu, S, Shi, M, Wu, S, Shen, M, Liu, X & Wen, S 2023, 'Event-Triggered Finite-Time Synchronization Control for Quaternion-Valued Memristive Neural Networks by an Non-Decomposition Method', IEEE Transactions on Network Science and Engineering, pp. 1-10. View/Download from: Publisher's site
Pinilla, A, Voigt-Antons, J-N, Garcia, J, Raffe, W & Möller, S 2023, 'Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals', Frontiers in Virtual Reality, vol. 3, p. 964754. View/Download from: Publisher's site View description>>
This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.
Pira, L & Ferrie, C 2023, 'An invitation to distributed quantum neural networks', Quantum Machine Intelligence, vol. 5, no. 2. View/Download from: Publisher's site View description>>
AbstractDeep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise to the concept of distributed deep learning. Distributed techniques are often employed in training large models or large datasets either out of necessity or simply for speed. Quantum machine learning, on the other hand, is the interplay between machine learning and quantum computing. It seeks to understand the advantages of employing quantum devices in developing new learning algorithms as well as improving the existing ones. A set of architectures that are heavily explored in quantum machine learning are quantum neural networks. In this review, we consider ideas from distributed deep learning as they apply to quantum neural networks. We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models, though the unique aspects of quantum data introduce new vulnerabilities to both approaches. We review the current state of the art in distributed quantum neural networks, including recent numerical experiments and the concept of circuit-cutting.
Pourhamzeh, M, Asadian, S, Mirzaei, H, Minaei, A, Shahriari, E, Shpichka, A, Es, HA, Timashev, P, Hassan, M & Vosough, M 2023, 'Novel antigens for targeted radioimmunotherapy in hepatocellular carcinoma', Molecular and Cellular Biochemistry, vol. 478, no. 1, pp. 23-37. View/Download from: Publisher's site
Pourpanah, F, Wang, R, Lim, CP, Wang, X-Z & Yazdani, D 2023, 'A review of artificial fish swarm algorithms: recent advances and applications', Artificial Intelligence Review, vol. 56, no. 3, pp. 1867-1903. View/Download from: Publisher's site
Poursafar, N, Taghizadeh, S, Hossain, MJ & Blaabjerg, F 2023, 'An enhanced control strategy for an ultra-fast EV charging station in a DC microgrid', International Journal of Electrical Power & Energy Systems, vol. 146, pp. 108727-108727. View/Download from: Publisher's site
Poursafar, N, Taghizadeh, S, Jahangir Hossain, M & Karimi-Ghartemani, M 2023, 'A Voltage-supportive Controller for Ultra-fast Electric Vehicle Chargers in Islanded DC Microgrids', Journal of Modern Power Systems and Clean Energy, vol. 11, no. 3, pp. 896-906. View/Download from: Publisher's site
Power, T, Kennedy, P, Chen, H, Martinez-Maldonado, R, McGregor, C, Johnson, A, Townsend, L & Hayes, C 2023, 'Learning to Manage De-escalation Through Simulation: An Exploratory Study', Clinical Simulation in Nursing, vol. 77, pp. 23-29. View/Download from: Publisher's site
Pradhan, B, Dikshit, A, Lee, S & Kim, H 2023, 'An explainable AI (XAI) model for landslide susceptibility modeling', Applied Soft Computing, vol. 142, pp. 110324-110324. View/Download from: Publisher's site
Pradhan, B, Lee, S, Dikshit, A & Kim, H 2023, 'Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model', Geoscience Frontiers, vol. 14, no. 6, pp. 101625-101625. View/Download from: Publisher's site
Pradhan, S, Qiu, X & Ji, J 2023, 'On Time–Frequency Domain Flexible Structure of Delayless Partitioned Block Adaptive Filtering Approach for Active Noise Control', Circuits, Systems, and Signal Processing, vol. 42, no. 12, pp. 7580-7595. View/Download from: Publisher's site View description>>
Frequencydomain filtered-x least mean square algorithms can reduce the computational complexity of the time domain counterpart with long filters; however, they suffer from large block delay, additional quantization error due to large size transformations and implementation difficulties in existing DSP hardware. In this paper, a time–frequencydomain flexible structure is proposed using the partitioned block frequencydomain adaptive filtering technique, which has no signal path delay and is well suited for low-cost DSP implementation. The proposed structure divides the long filters into many equal partitions and carries out the control filter update in frequency domain while generating the control signal in both time and frequency domains, thereby eliminating the forward path delay completely while maintaining low computational complexity. The proposed structure has a potential benefit for controlling broadband noise, where the causality constraint is more important. The simulation results using the measured acoustic paths demonstrate that the proposed structure maintains similar control performance as that of the time domain algorithm but with much less computational complexity. Furthermore, the tracking performance of the proposed structure under different levels of measurement noise is investigated.
Pradhan, S, Zhang, G, Qiu, X, Ji, JC & Parnell, J 2023, 'Robust improved multiband-structured subband adaptive filter for active noise control with impulsive interference', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A121-A121. View/Download from: Publisher's site View description>>
The feedforward filtered-x least mean square algorithm is extensively implemented for active control of broadband noise. However, the control performance is substantially deteriorated due to colored noise and the presence of uncorrelated disturbances near the reference and error sensors. To tackle these issues, in this paper, a robust improved multiband-structured subband adaptive filter based on logarithmic and total least squares method is proposed for active control. Unlike the conventional method of total least squares, the proposed algorithm adopts logarithmic and Rayleigh quotient functions. The closed loop implementation of the improved multiband-structured subband adaptive filter is adopted to meet the delayless requirement. The proposed algorithm is well-suited for environments where the reference signal is highly correlated and the residual noise is contaminated by impulsive noise. Furthermore, an affine combination of the proposed algorithm is developed to meet the complementary requirements of faster convergence and improved noise reduction. Eventually, the stability and computational complexity are studied. Simulation results using measured acoustic paths in an anechoic chamber and a normal room illustrate the effectiveness of the proposed algorithm for controlling broadband noise with impulsive interference. In addition, the tracking control performance is evaluated in a time-varying acoustic environment.
Pradhan, S, Zhang, G, Zhao, S, Niwa, K & Bastiaan Kleijn, W 2023, 'On eigenvalue shaping for two-channel decentralized active noise control systems', Applied Acoustics, vol. 205, pp. 109260-109260. View/Download from: Publisher's site View description>>
Recent works show that two-channel decentralized active noise control (DANC) systems are able to achieve optimal noise reduction performance with guaranteed convergence by proper matrix eigenvalue shaping for each frequency. In this paper, we study the impact of three eigenvalue shaping approaches on the performance of a time-domain two-channel DANC system, where the first two approaches are from the literature and the third one is newly proposed as an extension of one of the two approaches. By theoretical analysis and experimental investigation of the three approaches, it is found that the eigenvalues of the 2 × 2 so-called characteristic matrices in the frequency-domain should be shaped by considering two aspects. Firstly, the two eigenvalues for each matrix need to be pushed towards the positive real axis to ensure stability. Secondly, the eigenvalues inherently affect the two auxiliary filters in the time-domain. They should be shaped so that the two filters have roughly the same magnitudes to facilitate implementation. Simulation results using the measured acoustic paths demonstrate the efficacy of the proposed eigenvalue shaping approach and the adaptive filtering technique for controlling sinusoidal noise, multitone noise, white noise and traffic noise. Experimental result shows the efficacy of the proposed approach for controlling white noise in three dimensional space.
Pratt, L, Johnston, A & Pietroni, N 2023, 'Bending the light: Next generation anamorphic sculptures.', Comput. Graph., vol. 114, pp. 210-218. View/Download from: Publisher's site
Priharsari, D, Abedin, B, Burdon, S, Clegg, S & Clay, J 2023, 'National digital strategy development: Guidelines and lesson learnt from Asia Pacific countries', Technological Forecasting and Social Change, vol. 196, pp. 122855-122855. View/Download from: Publisher's site View description>>
Adoption of information and communication technology (ICT) to create a digital ecosystem gives rise to substantial economic and social benefits. Therefore, many countries develop National digital strategies (NDSs) to establish objectives, policy priorities and outline necessary actions for implementation of digital transformation. However, given the digital divide between different economies, it is difficult to compare consistently the priorities of NDS across various countries. This paper is an effort to explicate various priorities on different country groups. Using a macro-analysis lens, this research project explored specific policies and regulations that are perceived useful in identifying strategies for strengthening ICT adoption for economic and digital growth. We surveyed members of Asia Productivity Organization (APO) from mid-2020 to 2021 and interviewed three member countries (Indonesia, Malaysia, and the Republic of Korea). Findings offer a guideline for countries to develop their NDS and discuss implications for policy makers in the Asia Pacific region.
Prior, J & Leaney, J 2023, 'Software Development Practice as Baradian Entanglement', Human Organization, vol. 82, no. 1, pp. 25-35. View/Download from: Publisher's site View description>>
Software development practice is a messy, complicated, and constantly shifting human endeavor. Barad’s concept of “entanglement” helps to theorize complex sociotechnical systems. We are testing the application of this theory to understand and explain software development practices, as our work appears to be the only ethnographic research using Barad in any technology industry. Our continual aim is to understand large-scale, collaborative software development more deeply in practice and to discover appropriate theories that describe our observations and insights. Both authors are experienced software engineers and researchers. Through an ongoing longitudinal ethnographic study at a large Australian software development company, we explore, support, and improve the lived experience and practice of the software developers that work there. Ethnographic insights and an appreciation of the mutual constitution of situated phenomena have expanded over several years into an elaboration of entanglement as a more insightful explanation of software development practice. This research is having a significant impact on the participant developers and organization, including changes in measurement practices, mentoring, knowledge management, and innovation.
Punetha, P & Nimbalkar, S 2023, 'An innovative rheological approach for predicting the behaviour of critical zones in a railway track', Acta Geotechnica, vol. 18, no. 10, pp. 5457-5483. View/Download from: Publisher's site View description>>
AbstractThe poor performance of critical zones along a railway line has long been a subject of concern for rail infrastructure managers. The rapid deterioration of track geometry in these zones is primarily ascribed to limited understanding of the underlying mechanism and scarcity of adequate tools to assess the severity of the potential issue. Therefore, a comprehensive evaluation of their behaviour is paramount to improve the design and ensure adequate service quality. With this objective, a novel methodology is introduced, which can predict the differential plastic deformations at the critical zones and assess the suitability of different countermeasures in improving the track performance. The proposed technique employs a three-dimensional geotechnical rheological track model that considers varied support conditions of the critical zone. The approach is successfully validated with published field data and predictions from finite element analysis. This methodology is then applied to a bridge-open track transition zone, where it is observed that an increase in axle load exacerbates the track geometry degradation problem. The results show that the performance of critical zones with weak subgrade can be improved by increasing the granular layer thickness. Interpretation of the predicted differential settlement for different countermeasures exemplifies the practical significance of the proposed methodology.
Punetha, P & Nimbalkar, S 2023, 'Numerical investigation on dynamic behaviour of critical zones in railway tracks under moving train loads', Transportation Geotechnics, vol. 41, pp. 101009-101009. View/Download from: Publisher's site
Qamar, A, Shaukat, R, Imran, S, Farooq, M, Amjad, M, Anwar, Z, Ali, H, Farhan, M, Mujtaba, MA, Korakianitis, T, Kalam, MA & Almomani, F 2023, 'Effect of surfactants on the convective heat transfer and pressure drop characteristics of ZnO/DIW nanofluids: An experimental study', Case Studies in Thermal Engineering, vol. 42, pp. 102716-102716. View/Download from: Publisher's site
Qi, C, Cao, D, Gao, X, Jia, S, Yin, R, Nghiem, LD, Li, G & Luo, W 2023, 'Optimising organic composition of feedstock to improve microbial dynamics and symbiosis to advance solid-state anaerobic co-digestion of sewage sludge and organic waste', Applied Energy, vol. 351, pp. 121857-121857. View/Download from: Publisher's site
Qi, T, Lyu, B & Hoang, DT 2023, 'Pilot Sequences With Low Coherence and PAPR for Grant-Free Massive Access', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1254-1258. View/Download from: Publisher's site View description>>
To accommodate massive devices and facilitate activity detection in a grant-free access system, it is necessary to design non-orthogonal pilot sequences with low coherence. In this letter, we study the optimization problem to minimize the worst-case coherence among sequences under the peak-to-average power ratio (PAPR) constraint of each sequence. An efficient method is proposed to iteratively construct sequences by the conjugate gradient descent and space projection. Simulation results demonstrate that the proposed sequences can decrease the coherence under strict PAPR constraints by up to 62% compared with the benchmarks.
Qi, Y & Indraratna, B 2023, 'The effect of adding rubber crumbs on the cyclic permanent deformation of waste mixtures containing coal wash and steel furnace slag', Géotechnique, vol. 73, no. 11, pp. 951-960. View/Download from: Publisher's site View description>>
Among the numerous studies into the dynamic loading behaviour of rubber crumbs–soil/waste mixtures, the main focus is on how the content of rubber crumbs ([Formula: see text]) affects the damping ratio, shear modulus and total deformation. However, research into the influence of [Formula: see text] on the permanent strain rate ([Formula: see text]) and the deformation mechanism under repeated loading is very limited. In the current study, the cyclic deformation response for waste mixtures of steel furnace slag (SFS), coal wash (CW) and rubber crumbs (RC) is analysed and the test results reveal that [Formula: see text] has a significant influence on the initial [Formula: see text] and the slope of the permanent axial strain rate line, whereas cyclic deviator stress ([Formula: see text]) mainly affects the initial [Formula: see text]. The influence of [Formula: see text] and [Formula: see text] on the [Formula: see text] value of the waste mixture is incorporated in an empirical model, which enables prediction of the permanent deformation mechanism of SFS + CW + RC mixtures with wider-ranging amounts of RC and higher cyclic deviator stresses.
Qian, J, Lan, H, Huang, L, Zheng, S, Hu, X, Chen, M, Lee, JE-Y & Zhang, W 2023, 'Acoustofluidics for simultaneous droplet transport and centrifugation facilitating ultrasensitive biomarker detection', Lab on a Chip, vol. 23, no. 19, pp. 4343-4351. View/Download from: Publisher's site View description>>
An orthogonal tunable acoustic tweezer enables simultaneous droplet transport and centrifugation facilitating ultrasensitive miRNA biomarker detection.
Qian, J, Ma, R, Chen, Z, Wang, G, Zhang, Y, Du, Y, Chen, Y, An, T & Ni, B-J 2023, 'Hierarchical Co-Fe layered double hydroxides (LDH)/Ni foam composite as a recyclable peroxymonosulfate activator towards monomethylhydrazine degradation: Enhanced electron transfer and 1O2 dominated non-radical pathway', Chemical Engineering Journal, vol. 469, pp. 143554-143554. View/Download from: Publisher's site
Qian, J, Mi, X, Chen, Z, Xu, W, Liu, W, Ma, R, Zhang, Y, Du, Y & Ni, B-J 2023, 'Efficient emerging contaminants (EM) decomposition via peroxymonosulfate (PMS) activation by Co3O4/carbonized polyaniline (CPANI) composite: Characterization of tetracycline (TC) degradation property and application for the remediation of EM-polluted water body', Journal of Cleaner Production, vol. 405, pp. 137023-137023. View/Download from: Publisher's site
Qian, J, Zhang, Y, Chen, Z, Du, Y & Ni, B-J 2023, 'NiCo layered double hydroxides/NiFe layered double hydroxides composite (NiCo-LDH/NiFe-LDH) towards efficient oxygen evolution in different water matrices', Chemosphere, vol. 345, pp. 140472-140472. View/Download from: Publisher's site
Qian, J, Zhang, Y, Chen, Z, Yu, R, Ye, Y, Ma, R, Li, K, Wang, L, Wang, D & Ni, B-J 2023, 'Sulfur-decorated Fe/C composite synthesized from MIL-88A(Fe) for peroxymonosulfate activation towards tetracycline degradation: Multiple active sites and non-radical pathway dominated mechanism', Journal of Environmental Management, vol. 344, pp. 118440-118440. View/Download from: Publisher's site
Guided by the connections between hypergraphs and exterior algebras, we study Turán and Ramsey type problems for alternating multilinear maps. This study lies at the intersection of combinatorics, group theory, and algebraic geometry, and has origins in the works of Lovász (Proc. Sixth British Combinatorial Conf., 1977), Buhler, Gupta, and Harris (J. Algebra, 1987), and Feldman and Propp (Adv. Math., 1992). Our main result is a Ramsey theorem for alternating bilinear maps. Given s, t ∈ N, s, t ≥ 2, and an alternating bilinear map ϕ: V × V → U with dim(V) ≥ s ·t4, we show that there exists either a dimension-s subspaceW ≤ V such that dim(span(ϕ(W,W))) = 0, or a dimension-t subspaceW ≤ V such that dim(span(ϕ(W,W))) = (t2). This result has natural group-theoretic (for finite p-groups) and geometric (for Grassmannians) implications, and leads to new Ramsey-type questions for varieties of groups and Grassmannians.
Qin, H, Cheng, Z, Zhang, B, Zhou, R, Yu, Y, Li, X, Wen, S, Li, J & Jiang, J 2023, 'Thermoelectrical comprehensive analysis and optimization of multi-stack solid oxide fuel cell system', Energy Conversion and Management, vol. 291, pp. 117297-117297. View/Download from: Publisher's site
Qin, H, Mason, M & Stewart, MG 2023, 'Fragility assessment for new and deteriorated portal framed industrial buildings subjected to tropical cyclone winds', Structural Safety, vol. 100, pp. 102287-102287. View/Download from: Publisher's site
Qin, H, Xie, W, Li, Y, Jiang, K, Lei, J & Du, Q 2023, 'Weakly supervised adversarial learning via latent space for hyperspectral target detection', Pattern Recognition, vol. 135, pp. 109125-109125. View/Download from: Publisher's site
Qin, S, Yang, N, Zhu, X & Wang, Z 2023, 'Analytical Approach for Load-Carrying Capacity Evaluation of Tibetan Timber Beam-column Joint', International Journal of Architectural Heritage, vol. 17, no. 10, pp. 1719-1735. View/Download from: Publisher's site View description>>
Queti is an important component of Tibetan timber beam-column joint to transfer compression, shear, and bending moment from one structural component to another. The inclination of Queti is a common type of damage in Tibetan heritage buildings and it significantly reduces the load-carrying capacity and safety of the joint under vertical load. In this paper, an analytical model of the joint with Queti-inclination is proposed to predict the yield and ultimate loads of the joint and the corresponding failure modes. Laboratory tests have been conducted on typical Tibetan beam-column joints to verify the proposed model. A parametric study is also conducted on the effects of material property, Queti width and height, as well as the dowel height on the load-carrying capacity of the joint. Results obtained show that a weaker material property will significantly reduce the capacity of the joint. An increase in Queti width and dowel height have an ameliorative effect on the yield and ultimate loads, while the Queti height has the opposite effect.
Qin, Y, Jia, H, Liu, W, Lu, N, Ngo, HH & Wang, J 2023, 'Application of in-situ micro laser transmission on real-time monitoring of flocculation process', Journal of Water Process Engineering, vol. 51, pp. 103364-103364. View/Download from: Publisher's site
Qiu, N, Zhang, J, Li, C, Shen, Y & Fang, J 2023, 'Mechanical properties of three-dimensional functionally graded triply periodic minimum surface structures', International Journal of Mechanical Sciences, vol. 246, pp. 108118-108118. View/Download from: Publisher's site
Qiu, P, Gong, Y, Zhao, Y, Cao, L, Zhang, C & Dong, X 2023, 'An Efficient Method for Modeling Nonoccurring Behaviors by Negative Sequential Patterns With Loose Constraints', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 4, pp. 1864-1878. View/Download from: Publisher's site
Qiu, Y-X, Wen, D, Li, R-H, Qin, L, Yu, M & Lin, X 2023, 'Computing Significant Cliques in Large Labeled Networks', IEEE Transactions on Big Data, vol. 9, no. 3, pp. 904-917. View/Download from: Publisher's site View description>>
Mining cohesive subgraphs and communities is a fundamental problem in network analysis and has drawn much attention in the last decade. Most existing cohesive subgraph models mainly consider the structural cohesion but ignore the subgraph significance. In this paper, we formulate a new model, called statistically significant clique, to mine significant cohesive subgraphs in large vertex-labeled graphs. A statistically significant clique is a complete subgraph with a significance value exceeding a given threshold. The subgraph significance is evaluated by a widely used metric called chi-square statistic. We study the problem of enumerating all maximal statistically significant cliques. The problem is proved to be NP-hard. We propose an efficient branch-and-bound algorithm with several elegant pruning strategies to solve our problem. We conduct extensive experiments on seven large real-world datasets to show the practical efficiency of our algorithms. We also conduct a case study to evaluate the effectiveness of our proposed model.
Qu, F, Zhao, H, Wu, K, Liu, Y, Zhao, X & Li, W 2023, 'Phase transformation and microstructure of in-situ concrete after 20-year exposure to harsh mining environment: A case study', Case Studies in Construction Materials, vol. 19, pp. e02287-e02287. View/Download from: Publisher's site
Qu, Y, Ma, L, Ye, W, Zhai, X, Yu, S, Li, Y & Smith, D 2023, 'Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence', Big Data Mining and Analytics, vol. 6, no. 4, pp. 443-464. View/Download from: Publisher's site
Quyet Truong, D, Choo, Y, Akther, N, Roobavannan, S, Norouzi, A, Gupta, V, Blumenstein, M, Vinh Nguyen, T & Naidu, G 2023, 'Selective rubidium recovery from seawater with metal-organic framework incorporated potassium cobalt hexacyanoferrate nanomaterial', Chemical Engineering Journal, vol. 454, pp. 140107-140107. View/Download from: Publisher's site
Rabie, M, Ali, AYM, Abo-Zahhad, EM, Elkady, MF, El-Shazly, AH, Salem, MS, Radwan, A, Rajabzadeh, S, Matsuyama, H & Shon, HK 2023, 'New hybrid concentrated photovoltaic/membrane distillation unit for simultaneous freshwater and electricity production', Desalination, vol. 559, pp. 116630-116630. View/Download from: Publisher's site
Rabiee, N, Dokmeci, MR, Zarrabi, A, Makvandi, P, Saeb, MR, Karimi-Maleh, H, Jafarzadeh, S, Karaman, C, Yamauchi, Y, Warkiani, ME, Bencherif, SA, Mehta, G, Eguchi, M, Kaushik, A, Shahbazi, M-A, Paiva-Santos, AC, Ryl, J, Lima, EC, Hamblin, MR, Varma, RS, Huh, Y, Vilian, ATE, Gupta, PK, Lakhera, SK, Kesari, KK, Liu, Y-T, Tahriri, M, Rama Raju, GS, Adeli, M, Mohammadi, A, Wang, J, Ansari, MZ, Aminabhavi, T, Savoji, H, Sethi, G, Bączek, T, Kot-Wasik, A, Penoff, ME, Nafchi, AM, Kucinska-Lipka, J, Zargar, M, Asadnia, M, Aref, AR, Safarkhani, M, Ashrafizadeh, M, Umapathi, R, Ghasemi, A & Radisic, M 2023, 'Green Biomaterials : fundamental principles', Green Biomaterials, vol. 1, no. 1, pp. 1-4. View/Download from: Publisher's site
Radfar, P, Ding, L, de la Fuente, LR, Aboulkheyr, H, Gallego-Ortega, D & Warkiani, ME 2023, 'Rapid metabolomic screening of cancer cells via high-throughput static droplet microfluidics', Biosensors and Bioelectronics, vol. 223, pp. 114966-114966. View/Download from: Publisher's site View description>>
Effective isolation and in-depth analysis of Circulating Tumour Cells (CTCs) are greatly needed in diagnosis, prognosis and monitoring of the therapeutic response of cancer patients but have not been completely fulfilled by conventional approaches. The rarity of CTCs and the lack of reliable biomarkers to distinguish them from peripheral blood cells have remained outstanding challenges for their clinical implementation. Herein, we developed a high throughput Static Droplet Microfluidic (SDM) device with 38,400 chambers, capable of isolating and classifying the number of metabolically active CTCs in peripheral blood at single-cell resolution. Owing to the miniaturisation and compartmentalisation capability of our device, we first demonstrated the ability to precisely measure the lactate production of different types of cancer cells inside 125 pL droplets at single-cell resolution. Furthermore, we compared the metabolomic activity of leukocytes from healthy donors to cancer cells and showed the ability to differentiate them. To further prove the clinical relevance, we spiked cancer cell lines in human healthy blood and showed the possibility to detect the cancer cells from leukocytes. Lastly, we tested the workflow on 8 preclinical mammary mouse models including syngeneic 67NR (non-metastatic) and 4T1.2 (metastatic) models with Triple-Negative Breast Cancer (TNBC) as well as transgenic mouses (12-week-old MMTV-PyMT). The results have shown the ability to precisely distinguish metabolically active CTCs from the blood using the proposed SDM device. The workflow is simple and robust which can eliminate the need for specialised equipment and expertise required for single-cell analysis of CTCs and facilitate on-site metabolic screening of cancer cells.
Radfar, P, Ding, L, Es, HA & Warkiani, ME 2023, 'A Microfluidic Approach for Enrichment and Single-Cell Characterization of Circulating Tumor Cells from Peripheral Blood', pp. 141-150. View/Download from: Publisher's site
Raggam, S, Mohammad, M, Choo, Y, Naidu, G, Zargar, M, Shon, HK & Razmjou, A 2023, 'Advances in metal organic framework (MOF) – Based membranes and adsorbents for lithium-ion extraction', Separation and Purification Technology, vol. 307, pp. 122628-122628. View/Download from: Publisher's site View description>>
Lithium plays a vital role in energy storage which is crucial for the transition to renewable energy, where it enables a stable and continuous release of the harvested energy from batteries. From both primary and secondary sources, there are various cost-effective and environmentally friendly methods of obtaining Lithium. This review highlights the development of novel metal organic framework (MOF)-based technologies (i.e., thin film membranes, mixed matrix membranes and adsorbents) for Lithium-ion extraction from aqueous sources like brine or seawater. The synthesis methods and the performance of the MOF-based membranes and adsorbents are further discussed in detail. MOF-based membranes and adsorbents can achieve a high selectivity towards Lithium ions up to the range of ∼270 times higher than competing ions such as Potassium. However, these materials have drawbacks in terms of water stability or their requirement of highly sophisticated fabrication methods which need to be considered before scaling-up processes. ZIF-8, UiO-66 and HKUST-1 are among the most researched MOFs for the desired application in this work and future progress should be done to address the aforementioned issues. This review compares the development and performance of a variety of different MOF-based materials for Lithium-ion extraction which will give an insight into the commercialization of this material in the industry.
Raghavendra, U, Gudigar, A, Paul, A, Goutham, TS, Inamdar, MA, Hegde, A, Devi, A, Ooi, CP, Deo, RC, Barua, PD, Molinari, F, Ciaccio, EJ & Acharya, UR 2023, 'Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives', Computers in Biology and Medicine, vol. 163, pp. 107063-107063. View/Download from: Publisher's site
Rahaman, MM, Bhowmick, S, Mondal, RN & Saha, SC 2023, 'A Computational Study of Chaotic Flow and Heat Transfer within a Trapezoidal Cavity', Energies, vol. 16, no. 13, pp. 5031-5031. View/Download from: Publisher's site View description>>
Numerical findings of natural convection flows in a trapezoidal cavity are reported in this study. This study focuses on the shift from symmetric steady to chaotic flow within the cavity. This cavity has a heated bottom wall, a cooled top wall, and adiabatic inclined sidewalls. The unsteady natural convection flows occurring within the cavity are numerically simulated using the finite volume (FV) method. The fluid used in the study is air, and the calculations are performed for different dimensionless parameters, including the Prandtl number (Pr), which is kept constant at 0.71, while varying the Rayleigh numbers (Ra) from 100 to 108 and using a fixed aspect ratio (AR) of 0.5. This study focuses on the effect of the Rayleigh numbers on the transition to chaos. In the transition to chaos, a number of bifurcations occur. The first primary transition is found from the steady symmetric to the steady asymmetric stage, known as a pitchfork bifurcation. The second leading transition is found from a steady asymmetric to an unsteady periodic stage, known as Hopf bifurcation. Another prominent bifurcation happens on the changeover of the unsteady flow from the periodic to the chaotic stage. The attractor bifurcates from a stable fixed point to a limit cycle for the Rayleigh numbers between 4 × 106 and 5 × 106. A spectral analysis and the largest Lyapunov exponents are analyzed to investigate the natural convection flows during the shift from periodic to chaos. Moreover, the cavity’s heat transfers are computed for various regimes. The cavity’s flow phenomena are measured and verified.
Rahimi, I, Chen, F & Gandomi, AH 2023, 'A review on COVID-19 forecasting models', Neural Computing and Applications, vol. 35, no. 33, pp. 23671-23681. View/Download from: Publisher's site View description>>
The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.
Rahimi, I, Gandomi, AH, Chen, F & Mezura-Montes, E 2023, 'A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization', Archives of Computational Methods in Engineering, vol. 30, no. 3, pp. 2181-2209. View/Download from: Publisher's site View description>>
AbstractMost real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for analysis. The results indicate that the constraint-handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” and “ Mathematics” were identified as the top three research fields in which future research work is anticipated to increase.
Rahimi, I, Gandomi, AH, Nikoo, MR & Chen, F 2023, 'A comparative study on evolutionary multi-objective algorithms for next release problem', Applied Soft Computing, vol. 144, pp. 110472-110472. View/Download from: Publisher's site
Rahman, SMA & Fattah, IMR 2023, 'Evaluation of a compression ignition engine performance and emission characteristics using diesel-essential oil blends of high orange oil content', Australian Journal of Mechanical Engineering, vol. 21, no. 3, pp. 725-732. View/Download from: Publisher's site View description>>
In this research, waste stream essential oil such as orange oil is used as a diesel fuel partial replacement to be tested in a diesel engine. Like diesel fuel, orange oil does not contain any oxygen since it is constituted of limonene (a colourless liquid aliphatic hydrocarbon) and has almost similar density. A 6-cylinder diesel engine is operated using various blends of orange and diesel fuel. The engine was operated with three different fuel blends: neat diesel, 74% diesel + 26% orange oil (D74O26) and 59% diesel + 41% orange oil (D59O41). All the orange oil blends produced nearly the same brake power from the engine experiment compared to neat diesel fuel. Furthermore, all orange oil blends emit less particulate matter, and the ‘count mean diameter’ of the emitted particles is also lower than base diesel. Based on the obtained results, these blends can be suggested to be used in a diesel engine.
Rajabipour, A, Kutay, C, Guenther, J & Bazli, M 2023, 'Factors to be considered in the design of indigenous communities' houses, with a focus on Australian first nation housing in the Northern Territory', Development Engineering, vol. 8, pp. 100109-100109. View/Download from: Publisher's site
Rajawat, AS, Goyal, SB, Chauhan, C, Bedi, P, Prasad, M & Jan, T 2023, 'Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm', Electronics, vol. 12, no. 1, pp. 217-217. View/Download from: Publisher's site View description>>
Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.
Ramakrishna, VAS, Chamoli, U, Larosa, AG, Mukhopadhyay, SC, Gangadhara Prusty, B & Diwan, AD 2023, 'A biomechanical comparison of posterior fixation approaches in lumbar fusion using computed tomography based lumbosacral spine modelling', Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 237, no. 2, pp. 243-253. View/Download from: Publisher's site View description>>
Extreme lateral interbody fusion (XLIF) may be performed with a standalone interbody cage, or with the addition of unilateral or bilateral pedicle screws; however, decisions regarding supplemental fixation are predominantly based on clinical indicators. This study examines the impact of posterior supplemental fixation on facet micromotions, cage loads and load-patterns at adjacent levels in a L4-L5 XLIF at early and late fusion stages. CT data from an asymptomatic subject were segmented into anatomical regions and digitally stitched into a surface mesh of the lumbosacral spine (L1-S1). The interbody cage and posterior instrumentation (unilateral and bilateral) were inserted at L4-L5. The volumetric mesh was imported into finite element software for pre-processing, running nonlinear static solves and post-processing. Loads and micromotions at the index-level facets reduced commensurately with the extent of posterior fixation accompanying the XLIF, while load-pattern changes observed at adjacent facets may be anatomically dependent. In flexion at partial fusion, compressive stress on the cage reduced by 54% and 72% in unilateral and bilateral models respectively; in extension the reductions were 58% and 75% compared to standalone XLIF. A similar pattern was observed at full fusion. Unilateral fixation provided similar stability compared to bilateral, however there was a reduction in cage stress-risers with the bilateral instrumentation. No changes were found at adjacent discs. Posterior supplemental fixation alters biomechanics at the index and adjacent levels in a manner that warrants consideration alongside clinical information. Unilateral instrumentation is a more efficient option where the stability requirements and subsidence risk are not excessive.
Ramakrishna, VAS, Chamoli, U, Mukhopadhyay, SC, Diwan, AD & Prusty, BG 2023, 'Measuring compressive loads on a ‘smart’ lumbar interbody fusion cage: Proof of concept', Journal of Biomechanics, vol. 147, pp. 111440-111440. View/Download from: Publisher's site
Ramakrishnan, N, Tomamichel, M & Berta, M 2023, 'Moderate Deviation Expansion for Fully Quantum Tasks', IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5041-5059. View/Download from: Publisher's site
Rambach, M, Youssry, A, Tomamichel, M & Romero, J 2023, 'Efficient quantum state tracking in noisy environments', Quantum Science and Technology, vol. 8, no. 1, pp. 015010-015010. View/Download from: Publisher's site View description>>
AbstractQuantum state tomography, which aims to find the best description of a quantum state—the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient (MEG) tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with very noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of MEG tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
Ramu, YK, Thomas, PS, Sirivivatnanon, V & Vessalas, K 2023, 'Non-expansive delayed ettringite formation in low sulphate and low alkali cement mortars', Australian Journal of Civil Engineering, vol. 21, no. 1, pp. 68-79. View/Download from: Publisher's site
Rao, P, Feng, W, Ouyang, P, Cui, J, Nimbalkar, S & Chen, Q 2023, 'Numerical Simulation of Pipeline Failure Mechanisms Under Lightning Strikes, Capturing Electric Disruption and Thermal Damage', Journal of Failure Analysis and Prevention, vol. 23, no. 5, pp. 2065-2074. View/Download from: Publisher's site
Rashed, AO, Huynh, C, Merenda, A, Qin, S, Maghe, M, Kong, L, Kondo, T, Dumée, LF & Razal, JM 2023, 'Carbon nanofibre microfiltration membranes tailored by oxygen plasma for electrocatalytic wastewater treatment in cross-flow reactors', Journal of Membrane Science, vol. 673, pp. 121475-121475. View/Download from: Publisher's site View description>>
The engineering of electrocatalytic membrane reactors provides potential perspectives to integrate membrane separation with electrocatalytic technology for efficient removal of emerging organic pollutants from wastewater. Here, electro-responsive microfiltration carbon nanofibre (CNF) membranes were synthesized by electrospinning of poly(acrylonitrile) PAN and subsequent carbonization, followed by oxygen plasma treatment to induce their surface wettability and reactivity for electrocatalytic water treatment. The electrocatalytic performance of CNF membranes was fine-tuned via oxygen plasma treatment to yield reaction kinetic constants up to 29.6 × 10−3 and 15.6 × 10−3 min−1 against methylene blue (MB) and acetaminophen (ACP), respectively, which were 1.4–1.8 times higher than that exhibited by pristine CNF membranes. The water permeance across CNF membrane was gradually enhanced with increasing the plasma exposure time up to 5 min to exhibit 4.65 × 103 L m−2 h−1.bar−1, while the removal efficiency of MB and ACP was significantly improved to reach 99 and 91%, respectively during combined microfiltration and electrocatalytic reaction, which was 2.4–10.3 times higher than that achieved during microfiltration alone. The achieved performance of oxygen plasma treated CNF membranes was attributed to their enhanced wettability (water contact angle ∼24°) and raised electro-oxidation capacity (oxygen evolution potential ∼1.6 V) with introducing oxygen-containing groups on the membrane surface. This work offers an effective scalable fabrication methodology to engineer flexible and functional CNF membranes with excellent electrocatalytic performance towards cost-effective water treatment.
Rashed, AO, Huynh, C, Merenda, A, Qin, S, Maghe, M, Kong, L, Kondo, T, Razal, JM & Dumée, LF 2023, 'Electrocatalytic ultrafiltration membrane reactors designed from dry-spun self-standing carbon nanotube sheets', Chemical Engineering Journal, vol. 458, pp. 141517-141517. View/Download from: Publisher's site View description>>
The development of electrochemically active ultrafiltration membrane reactors offers promising perspectives to achieve simultaneous separation and degradation of persistent organic pollutants and support triggered self-cleaning of membrane materials upon surface fouling. Here, electro-responsive ultrafiltration membranes were synthesised from drawable carbon nanotubes (CNT) dry-spun as ultra-thin sheets onto preformed carbon nanofibre (CNF) supports to generate a unique class of electrically conductive and flexible ultrafiltration membranes. The pore size of the CNT-based membranes, on the order of ∼ 28 nm, was fine-tuned by controlling the dry layering and orientation of the CNT sheets to manage the membrane selectivity. The CNT-based membranes were used as effective conductive platforms to promote charge transfer during electrocatalytic degradation of acetaminophen, as a model contaminant. The CNT-based membranes, besides offering water permeance up to 2.77 × 103 L.m−2.h−1.bar−1, yielded electrocatalytic kinetic constant up to 46.5 × 10−3 min−1 during combined electrochemical reaction and ultrafiltration process, which is 1.4 to 39 times larger than previously reported values. Such high performance was maintained quite stable even after 8 reuse cycles. These results demonstrate the potential of CNT dry spinning technology for the scalable fabrication of highly permeable, but selective CNT-based membranes with remarkable electrochemical properties towards cost-effective water treatment at an exceptional reaction rate.
Rashed, AO, Huynh, C, Merenda, A, Qin, S, Usman, KAS, Sadek, A, Kong, L, Kondo, T, Dumée, LF & Razal, JM 2023, 'Schottky-like photo/electro-catalytic carbon nanotube composite ultrafiltration membrane reactors', Carbon, vol. 204, pp. 238-253. View/Download from: Publisher's site View description>>
Stimuli-responsive membrane reactors offer advanced strategies towards simultaneous separation and degradation of persistent organic contaminants, allowing for triggered response through photo or electro stimuli. However, the scalable fabrication of stimuli-responsive nanoporous membranes with high selectivity and catalytic reactivity is still challenging in practical application. Here, photo-electro responsive membranes were designed by assembling carbon nanotubes (CNT) scaffolds decorated with conformal nanoscale SnO2 coatings. The membranes, built from spinnable CNT materials supported the generation of a unique class of ultrathin and flexible ultrafiltration membranes with thicknesses down to 25 nm and pore size as narrow as ∼20 nm. The CNTs were used as effective conductive photosensitizers to promote charge transfer from the graphitic to the SnO2 layer, supporting synergistic effects arising from generated Schottky-like diodes to improve the photo/electro-catalytic activity of nanocomposite membranes. The SnO2-CNT membranes exhibited high water permeance of 2.24 × 103 L m−2 h−1.bar−1, and faster reaction kinetics of 77.6 × 10−3 for acetaminophen degradation, which was 2–10 times higher than the kinetics achieved by currently available catalytic membrane reactors. The structural stability and outstanding performance of SnO2-CNT membranes were maintained over 8 reuse cycles with ∼99% degradation efficiency against acetaminophen in 60 min. The unique solid-state fabrication method of uniformly coated catalytic metal oxide on well aligned CNTs provides a scalable feasible approach to produce high-performance ultrafiltration photo/electro-catalytic membrane reactors towards cost-effective water purification at a competent reaction rate.
Rashed, AO, Huynh, C, Merenda, A, Rodriguez-Andres, J, Kong, L, Kondo, T, Razal, JM & Dumée, LF 2023, 'Dry-spun carbon nanotube ultrafiltration membranes tailored by anti-viral metal oxide coatings for human coronavirus 229E capture in water', Journal of Environmental Chemical Engineering, vol. 11, no. 3, pp. 110176-110176. View/Download from: Publisher's site View description>>
Although waterborne virus removal may be achieved using separation membrane technologies, such technologies remain largely inefficient at generating virus-free effluents due to the lack of anti-viral reactivity of conventional membrane materials required to deactivating viruses. Here, a stepwise approach towards simultaneous filtration and disinfection of Human Coronavirus 229E (HCoV-229E) in water effluents, is proposed by engineering dry-spun ultrafiltration carbon nanotube (CNT) membranes, coated with anti-viral SnO2 thin films via atomic layer deposition. The thickness and pore size of the engineered CNT membranes were fine-tuned by varying spinnable CNT sheets and their relative orientations on carbon nanofibre (CNF) porous supports to reach thicknesses less than 1 µm and pore size around 28 nm. The nanoscale SnO2 coatings were found to further reduce the pore size down to ∼21 nm and provide more functional groups on the membrane surface to capture the viruses via size exclusion and electrostatic attractions. The synthesized CNT and SnO2 coated CNT membranes were shown to attain a viral removal efficiency above 6.7 log10 against HCoV-229E virus with fast water permeance up to ∼4 × 103 and 3.5 × 103 L.m−2.h−1.bar−1, respectively. Such high performance was achieved by increasing the dry-spun CNT sheets up to 60 layers, orienting successive 30 CNT layers at 45°, and coating 40 nm SnO2 on the synthesized membranes. The current study provides an efficient scalable fabrication scheme to engineer flexible ultrafiltration CNT-based membranes for cost-effective filtration and inactivation of waterborne viruses to outperform the state-of-the-art ultrafiltration membranes.
Rathinasuriyan, C, Elumalai, PV, Bharani Chandar, J, Karthik, K, Medapati, SR, Alahmadi, AA, Alwetaishi, M, Alzaed, AN, Kalam, MA & Shahapurkar, K 2023, 'Welding-based additive manufacturing processes for fabrication of metallic parts', Composites and Advanced Materials, vol. 32. View/Download from: Publisher's site View description>>
Additive Manufacturing (AM) is modernizing the manufacturing industry by enabling the layer-by-layer deposition process to manufacture objects in nearly any form with minimum material waste. However, components developed utilizing the AM process have dimensional constraints. To address this issue, AM-produced metal materials can be coupled with various welding processes. This article focuses on the foundations, highlighting the distinguishing features, capabilities, and challenges of welding-based AM processes by categorizing them into two major groups; arc welding-based AM like Cold Metal Transfer (CMT), Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW), Plasma Arc Welding (PAW), and high-energy density welding based AM like Laser Beam Welding (LBW) and Electron Beam Welding (EBW). The prior study findings of welding-based AM metal components on mechanical characteristics and microstructural characterization have been addressed. This work will aid researchers, academicians, and professional welders since it gathers vital information on welding-based AM processes. Furthermore, current research in the arena of welding-based AM and its future opportunities has been discussed.
Ray, MK, Mandal, K, Moloudian, G & Lalbakhsh, A 2023, 'Axial ratio beamwidth enhancement of a low-profile circularly polarized antenna using defected ground structures', International Journal of Microwave and Wireless Technologies, vol. 15, no. 8, pp. 1434-1442. View/Download from: Publisher's site View description>>
AbstractThis article explores the possibilities of incorporating a defected ground structure (DGS) for widening the 3 dB axial ratio beamwidth (ARBW) of a simple low-profile planar circularly polarized (CP) antenna. Two pairs of DGSs are etched symmetrically along the diagonals of the ground plane to overcome the limited 3 dB ARBW performance of a crossed-slotted circular-shaped planar CP antenna. Here, one pair of DGSs is orthogonal to another pair of DGSs. The distance between the pairs of DGSs plays a key role in enhancing the 3 dB ARBW by 40.62%. A gap of 0.18λ0 is considered between the DGSs to maintain the symmetrical electric field distribution in the substrate. It also helps to reduce orthogonal current components and provides uniformly distributed patch surface currents. These phenomena collectively help the proposed CP antenna to exhibit right-handed circular polarization radiation with the 3 dB ARBW of 186° and 188° in the E-plane and H-plane, respectively. The measured results yield impedance bandwidth (S11 ≤ −10 dB) of 2.6% (2.283–2.342 GHz), CP bandwidth of 0.9% (2.294–2.315 GHz), gain higher than 4.8 dBic, and total antenna efficiency more than 64% across the entire CP band.
Raza, A, Keshavarz, R, Dutkiewicz, E & Shariati, N 2023, 'Compact Multiservice Antenna for Sensing and Communication Using Reconfigurable Complementary Spiral Resonator', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-9. View/Download from: Publisher's site
Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12219-12233. View/Download from: Publisher's site
Cancer-specific small extracellular vesicles (sEVs), known as exosomes, have shown a great promise to serve as novel biomarkers for cancer diagnosis and prognosis in liquid biopsies. However, the high heterogeneity of sEVs posed great technical challenges to acquiring their molecular information. A simple and reproducible method for isolating subpopulations of sEVs can significantly enhance the detection and stratification of these circulating biomarkers and their function. This study used the synergic effects of the immunoaffinity-based approach and inertial microfluidics (ImmunoInertial microfluidics) to isolate specific subpopulations of sEVs. At first, the cancer cell-derived sEVs were captured on microbeads of varying sizes which were functionalized with specific capture antibodies such as epithelial cell adhesion molecule (EpCAM), epidermal growth factor receptor (EGFR), and the programmed death-ligand 1 (PD-L1), facilitating the selective capture of sEVs. The sEVs-bearing microbeads were subsequently introduced to a series of inertial microfluidic channels, called iZExoSub (inertial zigzag microfluidics for exosome subpopulation separation), for size-based bead separation. Results revealed more than 90% efficiency in sEVs subpopulation separation, further proved via flow cytometry analysis data. Our approach is capable of selective isolation and quantitative detection of important biomarkers from sEVs subpopulations with high sensitivity and low cost and has the capacity to process samples of varying volumes, ranging from µL up to mL continuously. This system can outperform FACS machines in terms of sample throughput by orders of magnitudes. In addition, this study emphasized the necessity of using a consistent sEV marker (as capture and detector) across different samples for accurate assessment of subpopulations.
Razzak, I, Khan, MK, Xu, G & Khalifa, F 2023, 'Guest Editorial Open and Interpretable AI in Computational Pathology', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 4, pp. 1657-1660. View/Download from: Publisher's site
Razzaq, L, Abbas, MM, Waseem, A, Jauhar, TA, Fayaz, H, Kalam, MA, Soudagar, MEM, A.S.Silitonga, Samr-Ul-Husnain & Ishtiaq, U 2023, 'Influence of varying concentrations of TiO2 nanoparticles and engine speed on the performance and emissions of diesel engine operated on waste cooking oil biodiesel blends using response surface methodology', Heliyon, vol. 9, no. 7, pp. e17758-e17758. View/Download from: Publisher's site
Rehman, A, Razzak, I & Xu, G 2023, 'Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 684-690. View/Download from: Publisher's site View description>>
Federated learning has recently emerged as a striking framework for allowing machine and deep learning models with thousands of participants to have distributed training to preserve the privacy of users' data. Federated learning comes with the pros of allowing all participants the possibility of creating robust models even in the absence of sufficient training data. Meanwhile, the participants are allowed to stay anonymous in the process. Recently, Smartphone usage has increased on a huge scale due to its portability and ability to perform many daily life tasks. Typing on a smartphone's soft keyboard generates vibrations that could be abused to distinguish the typed keys, aiding side-channel attacks. This data can be in the form of clinical notes, medical information, username, and passwords. The attackers can steal this data using smartphone hardware sensors. This study proposes a novel framework based on federated learning for side-channel attack detection to secure this information. We collected a dataset from 10 Android smartphone users who were asked to type on the smartphone soft keyboard. We convert this dataset into two windows of five users to make two clients train local models. The federated learning-based framework aggregates model updates contributed by two clients and trains the DNN model individually on the dataset. To reduce the over-fitting factor, each client examines the findings three times. Experiments reveal that the DNN model has a higher accuracy of 80.09\%, showing that the proposed framework can efficiently detect side-channel attacks.
Rehman, J, Hawryszkiewycz, I, Sohaib, O, Namisango, F & Dahri, AS 2023, 'Towards the Knowledge-Smart Professional Service Firms: How High-Performance Work Systems Support the Transformation', Journal of the Knowledge Economy, vol. 14, no. 4, pp. 3640-3670. View/Download from: Publisher's site
Rehman, J, Hawryszkiewycz, IT, Sohaib, O, Namisango, F & Dahri, AS 2023, 'How Professional Service Firms Derive Triple Value Bottomline: An IC Perspective.', J. Inf. Knowl. Manag., vol. 22, no. 01, pp. 2250087:1-2250087:1. View/Download from: Publisher's site View description>>
The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of Information & Communication Technologies (ICTs) globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, these tools & technologies would only be meaningful if these are appropriately utilized by a knowledgeable workforce. In other words, this knowledge age has changed the success mantra of business competitiveness by re-shifting the focus from ICT-based transformations to knowledge-based transformations, though the availability of ICT systems has further augmented the organizational capabilities. Moreover, truly capitalizing on these warrants a knowledge-enabled work culture and recognizing as such the strategic significance of in-house Intellectual Capital (IC) that serves as a prime mover of achieving a sustainable competitive advantage. However, the maximum potential of IC for deriving multi-stakeholder value has not been fully achieved. Therefore, by administering 12 face-to-face semi-structured interviews at Australian Professional Service Firms (PSFs), this research offers a novel perspective on IC valuation by presenting the concept of ‘Triple Value Bottomline’ coupled with ‘IC Best Practices for PSFs’. These collectively offer IC evaluation, measurement and management mechanisms. Overall, the findings reveal immense potential of IC for achieving diverse value outcomes for multi-stakeholders in PSFs.
Reinhartz-Berger, I, Zdravkovic, J & Gill, A 2023, 'Guest editorial for EMMSAD’2021 special section', Software and Systems Modeling, vol. 22, no. 1, pp. 9-11. View/Download from: Publisher's site
Ren, J, Zhu, X & Li, S 2023, 'Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method', Sensors, vol. 23, no. 2, pp. 860-860. View/Download from: Publisher's site View description>>
A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force method. The difference between the maximum bending strains of the bridges with intact and damaged cables is used to obtain the damage index vectors (DIXVs). Then, BVM is obtained by the normalized DIXV. Finally, the damage indicator vector (DIV) is obtained by DIXV and BVM to identify the damaged cables. The damage indicator is substituted into the damage severity function to identify the corresponding damage severity. A field cable-stayed bridge is used to verify the proposed method. The three-dimensional finite element model is established using ANSYS, and the model is validated using the field measurements. The validated model is used to simulate the strain response of the bridge with different damage scenarios subject to a moving vehicle load, including one, two, three, and four damaged cables with damage severity of 10%, 20%, and 30%, respectively. The noise effect is also discussed. The results show that the BVM method has good anti-noise capability and robustness.
Ren, L, Guo, Z, Zhang, L, Hu, H, Li, C, Lin, Z, Zhen, Z & Zhou, JL 2023, 'A novel aerobic denitrifying phosphate-accumulating bacterium efficiently removes phthalic acid ester, total nitrogen and phosphate from municipal wastewater', Journal of Water Process Engineering, vol. 52, pp. 103532-103532. View/Download from: Publisher's site View description>>
Simultaneous removal of nitrogen, phosphate and emerging pollutants are critical for safe reuse of wastewater, but research in this field is limited. In the present study, a novel aerobic denitrifying phosphate-accumulating bacterial strain RL-GZ01 was found to be able to utilize phthalic acid esters (PAEs) as carbon resource for cell growth. Based on 16S rRNA gene analysis, physiological and biochemical characterization, and genome-based average nucleotide identity calculation, RL-GZ01 was identified as Rhodococcus pyridinivorans. Strain RL-GZ01 showed high DEHP degradation in alkaline conditions and good tolerance of salinity and organic solvents. The degradation of DEHP by RL-GZ01 fitted well with a modified Gompertz model (R2 = 0.9985). Metabolic intermediates of DEHP were identified via UHPLC-MS/MS analysis and the catabolic pathway was proposed thereafter. Genes and gene clusters contributed to the utilization of DEHP were analyzed through genomic analysis. Analysis of KEGG nitrogen metabolism pathway indicated that nitrate and nitrite were further transformed into ammonium which was further used for the biosynthesis of L-glutamine and L-glutamate. Strain RL-GZ01 was further identified as a denitrifying phosphate accumulating organism which can accumulate phosphate by generating polyphosphate. Finally, strain RL-GZ01 was applied to municipal wastewater treatment for simultaneous removal of nitrogen, phosphate and DEHP. The removal percentages of DEHP (5 mg/L), TN (71.2 mg/L), NH4+-N (70.9 mg/L), PO43−-P (10.89 mg/L) and COD (622.4 mg/L) by strain RL-GZ01 were 89.94 %, 64.45 %, 64.94 %, 76.30 % and 63.23 % within 84 h, respectively. These demonstrated the capability of strain RL-GZ01 for the biological treatment of wastewater containing PAEs.
Ren, L, Weng, L, Chen, D, Hu, H, Jia, Y & Zhou, JL 2023, 'Bioremediation of PAEs-contaminated saline soil: The application of a marine bacterial strain isolated from mangrove sediment', Marine Pollution Bulletin, vol. 192, pp. 115071-115071. View/Download from: Publisher's site
Ren, Y, Ren, B, Zhang, X, Lv, T, Ni, W & Lu, G 2023, 'Impartial Cooperation in SWIPT-Assisted NOMA Systems With Random User Distribution', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10488-10504. View/Download from: Publisher's site
Ren, Z, Cao, H, Desmond, P, Liu, B, Ngo, HH, He, X, Li, G, Ma, J & Ding, A 2023, 'Ions play different roles in virus removal caused by different NOMs in UF process: Removal efficiency and mechanism analysis', Chemosphere, vol. 313, pp. 137644-137644. View/Download from: Publisher's site
Ren, Z, Gao, D, Zhu, Y, Ni, Q, Yan, K & Hong, J 2023, 'Generative adversarial networks driven by multi-domain information for improving the quality of generated samples in fault diagnosis', Engineering Applications of Artificial Intelligence, vol. 124, pp. 106542-106542. View/Download from: Publisher's site
Ren, Z, Ji, J, Zhu, Y, Hong, J & Feng, K 2023, 'Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-17. View/Download from: Publisher's site
Ren, Z, Shi, H, Zeng, J, He, X, Li, G, Ngo, HH, Ma, J, Tang, CY & Ding, A 2023, 'In Situ Utilization of Iron Flocs after Fe3+ Coagulation Enhances H2O2 Chemical Cleaning to Eliminate Viruses and Mitigate Ultrafiltration Membrane Fouling', ACS ES&T Water, vol. 3, no. 8, pp. 2718-2729. View/Download from: Publisher's site
Background: Implantation of insulin-secreting cells has been trialed as a treatment for Type 1 diabetes mellitus; however, the host immunogenic response limits their effectiveness. Methodology: The authors developed a core-shell nanostructure of upconversion nanoparticle-mesoporous silica for controlled local delivery of an immunomodulatory agent, MCC950, using near-infrared light and validated it in in vitro models of fibrosis. Results: The individual components of the nanosystem did not affect the proliferation of insulin-secreting cells, unlike fibroblast proliferation (p < 0.01). The nanosystem is effective at releasing MCC950 and preventing fibroblast differentiation (p < 0.01), inflammation (IL-6 expression; p < 0.05) and monocyte adhesion (p < 0.01). Conclusion: This MCC950-loaded nanomedicine system could be used in the future together with insulin-secreting cell implants to increase their longevity as a curative treatment for Type 1 diabetes mellitus.
Reza, MS, Hannan, MA, Ker, PJ, Mansor, M, Lipu, MSH, Hossain, MJ & Mahlia, TMI 2023, 'Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions', Journal of Energy Storage, vol. 68, pp. 107698-107698. View/Download from: Publisher's site
Richter, R, Syberg, M, Deuse, J, Willats, P & Lenze, D 2023, 'Creating lean value streams through proactive variability management', International Journal of Production Research, vol. 61, no. 16, pp. 5692-5703. View/Download from: Publisher's site View description>>
Complex product and production systems often result in high variability in the production flow, prohibiting the sustainable implementation of lean practices. In this paper the authors introduce a PDCA cycle to analyse and reduce variability in value streams. The value stream is divided into zones, which are then qualified as stable or unstable. Lean practices can be applied in stable zones, unstable zones remain expert-driven. Measures are introduced to reduce variability in unstable zones with the ultimate target, to turn them into stable zones, extending sustainable lean activities in the value stream, step by step. An IT system is developed to acquire, process and visualise the vast amount of data to provide structured information for experts and management for the reduction of variability in production.
Roche, CD, Lin, H, Huang, Y, de Bock, CE, Beck, D, Xue, M & Gentile, C 2023, '3D bioprinted alginate-gelatin hydrogel patches containing cardiac spheroids recover heart function in a mouse model of myocardial infarction', Bioprinting, vol. 30, pp. e00263-e00263. View/Download from: Publisher's site
Romeijn, T, Fletcher, DF & de Andrade, A 2023, 'Evaluation of numerical approaches for the simulation of water-flow in gravity-driven helical mineral separators', Separation Science and Technology, vol. 58, no. 14, pp. 2519-2538. View/Download from: Publisher's site
Rony, ZI, Mofijur, M, Hasan, MM, Ahmed, SF, Almomani, F, Rasul, MG, Jahirul, MI, Loke Show, P, Kalam, MA & Mahlia, TMI 2023, 'Unanswered issues on decarbonizing the aviation industry through the development of sustainable aviation fuel from microalgae', Fuel, vol. 334, pp. 126553-126553. View/Download from: Publisher's site View description>>
Concerns have been raised about the effects of fossil fuel combustion on global warming and climate change. Fuel consumer behavior is also heavily influenced by factors such as fluctuating fuel prices and the need for a consistent and reliable fuel supply. Microalgae fuel is gaining popularity in the aviation industry as a potential source of energy diversification. Microalgae can grow in saltwater or wastewater, capture CO2 from the atmosphere and produce lipids without requiring a large amount of land. As a result, the production of oil from microalgae poses no threat to food availability. The low carbon footprint of microalgae-derived fuels has the potential to mitigate the impact of traditional aviation fuels derived from petroleum on climate change and global warming. Therefore, aviation fuels derived from microalgae have the potential to be a more environmentally friendly and sustainable alternative to conventional fuels. Gathering microalgal species with a high lipid content, drying them, and turning them into aviation fuel is an expensive process. The use of biofuels derived from microalgae in the aviation industry is still in its infancy, but there is room for growth. This study analyses the potential routes already researched, their drawbacks in implementation, and the many different conceptual approaches that can be used to produce sustainable aviation fuel from microalgal lipids. Microalgae species with fast-growing rates require less space and generate lipids that can be converted into biofuel without imperiling food security. The key challenges in algal-based aviation biofuel include decreased lipid content, harvesting expenses, and drying procedure that should be enhanced and optimized to increase process viability.
Roobavannan, S, Choo, Y, Truong, DQ, Han, DS, Shon, HK & Naidu, G 2023, 'Seawater lithium mining by zeolitic imidazolate framework encapsulated manganese oxide ion sieve nanomaterial', Chemical Engineering Journal, vol. 474, pp. 145957-145957. View/Download from: Publisher's site
Roohani, I, Entezari, A & Zreiqat, H 2023, 'Liquid crystal display technique (LCD) for high resolution 3D printing of triply periodic minimal surface lattices bioceramics', Additive Manufacturing, vol. 74, pp. 103720-103720. View/Download from: Publisher's site
Roopa, AK, Hunashyal, AM, Patil, AY, Kamadollishettar, A, Patil, B, Soudagar, MEM, Shahapurkar, K, Khan, TMY & Kalam, MA 2023, 'Study on Interfacial Interaction of Cement-Based Nanocomposite by Molecular Dynamic Analysis and an RVE Approach', Advances in Civil Engineering, vol. 2023, pp. 1-18. View/Download from: Publisher's site View description>>
There is an increased demand for cement nanocomposites in the twenty-first century due to their composition, higher strength, high efficiency, and multiscale nature. As carbon nanotubes (CNTs) possess extremely high strength, resilience, and stiffness, inclusion of carbon nanotubes in small quantities to the concrete mix makes them a multifunctional material. A molecular level understanding is significant to capacitate the macrolevel properties of these composites. In the proposed work, molecular dynamics (MD) simulations are used to understand the behaviour of the composites at the atomic level and continuum mechanics with representative volume element (RVE) homogenization modelling is carried out for interfacial interaction study of composites. The mechanical properties such as Young’s modulus, shear modulus, and poisons are evaluated using previous methods of simulations for different compositions of nanomaterials in cement matrix. The FORCITE module of MD simulation and square RVE model is used to determine the mechanical, electrical properties, and elastic constants of the cement nanocomposite. The MD simulation describes the linking effect of CNT into cement matric, and the RVE modelling study reveals the pull-out effect of CNT from matrix. From experimental and analytical studies, it is found that increase in CNT till 0.5% weight fraction increases the mechanical properties about 12% and further increasing of CNT weight fraction causes a reduction in mechanical properties about 5% due to the agglomeration of nanotubes. The density of states method in MD simulation indicates that mobility of the electrons increases with an increase in carbon nanotube proportion in the composites. The experimental test results substantiate the analytical studies, and the error obtained from both approaches is less than 20%. From the analytical study, the average maximum Young’s modulus, shear modulus, and bulk modulus are obtained as 46 GPa, 31 GPa, and 32 ...
Rout, JK, Dalmia, A, Rath, SK, Mohanta, BK, Ramasubbareddy, S & Gandomi, AH 2023, 'Detecting Product Review Spammers Using Principles of Big Data', IEEE Transactions on Engineering Management, vol. 70, no. 7, pp. 2516-2527. View/Download from: Publisher's site
Roy, NC, Saha, G & Saha, SC 2023, 'MHD CASSON OR CARREAU FLUIDS FLOW WITH MICROORGANISMS OVER A PERMEABLE SHRINKING SURFACE', Journal of Naval Architecture and Marine Engineering, vol. 20, no. 2, pp. 101-114. View/Download from: Publisher's site View description>>
The characteristics of dual solutions of the flow of Casson or Carreau fluids mixed with microorganisms over a permeable shrinking surface are investigated considering the effects of the magnetic field, heat dissipation, Dufour, and thermophoretic diffusivity. By using similarity transformations, the governing equations are converted into a set of ordinary differential equations. These reduced equations are then solved by the well-known shooting technique along with the fourth-order Runge-Kutta method. A comparison is provided and the present solutions hold a good agreement with available published results. The local skin friction coefficient, local Nusselt number, local Sherwood number, and local density number of the microorganisms are found to increase with the increase of non-Newtonian Casson parameter, Dufour, and thermophoretic diffusivity number, however, it decreases with Weissenberg number. The remarkable finding which is not revealed yet is that dual solutions exist in the flow of Casson or Carreau fluids with microorganisms over a permeable shrinking sheet subject to a certain combination of parameters. Moreover, the domain of the occurrence of dual solutions broadens on account of higher values of all physical parameters of the problem except the Eckert number.
Roy, NC, Saha, G & Saha, SC 2023, 'Williamson Fluid Flow Having Microorganisms Over a Permeable Shrinking Sheet', Science and Technology Asia, vol. 28, no. 3, pp. 1-17. View description>>
This study examines the characteristics of fluid flow of microorganisms over a permeable vertical shrinking sheet in the presence of a magnetic field and thermal radiation. The governing equations are simplified to a nonlinear system of ODEs and solved using the nonlinear shooting method. The results of the study show that an increase in certain parameters, such as the Eckert number and Hartmann number, leads to an increase in local skin friction coefficient and density of microorganisms, and a decrease in the local Nusselt number. However, when the Weissenberg number is higher, the opposite characteristics are observed. The study also found that the domain of dual solutions increases with the increase of certain parameters but decreases with a higher Weissenberg number and boundary layer separation is delayed with the increase of dual solutions.
Ruan, J, Cui, H, Huang, Y, Li, T, Wu, C & Zhang, K 2023, 'A review of occluded objects detection in real complex scenarios for autonomous driving', Green Energy and Intelligent Transportation, vol. 2, no. 3, pp. 100092-100092. View/Download from: Publisher's site View description>>
Autonomous driving is a promising way to future safe, efficient, and low-carbon transportation. Real-time accurate target detection is an essential precondition for the generation of proper following decision and control signals. However, considering the complex practical scenarios, accurate recognition of occluded targets is a major challenge of target detection for autonomous driving with limited computational capability. To reveal the overlap and difference between various occluded object detection by sharing the same available sensors, this paper presents a review of detection methods for occluded objects in complex real-driving scenarios. Considering the rapid development of autonomous driving technologies, the research analyzed in this study is limited to the recent five years. The study of occluded object detection is divided into three parts, namely occluded vehicles, pedestrians and traffic signs. This paper provided a detailed summary of the target detection methods used in these three parts according to the differences in detection methods and ideas, which is followed by the comparison of advantages and disadvantages of different detection methods for the same object. Finally, the shortcomings and limitations of the existing detection methods are summarized, and the challenges and future development prospects in this field are discussed.
Ruan, Z, Song, W, Zhang, Y, Yao, G & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulsewidth Modulation Technique Using Virtual Flux Ripple', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 2051-2060. View/Download from: Publisher's site
Ruan, Z, Song, W, Zhao, L, Zhang, Y & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulse Width Modulation Control Strategy of Induction Motor Drive System With Torque Ripple Prediction', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 993-1003. View/Download from: Publisher's site
Rubina Aktar, M, Shamim Anower, M, Zahurul Islam Sarkar, M, Sayem, ASM, Rashedul Islam, M, Akash, AI, Rumana Akter Rume, M, Moloudian, G & Lalbakhsh, A 2023, 'Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G', IEEE Access, vol. 11, pp. 3208-3220. View/Download from: Publisher's site
Rybarczyk, A, Smułek, W, Grzywaczyk, A, Kaczorek, E, Jesionowski, T, Nghiem, LD & Zdarta, J 2023, '3D printed polylactide scaffolding for laccase immobilization to improve enzyme stability and estrogen removal from wastewater', Bioresource Technology, vol. 381, pp. 129144-129144. View/Download from: Publisher's site View description>>
This study reports a biocatalytic system of immobilized laccase and 3D printed open-structure biopolymer scaffoldings. The scaffoldings were computer-designed and 3D printed using polylactide (PLA) filament. The immobilization of laccase onto the 3D printed PLA scaffolds were optimized with regard to pH, enzyme concentration, and immobilization time. Laccase immobilization resulted in a small reduction in reactivity (in terms of Michaelis constant and maximum reaction rate) but led to significant improvement in chemical and thermal stability. After 20 days of storage, the immobilized and free laccase showed 80% and 35% retention of the initial enzymatic activity, respectively. The immobilized laccase on 3D printed PLA scaffolds achieved 10% improvement in the removal of estrogens from real wastewater as compared to free laccase and showed the significant reusability potential. Results here are promising but also highlight the need for further study to improve enzymatic activity and reusability.
Saberi, Z, K. Hussain, O & Saberi, M 2023, 'Data-driven personalized assortment optimization by considering customers’ value and their risk of churning: Case of online grocery shopping', Computers & Industrial Engineering, vol. 182, pp. 109328-109328. View/Download from: Publisher's site
Saboj, JH, Nag, P, Saha, G & Saha, SC 2023, 'Entropy Production Analysis in an Octagonal Cavity with an Inner Cold Cylinder: A Thermodynamic Aspect', Energies, vol. 16, no. 14, pp. 5487-5487. View/Download from: Publisher's site View description>>
Understanding fluid dynamics and heat transfer is crucial for designing and improving various engineering systems. This study examines the heat transfer characteristics of a buoyancy-driven natural convection flow that is laminar and incompressible. The investigation also considers entropy generation (Egen) within an octagonal cavity subject to a cold cylinder inside the cavity. The dimensionless version of the governing equations and their corresponding boundary conditions have been solved numerically using the finite element method, employing triangular mesh elements for discretization. The findings indicated that incorporating a cold cylinder inside the octagonal cavity resulted in a higher heat transfer (HT) rate than in the absence of a cold cylinder. Furthermore, using the heat flux condition led to a higher average Nusselt number (Nuavg) and a lower Bejan number (Be) than the isothermal boundary condition. The results also showed that HT and Egen were more significant in the Al2O3-H2O nanofluid than the basic fluids such as air and water, and HT increased as χ increased. The current research demonstrates that employing the heat flux condition and incorporating nanoparticles can enhance the rate of HT and Egen. Furthermore, the thermo-fluid system should be operated at low Ra to achieve greater HT effectiveness for nanofluid concerns.
Sadeghi, F, Mousavi, M, Zhu, X, Rashidi, M, Samali, B & Gandomi, AH 2023, 'Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data', Journal of Structural Engineering, vol. 149, no. 1. View/Download from: Publisher's site View description>>
Damage of shear connectors in steel-concrete composite (SCC) beams affects the composite action and appears as abnormalities in the shear slip between the composite components. The shear slip at the composite interface causes nonlinearity in the global composite beam response which is an issue beyond the inherent complexity of the composite system. This paper presents a novel approach for damage detection of SCCs by variational mode decomposition (VMD) of shear slip data. Numerical and experimental studies were conducted on steel-concrete composite beams to generate noise-contaminated shear slip data from undamaged and damaged states of the structure. The VMD algorithm is employed to decompose shear slip signals into intrinsic mode functions (IMFs) and then, the center frequency of each mode is captured. The higher center frequency realized in the second mode is taken as a damage sensitive feature. Because IMFs curves are extracted from the signal splines interpolated between the average of the peaks and troughs, the change of energy in the center frequencies of the undamaged and damaged states can be defined as a damage index. Welch power spectral densities of the IMFs are calculated to further investigate changes in the center frequencies of IMFs obtained using the VMD algorithm. The empirical mode decomposition (EMD) technique is also utilized to decompose shear slip signals into IMFs for comparison purposes. The results show that the EMD is not able to detect abnormalities in the shear slip signals affected by damage because of losing information through several mode decompositions and a phenomenon termed mode mixing. However, when the VMD was set to decompose the signal into two IMFs, it was found that it is more efficient in maintaining the frequency content of the shear slip signal. According to the results, the proposed method has been proved successful in detecting damage of composite beams and can be employed as a reliable and robust technique.
Sadeghirad, H, Bahrami, T, Layeghi, SM, Yousefi, H, Rezaei, M, Hosseini‐Fard, SR, Radfar, P, Warkiani, ME, O'Byrne, K & Kulasinghe, A 2023, 'Immunotherapeutic targets in non‐small cell lung cancer', Immunology, vol. 168, no. 2, pp. 256-272. View/Download from: Publisher's site View description>>
AbstractNon‐small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and has a 5‐year survival rate of ~20%. Immunotherapies have shown promising results leading to durable responses, however, they are only effective for a subset of patients. To determine the best therapeutic approach, a thorough and in‐depth profiling of the tumour microenvironment (TME) is required. The TME is a complex network of cell types that form an interconnected network, promoting tumour cell initiation, growth and dissemination. The stroma, immune cells and endothelial cells that comprise the TME generate a plethora of cytotoxic or cytoprotective signalling pathways. In this review, we discuss immunotherapeutic targets in NSCLC tumours and how the TME may influence patients' response to immunotherapy.
Saha, G, Al-Waaly, AAY, Paul, MC & Saha, SC 2023, 'Heat Transfer in Cavities: Configurative Systematic Review', Energies, vol. 16, no. 5, pp. 2338-2338. View/Download from: Publisher's site View description>>
This study is a systematic review of research on heat transfer analysis in cavities and aims to provide a comprehensive understanding of flow and heat transfer performance in various kinds of cavities with or without the presence of fins, obstacles, cylinders, and baffles. The study also examines the effects of different forces, such as magnetic force, buoyancy force, and thermophoresis effect on heat transfer in cavities. This study also focuses on different types of fluids, such as air, water, nanofluids, and hybrid nanofluids in cavities. Moreover, this review deals with aspects of flow and heat transfer phenomena for only single-phase flows. It discusses various validation techniques used in numerical studies and the different types and sizes of mesh used by researchers. The study is a comprehensive review of 297 research articles, mostly published since 2000, and covers the current progress in the area of heat transfer analysis in cavities. The literature review in this study shows that cavities with obstacles such as fins and rotating cylinders have a significant impact on enhancing heat transfer. Additionally, it is found that the use of nanofluids and hybrid nanofluids has a greater effect on enhancing heat transfer. Lastly, the study suggests future research directions in the field of heat transfer in cavities. This study’s findings have significant implications for a range of areas, including electronic cooling, energy storage systems, solar thermal technologies, and nuclear reactor systems.
Saha, S, Kundu, B, Paul, GC & Pradhan, B 2023, 'Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models', Stochastic Environmental Research and Risk Assessment, vol. 37, no. 7, pp. 2513-2540. View/Download from: Publisher's site View description>>
AbstractDrought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.
Saha, S, Kundu, B, Saha, A, Mukherjee, K & Pradhan, B 2023, 'Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India', Theoretical and Applied Climatology, vol. 151, no. 1-2, pp. 891-913. View/Download from: Publisher's site
Saha, SC, Ahmed, SF, Ahmed, B, Mehnaz, T & Musharrat, A 2023, 'A review of phase change materials in multi-designed tubes and buildings: Testing methods, applications, and heat transfer enhancement', Journal of Energy Storage, vol. 63, pp. 106990-106990. View/Download from: Publisher's site
Sahin, SE, Gulhan, G, Barua, PD, Tuncer, T, Dogan, S, Faust, O & Acharya, UR 2023, 'PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals', Expert Systems, vol. 40, no. 8. View/Download from: Publisher's site View description>>
AbstractEngines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet m...
Sahoo, SS, Mohanty, S, Sahoo, KS, Daneshmand, M & Gandomi, AH 2023, 'A Three-Factor-Based Authentication Scheme of 5G Wireless Sensor Networks for IoT System', IEEE Internet of Things Journal, vol. 10, no. 17, pp. 15087-15099. View/Download from: Publisher's site
Sajjad, MB, Indraratna, B, Ngo, T, Kelly, R & Rujikiatkamjorn, C 2023, 'A Computational Approach to Smoothen the Abrupt Stiffness Variation along Railway Transitions', Journal of Geotechnical and Geoenvironmental Engineering, vol. 149, no. 8. View/Download from: Publisher's site
Sakhare, A, Punetha, P, Meena, NK, Nimbalkar, S & Dodagoudar, G-R 2023, 'Dynamic behaviour of integral abutment bridge transition under moving train loads', Transportation Geotechnics, vol. 40, pp. 100989-100989. View/Download from: Publisher's site View description>>
Transition zones, such as bridge approaches, are discontinuities along a railway line that are highly prone to differential movement due to a rapid variation of support conditions along the track. The concrete approach slabs are often provided before and after the bridges to reduce this differential movement and provide a gradual variation in track stiffness. This paper provides insights into the dynamic behaviour of an integral abutment railway bridge (IARB) transition zone consisting of approach slab under moving train loads using finite element (FE) analyses. Firstly, the FE model is successfully validated against the published field data. Subsequently, the validated model is employed to investigate the influence of parameters such as approach slab geometry (length, thickness, inclination, and shape), backfill soil type, direction of train movement and train speed. Results show that the behaviour of IARB is sensitive to the length of the approach slab, backfill soil type and train speed. The findings of this study enhance the current understanding of the behaviour of IARBs subjected to moving train loading and identify the important parameters that influence their performance.
Sakti, AD, Anggraini, TS, Ihsan, KTN, Misra, P, Trang, NTQ, Pradhan, B, Wenten, IG, Hadi, PO & Wikantika, K 2023, 'Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products', Science of The Total Environment, vol. 854, pp. 158825-158825. View/Download from: Publisher's site
Salah, A, Bekhit, M, Eldesouky, E, Ali, A & Fathalla, A 2023, 'Price Prediction of Seasonal Items Using Time Series Analysis', Computer Systems Science and Engineering, vol. 46, no. 1, pp. 445-460. View/Download from: Publisher's site
Saleem, R, Ni, W, Ikram, M & Jamalipour, A 2023, 'Deep-Reinforcement-Learning-Driven Secrecy Design for Intelligent-Reflecting-Surface-Based 6G-IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8812-8824. View/Download from: Publisher's site View description>>
The sixth-generation (6G) wireless communication has called for higher bandwidth and massive connectivity of Internet-of-Things (IoT) devices. The increased connectivity also demands advanced levels of network security, which are critical to maintaining due to severe signal attenuation at higher frequencies. Intelligent reflecting surface (IRS) is an increasingly popular, efficient, solution to cater to higher data rates, better coverage range, and reduced signal blockages. In this paper, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A mathematical design of the system model is presented, and an optimization problem is formulated. The secrecy rate of the trusted devices is maximized while guaranteeing Quality-of-Service (QoS) to all the legitimate, trusted and untrusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices owing to the complex and continuous nature of action and state spaces. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices under the proposed model, as compared to the benchmark cases. The results also ensure the throughput performance of all trusted and untrusted devices. The performance of the proposed DDPG model is evaluated under meticulously selected hyper-parameters.
Salis, Z, Gallego, B, Nguyen, TV & Sainsbury, A 2023, 'Association of Decrease in Body Mass Index With Reduced Incidence and Progression of the Structural Defects of Knee Osteoarthritis: A Prospective Multi‐Cohort Study', Arthritis & Rheumatology, vol. 75, no. 4, pp. 533-543. View/Download from: Publisher's site View description>>
ObjectiveTo define the association between change in body mass index (BMI) and the incidence and progression of the structural defects of knee osteoarthritis as assessed by radiography.MethodsRadiographic analyses of knees at baseline and at 4–5 years of follow‐up were obtained from the following 3 independent cohort studies: the Osteoarthritis Initiative (OAI) study, the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study. Logistic regression analyses using generalized estimating equations, with clustering of both knees within individuals, were used to investigate the association between change in BMI from baseline to 4–5 years of follow‐up and the incidence and progression of knee osteoarthritis.ResultsA total of 9,683 knees (from 5,774 participants) in an “incidence cohort” and 6,075 knees (from 3,988 participants) in a “progression cohort” were investigated. Change in BMI was positively associated with both the incidence and progression of the structural defects of knee osteoarthritis. The adjusted odds ratio (OR) for osteoarthritis incidence was 1.05 (95% confidence interval [95% CI] 1.02–1.09), and the adjusted OR for osteoarthritis progression was 1.05 (95% CI 1.01–1.09). Change in BMI was also positively associated with degeneration (i.e., narrowing) of the joint space and with degeneration of the femoral and tibial surfaces (as indicated by osteophytes) on the medial but not on the lateral side of the knee.ConclusionA decrease in BMI was independently associated with lower odds of incidence and progression of the structural defects of knee osteoarthritis and could be a component in preventing the onset or worsening of knee osteoarthritis.
Samadi, A, Ni, T, Fontananova, E, Tang, G, Shon, H & Zhao, S 2023, 'Engineering antiwetting hydrophobic surfaces for membrane distillation: A review', Desalination, vol. 563, pp. 116722-116722. View/Download from: Publisher's site View description>>
Membrane distillation (MD) is an emerging membrane separation technology with great potential for desalination, wastewater treatment and volatile resource recovery. It becomes even more attractive as it can utilize low-grade heat or renewable energy, and treat high-salinity waste liquids towards zero liquid discharge. However, the performance of MD is often limited by the wetting of hydrophobic porous membranes during operation, leading to reduced flux and efficiency. To overcome this challenge, the development of antiwetting hydrophobic MD membranes has gained increasing attention in recent years. In this review, we examine the liquid entry pressure (LEP) and its influencing factors (e.g. the maximum pore size, surface chemistry/free energy and surface roughness/architecture) of an MD membrane, which determine the antiwetting performance of the porous MD membrane. From enhancing the LEP point of view, we propose two key strategies for engineering antiwetting surfaces: (1) reducing the membrane pore size, and (2) increasing the liquid contact angle by minimizing the surface free energy and the liquid/solid contact area through enhancing the surface roughness and/or creating hierarchical/re-entrant structures. These strategies include various specific fabrication techniques, such as surface coating, vapor deposition, layer-by-layer assembly, surface fluorination, and surface functionalization. Green surface modification materials and methods are also discussed to reduce the application of less environmentally friendly fluoride-containing compounds. Furthermore, we provide insights and future directions for the design and engineering of high-performance antiwetting hydrophobic MD membranes. Overall, this review offers a comprehensive analysis of the current state-of-the-art research in engineering antiwetting hydrophobic MD membranes, and highlights the potential for the development of next-generation MD membranes with improved performance and efficiency.
Samadi-Boroujeni, H, Haghshenas-Adarmanabadi, A, Shayannejad, M & Khabbaz, H 2023, 'Comparison of Mohr-Coulomb and hardening soil constitutive models for simulation of settlements in the Karkheh earth dam', Australian Geomechanics Journal, vol. 58, no. 3, pp. 143-158. View/Download from: Publisher's site View description>>
This paper presents the settlement behaviour of Karkheh earth dam during its construction and operation stages. Karkheh is one of the largest earth dams in the world in terms of its reservoir capacity and body volume. The settlement of such a large body of soil can affect the performance of the dam elements and endanger downstream areas; should a breach or failure occur in the dam, more than two million people will be affected. It is crucial to know the settlement behaviour of this structure and use the existing results to predict its future settlements and calibrate the existing stress-strain models. For anticipation of dam settlement the measured displacement from the portable probe anchor magnets installed in the dam body are compared to the results of numerical simulations. The available data cover a period of 12 years including construction, and two material impounding and operation periods of the dam. The numerical analysis is performed in 2D plane-strain conditions and two material models are used, including Mohr-Coulomb (MC) and Hardening Soil (HS) models. The comparison between the calculation results and the measured vertical deformations in the dam site reveals that the accuracy of model for the deformations in the middle levels of dam is better than those of the crest for both applied material models in construction and impounding stages. The maximum settlement differences between computed and observed values are 0.05 m for MC model and 0.01 m for HS model. For the operation stage, the error of calculated settlements for the MC model is smaller; hence the results of this model might be more reliable for prediction of future dam settlements. The similar trends, obtained from both material models, exhibit the suitability of the model parameters used in the simulations.
Samal, PB, Chen, SJ & Fumeaux, C 2023, 'Wearable Textile Multiband Antenna for WBAN Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 2, pp. 1391-1402. View/Download from: Publisher's site
Samal, PB, Chen, SJ, Tung, TT, Losic, D & Fumeaux, C 2023, 'Efficiency-Driven Design for Planar Antennas With Lossy Materials', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 23-33. View/Download from: Publisher's site
Samsuddin Sah, S, Abdul Maulud, KN, Sharil, S, A. Karim, O & Pradhan, B 2023, 'Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images', The Egyptian Journal of Remote Sensing and Space Sciences, vol. 26, no. 4, pp. 989-998. View/Download from: Publisher's site
Sanderson, B, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, EW 2023, 'Multicenter, multidisciplinary user‐centered design of a clinical decision‐support and simulation system for massive transfusion', Transfusion, vol. 63, no. 5, pp. 993-1004. View/Download from: Publisher's site View description>>
AbstractBackgroundManaging critical bleeding with massive transfusion (MT) requires a multidisciplinary team, often physically separated, to perform several simultaneous tasks at short notice. This places a significant cognitive load on team members, who must maintain situational awareness in rapidly changing scenarios. Similar resuscitation scenarios have benefited from the use of clinical decision support (CDS) tools.Study Design and MethodsA multicenter, multidisciplinary, user‐centered design (UCD) study was conducted to design a computerized CDS for MT. This study included analysis of the problem context with a cognitive walkthrough, development of a user requirement statement, and co‐design with users of prototypes for testing. The final prototype was evaluated using qualitative assessment and the System Usability Scale (SUS).ResultsEighteen participants were recruited across four institutions. The first UCD cycle resulted in the development of four prototype interfaces that addressed the user requirements and context of implementation. Of these, the preferred interface was further developed in the second UCD cycle to create a high‐fidelity web‐based CDS for MT. This prototype was evaluated by 15 participants using a simulated bleeding scenario and demonstrated an average SUS of 69.3 (above average, SD 16) and a clear interface with easy‐to‐follow blood product tracking.DiscussionWe used a UCD process to explore a highly complex clinical scenario and develop a prototype CDS for MT that incorporates distributive situational awareness, supports multiple user roles, and allows simulated MT training. Evaluation of the impact of this prototype on the efficacy and efficiency of managing MT is currentl...
Sanderson, BJ, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, E 2023, 'Clinical decision support versus a paper‐based protocol for massive transfusion: Impact on decision outcomes in a simulation study', Transfusion, vol. 63, no. 12, pp. 2225-2233. View/Download from: Publisher's site View description>>
AbstractBackgroundManagement of major hemorrhage frequently requires massive transfusion (MT) support, which should be delivered effectively and efficiently. We have previously developed a clinical decision support system (CDS) for MT using a multicenter multidisciplinary user‐centered design study. Here we examine its impact when administering a MT.Study Design and MethodsWe conducted a randomized simulation trial to compare a CDS for MT with a paper‐based MT protocol for the management of simulated hemorrhage. A total of 44 specialist physicians, trainees (residents), and nurses were recruited across critical care to participate in two 20‐min simulated bleeding scenarios. The primary outcome was the decision velocity (correct decisions per hour) and overall task completion. Secondary outcomes included cognitive workload and System Usability Scale (SUS).ResultsThere was a statistically significant increase in decision velocity for CDS‐based management (mean 8.5 decisions per hour) compared to paper based (mean 6.9 decisions per hour; p .003, 95% CI 0.6–2.6). There was no significant difference in the overall task completion using CDS‐based management (mean 13.3) compared to paper‐based (mean 13.2; p .92, 95% CI ‐1.2–1.3). Cognitive workload was statistically significantly lower using the CDS compared to the paper protocol (mean 57.1 vs. mean 64.5, p .005, 95% CI 2.4–12.5). CDS usability was assessed as a SUS score of 82.5 (IQR 75–87.5).DiscussionCompared to paper‐based management, CDS‐based MT supports more time‐efficient decision‐making by users with limited CDS training and achieves similar overall task completion whil...
Sang, L, Xu, M, Qian, S & Wu, X 2023, 'Adversarial Heterogeneous Graph Neural Network for Robust Recommendation', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2660-2671. View/Download from: Publisher's site
Saputra, YM, Hoang, DT, Nguyen, DN, Tran, L-N, Gong, S & Dutkiewicz, E 2023, 'Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115. View/Download from: Publisher's site View description>>
Federated learning (FL) can empower Internet-of-Vehicles (IoV) to help the vehicular service provider (VSP) improve the global model accuracy for road safety and better profits for both VSP and participating smart vehicles (SVs). Nonetheless, there exist major challenges when implementing FL in IoV including dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information at each learning round. Then, each selected SV can collect on-road information and offer a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the asymmetric information between them. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster and obtain social welfare of the network up to 27.2 times compared with those of other baseline FL methods.
Sarafianou, M, Choong, DSW, Chen, DS-H, Goh, DJ, Yao, Z, Sharma, J, Merugu, S, Ng, EJ & Lee, JE-Y 2023, 'Long-Range High-Resolution Imaging With Silicon-on-Nothing ScAlN pMUTs', IEEE Sensors Journal, vol. 23, no. 20, pp. 24254-24263. View/Download from: Publisher's site
Sateesh, KA, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Yunus Khan, TM, Harari, PA, El-Shafay, AS, Mujtaba, MA, Elfaskhany, A & Kalam, MA 2023, 'Effect of MWCNTs nano-additive on a dual-fuel engine characteristics utilizing dairy scum oil methyl ester and producer gas', Case Studies in Thermal Engineering, vol. 42, pp. 102661-102661. View/Download from: Publisher's site
Savkin, AV, Huang, C & Ni, W 2023, 'Collision-Free 3-D Navigation of a UAV Team for Optimal Data Collection in Internet-of-Things Networks With Reconfigurable Intelligent Surfaces', IEEE Systems Journal, vol. 17, no. 3, pp. 4070-4077. View/Download from: Publisher's site
Savkin, AV, Huang, C & Ni, W 2023, 'Joint Multi-UAV Path Planning and LoS Communication for Mobile-Edge Computing in IoT Networks With RISs', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2720-2727. View/Download from: Publisher's site
Savkin, AV, Huang, C & Ni, W 2023, 'On-Demand Deployment of Aerial Base Stations for Coverage Enhancement in Reconfigurable Intelligent Surface-Assisted Cellular Networks on Uneven Terrains', IEEE Communications Letters, vol. 27, no. 2, pp. 666-670. View/Download from: Publisher's site
Savkin, AV, Verma, SC & Ni, W 2023, 'Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains', Defence Technology, vol. 30, pp. 154-160. View/Download from: Publisher's site
Sayem, ASM, Lalbakhsh, A, Esselle, KP, Moloudian, G, Buckley, JL & Simorangkir, RBVB 2023, 'Advancements, Challenges, and Prospects of Water-Filled Antennas', IEEE Access, vol. 11, pp. 8301-8323. View/Download from: Publisher's site
Schneider, PJ & Rizoiu, M-A 2023, 'The effectiveness of moderating harmful online content', Proceedings of the National Academy of Sciences, vol. 120, no. 34. View/Download from: Publisher's site View description>>
In 2022, the European Union introduced the Digital Services Act (DSA), a new legislation to report and moderate harmful content from online social networks. Trusted flaggers are mandated to identify harmful content, which platforms must remove within a set delay (currently 24 h). Here, we analyze the likely effectiveness of EU-mandated mechanisms for regulating highly viral online content with short half-lives. We deploy self-exciting point processes to determine the relationship between the regulated moderation delay and the likely harm reduction achieved. We find that harm reduction is achievable for the most harmful content, even for fast-paced platforms such as Twitter. Our method estimates moderation effectiveness for a given platform and provides a rule of thumb for selecting content for investigation and flagging, managing flaggers’ workload.
Scriven, A, Kedziora, DJ, Musial, K & Gabrys, B 2023, 'The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry', Foundations and Trends® in Information Systems, vol. 7, no. 1-2, pp. 1-252. View/Download from: Publisher's site
Scussel, O, Brennan, MJ, Iwanaga, MK, Almeida, FCL, Karimi, M, Muggleton, JM, Joseph, PF & Rustighi, E 2023, 'Analysis of phase data from ground vibration measurements above a leaking plastic water pipe', Journal of Sound and Vibration, vol. 564, pp. 117873-117873. View/Download from: Publisher's site
Sebayang, AH, Ideris, F, Silitonga, AS, Shamsuddin, AH, Zamri, MFMA, Pulungan, MA, Siahaan, S, Alfansury, M, Kusumo, F & Milano, J 2023, 'Optimization of ultrasound-assisted oil extraction from Carica candamarcensis; A potential Oleaginous tropical seed oil for biodiesel production', Renewable Energy, vol. 211, pp. 434-444. View/Download from: Publisher's site
Sebayang, AH, Kusumo, F, Milano, J, Shamsuddin, AH, Silitonga, AS, Ideris, F, Siswantoro, J, Veza, I, Mofijur, M & Reen Chia, S 2023, 'Optimization of biodiesel production from rice bran oil by ultrasound and infrared radiation using ANN-GWO', Fuel, vol. 346, pp. 128404-128404. View/Download from: Publisher's site
Seifollahi, S & Piccardi, M 2023, 'Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis', pp. 575-586. View/Download from: Publisher's site
Seneviratne, JA, Carter, DR, Mittra, R, Gifford, A, Kim, PY, Luo, J, Mayoh, C, Salib, A, Rahmanto, AS, Murray, J, Cheng, NC, Nagy, Z, Wang, Q, Kleynhans, A, Tan, O, Sutton, SK, Xue, C, Chung, SA, Zhang, Y, Sun, C, Zhang, L, Haber, M, Norris, MD, Fletcher, JI, Liu, T, Dilda, PJ, Hogg, PJ, Cheung, BB & Marshall, GM 2023, 'Inhibition of mitochondrial translocase SLC25A5 and histone deacetylation is an effective combination therapy in neuroblastoma', International Journal of Cancer, vol. 152, no. 7, pp. 1399-1413. View/Download from: Publisher's site View description>>
AbstractThe mitochondrion is a gatekeeper of apoptotic processes, and mediates drug resistance to several chemotherapy agents used to treat cancer. Neuroblastoma is a common solid cancer in young children with poor clinical outcomes following conventional chemotherapy. We sought druggable mitochondrial protein targets in neuroblastoma cells. Among mitochondria‐associated gene targets, we found that high expression of the mitochondrial adenine nucleotide translocase 2 (SLC25A5/ANT2), was a strong predictor of poor neuroblastoma patient prognosis and contributed to a more malignant phenotype in pre‐clinical models. Inhibiting this transporter with PENAO reduced cell viability in a panel of neuroblastoma cell lines in a TP53‐status‐dependant manner. We identified the histone deacetylase inhibitor, suberanilohydroxamic acid (SAHA), as the most effective drug in clinical use against mutant TP53 neuroblastoma cells. SAHA and PENAO synergistically reduced cell viability, and induced apoptosis, in neuroblastoma cells independent of TP53‐status. The SAHA and PENAO drug combination significantly delayed tumour progression in pre‐clinical neuroblastoma mouse models, suggesting that these clinically advanced inhibitors may be effective in treating the disease.
Seoni, S, Jahmunah, V, Salvi, M, Barua, PD, Molinari, F & Acharya, UR 2023, 'Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)', Computers in Biology and Medicine, vol. 165, pp. 107441-107441. View/Download from: Publisher's site
Sha, C, Yang, L, Cairney, JM, Zhang, J & Young, DJ 2023, 'Sulphur diffusion through a growing chromia scale and effects of water vapour', Corrosion Science, vol. 222, pp. 111410-111410. View/Download from: Publisher's site
Shahabuddin, M, Uddin, MN, Chowdhury, JI, Ahmed, SF, Uddin, MN, Mofijur, M & Uddin, MA 2023, 'A review of the recent development, challenges, and opportunities of electronic waste (e-waste)', International Journal of Environmental Science and Technology, vol. 20, no. 4, pp. 4513-4520. View/Download from: Publisher's site View description>>
AbstractThis study reviews recent developments, challenges, and the prospect of electronic waste (e-waste). Various aspects of e-waste, including collection, pre-treatment, and recycling, are discussed briefly. It is found that Europe is the leading collector of e-waste, followed by Asia, America, Oceania, and Africa. The monetary worth of e-waste raw materials is estimated to be $57.0 billion. However, only $10.0 billion worth of e-waste is recycled and recovered sustainably, offsetting 15.0 million tonnes (Mt) of CO2. The major challenges of e-waste treatment include collection, sorting and inhomogeneity of waste, low energy density, prevention of further waste, emission, and cost-effective recycling. Only 78 countries in the world now have e-waste related legislation. Such legislation is not effectively implemented in most regions. Developing countries like south-eastern Asia and Northern Africa have limited or no e-waste legislation. Therefore, country-specific standards and legislation, public awareness, effective implementation, and government incentives for developing cost-effective technologies are sought to manage e-waste, which will play an important role in the circular economy.
Shahariar, GMH, Bodisco, TA, Surawski, N, Komol, MMR, Sajjad, M, Chu-Van, T, Ristovski, Z & Brown, RJ 2023, 'Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches', Next Energy, vol. 1, no. 4, pp. 100060-100060. View/Download from: Publisher's site
Shaharuddin, S, Abdul Maulud, KN, Syed Abdul Rahman, SAF, Che Ani, AI & Pradhan, B 2023, 'The role of IoT sensor in smart building context for indoor fire hazard scenario: A systematic review of interdisciplinary articles', Internet of Things, vol. 22, pp. 100803-100803. View/Download from: Publisher's site
Shahriari Felordi, M, Alikhani, M, Farzaneh, Z, Alipour Choshali, M, Ebrahimi, M, Aboulkheyr Es, H, Piryaei, A, Najimi, M & Vosough, M 2023, '(‐)‐Epigallocatechin‐3‐gallate induced apoptosis by dissociation of c‐FLIP/Ku70 complex in gastric cancer cells', Journal of Cellular and Molecular Medicine, vol. 27, no. 17, pp. 2572-2582. View/Download from: Publisher's site View description>>
AbstractAnti‐cancer properties of (‐)‐epigallocatechin‐3‐gallate (EGCG) are mediated via apoptosis induction, as well as inhibition of cell proliferation and histone deacetylase. Accumulation of stabilized cellular FLICE‐inhibitory protein (c‐FLIP)/Ku70 complex in the cytoplasm inhibits apoptosis through interruption of extrinsic apoptosis pathway. In this study, we evaluated the anti‐cancer role of EGCG in gastric cancer (GC) cells through dissociation of c‐FLIP/Ku70 complex. MKN‐45 cells were treated with EGCG or its antagonist MG149 for 24 h. Apoptosis was evaluated by flow cytometry and quantitative RT‐PCR. Protein expression of c‐FLIP and Ku70 was analysed using western blot and immunofluorescence. Dissociation of c‐FLIP/Ku70 complex as well as Ku70 translocation were studied by sub‐cellular fractionation and co‐immunoprecipitation. EGCG induced apoptosis in MKN‐45 cells with substantial up‐regulation of P53 and P21, down‐regulation of c‐Myc and Cyclin D1 as well as cell cycle arrest in S and G2/M check points. Moreover, EGCG treatment suppressed the expression of c‐FLIP and Ku70, decreased their interaction while increasing the Ku70 nuclear content. By dissociating the c‐FLIP/Ku70 complex, EGCG could be an alternative component to the conventional HDAC inhibitors in order to induce apoptosis in GC cells. Thus, its combination with other cancer therapy protocols could result in a better therapeutic outcome.
Shakeel, K, Wijayaratna, K, Barbieri, DM, Lou, B & Rashidi, TH 2023, 'Mobility perceptions regarding the COVID-19 pandemic from around the world', Travel Behaviour and Society, vol. 33, pp. 100631-100631. View/Download from: Publisher's site
Shakor, P, Nejadi, S, Paul, G & Gowripalan, N 2023, 'Effects of Different Orientation Angle, Size, Surface Roughness, and Heat Curing on Mechanical Behavior of 3D Printed Cement Mortar With/Without Glass Fiber in Powder-Based 3DP', 3D Printing and Additive Manufacturing, vol. 10, no. 2, pp. 330-355. View/Download from: Publisher's site
Shamayleh, OA & Far, H 2023, 'Utilising artificial neural networks for prediction of properties of geopolymer concrete', Computers and Concrete, vol. 31, no. 4, pp. 327-335. View/Download from: Publisher's site View description>>
The most popular building material, concrete, is intrinsically linked to the advancement of humanity. Due to the ever-increasing complexity of cementitious systems, concrete formulation for desired qualities remains a difficult undertaking despite conceptual and methodological advancement in the field of concrete science. Recognising the significant pollution caused by the traditional cement industry, construction of civil engineering structures has been carried out successfully using Geopolymer Concrete (GPC), also known as High Performance Concrete (HPC). These are concretes formed by the reaction of inorganic materials with a high content of Silicon and Aluminium (Pozzolans) with alkalis to achieve cementitious properties. These supplementary cementitious materials include Ground Granulated Blast Furnace Slag (GGBFS), a waste material generated in the steel manufacturing industry; Fly Ash, which is a fine waste product produced by coal-fired power stations and Silica Fume, a by-product of producing silicon metal or ferrosilicon alloys. This result demonstrated that GPC/HPC can be utilised as a substitute for traditional Portland cement-based concrete, resulting in improvements in concrete properties in addition to environmental and economic benefits. This study explores utilising experimental data to train artificial neural networks, which are then used to determine the effect of supplementary cementitious material replacement, namely fly ash, Ground Granulated Blast Furnace Slag (GGBFS) and silica fume, on the compressive strength, tensile strength, and modulus of elasticity of concrete and to predict these values accordingly.
Shamsi, A, Asgharnezhad, H, Bouchani, Z, Jahanian, K, Saberi, M, Wang, X, Razzak, I, Alizadehsani, R, Mohammadi, A & Alinejad-Rokny, H 2023, 'A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis', Neural Computing and Applications, vol. 35, no. 30, pp. 22179-22188. View/Download from: Publisher's site View description>>
AbstractSkin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error ou...
Shan, B, Ni, W, Yuan, X, Yang, D, Wang, X & Liu, RP 2023, 'Graph learning from band-limited data by graph Fourier transform analysis', Signal Processing, vol. 207, pp. 108950-108950. View/Download from: Publisher's site
Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 6, pp. 2693-2704. View/Download from: Publisher's site
Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Preserving the Privacy of Latent Information for Graph-Structured Data', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5041-5055. View/Download from: Publisher's site
Shan, F, He, X, Armaghani, DJ & Sheng, D 2023, 'Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance', Journal of Rock Mechanics and Geotechnical Engineering. View/Download from: Publisher's site
Shan, F, He, X, Armaghani, DJ, Zhang, P & Sheng, D 2023, 'Response to Discussion on “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Georg H. Erharter, Thomas Marcher', Tunnelling and Underground Space Technology, vol. 139, pp. 105064-105064. View/Download from: Publisher's site
Shanmugam, S, Mathimani, T, Rajendran, K, Sekar, M, Rene, ER, Chi, NTL, Ngo, HH & Pugazhendhi, A 2023, 'Perspective on the strategies and challenges in hydrogen production from food and food processing wastes', Fuel, vol. 338, pp. 127376-127376. View/Download from: Publisher's site
Shao, D, Su, F, Zou, X, Lu, J, Wu, S, Tian, R, Ran, D, Guo, Z & Jin, D 2023, 'Pixel-Level Classification of Five Histologic Patterns of Lung Adenocarcinoma', Analytical Chemistry, vol. 95, no. 5, pp. 2664-2670. View/Download from: Publisher's site
Shao, R, Wu, C, Li, J & Liu, Z 2023, 'Repeated impact resistance of steel fibre-reinforced dry UHPC: Effects of fibre length, mixing method, fly ash content and crumb rubber', Composite Structures, vol. 321, pp. 117274-117274. View/Download from: Publisher's site
Shao, R, Wu, C, Li, J, Liu, Z, Wu, P & Yang, Y 2023, 'Mechanical behaviour and environmental benefit of eco-friendly steel fibre-reinforced dry UHPC incorporating high-volume fly ash and crumb rubber', Journal of Building Engineering, vol. 65, pp. 105747-105747. View/Download from: Publisher's site View description>>
This study evaluates the impact of high-volume fly ash (HVFA) and waste crumb rubber (CR) on the mechanical property and environmental benefit of steel fibre-reinforced dry UHPC (FR-DUHPC) designed in a previous study. FA was introduced at 20–60% by mass substitution for cement with fibre dosage of 1.5 vol. %. Then, waste CR with different meshes were added as partial/completed replacements of coarse and medium sand with three volume contents of fibres (0.5%, 1.0% and 1.5%). Test results indicated that in the case of 1.5% fibre reinforcement, the increase in FA content and the addition of CR aggregate markedly reduced the density, modulus of elasticity and strength behaviour, whereas had minimal effect on the post-peak ductility of the assessed mixtures under compression and bending loads. Owing to the adopted moist/steam curing and the continuous pozzolanic reaction, the contribution of FA effect to both strengths at various ages was apparently increased and 50% of cement substitution was considered to be the most suitable FA addition in this study. For rubberized concrete reinforced with 0.5–1.5% steel fibres, the mechanical properties increased gradually with fibre dosage and curing age. However, the effect was evidently weakened with the addition of finer CR aggregate, and increasing the fibre dosage contributed to more positive impact on ductility rather than the load-carrying capacity. In summary, the flexural property benefits derived from the inclusion of steel fibre, FA and waste CR, as well as the eco-friendly benefits derived from the cost saving, energy conservation and carbon emission reduction, render the developed lightweight concrete mixture to be broadly used in dry concrete applications with different strength requirements that are mainly subjected to bending loads during serviceability.
Sharma, K, Akther, N, Choo, Y, Zhang, P, Matsuyama, H, Shon, HK & Naidu, G 2023, 'Positively charged nanofiltration membranes for enhancing magnesium separation from seawater', Desalination, vol. 568, pp. 117026-117026. View/Download from: Publisher's site
Sharma, M, Joshi, S, Prasad, M & Bartwal, S 2023, 'Overcoming barriers to circular economy implementation in the oil & gas industry: Environmental and social implications', Journal of Cleaner Production, vol. 391, pp. 136133-136133. View/Download from: Publisher's site View description>>
This anticipated consumer demand has put unprecedented pressure on natural resources. Being the highest contributor in the energy transition, Oil & gas (O&G) industry needs to lessen the negative impact of climate change and natural disasters. To combat the impact of emissions and a move towards circularity, O&G industry has undertaken numerous initiatives including energy efficiency, process fuel improvements, and technological transformation etc. But due to certain barriers O&G industry is unable to embrace Circular Economy (CE) implementation in the firms. Therefore, this study has proposed a model to examine the existing critical barriers and suggest strategies to overcome the barriers. The current study has employed an extensive analysis using a hybrid methodology of Fuzzy-DEMATEL (F-DEMATEL) and Best Worst Method (BWM) for assessing the barriers and ranking the strategies. The results showed that ‘knowledge barriers’ are the most critical in the O&G industry that hampers the implementation of CE currently. Further, the strategies ‘Developing collaborative model’ and ‘Internal research and development, innovation’ are the two most significant strategies that may help to reduce the barriers to a minimum. The findings, social and environmental implications are beneficial for the stakeholders and policy-makers to support the transition to CE.
Sharma, R, Goel, T, Tanveer, M, Lin, CT & Murugan, R 2023, 'Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 3, pp. 1123-1138. View/Download from: Publisher's site
Sharma, RK, Bharathy, G, Karimi, F, Mishra, AV & Prasad, M 2023, 'Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review.', Inf., vol. 14, no. 10, pp. 577-577. View/Download from: Publisher's site View description>>
This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.
Sharma, SK, Truong, DQ, Guo, J, An, AK, Naidu, G & Deka, BJ 2023, 'Recovery of rubidium from brine sources utilizing diverse separation technologies', Desalination, vol. 556, pp. 116578-116578. View/Download from: Publisher's site
Shelare, SD, Belkhode, PN, Nikam, KC, Jathar, LD, Shahapurkar, K, Soudagar, MEM, Veza, I, Khan, TMY, Kalam, MA, Nizami, A-S & Rehan, M 2023, 'Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production', Energy, vol. 282, pp. 128874-128874. View/Download from: Publisher's site
Shen, M, Ye, K, Liu, X, Zhu, L, Kang, J, Yu, S, Li, Q & Xu, K 2023, 'Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 791-824. View/Download from: Publisher's site
Shen, S, Wu, X, Sun, P, Zhou, H, Wu, Z & Yu, S 2023, 'Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems', Expert Systems with Applications, vol. 225, pp. 120192-120192. View/Download from: Publisher's site
Shen, S, Xie, L, Zhang, Y, Wu, G, Zhang, H & Yu, S 2023, 'Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5302-5315. View/Download from: Publisher's site
Sheng, Z, Wen, S, Feng, Z-K, Gong, J, Shi, K, Guo, Z, Yang, Y & Huang, T 2023, 'A Survey on Data-Driven Runoff Forecasting Models Based on Neural Networks', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 4, pp. 1083-1097. View/Download from: Publisher's site
Shephard, RW & Maloney, SK 2023, 'A review of thermal stress in cattle', Australian Veterinary Journal, vol. 101, no. 11, pp. 417-429. View/Download from: Publisher's site View description>>
Cattle control body temperature in a narrow range over varying climatic conditions. Endogenous body heat is generated by metabolism, digestion and activity. Radiation is the primary external source of heat transfer into the body of cattle. Cattle homeothermy uses behavioural and physiological controls to manage radiation, convection, conduction, and evaporative exchange of heat between the body and the environment, noting that evaporative mechanisms almost exclusively transfer body heat to the environment. Cattle control radiation by shade seeking (hot) and shelter (cold) and by huddling or standing further apart, noting there are intrinsic breed and age differences in radiative transfer potential. The temperature gradient between the skin and the external environment and wind speed (convection) determines heat transfer by these means. Cattle control these mechanisms by managing blood flow to the periphery (physiology), by shelter‐seeking and standing/lying activity in the short term (behaviourally) and by modifying their coats and adjusting their metabolic rates in the longer term (acclimatisation). Evaporative heat loss in cattle is primarily from sweating, with some respiratory contribution, and is the primary mechanism for dissipating excess heat when environmental temperatures exceed skin temperature (~36°C). Cattle tend to be better adapted to cooler rather than hotter external conditions, with Bos indicus breeds more adapted to hotter conditions than Bos taurus. Management can minimise the risk of thermal stress by ensuring appropriate breeds of suitably acclimatised cattle, at appropriate stocking densities, fed appropriate diets (and water), and with access to suitable shelter and ventilation are better suited to their expected farm environment.
Sheu, A, Blank, RD, Tran, T, Bliuc, D, Greenfield, JR, White, CP & Center, JR 2023, 'Associations of Type 2 Diabetes, Body Composition, and Insulin Resistance with Bone Parameters: The Dubbo Osteoporosis Epidemiology Study', JBMR Plus, vol. 7, no. 9. View/Download from: Publisher's site View description>>
ABSTRACTType 2 diabetes (T2D) may be associated with increased risk of fractures, despite preserved bone mineral density (BMD). Obesity and insulin resistance (IR) may have separate effects on bone turnover and bone strength, which contribute to skeletal fragility. We characterized and assessed the relative associations of obesity, body composition, IR, and T2D on bone turnover markers (BTMs), BMD, and advanced hip analysis (AHA). In this cross‐sectional analysis of Dubbo Osteoporosis Epidemiology Study, 525 (61.3% women) participants were grouped according to T2D, IR (homeostasis model assessment insulin resistance [HOMA‐IR] </≥2.5), and BMI (</≥25 kg/m2): insulin‐sensitive lean (IS‐L), insulin‐sensitive overweight/obese (IS‐O), insulin‐resistant (IR), and T2D. BMD, AHA, and body composition, including visceral adipose tissue (VAT) (on dual‐energy x‐ray absorptiometry scan) and fasting BTMs, were assessed. Analyses performed using Bayesian model averaging and principal component analysis. T2D was associated with low BTMs (by 26%–30% [95% confidence interval [CI] 11%–46%] in women, 35% [95% CI 18%–48%] in men compared to IS‐L), which persisted after adjustment for VAT. BTMs were similar among IR/IS‐O/IS‐L. BMD was similar among T2D/IR/IS‐O; BMD was low only in IS‐L. All groups were similar after adjustment for BMI. Similarly, AHA components were lowest in IS‐L (attenuated following adjustment). On multivariate analysis, T2D was independently associated with BTMs. IR was also associated with C‐terminal telopeptide of type 1 collagen in men. Age and body size were the strongest independent contributors to BMD and AHA. VAT was inversely associated with section modulus, cross‐sectional area, cross‐sectional moment of inertia in women, and hip axis length in men. Low bone turnover is associated with T2D and IR (in men), while BMD and hip strength/geometry are predominantly associated with body size....
Sheu, A, O’Connell, RL, Jenkins, AJ, Tran, T, Drury, PL, Sullivan, DR, Li, L, Colman, P, O’Brien, R, Kesäniemi, YA, Center, JR, White, CP & Keech, AC 2023, 'Factors associated with fragility fractures in type 2 diabetes: An analysis of the randomised controlled Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study', Diabetes/Metabolism Research and Reviews, vol. 39, no. 5. View/Download from: Publisher's site View description>>
AbstractAimsFracture risk is elevated in some type 2 diabetes patients. Bone fragility may be associated with more clinically severe type 2 diabetes, although prospective studies are lacking. It is unknown which diabetes‐related characteristics are independently associated with fracture risk. In this post‐hoc analysis of fracture data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial (ISRCTN#64783481), we hypothesised that diabetic microvascular complications are associated with bone fragility.Materials and MethodsThe FIELD trial randomly assigned 9795 type 2 diabetes participants (aged 50–75 years) to receive oral co‐micronised fenofibrate 200 mg (n = 4895) or placebo (n = 4900) daily for a median of 5 years. We used Cox proportional hazards models to identify baseline sex‐specific diabetes‐related parameters independently associated with incident fractures.ResultsOver 49,470 person‐years, 137/6138 men experienced 141 fractures and 143/3657 women experienced 145 fractures; incidence rates for the first fracture of 4∙4 (95% CI 3∙8–5∙2) and 7∙7 per 1000 person‐years (95% CI 6∙5–9∙1), respectively. Fenofibrate had no effect on fracture outcomes. In men, baseline macrovascular disease (HR 1∙52, 95% CI 1∙05–2∙21, p = 0∙03), insulin use (HR 1∙62, HR 1∙03–2∙55, p = 0∙03), and HDL‐cholesterol (HR 2∙20, 95% CI 1∙11–4∙36, p = 0∙02) were independently associated with fracture. In women, independent risk factors included baseline peripheral neuropathy (HR 2∙04, 95% CI 1∙16–3∙59, p = 0∙01) and insulin use (HR 1∙55, 95% CI 1∙02–2∙33, p = 0∙04).
Shi, AC, Maidi, AM, Shamsuddin, N, Kalam, MA & Begum, F 2023, 'Photonic crystal fibre sensor for alcohol detection with extremely low birefringence', International Journal of Applied Science and Engineering, vol. 20, no. 2, pp. 1-7. View/Download from: Publisher's site
Shi, D, Zhu, L, Li, J, Zhang, Z & Chang, X 2023, 'Unsupervised Adaptive Feature Selection With Binary Hashing', IEEE Transactions on Image Processing, vol. 32, pp. 838-853. View/Download from: Publisher's site View description>>
Unsupervised feature selection chooses a subset of discriminative features to reduce feature dimension under the unsupervised learning paradigm. Although lots of efforts have been made so far, existing solutions perform feature selection either without any label guidance or with only single pseudo label guidance. They may cause significant information loss and lead to semantic shortage of the selected features as many real-world data, such as images and videos are generally annotated with multiple labels. In this paper, we propose a new Unsupervised Adaptive Feature Selection with Binary Hashing (UAFS-BH) model, which learns binary hash codes as weakly-supervised multi-labels and simultaneously exploits the learned labels to guide feature selection. Specifically, in order to exploit the discriminative information under the unsupervised scenarios, the weakly-supervised multi-labels are learned automatically by specially imposing binary hash constraints on the spectral embedding process to guide the ultimate feature selection. The number of weakly-supervised multi-labels (the number of '1' in binary hash codes) is adaptively determined according to the specific data content. Further, to enhance the discriminative capability of binary labels, we model the intrinsic data structure by adaptively constructing the dynamic similarity graph. Finally, we extend UAFS-BH to multi-view setting as Multi-view Feature Selection with Binary Hashing (MVFS-BH) to handle the multi-view feature selection problem. An effective binary optimization method based on the Augmented Lagrangian Multiple (ALM) is derived to iteratively solve the formulated problem. Extensive experiments on widely tested benchmarks demonstrate the state-of-the-art performance of the proposed method on both single-view and multi-view feature selection tasks. For the purpose of reproducibility, we provide the source codes and testing datasets at https://github.com/shidan0122/UMFS.git..
Shi, K, Cai, X, She, K, Wen, S, Zhong, S, Park, P & Kwon, O-M 2023, 'Stability Analysis and Security-Based Event-Triggered Mechanism Design for T-S Fuzzy NCS With Traffic Congestion via DoS Attack and Its Application', IEEE Transactions on Fuzzy Systems, vol. 31, no. 10, pp. 3639-3651. View/Download from: Publisher's site
Shi, K, Peng, X, Lu, H, Zhu, Y & Niu, Z 2023, 'Application of Social Sensors in Natural Disasters Emergency Management: A Review', IEEE Transactions on Computational Social Systems, vol. 10, no. 6, pp. 3143-3158. View/Download from: Publisher's site View description>>
Natural disasters are public emergencies characterized by suddenness, universality, and nonconventionality. Realizing the early warning, monitoring, and intervention of natural disasters and their derivative social impacts is significant for reducing the disasters’ damage and benefits the maintenance of social stability. Social sensors are ubiquitous sensors based on social network platforms. It uses the concepts and methods of physical space to mine social signals that integrate human perception and intelligence in cyberspace. Compared with traditional physical sensors, social sensors represent a crucial data acquisition channel in the emergency management of natural disasters and have the advantages of real time, comprehensive coverage, low cost, and flexible deployment. This article reviews the application of social sensors in natural disasters emergency management. We summarize the application functions of social sensors into three categories: natural disaster situation awareness and event detection, disaster information dissemination and communication, and disaster sentiment analysis and public opinion mining. Based on the above functions, this article analyzes the research status, data, technical methods, and application systems. Finally, this article proposes a research trend of applying social sensors in natural disaster emergency management according to the requirements of real scenarios.
Shi, M, Zhao, X, Yin, X, Chang, X, Niu, F & Guo, J 2023, 'Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis', Knowledge-Based Systems, vol. 276, pp. 110707-110707. View/Download from: Publisher's site
Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Chen, M, Yang, H, Li, L & Li, W 2023, 'Nondetection Zone Elimination and Detection Speed Improvement for DC Microgrids Islanding Detection With Adaptive Resonant Frequency', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 6, pp. 5750-5765. View/Download from: Publisher's site
Shi, X, Chen, Z, Wei, W, Chen, J & Ni, B-J 2023, 'Toxicity of micro/nanoplastics in the environment: Roles of plastisphere and eco-corona', Soil & Environmental Health, vol. 1, no. 1, pp. 100002-100002. View/Download from: Publisher's site
Shi, X, Chen, Z, Wu, L, Wei, W & Ni, B-J 2023, 'Microplastics in municipal solid waste landfills: Detection, formation and potential environmental risks', Current Opinion in Environmental Science & Health, vol. 31, pp. 100433-100433. View/Download from: Publisher's site
Shi, Y, Han, Y, Hu, Q, Yang, Y & Tian, Q 2023, 'Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2226-2245. View/Download from: Publisher's site View description>>
It is challenging to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. We embed and benchmark existing adversarial attack methods under the new framework. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.
Shoeibi, A, Khodatars, M, Jafari, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Ling, SH, Khosravi, A, Alinejad-Rokny, H, Lam, H-K, Fuller-Tyszkiewicz, M, Acharya, UR, Anderson, D, Zhang, Y & Górriz, JM 2023, 'Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review.', Inf. Fusion, vol. 93, pp. 85-117. View/Download from: Publisher's site
Shokouhian, B, Negahdari, B, Heydari, Z, Totonchi, M, Aboulkheyr Es, H, Piryaei, A, Mostafavi, E & Vosough, M 2023, 'HNF4α is possibly the missing link between epithelial–mesenchymal transition and Warburg effect during hepatocarcinogenesis', Cancer Science, vol. 114, no. 4, pp. 1337-1352. View/Download from: Publisher's site View description>>
AbstractHepatocellular carcinoma (HCC) is a heterogeneous, late‐diagnosed, and highly recurrent malignancy that often affects the whole body's metabolism. Finding certain theranostic molecules that can address current concerns simultaneously is one of the priorities in HCC management. In this study, performing protein–protein interaction network analysis proposed hepatocyte nuclear factor 4 alpha (HNF4α) as a hub protein, associating epithelial–mesenchymal transition (EMT) to reprogrammed cancer metabolism, formerly known as the Warburg effect. Both phenomena improved the compensation of cancerous cells in competitive conditions. Mounting evidence has demonstrated that HNF4α is commonly downregulated and serves as a tumor suppressor in the HCC. Enhancing the HNF4α mRNA translation through a specific synthetic antisense long non‐coding RNA, profoundly affects both EMT and onco‐metabolic modules in HCC cells. HNF4α overexpression decreased featured mesenchymal transcription factors and improved hepatocytic function, decelerated glycolysis, accelerated gluconeogenesis, and improved dysregulated cholesterol metabolism. Moreover, HNF4α overexpression inhibited the migration, invasion, and proliferation of HCC cells and decreased metastasis rate and tumor growth in xenografted nude mice. Our findings suggest a central regulatory role for HNF4α through its broad access to a wide variety of gene promoters involved in EMT and the Warburg effect in human hepatocytes. This essential impact indicates that HNF4α may be a potential target for HCC treatment.
AbstractThe global burden of respiratory diseases is enormous, with many millions of people suffering and dying prematurely every year. The global COVID‐19 pandemic witnessed recently, along with increased air pollution and wildfire events, increases the urgency of identifying the most effective therapeutic measures to combat these diseases even further. Despite increasing expenditure and extensive collaborative efforts to identify and develop the most effective and safe treatments, the failure rates of drugs evaluated in human clinical trials are high. To reverse these trends and minimize the cost of drug development, ineffective drug candidates must be eliminated as early as possible by employing new, efficient, and accurate preclinical screening approaches. Animal models have been the mainstay of pulmonary research as they recapitulate the complex physiological processes, Multiorgan interplay, disease phenotypes of disease, and the pharmacokinetic behavior of drugs. Recently, the use of advanced culture technologies such as organoids and lung‐on‐a‐chip models has gained increasing attention because of their potential to reproduce human diseased states and physiology, with clinically relevant responses to drugs and toxins. This review provides an overview of different animal models for studying respiratory diseases and evaluating drugs. We also highlight recent progress in cell culture technologies to advance integrated models and discuss current challenges and present future perspectives.
Shu, X, Yang, Y, Liu, J, Chang, X & Wu, B 2023, 'ALVLS: Adaptive local variances-Based levelset framework for medical images segmentation', Pattern Recognition, vol. 136, pp. 109257-109257. View/Download from: Publisher's site
Shuvo, SB, Alam, SS, Ayman, SU, Chakma, A, Barua, PD & Acharya, UR 2023, 'NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals', Biomedical Signal Processing and Control, vol. 86, pp. 105272-105272. View/Download from: Publisher's site
Shvetcov, A, Thomson, S, Spathos, J, Cho, A-N, Wilkins, HM, Andrews, SJ, Delerue, F, Couttas, TA, Issar, JK, Isik, F, Kaur, S, Drummond, E, Dobson-Stone, C, Duffy, SL, Rogers, NM, Catchpoole, D, Gold, WA, Swerdlow, RH, Brown, DA & Finney, CA 2023, 'Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer’s Disease', International Journal of Molecular Sciences, vol. 24, no. 19, pp. 15011-15011. View/Download from: Publisher's site View description>>
Alzheimer’s disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.
This study presents the use of rubber grids (RGs) fabricated from end-of-life conveyor belts (i.e. discarded from the mining industry) to improve the performance of ballast tracks. The square apertures of these recycled rubber sheets were cast using a waterjet cutting process. A series of large-scale impact tests were performed on ballast specimens stabilised with three different grids of varied effective area ratios (KA.eff) to evaluate their effectiveness in mitigating the applied impact forces, in relation to both displacement and breakage of the ballast aggregates. Smart Ballast particles with motion-sensing capabilities were adopted to monitor the interaction between the grid and ballast assembly. The impact test results indicate that the inclusion of a RG decreases the deformation and breakage of ballast as well as reduces its vibrations. This study demonstrates that these recycled RGs with optimum effective area ratios can be more effective than conventional polymer geogrids, apart from the obvious environmental benefits.
Siddiqui, SA, Mahmood, A, Sheng, QZ, Suzuki, H & Ni, W 2023, 'Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9546-9560. View/Download from: Publisher's site
In this paper, we propose a reconfigurable intelligent surface (RIS) that can dynamically switch the transmission and reflection phase of incident electromagnetic waves in real time to realize the dual-beam or quad-beam and convert the polarization of the transmitted beam. Such surfaces can redirect a wireless signal at will to establish robust connectivity when the designated line-of-sight channel is disturbed, thereby enhancing the performance of wireless communication systems by creating an intelligent radio environment. When integrated with a sensing element, they are integral to performing joint detection and communication functions in future wireless sensor networks. In this work, we first analyze the scattering performance of a reconfigurable unit element and then design a RIS. The dynamic field scattering manipulation capability of the RIS is validated by full-wave electromagnetic simulations to realize six different functions. The scattering characteristics of the proposed unit element, which incorporates two p-i-n diodes have been substantiated through practical implementation. This involved the construction of a simple prototype and the subsequent examination of its scattering properties via the free-space measurement method. The obtained transmission and reflection coefficients from the measurements are in agreement with the anticipated outcomes from simulations.
Sirivivatnanon, V, Thomas, P, Joshua Tapas, M & Nhu Nguyen, T 2023, 'Reliability of AMBT and CPT in testing the effectiveness of SCM to mitigate alkali–silica reaction of field concrete', Construction and Building Materials, vol. 369, pp. 130510-130510. View/Download from: Publisher's site
Sirivivatnanon, V, Xue, C & Khatri, R 2023, 'Long-term reinforcement corrosion in low carbon concrete with a high volume of SCMs exposed to NaCl solutions and field marine environment', Construction and Building Materials, vol. 393, pp. 132071-132071. View/Download from: Publisher's site View description>>
Chloride-induced corrosion in low carbon concrete with a high volume of SCMs exposed to (i) accelerated corrosion by impressed current, (ii) 9-year NaCl-simulated tidal condition and (iii) 5-year field marine tidal condition was compared in this study. The effectiveness of different exposure conditions and indicators in distinguishing the parameters affecting the corrosion resistance of concrete was investigated. The results showed that at a high w/b ratio of 0.6, the accelerated corrosion test provided inconsistent ranking for the corrosion resistance of binders when continuous and intermittent impressed voltage were applied. At a w/b ratio of 0.4, the electrochemical measurements from both accelerated corrosion and laboratory-simulated tidal exposure confirmed the advantage of 30 % FA blended cement concrete in protecting reinforcement, whereas results from two exposure conditions denoted opposite ranking for the corrosion resistance of GP cement and 50 % slag blended cement concrete. The statistical analysis of the 674 independent data points (Ecorr, Rp) suggested that using −465 mVSCE as the upper limit for indicating a higher than 90 % corrosion probability was more reliable than the −350 mVSCE from ASTM C876. Compared to field marine tidal exposure, long-term wet/dry cycles in 3 % NaCl induced faster corrosion initiation and propagation, thereby higher weight loss of reinforcement. The chloride contents permitting corrosion initiation were higher for concrete with more binder due to a higher alkalinity. The complex interaction between chloride, alkalis and reinforcement corrosion leads to difficulty in discriminating the corrosion resistance of various binders, especially in long-term exposure.
Siva, V, Raghuram, M, Singh, A, Singh, SK & Siwakoti, YP 2023, 'Switching Strategy to Reduce Inductor Current Ripple and Common Mode Voltage in Quasi Z-Source Ultra Sparse Matrix Converter', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 4, pp. 1159-1169. View/Download from: Publisher's site
Type 1 diabetes (T1D) is a chronic, lifelong metabolic disease. It is characterised by the autoimmune-mediated loss of insulin-producing pancreatic β cells in the islets of Langerhans (β-islets), resulting in disrupted glucose homeostasis. Administration of exogenous insulin is the most common management method for T1D, but this requires lifelong reliance on insulin injections and invasive blood glucose monitoring. Replacement therapies with beta cells are being developed as an advanced curative treatment for T1D. Unfortunately, this approach is limited by the lack of donated pancreatic tissue, the difficulties in beta cell isolation and viability maintenance, the longevity of the transplanted cells in vivo, and consequently high costs. Emerging approaches to address these limitations are under intensive investigations, including the production of insulin-producing beta cells from various stem cells, and the development of bioengineered devices including nanotechnologies for improving islet transplantation efficacy without the need for recipients taking toxic anti-rejection drugs. These emerging approaches present promising prospects, while the challenges with the new techniques need to be tackled for ultimately clinical treatment of T1D. This review discussed the benefits and limitations of the cell-based therapies for beta cell replacement as potential curative treatment for T1D, and the applications of bioengineered devices including nanotechnology to overcome the challenges associated with beta cell transplantation.
Smith, H & Hussain, W 2023, 'Is it time we changed the way we manage melanoma in situ of the trunk and limbs?', British Journal of Dermatology, vol. 188, no. 5, pp. 685-687. View/Download from: Publisher's site View description>>
There is little evidence on the optimal clinical and histological margins required to reduce local recurrence in melanoma in situ (MIS). Our aim was to identify the number of lesions on the trunk and limbs with histological clearance > 1 mm after initial narrow-margin excision. In our cohort 93.6% were considered clear after initial exclusion with no residual MIS seen when further wide local excision was carried out.
Smith, MAA, Khot, MI, Taccola, S, Fry, NR, Muhonen, PL, Tipper, JL, Jayne, DG, Kay, RW & Harris, RA 2023, 'A digitally driven manufacturing process for high resolution patterning of cell formations', Biomedical Microdevices, vol. 25, no. 2. View/Download from: Publisher's site View description>>
AbstractThis paper presents the engineering and validation of an enabling technology that facilitates new capabilities in in vitro cell models for high-throughput screening and tissue engineering applications. This is conducted through a computerized system that allows the design and deposition of high-fidelity microscale patterned coatings that selectively alter the chemical and topographical properties of cell culturing surfaces. Significantly, compared to alternative methods for microscale surface patterning, this is a digitally controlled and automated process thereby allowing scientists to rapidly create and explore an almost infinite range of cell culture patterns. This new capability is experimentally validated across six different cell lines demonstrating how the precise microscale deposition of these patterned coatings can influence spatiotemporal growth and movement of endothelial, fibroblast, neuronal and macrophage cells. To further demonstrate this platform, more complex patterns are then created and shown to guide the behavioral response of colorectal carcinoma cells.Graphical Abstract
Sohn, W, Jiang, J, Phuntsho, S, Choden, Y, Tran, VH & Shon, HK 2023, 'Nutrients in a circular economy: Role of urine separation and treatment', Desalination, vol. 560, pp. 116663-116663. View/Download from: Publisher's site
Son Tran, V, Hao Ngo, H, Guo, W, Ha Nguyen, T, Mai Ly Luong, T, Huan Nguyen, X, Lan Anh Phan, T, Trong Le, V, Phuong Nguyen, M & Khai Nguyen, M 2023, 'New chitosan-biochar composite derived from agricultural waste for removing sulfamethoxazole antibiotics in water', Bioresource Technology, vol. 385, pp. 129384-129384. View/Download from: Publisher's site
Song 宋, B波, Wu 吴, H-M惠, Song 宋, Y-R玉, Jiang 蒋, G-P国, Xia 夏, L-L玲 & Wang 王, X旭 2023, 'Robustness of community networks against cascading failures with heterogeneous redistribution strategies', Chinese Physics B, vol. 32, no. 9, pp. 098905-098905. View/Download from: Publisher's site View description>>
Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering–LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.
Song, L, Wang, H, Zhang, G & Yu, S 2023, 'FedInf: Social influence prediction with federated learning', Neurocomputing, vol. 548, pp. 126407-126407. View/Download from: Publisher's site
Song, L-Z, Ansari, M, Qin, P-Y, Maci, S, Du, J & Guo, YJ 2023, 'Two-Dimensional Wide-Angle Multibeam Flat GRIN Lens With a High Aperture Efficiency', IEEE Transactions on Antennas and Propagation, vol. 71, no. 10, pp. 8018-8029. View/Download from: Publisher's site View description>>
High-aperture-efficiency 2-dimensional (2-D) multi-beam flat gradient-index (GRIN) lenses are developed in this work. New methods based on bifocal analysis are found to determine the refractive-index profile of the lens as well as feed positions along a circular feed locus to enable independent wide-angle multi-beam radiations. Distinct formulas for the GRIN profile are provided for two different purposes: i) multi-beam with good average performance in all azimuthal planes or ii) radiation performance optimized in a specific azimuthal plane. The latter solution requires a GRIN variation in both azimuthal and radial variables. Subwavelength triple-metal-layer unit cells are designed to emulate the local refractive indices. A 2-D multi-beam GRIN lens, fed by 13-modules of 2×2 patch arrays displaced along xoz and yoz focus loci, has been successfully simulated, fabricated, and measured. Wide-angle multi-beam radiations have been obtained with a beam coverage of around ±45° in both xoz and yoz planes. The multi-beam radiation patterns are stable in a 22.2% bandwidth from 12 GHz to 15 GHz. The beam-scanning losses in this operating band are 1-2.6 dB and 2.1-3.9 dB in xoz and yoz planes, respectively. The measured peak realized gain is 22.3 dBi at 13.4 GHz, corresponding to an aperture efficiency of 66.4%.
Song, W, Ma, Z, Wang, X, Wang, Y, Wu, D, Wang, C, He, D, Kong, L, Yu, W, Li, JJ, Li, H & He, Y 2023, 'Macroporous Granular Hydrogels Functionalized with Aligned Architecture and Small Extracellular Vesicles Stimulate Osteoporotic Tendon‐To‐Bone Healing', Advanced Science, vol. 10, no. 34. View/Download from: Publisher's site View description>>
AbstractOsteoporotic tendon‐to‐bone healing (TBH) after rotator cuff repair (RCR) is a significant orthopedic challenge. Considering the aligned architecture of the tendon, inflammatory microenvironment at the injury site, and the need for endogenous cell/tissue infiltration, there is an imminent need for an ideal scaffold to promote TBH that has aligned architecture, ability to modulate inflammation, and macroporous structure. Herein, a novel macroporous hydrogel comprising sodium alginate/hyaluronic acid/small extracellular vesicles from adipose‐derived stem cells (sEVs) (MHA‐sEVs) with aligned architecture and immunomodulatory ability is fabricated. When implanted subcutaneously, MHA‐sEVs significantly improve cell infiltration and tissue integration through its macroporous structure. When applied to the osteoporotic RCR model, MHA‐sEVs promote TBH by improving tendon repair through macroporous aligned architecture while enhancing bone regeneration by modulating inflammation. Notably, the biomechanical strength of MHA‐sEVs is approximately two times higher than the control group, indicating great potential in reducing postoperative retear rates. Further cell‐hydrogel interaction studies reveal that the alignment of microfiber gels in MHA‐sEVs induces tenogenic differentiation of tendon‐derived stem cells, while sEVs improve mitochondrial dysfunction in M1 macrophages (Mφ) and inhibit Mφ polarization toward M1 via nuclear factor‐kappaB (NF‐κb) signaling pathway. Taken together, MHA‐sEVs provide a promising strategy for future clinical application in promoting osteoporotic TBH.
Song, X, Lu, X, Fang, G, He, X, Fan, X, Cai, L, Jia, W & Wang, Z 2023, 'ABUSDet: A Novel 2.5D deep learning model for automated breast ultrasound tumor detection', Applied Intelligence, vol. 53, no. 21, pp. 26255-26269. View/Download from: Publisher's site View description>>
Automated Breast Ultrasound is a highly advanced breast tumor detection modality that produces hundreds of 2D slices in each scan. However, this large number of slices poses a significant burden for physicians to review. This paper proposes a novel 2.5D tumor detection model, named “ABUSDet,” to assist physicians in automatically reviewing ABUS images and predicting the locations of breast tumors in images. At the core of this approach, a sequence of data blocks partitioned from a pre-processed 3D volume are fed to a 2.5D tumor detection model, which outputs a sequence of 2D tumor candidates. An aggregation module then clusters the 2D tumor candidates to produce the ultimate 3D coordinates of the tumors. To further improve the accuracy of the model, a novel mechanism for training deep learning models, called “Deliberate Training,” is proposed. The proposed model is trained and tested on a dataset of 87 patients with 235 ABUS volumes. It achieves sensitivities of 77.94%, 75.49%, and 65.19% at FPs/volume of 3, 2, and 1, respectively. Compared with the 2D and 3D object detection models, the proposed ABUSDet model achieves the highest sensitivity with relatively low false-positive rates. Graphical abstract: [Figure not available: see fulltext.]
Song, Y, He, W, Sun, X, Lei, J, Nghiem, LD, Duan, J, Liu, W, Liu, Y & Cai, Z 2023, 'C-doped Bi3O4X nanosheets with self-induced internal electric fields for pyrene degradation: Effects of carbon and halogen element type on photocatalytic activity', Separation and Purification Technology, vol. 323, pp. 124426-124426. View/Download from: Publisher's site
Song, Y, Lu, J, Lu, H & Zhang, G 2023, 'Learning Data Streams With Changing Distributions and Temporal Dependency', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 3952-3965. View/Download from: Publisher's site View description>>
In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus on a data stream with changing distributions. However, since each variable of a data stream is a time series, these variables normally have temporal dependency problems in the real world. How to solve concept drift and temporal dependency problems at the same time is rarely discussed in the concept-drift literature. To solve this situation, this article proves and validates that the testing error decreases faster if a predictor is trained on a temporally reconstructed space when drift occurs. Based on this theory, a novel drift adaptation regression (DAR) framework is designed to predict the label variable for data streams with concept drift and temporal dependency. A new statistic called local drift degree (LDD⁺) is proposed and used as a drift adaptation technique in the DAR framework to discard outdated instances in a timely way, thereby guaranteeing that the most relevant instances will be selected during the training process. The performance of DAR is demonstrated by a set of experimental evaluations on both synthetic data and real-world data streams.
Soo, A, Wang, L, Wang, C & Shon, HK 2023, 'MachIne learning for nutrient recovery in the smart city circular economy – A review', Process Safety and Environmental Protection, vol. 173, pp. 529-557. View/Download from: Publisher's site View description>>
Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking.
Sood, K, Nguyen, DDN, Qu, Y, Cui, L, Karmakar, KK & Yu, S 2023, 'Security Challenges and Potential Solutions in Aerial-Terrestrial Wireless Networking', IEEE Internet of Things Magazine, vol. 6, no. 4, pp. 118-123. View/Download from: Publisher's site
Sood, K, Nosouhi, MR, Kumar, N, Gaddam, A, Feng, B & Yu, S 2023, 'Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 288-300. View/Download from: Publisher's site View description>>
In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.
Sood, S & Pattinson, H 2023, 'Marketing Education Renaissance Through Big Data Curriculum: Developing Marketing Expertise Using AI Large Language Models', International Journal of Innovation and Economic Development, vol. 8, no. 6, pp. 23-40. View/Download from: Publisher's site View description>>
Utilising big data sources and artificial intelligence (AI) tools with marketing activities and analysis contrast with questionnaires and small n observations, essentially creating a renaissance in marketing education. As a result, marketing education keeps pace with AI developments and ensures learners (or students) prepare for the demands of the modern marketing landscape 2025-30. The authors advocate a central focus on a big data-driven marketing curriculum for marketing education. Such a curriculum places AI and machine learning center stage to help understand, analyze and utilize large and complex marketing datasets for predictive marketing. In doing so, the potential exists for practitioners to link marketing strategy directly with marketing execution, allowing learners to use big data and AI for upstream strategy design and marketing plan development while downstream predicting the results of marketing campaigns, programs, and initiatives But necessary changes in pedagogy are creating adaptive learning experiences breaking free from traditional assessments In our model of learning educators enable the development of practical marketing expertise using the techniques and tools of micro-testing to nudge learners using Python data science notebooks. Overall, a renaissance in marketing education is made possible with a focus on a big data AI tools-driven curriculum. Such attention ensures learners prepare for the demands of the modern marketing landscape, moving well beyond marketing analytics using the AI technologies of Large Language Models, further expanding the use of big data Learners use role play, witnessing firsthand experiences fulfilling new hitherto emerging marketing roles By 2025, Educators fostering a big data AI-focused marketing education curriculum ensure the next generation of AI marketers will eagerly shape the future of marketing practice and behavior with new roles combining human work with AI.
Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2023, 'AC Loss in High-Temperature Superconducting Bulks Subjected to Alternating and Rotating Magnetic Fields', Materials, vol. 16, no. 2, pp. 633-633. View/Download from: Publisher's site View description>>
High-temperature superconductor (HTS) bulks have demonstrated extremely intriguing potential for industrial and commercial applications due to their capability to trap significantly larger magnetic fields than conventional permanent magnets. The magnetic field in electrical rotating machines is a combination of alternating and rotational fields. In contrast, all previous research on the characterization of electromagnetic properties of HTS have solely engrossed on the alternating AC magnetic fields and the associated AC loss. This research paper gives a thorough examination of the AC loss measurement under various conditions. The obtained results are compared to the finite element-based H-formulation. The AC loss is measured at various amplitudes of circular flux density patterns and compared with the AC loss under one-dimensional alternating flux density. The loss variation has also been studied at other frequencies. The findings in this research paper provide more insights into material characterization, which will be useful in the design of future large-scale HTS applications.
Soudagar, MEM, Nik-Ghazali, N-N, Kalam, MA, Badruddin, IA, Banapurmath, NR, Khan, TMY, Bashir, MN, Akram, N, Farade, R & Afzal, A 2023, 'Corrigendum to “The effects of graphene oxide nanoparticle additive stably dispersed in dairy scum oil biodiesel-diesel fuel blend on CI engine: Performance, emission and combustion characteristics” [Fuel 257 (2019) 116015]', Fuel, vol. 352, pp. 128943-128943. View/Download from: Publisher's site
Stapleton, MJ, Ansari, AJ & Hai, FI 2023, 'Antibiotic sorption onto microplastics in water: A critical review of the factors, mechanisms and implications', Water Research, vol. 233, pp. 119790-119790. View/Download from: Publisher's site
Stapleton, MJ, Ansari, AJ, Ahmed, A & Hai, FI 2023, 'Change in the chemical, mechanical and physical properties of plastics due to UVA degradation in different water matrices: A study on the recyclability of littered plastics', Environmental Pollution, vol. 334, pp. 122226-122226. View/Download from: Publisher's site
Stapleton, MJ, Ansari, AJ, Ahmed, A & Hai, FI 2023, 'Evaluating the generation of microplastics from an unlikely source: The unintentional consequence of the current plastic recycling process', Science of The Total Environment, vol. 902, pp. 166090-166090. View/Download from: Publisher's site View description>>
This study casts light on the potential of microplastic generation during plastic recycling - an unintended consequence of the process. To date, microplastics have been detected in the wastewater and sludge from plastic recycling facilities; however, generation pathways, factors and minimisation strategies are understudied. The purpose of this study is to identify the factors affecting microplastic generation, namely, plastic type and weathering conditions. The size reduction phase, which involved the mechanical shredding of the plastic waste material, was identified to be the predominate source of microplastic generation. Material type was found to significantly affect microplastic generation rates. Focussing on the microplastic particles in the size range of 0.212-1.18 mm, polycarbonate (PC), polyethylene terephthalate (PET), polypropylene (PP), and high-density polyethylene (HDPE) generated 28,600 ± 3961, 21,093 ± 2211, 18,987 ± 752 and 6807 ± 393 particles/kg of plastic material shredded, respectively. The significant variations between different plastic types were correlated (R2 = 0.88) to the hardness of the plastic. Environmental weathering was observed to significantly affect microplastic generation rates. Generation rates increased for PC, PET, PP, and HDPE by 185.05 %, 159.80 %, 123.70 % and 121.74 %, respectively, over a six-month environmental exposure period. The results in this study confirm production of large amounts of microplastics from the plastic recycling industry through its operational processes, which may be a significant source for microplastic pollution if measures to reduce their production and removal from wastewater and sludge are not considered.
Stewart, M 2023, 'Risk‐based thinking for extreme events: What do terrorism and climate change have in common?', Risk Management and Insurance Review, vol. 26, no. 4, pp. 467-484. View/Download from: Publisher's site View description>>
AbstractTerrorism and climate change debates are often characterized by worst‐case thinking, cost neglect, probability neglect, and avoidance of the notion of acceptable risk. This is not unexpected when dealing with extreme events. However, it can result in a frightened public, costly policy outcomes, and wasteful expenditures. The paper will describe how risk‐based approaches are well suited to infrastructure decision‐making for extreme events. Risk management concepts will be illustrated with current research of risk‐based assessment of climate adaptation engineering strategies including designing new houses in Australia subject to cyclones and extreme wind events. It will be shown that small improvements to house designs at a one‐off cost of several thousand dollars per house can reduce damage risks by 70%–80% and achieve billions of dollars of net benefit for community resilience—this helps offset some the predicted adverse effects of climate change for a modest cost. The effect of risk perceptions, insurance, and economic incentives is explored for another climate adaption measure. The paper will also highlight that there is much to be optimistic about the future, and in the ability of risk‐based thinking to meet many challenges.
Stewart, MG 2023, 'Climate Adaptation Engineering: An Optimist’s View', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 9, no. 1. View/Download from: Publisher's site
Stewart, MG 2023, 'Spatial variability of explosive blast loading and its effect on damage risks to reinforced concrete buildings', Engineering Structures, vol. 285, pp. 115650-115650. View/Download from: Publisher's site
Stewart, MG, Thöns, S & Beck, AT 2023, 'Assessment of risk reduction strategies for terrorist attacks on structures', Structural Safety, vol. 104, pp. 102353-102353. View/Download from: Publisher's site
Stone, DG, Chen, Y, Ekimov, EA, Tran, TT & Bradac, C 2023, 'Diamond Nanothermometry Using a Machine Learning Approach', ACS Applied Optical Materials, vol. 1, no. 4, pp. 898-905. View/Download from: Publisher's site
Stone, RC, Farhangi, V, Fatahi, B & Karakouzian, M 2023, 'A novel short pile foundation system bonded to highly cemented layers for settlement control', Canadian Geotechnical Journal, vol. 60, no. 9, pp. 1332-1351. View/Download from: Publisher's site View description>>
While design methods of deep foundations are mainly developed for homogenous soil deposits, the presence of highly cemented layers could lead to underestimation of resistance and overestimation of settlement of pile foundations. This study presents a novel approach using competent caliche layers bonded to the top and bottom of a continuous flight auger (CFA) pile as a new composite foundation system named caliche stiffened pile (CSP). The key objective is to optimize the required pile length in a cost-effective approach without ameliorating soil properties. Settlements of the CSP foundation for a high-rise building were monitored and full-scale tests were conducted to measure piles’ capacity. Finite element back analyses were performed to avoid adverse effect of sample disturbance in settlement calculations. A back calculation of a test fill embankment was performed to determine soil stiffness parameters by simulating an unscheduled imposed load to the structure. Impacts of the CSP on controlling the settlement of pile foundation and optimizing the required pile length are investigated using finite element analysis and a parametric study. The proposed CSP foundation can reduce the CFA pile settlement significantly in the presence of caliche layers with thickness equal or greater than a pile diameter at CFA pile head and toe, where the CSP is located.
Stratton-Powell, AA, Williams, S, Tipper, JL, Redmond, AC & Brockett, CL 2023, 'Isolation and characterisation of wear debris surrounding failed total ankle replacements', Acta Biomaterialia, vol. 159, pp. 410-422. View/Download from: Publisher's site
Stuart, B, Wong, S, Goodall, R, Beale, A, Chu, C, Nel, P, Amin-Jones, H & Thomas, P 2023, 'Safe Storage? An Assessment of Polyethylene for the Storage of Heritage Objects', Studies in Conservation, vol. 68, no. 6, pp. 669-678. View/Download from: Publisher's site View description>>
Sealable polyethylene bags are widely used to protect and store heritage items. While polyethylene is regarded as a stable material, consideration should be given to potential chemical interactions between the polymer and stored objects. The presence of additives used in manufactured polyethylene storage materials should also be considered when they are used in contact with objects. For this study, infrared spectroscopy has been successfully used to identify storage materials and associated additives, as well as to characterise the chemical changes that they undergo. Three case studies are presented that demonstrate that polyethylene bags can undergo chemical changes when exposed to the typical storage conditions used for heritage objects. The storage of degrading cellulose nitrate items shows that polyethylene undergoes oxidation when exposed to the cellulose nitrate degradation products and is identified as detrimental to long term storage viability of the polyethylene. An investigation of the yellow discolouration of polyethylene bags suggests that the oxidation of antioxidant additives, rather than the polyethylene, is responsible for the colour change. It is also demonstrated that polyethylene bags used in a procedure for the consolidation of archaeological ceramics show an interaction between the adhesive solvent employed and the bag additives.
Su, G, Jiang, P, Ong, HC, Zhu, J, Amin, NAS, Zulkifli, NWM & Ibrahim, S 2023, 'Co-production of biochar and electricity from oil palm wastes for carbon dioxide mitigation in Malaysia', Journal of Cleaner Production, vol. 423, pp. 138749-138749. View/Download from: Publisher's site
Su, G, Zulkifli, NWM, Liu, L, Ong, HC, Ibrahim, S, Yu, KL, Wei, Y & Bin, F 2023, 'Carbon-negative co-production of methanol and activated carbon from bagasse pyrolysis, physical activation, chemical looping, and methanol synthesis', Energy Conversion and Management, vol. 293, pp. 117481-117481. View/Download from: Publisher's site
Su, G, Zulkifli, NWM, Ong, HC, Ibrahim, S, Cheah, MY, Zhu, R & Bu, Q 2023, 'Co-pyrolysis of medical protective clothing and oil palm wastes for biofuel: Experimental, techno-economic, and environmental analyses', Energy, vol. 273, pp. 127221-127221. View/Download from: Publisher's site
Su, Z, Diao, T, McGuire, H, Yao, C, Yang, L, Bao, G, Xu, X, He, B & Zheng, Y 2023, 'Nanomaterials Solutions for Contraception: Concerns, Advances, and Prospects', ACS Nano, vol. 17, no. 21, pp. 20753-20775. View/Download from: Publisher's site
Su, Z, Yao, C, Tipper, J, Yang, L, Xu, X, Chen, X, Bao, G, He, B, Xu, X & Zheng, Y 2023, 'Nanostrategy of Targeting at Embryonic Trophoblast Cells Using CuO Nanoparticles for Female Contraception', ACS Nano, vol. 17, no. 24, pp. 25185-25204. View/Download from: Publisher's site View description>>
Effective contraceptives have been comprehensively adopted by women to prevent the negative consequences of unintended pregnancy for women, families, and societies. With great contributions of traditional hormonal drugs and intrauterine devices (IUDs) to effective female contraception by inhibiting ovulation and deactivating sperm, their long-standing side effects on hormonal homeostasis and reproductive organs for females remain concerns. Herein, we proposed a nanostrategy for female contraceptives, inducing embryonic trophoblast cell death using nanoparticles to prevent embryo implantation. Cupric oxide nanoparticles (CuO NPs) were adopted in this work to verify the feasibility of the nanostrategy and its contraceptive efficacy. We carried out the in vitro assessment on the interaction of CuO NPs with trophoblast cells using the HTR8/SVneo cell line. The results showed that the CuO NPs were able to be preferably uptaken into cells and induced cell damage via a variety of pathways including oxidative stress, mitochondrial damage, DNA damage, and cell cycle arrest to induce cell death of apoptosis, ferroptosis, and cuproptosis. Moreover, the key regulatory processes and the key genes for cell damage and cell death caused by CuO NPs were revealed by RNA-Seq. We also conducted in vivo experiments using a rat model to examine the contraceptive efficacy of both the bare CuO NPs and the CuO/thermosensitive hydrogel nanocomposite. The results demonstrated that the CuO NPs were highly effective for contraception. There was no sign of disrupting the homeostasis of copper and hormone, or causing inflammation and organ damage in vivo. In all, this nanostrategy exhibited huge potential for contraceptive development with high biosafety, efficacy, clinical translation, nonhormonal style, and on-demand for women.
Subramanian, S, Thoms, JAI, Huang, Y, Cornejo-Páramo, P, Koch, FC, Jacquelin, S, Shen, S, Song, E, Joshi, S, Brownlee, C, Woll, PS, Chacon-Fajardo, D, Beck, D, Curtis, DJ, Yehson, K, Antonenas, V, O'Brien, T, Trickett, A, Powell, JA, Lewis, ID, Pitson, SM, Gandhi, MK, Lane, SW, Vafaee, F, Wong, ES, Göttgens, B, Alinejad-Rokny, H, Wong, JWH & Pimanda, JE 2023, 'Genome-wide transcription factor–binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs', Blood, vol. 142, no. 17, pp. 1448-1462. View/Download from: Publisher's site View description>>
AbstractHematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34+ HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type–specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell–specific elements with ERG, and myeloid- and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type–specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.
Suherman, Abdullah, I, Sabri, M, Turmuzi, M, Silitonga, AS, Dharma, S & Yusfiani, M 2023, 'A Review of Properties, Engine Performance, Emission Characteristics and Material Compatibility Biodiesel From Waste Cooking Oil (WCO)', Automotive Experiences, vol. 6, no. 3, pp. 624-651. View/Download from: Publisher's site View description>>
Biodiesel is one of the renewable energy sources, non-fossil. The chosen feedstock should ideally be low-cost. Using waste cooking oil can reduce synthetic biodiesel's price by up to 70%. However, biodiesel has the advantage of lower heating value and higher density, causing increased fuel consumption and NOx emissions. Biodiesel has physicochemical properties such as a more significant cetane number than fossil diesel, a high flash point, and the absence of sulfur. This study identifies the potential availability of WCO as biodiesel and summarizes recent studies on the physiochemical properties of WCO biodiesel. This study also aims to clarify the use of WCO biodiesel on engine performance and exhaust emission characteristics (H.C., CO, CO2, NOx) when this biodiesel is used. Engine type and biodiesel ratio were identified for all articles. This study also discusses the effect of adding nanoparticles on engine performance and exhaust emissions in WCO biodiesel. This study also clarifies material compatibility (corrosion, wear, and friction). The corrosion rate in various types of materials and corrosion testing methods. Finally, this paper presents the opportunity for WCO biodiesel to be very feasible to reduce fossil diesel use.
Suherman, S, Abdullah, I, Sabri, M & Silitonga, AS 2023, 'Evaluation of Physicochemical Properties Composite Biodiesel from Waste Cooking Oil and Schleichera oleosa Oil', Energies, vol. 16, no. 15, pp. 5771-5771. View/Download from: Publisher's site View description>>
Waste cooking oil (WCO) biodiesel has some disadvantages, such as poor cold flow properties, low oxidation stability, and flash point during storage. These poor physicochemical properties can be improved by different ways, such as the addition of non-edible oil. The aim of this study to analyse physicochemical properties of the biodiesel made by between WCO and Schleichera oleosa (SO). The biodiesel produced with 70:30% of WCO and SO respectively as crude oil, further introducing of different KOH-based catalyst into this oil to obtained the methyl ester. The optimum yield transesterification process are 94% with 60 min. of the reaction time, 1 wt.% KOH, and 12:1 molar ratio the methanol to oil. On the other hand, the Schleichera oleosa blend shows oxidation stability at 6.8 h and 3.3 h for Waste cooking oil methyl ester (WCME). The reduction of cold flow and, on the contrary, the flash point increase were obtained with a 70:30% ratio of WCO and SO. The cold flow properties and flash point of the fuel. Thus, mixed WCO and Schleichera oleosa oil improve the physiochemical properties such as oxidation stability, flash point, and cold flow of biodiesel without the need for synthetic antioxidants.
Sulaiman, M, Rabbani, FA, Iqbal, T, Kazmi, MA, Yasin, S, Mujtaba, MA, Kalam, MA & Almomani, F 2023, 'Impact of eco-friendly chemical pretreatment on physicochemical and surface mechanical properties of sustainable lignocellulosic agricultural waste', Algal Research, vol. 71, pp. 103051-103051. View/Download from: Publisher's site
Sulimani, H, Sajjad, AM, Alghamdi, WY, Kaiwartya, O, Jan, T, Simoff, S & Prasad, M 2023, 'Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment', Transactions on Emerging Telecommunications Technologies, vol. 34, no. 11. View/Download from: Publisher's site View description>>
AbstractA decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.
Sun, C, Xiong, X, Zhai, Z, Ni, W, Ohtsuki, T & Wang, X 2023, 'Max–Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8650-8665. View/Download from: Publisher's site
Sun, C, Zheng, Z, Wang, X, Xu, M & Yang, Y 2023, 'Self-Supervised Point Cloud Representation Learning via Separating Mixed Shapes', IEEE Transactions on Multimedia, vol. 25, pp. 6207-6218. View/Download from: Publisher's site
Sun, H, Tian, Y, Zhan, W, Zhang, H, Meng, Y, Li, L, Zhou, X, Zuo, W & Ngo, HH 2023, 'Estimating Yangtze River basin's riverine N2O emissions through hybrid modeling of land-river-atmosphere nitrogen flows', Water Research, vol. 247, pp. 120779-120779. View/Download from: Publisher's site
Sun, J, Wang, Y, Liu, P, Wen, S & Wang, Y 2023, 'Memristor-Based Circuit Design of PAD Emotional Space and Its Application in Mood Congruity', IEEE Internet of Things Journal, vol. 10, no. 18, pp. 16332-16342. View/Download from: Publisher's site
Sun, J, Wang, Y, Liu, P, Wen, S & Wang, Y 2023, 'Memristor-Based Neural Network Circuit With Multimode Generalization and Differentiation on Pavlov Associative Memory', IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3351-3362. View/Download from: Publisher's site View description>>
Most of the classical conditioning laws implemented by existing circuits are involved in learning and forgetting between only three neurons, and the problems between multiple neurons are not considered. In this article, a multimode generalization and differentiation circuit for the Pavlov associative memory is proposed based on memristors. The designed circuit is mainly composed of voltage control modules, synaptic neuron modules, and inhibition modules. The secondary differentiation is accomplished through the process of associative learning and forgetting among multiple neurons. The process of multiple generalization and differentiation is realized based on the nonvolatility and thresholding properties of memristors. The extinction inhibition and differentiation inhibition in forgetting is considered through the inhibition modules. The Pavlov associative memory neural network with multimodal generalization and differentiation may provide a reference for the further development of brain-like intelligence.
Sun, J, Zhao, L, Wen, S & Wang, Y 2023, 'Memristor-Based Neural Network Circuit of Emotional Habituation With Contextual Dependency', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 17382-17391. View/Download from: Publisher's site
Sun, L, Chang, Y-C, Lyu, C, Shi, Y, Shi, Y & Lin, C-T 2023, 'Toward multi-target self-organizing pursuit in a partially observable Markov game', Information Sciences, vol. 648, pp. 119475-119475. View/Download from: Publisher's site
Sun, W, Guo, W, Li, B, Wen, S & Wu, X 2023, 'Interval Bipartite Synchronization of Delayed Nonlinear Neural Networks With Signed Graphs', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 3, pp. 1723-1733. View/Download from: Publisher's site View description>>
The cooperation of linear multiagent systems (MASs) in the signed graphs has received wide attention. However, time delays and nonlinearity are ignored. This article deals with the cooperation behavior, especially interval bipartite synchronization (IBS) of delayed nonlinear neural networks (NNs) with signed graphs. A generalized matrix is proposed for the construction of the Lyapunov functionals, establishing sufficient conditions in a linear matrix inequality related to the coupling strength, the delays, and the network structure. It suggests that the negative rooted cycles and the negative nonrooted cycles take an important part in stabilizing delayed nonlinear NNs and leading to their diversity, and time delays, especially communication delays, significantly impact cooperation performance. Numerical examples are employed to validate our derived results.
Sun, X, Cheng, H, Liu, B, Li, J, Chen, H, Xu, G & Yin, H 2023, 'Self-Supervised Hypergraph Representation Learning for Sociological Analysis', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11860-11871. View/Download from: Publisher's site
Sun, X, Dong, Z, Jin, Z, Lei, G & Tian, X 2023, 'System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design', IEEE Transactions on Industrial Electronics, pp. 1-11. View/Download from: Publisher's site
Sun, X, Pan, Y, Song, Y, Liu, W, Nghiem, LD, Wang, Q & Cai, Z 2023, 'Ceftriaxone sodium degradation by carbon quantum dots (CQDs)-decorated C-doped α-Bi2O3 nanorods', Environmental Science and Ecotechnology, vol. 13, pp. 100219-100219. View/Download from: Publisher's site View description>>
A novel carbon quantum dots decorated C-doped α-Bi2O3 photocatalyst (CBO/CQDs) was synthesized by solvothermal method. The synergistic effect of adsorption and photocatalysis highly improved contaminants removal efficiencies. The ceftriaxone sodium degradation rate constant (k) of CBO/CQDs was 11.4 and 3.2 times that of pure α-Bi2O3 and C-doped α-Bi2O3, respectively. The interstitial carbon doping generated localized states above the valence band, which enhanced the utilization of visible light and facilitated the separation of photogenerated electrons and holes; the loading of CQDs improved the charge carrier separation and extended the visible light response; the reduced particle size of CBO/CQDs accelerated the migration of photogenerated carriers. The •O2 - and h+ were identified as the dominant reactive species in ceftriaxone sodium degradation, and the key role of •O2 - was further investigated by NBT transformation experiments. The Fukui index was applied to ascertain the molecular bonds of ceftriaxone sodium susceptible to radical attack, and intermediates analysis was conducted to explore the possible degradation pathways. The toxicity evaluation revealed that some degradation intermediates possessed high toxicity, thus the contaminants require sufficient mineralization to ensure safe discharge. The present study makes new insights into synchronous carbon dopping and CQDs decoration on modification of α-Bi2O3, which provides references for future studies.
Sun, X, Su, Z, Lei, G & Yao, M 2023, 'Robust Predictive Cascaded Speed and Current Control for PMSM Drives Considering Parameter Variations', IEEE Transactions on Industrial Electronics, pp. 1-11. View/Download from: Publisher's site
Joint Communications and Sensing (JCAS) in mobile networks are typically based on Orthogonal Frequency Division Multiplexing (OFDM) systems. For time-varying channels, large Doppler frequencies in OFDM JCAS can cause notable intercarrier interference, which has not been considered for sensing. In this paper, we propose a frequency-domain sensing framework for OFDM JCAS systems. We first derive a frequency-domain closed-form expression of the received signals, to characterise the delay and Doppler frequency impact within and across OFDM blocks. We then develop intra-block and inter-block sensing algorithms, based on the expression. The framework is further completed with exemplified pilot design and periodogram sensing algorithm. Simulation results demonstrate the effectiveness of the proposed framework.
Sun, Z, Yao, Y, Wei, X-S, Shen, F, Zhang, J & Hua, X-S 2023, 'Boosting Robust Learning Via Leveraging Reusable Samples in Noisy Web Data', IEEE Transactions on Multimedia, vol. 25, pp. 3284-3295. View/Download from: Publisher's site
Sunku Mohan, V, Sankaran, S, Nanda, P & Achuthan, K 2023, 'Enabling secure lightweight mobile Narrowband Internet of Things (NB-IoT) applications using blockchain', Journal of Network and Computer Applications, vol. 219, pp. 103723-103723. View/Download from: Publisher's site
Swaminathan, GV, Periasamy, S & Lu, DD-C 2023, 'Capacitor Current Control Based Virtual Inertia Control of Autonomous DC Microgrid', IEEE Transactions on Industrial Electronics, vol. 70, no. 7, pp. 6908-6918. View/Download from: Publisher's site
Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Li, L, Lu, DD-C, Aguilera, RP & Siwakoti, YP 2023, 'HERIC-Clamped and PN-NPC Inverters With Five-Level Output Voltage and Reduced Grid-Interfaced Filter Size', IEEE Open Journal of Power Electronics, vol. 4, pp. 306-318. View/Download from: Publisher's site
Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Siwakoti, YP, Li, L, Lu, DD-C, Aguilera, RP & Pou, J 2023, 'A 5-Level HERIC Active-Clamped Inverter With Full Reactive Power Capability for Grid-Connected Applications', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 135-148. View/Download from: Publisher's site
Syberg, M, West, N, Lenze, D & Deuse, J 2023, 'Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning', Procedia CIRP, vol. 120, pp. 1107-1112. View/Download from: Publisher's site
Szczygiełda, M, Krajewska, M, Andrzejewski, A, Zheng, L, Nghiem, LD, Oleskowicz-Popiel, P, Szymanowska, D & Prochaska, K 2023, 'Dewatering fermentation broth for keto carboxylic acid enrichment by forward osmosis: A techno-economic analysis', Journal of Membrane Science, vol. 679, pp. 121699-121699. View/Download from: Publisher's site
T., Y & D., C 2023, 'A Typology of Competitive Strategies for Social Enterprises', Journal of Social Entrepreneurship, pp. 1-27. View/Download from: Publisher's site View description>>
This article tackles the limited theorising on social enterprises’ (SEs) decisions on the product or service mix, quality, pricing, and the targeted beneficiaries by proposing a typology of competitive strategies for them. The paper empirically observes how SEs react to the challenges faced by the marketisation of their fields. The context of this study is the supplementary education of the disabled in Turkey, a field where increased state coverage led to the entrance of many profit-focused counterparts. Based on a Grounded Theory methodology and a longitudinal dataset including ten cases, the study developed a unique typology comprising three competitive strategies, i.e., innovator, enforcer, and includer. The findings illustrate various strategic responses to heightened competition from incumbent SEs. However, deviation of these strategic responses from the typology appeared to be detrimental in the long-term. By shedding light on the intricacies of the hybrid nature of SEs and considering changes in their competitive environment over time, this study concludes with a summary of contributions to theory, practice, and policy.
Taheri, MH, Askari, N, Feng, Y, Nabaei, M, Islam, MS, Farnoud, A & Cui, X 2023, 'Swirling flow and capillary diameter effect on the performance of an active dry powder inhalers', Medicine in Novel Technology and Devices, vol. 18, pp. 100240-100240. View/Download from: Publisher's site
Tai, M-R, Ji, H-W, Chen, J-P, Liu, X-F, Song, B-B, Zhong, S-Y, Rifai, A, Nisbet, DR, Barrow, CJ, Williams, RJ & Li, R 2023, 'Biomimetic triumvirate nanogel complexes via peptide-polysaccharide-polyphenol self-assembly', International Journal of Biological Macromolecules, vol. 251, pp. 126232-126232. View/Download from: Publisher's site
Talaei, S, Zhu, X, Li, J, Yu, Y & Chan, THT 2023, 'Transfer learning based bridge damage detection: Leveraging time-frequency features', Structures, vol. 57, pp. 105052-105052. View/Download from: Publisher's site
Tan, J, Goyal, SB, Singh Rajawat, A, Jan, T, Azizi, N & Prasad, M 2023, 'Anti-Counterfeiting and Traceability Consensus Algorithm Based on Weightage to Contributors in a Food Supply Chain of Industry 4.0', Sustainability, vol. 15, no. 10, pp. 7855-7855. View/Download from: Publisher's site View description>>
Supply chain management can significantly benefit from contemporary technologies. Among these technologies, blockchain is considered suitable for anti-counterfeiting and traceability applications due to its openness, decentralization, anonymity, and other characteristics. This article introduces different types of blockchains and standard algorithms used in blockchain technology and discusses their advantages and disadvantages. To improve the work efficiency of anti-counterfeiting traceability systems in supply chains and reduce their energy consumption, this paper proposes a model based on the practical Byzantine fault tolerance (PBFT) algorithm of alliance chains. This model uses a credit evaluation system to select the primary node and integrates the weightage to contributors (WtC) algorithm based on the consensus mechanism. This model can reduce the decline in the algorithm success rate while increasing the number of malicious transaction nodes, thereby reducing the computing cost. Additionally, the throughput of the algorithmic system increases rapidly, reaching approximately 680 transactions per second (TPS) in about 120 min after the malicious nodes are eliminated. The throughput rapidly increases as the blacklist mechanism reduces the number of malicious nodes, which improves the system’s fault tolerance. To validate the effectiveness of the proposed model, a case study was conducted using data from the anti-counterfeiting traceability system of the real-life supply chain of a food company. The analysis results show that after a period of stable operation of the WtCPBFT algorithm in the proposed model, the overall communication cost of the system was reduced, the throughput and stability were improved, and the fault-tolerant performance of the system was improved. In conclusion, this paper presents a novel model that utilizes the PBFT algorithm of alliance chains and the WtC algorithm to improve the efficiency and security of anti-counte...
Tan, S, Liu, W, Dong, Q, Chan, S, Yu, S, Zhong, X & He, D 2023, 'Hitting Moving Targets: Intelligent Prevention of IoT Intrusions on the Fly', IEEE Internet of Things Journal, vol. 10, no. 23, pp. 21000-21012. View/Download from: Publisher's site
Tang, J, Yang, H, Pu, Y, Hu, Y, Qu, X, Chen, S, Wang, XC, Ngo, HH, Li, Y & Abomohra, A 2023, 'Bioenergy production from swine wastewater based on a combined process of anaerobic dynamic membrane reactor and microalgae cultivation: Feasibility and performance', Science of The Total Environment, vol. 899, pp. 165621-165621. View/Download from: Publisher's site
Tang, K, Yang, C, Guo, Y, Wang, N, Zhu, Y, Zhang, Y, Ng, EJ, Lee, JE-Y, Fang, Z, Wang, W, Jiang, H, Heng, C-H & Zheng, Y 2023, 'A 107 pJ/b TX 260 pJ/b RX Ultralow-Power MEMS-Based Transceiver With Wake-Up in ISM-Bands for IoT Applications', IEEE Journal of Solid-State Circuits, vol. 58, no. 5, pp. 1337-1349. View/Download from: Publisher's site
Tang, L, Pan, Z, Li, X, Li, J & Meng, J 2023, 'Antibiotics resistance removal from piggery wastewater by an integrated anaerobic–aerobic biofilm reactor: Efficiency and mechanism', Science of The Total Environment, vol. 905, pp. 167031-167031. View/Download from: Publisher's site
Tang, R, Yu, Z & Li, J 2023, 'KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences', Molecular Phylogenetics and Evolution, vol. 179, pp. 107662-107662. View/Download from: Publisher's site
Tang, X-W, Huang, Y, Shi, Y, Huang, X-L & Yu, S 2023, 'UAV Placement for VR Reconstruction: A Tradeoff Between Resolution and Delay', IEEE Communications Letters, vol. 27, no. 5, pp. 1382-1386. View/Download from: Publisher's site
Tang, Z, Li, W, Peng, Q & Yan, L 2023, 'Quasi-Static Cyclic Behavior of CFRP-Confined Geopolymeric Composites', Journal of Composites for Construction, vol. 27, no. 6. View/Download from: Publisher's site
Tanveer, M, Lin, C-T & Singh, AK 2023, 'Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging—Part II', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 188-189. View/Download from: Publisher's site
Tao, G, Guo, E, Yuan, J, Chen, Q & Nimbalkar, S 2023, 'Permeability and Cracking of Compacted Clay Liner Improved by Nano-SiO2 and Sisal Fiber', KSCE Journal of Civil Engineering, vol. 27, no. 12, pp. 5109-5122. View/Download from: Publisher's site
Tao, G, Ouyang, Q, Lei, D, Chen, Q, Nimbalkar, S, Bai, L & Zhu, Z 2023, 'Erratum for “NMR-Based Measurement of AWRC and Prediction of Shear Strength of Unsaturated Soils”', International Journal of Geomechanics, vol. 23, no. 9. View/Download from: Publisher's site
Tao, G, Peng, P, Chen, Q, Nimbalkar, S, Huang, Z, Peng, Y & Zhao, W 2023, 'A new fractal model for nonlinear seepage of saturated clay considering the initial hydraulic gradient of microscopic seepage channels', Journal of Hydrology, vol. 625, pp. 130055-130055. View/Download from: Publisher's site
Tao, X, Adak, C, Chun, P-J, Yan, S & Liu, H 2023, 'ViTALnet: Anomaly on Industrial Textured Surfaces With Hybrid Transformer', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13. View/Download from: Publisher's site
Tasci, G, Gun, MV, Keles, T, Tasci, B, Barua, PD, Tasci, I, Dogan, S, Baygin, M, Palmer, EE, Tuncer, T, Ooi, CP & Acharya, UR 2023, 'QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals', Chaos, Solitons & Fractals, vol. 172, pp. 113472-113472. View/Download from: Publisher's site
Tasci, G, Loh, HW, Barua, PD, Baygin, M, Tasci, B, Dogan, S, Tuncer, T, Palmer, EE, Tan, R-S & Acharya, UR 2023, 'Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals', Knowledge-Based Systems, vol. 260, pp. 110190-110190. View/Download from: Publisher's site
Tasci, I, Tasci, B, Barua, PD, Dogan, S, Tuncer, T, Palmer, EE, Fujita, H & Acharya, UR 2023, 'Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals', Information Fusion, vol. 96, pp. 252-268. View/Download from: Publisher's site
Tavakoli, J, Diwan, AD & Tipper, JL 2023, 'Intervertebral disc-on-a-chip: a precision engineered toolbox for low back pain studies', Trends in Biotechnology, vol. 41, no. 11, pp. 1339-1342. View/Download from: Publisher's site
Tawalbeh, M, Mohammed, S, Al-Othman, A, Yusuf, M, Mofijur, M & Kamyab, H 2023, 'MXenes and MXene-based materials for removal of pharmaceutical compounds from wastewater: Critical review', Environmental Research, vol. 228, pp. 115919-115919. View/Download from: Publisher's site
Tayari, S, Taghikhah, F, Bharathy, G & Voinov, A 2023, 'Designing a conceptual framework for strategic selection of Bushfire mitigation approaches', Journal of Environmental Management, vol. 344, pp. 118486-118486. View/Download from: Publisher's site
Teng, J, Liu, J, Zhang, S & Sheng, D 2023, 'Frost heave in coarse-grained soils: experimental evidence and numerical modelling', Géotechnique, vol. 73, no. 12, pp. 1100-1111. View/Download from: Publisher's site View description>>
Frost heave in coarse-grained soils caused by vapour transfer has attracted much attention, but little experimental or numerical evidence has been reported thus far. A series of laboratory experiments is carried out by a frost heave apparatus and an X-ray micro-computed tomography instrument. The only water supply mechanism to the tested specimen is vapour transfer. The results indicate that considerable frost heave occurs in coarse-grained soil specimens with a zero fines content. The ratio of frost heave to the initial height can reach 13·8% and 25·1% at 14 days and 18 days, respectively. Ice crystals first grow in pores causing the soil particle to rotate and move, and the soil porosity to increase. With continued ice crystal growth, they eventually become connected and form an ice lens. If a constant temperature gradient is applied, only one horizontal ice lens is formed, which differs from the layered ice lenses observed in fine-graded soils. A new numerical model is developed to simulate ice formation and frost heave in coarse-grained soils, which considers the process of vapour transfer and desublimation. The predicted frost heave results agree well with the measured results. This study provides a novel explanation for the frost heave mechanism in coarse-grained soils.
Thanikodi, S, Milano, J, Sebayang, AH, Shamsuddin, AH, Rangappa, SM, Siengchin, S, Silitonga, AS, Bahar, AH, Ibrahim, H & Benu, SM 2023, 'Enhancing the engine performance using multi fruits peel (exocarp) ash with nanoparticles in biodiesel production', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 1, pp. 2122-2143. View/Download from: Publisher's site
Thiri Zun, M, Shakeel Ahmad, M, Fayaz, H, Selvaraj, J, Ahmed, W, Wang, Y, Ben Khedher, N, Silitonga, AS, Elfasakhany, A, Kalam, MA & Rashid, B 2023, 'Towards techno-economics of green hydrogen as a primary combustion fuel for recreational vehicle vapor absorption refrigeration system', Sustainable Energy Technologies and Assessments, vol. 56, pp. 103007-103007. View/Download from: Publisher's site
Tian, A, Feng, B, Zhou, H, Huang, Y, Sood, K, Yu, S & Zhang, H 2023, 'Efficient Federated DRL-Based Cooperative Caching for Mobile Edge Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 246-260. View/Download from: Publisher's site View description>>
Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.
Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2023, 'CIFair: Constructing continuous domains of invariant features for image fair classifications', Knowledge-Based Systems, vol. 268, pp. 110417-110417. View/Download from: Publisher's site
Tian, J, Sun, X, Du, Y, Zhao, S, Liu, Q, Zhang, K, Yi, W, Huang, W, Wang, C, Wu, X, Hsieh, M-H, Liu, T, Yang, W & Tao, D 2023, 'Recent Advances for Quantum Neural Networks in Generative Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-20. View/Download from: Publisher's site
Tian, P & Yu, H 2023, 'Can we improve meta-learning model in few-shot learning by aligning data distributions?', Knowledge-Based Systems, vol. 277, pp. 110800-110800. View/Download from: Publisher's site
Tian, Y, Li, Q, Feng, Y, Yu, Y, Wu, D, Chen, X & Gao, W 2023, 'Nonlinear dynamic analysis of the functionally graded graphene platelets reinforced porous plate under moving mass', Thin-Walled Structures, vol. 183, pp. 110363-110363. View/Download from: Publisher's site View description>>
Recent studies demonstrated that porous materials could gain satisfying improvements in some mechanical properties by adding graphene platelet (GPL) reinforcements. Following this result, the present work exhibits a semi-analytical method to investigate the nonlinear dynamic characteristics of the functionally graded GPLs reinforced porous (FG-GPLRP) plate under a moving mass. Two types of boundary conditions, i.e., simply supported (SSSS) and clamped (CCCC) edges, are incorporated in the study. Based on the refined sinusoidal shear deformation theory (RSSDT) and von Kármán nonlinearity, the governing equations are transformed into a group of ordinary differential equations for the deflection of the plate. Then, the dynamic behaviours of the plate can be investigated by operating the fourth-order Runge–Kutta approach. After verification, several numerical examples are displayed to illustrate the effects of porosity coefficient, GPLs content, Winkler–Pasternak foundation, damping, initial imperfection, and compression stress on the moving-load-bearing capability of the plate. The obtained results demonstrate that, without harming its moving load capacity, it is possible to decrease the mass of the FG-GPLRP plate to a satisfying extent by altering the porosity and GPLs content.
Tian, Z, Cui, L, Liang, J & Yu, S 2023, 'A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning', ACM Computing Surveys, vol. 55, no. 8, pp. 1-35. View/Download from: Publisher's site View description>>
The prosperity of machine learning has been accompanied by increasing attacks on the training process. Among them, poisoning attacks have become an emerging threat during model training. Poisoning attacks have profound impacts on the target models, e.g., making them unable to converge or manipulating their prediction results. Moreover, the rapid development of recent distributed learning frameworks, especially federated learning, has further stimulated the development of poisoning attacks. Defending against poisoning attacks is challenging and urgent. However, the systematic review from a unified perspective remains blank. This survey provides an in-depth and up-to-date overview of poisoning attacks and corresponding countermeasures in both centralized and federated learning. We firstly categorize attack methods based on their goals. Secondly, we offer detailed analysis of the differences and connections among the attack techniques. Furthermore, we present countermeasures in different learning framework and highlight their advantages and disadvantages. Finally, we discuss the reasons for the feasibility of poisoning attacks and address the potential research directions from attacks and defenses perspectives, separately.
Tihin, GL, Mo, KH, Onn, CC, Ong, HC, Taufiq-Yap, YH & Lee, HV 2023, 'Overview of municipal solid wastes-derived refuse-derived fuels for cement co-processing', Alexandria Engineering Journal, vol. 84, pp. 153-174. View/Download from: Publisher's site
Tong, M, Huang, X & Zhang, JA 2023, 'Faster-Than-Nyquist Transmission With Frame-by-Frame Decision-Directed Successive Interference Cancellation', IEEE Transactions on Communications, vol. 71, no. 8, pp. 4851-4861. View/Download from: Publisher's site
Tonini de Araújo, M, Tonatto Ferrazzo, S, Mansur Chaves, H, Gravina da Rocha, C & Cesar Consoli, N 2023, 'Mechanical behavior, mineralogy, and microstructure of alkali-activated wastes-based binder for a clayey soil stabilization', Construction and Building Materials, vol. 362, pp. 129757-129757. View/Download from: Publisher's site View description>>
This paper evaluated the mechanical and microstructural behavior of a clayey soil stabilized by an alkali-activated binder composed of two residues (sugarcane bagasse ash and hydrated eggshell lime) and sodium hydroxide. The sugarcane bagasse ash, an agro-industrial waste, contains high aluminosilicates content (64.74 % silica and 13.25 % alumina), needed for alkali-activation processes; calcium additions from the lime (72.90 % calcium oxide) allow curing at room temperatures. An experimental design analyzed the mechanical behavior of soil-alkali activated binder, and soil-Portland cement mixtures. Unconfined compressive strength (UCS), tensile strength (STS), stiffness (G0), durability (accumulated loss of mass, ALM), and matric suction tests, and XRD and SEM-EDS investigations were performed. Statistical analysis showed a higher influence of the dry unit weight over the binders’ mechanical results. In addition, binders presented similar mechanical results for mixtures cured in room temperature (23 °C) (e.g., UCS around 5 MPa for high density-high binder content samples with 30 % moisture content, and STS around 0.6 MPa for high density-high binder content samples with 28 % moisture content). For both binders, the lowest ALM was 1.63 % for high density-high binder content samples. The porosity/binder content index was a reliable parameter when evaluating soil stabilization. XRD and SEM analysis of alkali-activated samples showed, respectively, an amorphous hump attributed to disordered structures (C[sbnd]S[sbnd]H and (C,N)-A[sbnd]S[sbnd]H) and soil particles embedded in a cementitious matrix. Higher temperature (40 °C) and curing period (28 days) resulted in a denser structure.
Topaloglu, I, Barua, PD, Yildiz, AM, Keles, T, Dogan, S, Baygin, M, Gul, HF, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds', Engineering Applications of Artificial Intelligence, vol. 126, pp. 106887-106887. View/Download from: Publisher's site
Tran, D-T, Nguyen, T-H, Doan, T-H, Dang, V-C & Nghiem, LD 2023, 'Removal of direct blue 71 and methylene blue from water by graphene oxide: effects of charge interaction and experimental parameters', Journal of Dispersion Science and Technology, vol. 44, no. 13, pp. 2508-2519. View/Download from: Publisher's site
Tran, LC, Le, AT, Huang, X, Dutkiewicz, E, Ngo, D & Taparugssanagorn, A 2023, 'Complexity Reduction for Hybrid TOA/AOA Localization in UAV-Assisted WSNs', IEEE Sensors Letters, vol. 7, no. 11, pp. 1-4. View/Download from: Publisher's site
Tran, T, Ho-Le, T, Bliuc, D, Abrahamsen, B, Hansen, L, Vestergaard, P, Center, JR & Nguyen, TV 2023, '‘Skeletal Age’ for mapping the impact of fracture on mortality', eLife, vol. 12, p. e83888. View/Download from: Publisher's site View description>>
Background:Fragility fracture is associated with an increased risk of mortality, but mortality is not part of doctor-patient communication. Here, we introduce a new concept called ‘Skeletal Age’ as the age of an individual’s skeleton resulting from a fragility fracture to convey the combined risk of fracture and fracture-associated mortality for an individual.Methods:We used the Danish National Hospital Discharge Register which includes the whole-country data of 1,667,339 adults in Denmark born on or before January 1, 1950, who were followed up to December 31, 2016 for incident low-trauma fracture and mortality. Skeletal age is defined as the sum of chronological age and the number of years of life lost (YLL) associated with a fracture. Cox’s proportional hazards model was employed to determine the hazard of mortality associated with a specific fracture for a given risk profile, and the hazard was then transformed into YLL using the Gompertz law of mortality.Results:During the median follow-up period of 16 years, there had been 307,870 fractures and 122,744 post-fracture deaths. A fracture was associated with between 1 and 7 years of life lost, with the loss being greater in men than women. Hip fractures incurred the greatest loss of life years. For instance, a 60-year-old individual with a hip fracture is estimated to have a skeletal age of 66 for men and 65 for women. Skeletal Age was estimated for each age and fracture site stratified by gender.Conclusions:We propose ‘Skeletal Age’ as a new metric to assess the impact of a fragility fracture on an individual’s life expectancy. This approach will enhance doctor-patient risk communication about the risks associated with osteoporosis.
The characterization of orthotropic materials has challenged the vibration and acoustics community for quite some time. Complex composite materials such as wooden structures require attention to factors including moisture, grain boundaries in addition to macroscopic features. Here we devise a basic model developed by measuring the vibrational response in two separate axes to determine the material characteristics of a timber dowel. A proposed benchtop procedure utilises vibrometers and accelerometers to gather data before the updating process, for which, FEMtools was used. Based on the input material parameters, uncovered by previous studies, provide a starting point for the model updating procedure where experimental mode shapes and frequency responses are correlated to the finite element model. With the focus on radiata pine, the results show radial and tangential values converge similar to previous literature but with variation in the longitudinal direction and shear planes. Overall, this study provides a solid foundation to the characterization process of orthotropic materials like timber which can be further expanded into fields of structural health monitoring, damage detection and potential use in digital twins. The authors acknowledge the support of the Australian Research Council Linkage Project LP200301196.
Tran, TS, Ho-Le, TP, Bliuc, D, Center, JR, Blank, RD & Nguyen, TV 2023, 'Prevention of Hip Fractures: Trade-off between Minor Benefits to Individuals and Large Benefits to the Community', Journal of Bone and Mineral Research, vol. 38, no. 11, pp. 1594-1602. View/Download from: Publisher's site View description>>
Tuan, HD, Nasir, AA, Chen, Y, Dutkiewicz, E & Poor, HV 2023, 'Quantized RIS-Aided Multi-User Secure Beamforming Against Multiple Eavesdroppers', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4695-4706. View/Download from: Publisher's site
Tuncer, I, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S, Yeong, CH & Acharya, UR 2023, 'Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography', Informatics in Medicine Unlocked, vol. 36, pp. 101158-101158. View/Download from: Publisher's site
This research explores how the cloud’s technological capability helps small and medium enterprises (SMEs) adapt to challenging business environments, providing long-term sustainability and strategic agility. The article uses a theoretical and quantitative empirical approach, known as the positivist research paradigm, in offering a unique capability called dynamic cloud capability that leverages the cloud’s technological capabilities. Based on the quantitative analysis of 222 Australian Information and Communication Technology (ICT) SMEs, dynamic cloud capability favourably improves the flexible allocation of resources (resource fluidity) and the ability to adapt business models (strategic agility). Additionally, because of the successful mediating effect of resource fluidity, it is inferred that dynamic cloud capability allows for the flexible allocation of resources leading to improved strategic agility. Hence, adopting dynamic cloud capability in an organisation’s strategy would be particularly appealing to ICT SMEs as it has been verified to enhance adaptability to a challenging business environment and flexible allocation of resources.
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2023, 'Multiservice Compact Pixelated Stacked Antenna With Different Pixel Shapes for IoT Applications', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19883-19897. View/Download from: Publisher's site
Unanue, IJ, Borzeshi, EZ & Piccardi, M 2023, 'Regressing Word and Sentence Embeddings for Low-Resource Neural Machine Translation', IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, pp. 450-463. View/Download from: Publisher's site
Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161. View/Download from: Publisher's site View description>>
AbstractCross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.
Uzair, M, Li, L, Eskandari, M, Hossain, J & Zhu, JG 2023, 'Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods', Renewable and Sustainable Energy Reviews, vol. 178, pp. 113228-113228. View/Download from: Publisher's site
Valenzuela-Fernández, L, Munoz Quezada, I & Merigo, JM 2023, 'Mapping the most competitive journals in advertising research. A bibliometric analysis in a 25-year period', Journal of Global Scholars of Marketing Science, vol. 33, no. 3, pp. 349-381. View/Download from: Publisher's site
Vasilescu, SA, Ding, L, Parast, FY, Nosrati, R & Warkiani, ME 2023, 'Sperm quality metrics were improved by a biomimetic microfluidic selection platform compared to swim-up methods', Microsystems & Nanoengineering, vol. 9, no. 1, p. 37. View/Download from: Publisher's site View description>>
AbstractSperm selection is an essential component of all assisted reproductive treatments (ARTs) and is by far the most neglected step in the ART workflow in regard to technological innovation. Conventional sperm selection methodologies typically produce a higher total number of sperm with variable motilities, morphologies, and levels of DNA integrity. Gold-standard techniques, including density gradient centrifugation (DGC) and swim-up (SU), have been shown to induce DNA fragmentation through introducing reactive oxygen species (ROS) during centrifugation. Here, we demonstrate a 3D printed, biologically inspired microfluidic sperm selection device (MSSP) that utilizes multiple methods to simulate a sperms journey toward selection. Sperm are first selected based on their motility and boundary-following behavior and then on their expression of apoptotic markers, yielding over 68% more motile sperm than that of previously reported methods with a lower incidence of DNA fragmentation and apoptosis. Sperm from the MSSP also demonstrated higher motile sperm recovery after cryopreservation than that of SU or neat semen. Experiments were conducted side-by-side against conventional SU methods using human semen (n = 33) and showed over an 85% improvement in DNA integrity with an average 90% reduction in sperm apoptosis. These results that the platform is easy-to-use for sperm selection and mimics the biological function of the female reproductive tract during conception.
Velumayil, R, Seikh, A, Balasubramanian, V, Kalam, M, Ravishankar, S, Venugopal, J, Chitra, L, Saravanakumar, L & Senthilkumar, TS 2023, 'Performance enhancement of water output via latent heat storage system with single slope solar stills', Thermal Science, vol. 27, no. 6 Part B, pp. 4851-4860. View/Download from: Publisher's site View description>>
The purpose of this study was to design, build, and assess the performance of a latent heat storage system in tandem with a single slope solar still. Using a solar accumulator to transfer hot water to a shell and a spiral finned tube filled with 30 kg of paraffin wax ? 1.2 wt.% of Al2O3 nanocomposites, latent heat was stored. To test the effect of the storage system?s performance, two trials were conducted, with and without storage, under as similar of conditions as could be arranged. The proposed storage system design eliminates any potential issues with usage of paraffin wax as the storage system in conjunction with the solar still. An outcome indicated that daily fresh water output was enhanced by 4.63% when the solar still was used in conjunction with the storage system.
Veza, I, Asy'ari, MZ, Idris, M, Epin, V, Rizwanul Fattah, IM & Spraggon, M 2023, 'Electric vehicle (EV) and driving towards sustainability: Comparison between EV, HEV, PHEV, and ICE vehicles to achieve net zero emissions by 2050 from EV', Alexandria Engineering Journal, vol. 82, pp. 459-467. View/Download from: Publisher's site
Veza, I, Irianto, Tuan Hoang, A, Yusuf, AA, Herawan, SG, Soudagar, MEM, Samuel, OD, Said, MFM & Silitonga, AS 2023, 'Effects of Acetone-Butanol-Ethanol (ABE) addition on HCCI-DI engine performance, combustion and emission', Fuel, vol. 333, pp. 126377-126377. View/Download from: Publisher's site
Veza, I, Spraggon, M, Fattah, IMR & Idris, M 2023, 'Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition', Results in Engineering, vol. 18, pp. 101213-101213. View/Download from: Publisher's site
Vidhya, V, Raghavendra, U, Gudigar, A, Basak, S, Mallappa, S, Hegde, A, Menon, GR, Barua, PD, Salvi, M, Ciaccio, EJ, Molinari, F & Acharya, UR 2023, 'YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images', IEEE Access, vol. 11, pp. 141309-141328. View/Download from: Publisher's site
Vishwakarma, V, Kandasamy, J & Vigneswaran, S 2023, 'Surface Treatment of Polymer Membranes for Effective Biofouling Control', Membranes, vol. 13, no. 8, pp. 736-736. View/Download from: Publisher's site View description>>
Membrane biofouling is the consequence of the deposition of microorganisms on polymer membrane surfaces. Polymeric membranes have garnered more attention for filtering and purifying water because of their ease of handling, low cost, effortless surface modification, and mechanical, chemical, and thermal properties. The sizes of the pores in the membranes enable micro- and nanofiltration, ultrafiltration, and reverse osmosis. Commonly used polymers for water filter membranes are polyvinyl chloride (PVA), polyvinylidene fluoride (PVDF), polyamide (PA), polyethylene glycol (PEG), polyethersulfone (PES), polyimide (PI), polyacrylonitrile (PAN), polyvinyl alcohol (PA), poly (methacrylic acid) (PMAA), polyaniline nanoparticles (PANI), poly (arylene ether ketone) (PAEK), polyvinylidene fluoride polysulfone (PSF), poly (ether imide) (PEI), etc. However, these polymer membranes are often susceptible to biofouling because of inorganic, organic, and microbial fouling, which deteriorates the membranes and minimizes their lives, and increases operating costs. Biofouling infection on polymer membranes is responsible for many chronic diseases in humans. This contamination cannot be eliminated by periodic pre- or post-treatment processes using biocides and other chemicals. For this reason, it is imperative to modify polymer membranes by surface treatments to enhance their efficiency and longevity. The main objective of this manuscript is to discuss application-oriented approaches to control biofouling on polymer membranes using various surface treatment methods, including nanomaterials and fouling characterizations utilizing advanced microscopy and spectroscopy techniques.
Vizuete-Luciano, E, Güzel, O & Merigó, JM 2023, 'Bibliometric research of the Pay-What-You-Want Topic', Journal of Revenue and Pricing Management, vol. 22, no. 5, pp. 413-426. View/Download from: Publisher's site View description>>
AbstractPay-What-You-Want (PWYW), is a pricing strategy increasingly applied in many different industries, both profitable and not. This study aims to identify influential cited works in PWYW research, determine the current status, and indicate the extent to which influential works have shaped the field addressing this concern, a set of bibliometric analyses conducted in this paper. The analysis was carried out on 136 research papers published between 2009 and 2022 have been analyzed based on Web of Science Core Collection (WoS) results. In order to identify the most cited authors and works, the co-citation analysis was applied. To scrutinize the intellectual structure of the field, bibliometric coupling was applied, to show the network structure of the themes, co-word analysis was applied. Building upon the results, this study suggests future research paths.
Vo, T-K-Q, Hoang, Q-H, Ngo, HH, Tran, C-S, Ninh, TNN, Le, S-L, Nguyen, A-T, Pham, TT, Nguyen, T-B, Lin, C & Bui, X-T 2023, 'Influence of salinity on microalgae-bacteria symbiosis treating shrimp farming wastewater', Science of The Total Environment, vol. 902, pp. 166111-166111. View/Download from: Publisher's site
Vo, TPT, Ngo, HH, Guo, W, Turney, C, Liu, Y, Nguyen, DD, Bui, XT & Varjani, S 2023, 'Influence of the COVID-19 pandemic on climate change summit negotiations from the climate governance perspective', Science of The Total Environment, vol. 878, pp. 162936-162936. View/Download from: Publisher's site View description>>
The COVID-19 pandemic has caused significant disruptions to the world since 2020, with over 647 million confirmed cases and 6.7 million reported deaths as of January 2023. Despite its far-reaching impact, the effects of COVID-19 on the progress of global climate change negotiations have yet to be thoroughly evaluated. This discussion paper conducts an examination of COVID-19's impact on climate change actions at global, national, and local levels through a comprehensive review of existing literature. This analysis reveals that the pandemic has resulted in delays in implementing climate policies and altered priorities from climate action to the pandemic response. Despite these setbacks, the pandemic has also presented opportunities for accelerating the transition to a low-carbon economy. The interplay between these outcomes and the different levels of governance will play a crucial role in determining the success or failure of future climate change negotiations.
Volpe, G, Maragò, OM, Rubinsztein-Dunlop, H, Pesce, G, Stilgoe, AB, Volpe, G, Tkachenko, G, Truong, VG, Chormaic, SN, Kalantarifard, F, Elahi, P, Käll, M, Callegari, A, Marqués, MI, Neves, AAR, Moreira, WL, Fontes, A, Cesar, CL, Saija, R, Saidi, A, Beck, P, Eismann, JS, Banzer, P, Fernandes, TFD, Pedaci, F, Bowen, WP, Vaippully, R, Lokesh, M, Roy, B, Thalhammer-Thurner, G, Ritsch-Marte, M, García, LP, Arzola, AV, Castillo, IP, Argun, A, Muenker, TM, Vos, BE, Betz, T, Cristiani, I, Minzioni, P, Reece, PJ, Wang, F, McGloin, D, Ndukaife, JC, Quidant, R, Roberts, RP, Laplane, C, Volz, T, Gordon, R, Hanstorp, D, Marmolejo, JT, Bruce, GD, Dholakia, K, Li, T, Brzobohatý, O, Simpson, SH, Zemánek, P, Ritort, F, Roichman, Y, Bobkova, V, Wittkowski, R, Denz, C, Kumar, GVP, Foti, A, Donato, MG, Gucciardi, PG, Gardini, L, Bianchi, G, Kashchuk, AV, Capitanio, M, Paterson, L, Jones, PH, Berg-Sørensen, K, Barooji, YF, Oddershede, LB, Pouladian, P, Preece, D, Adiels, CB, De Luca, AC, Magazzù, A, Bronte Ciriza, D, Iatì, MA & Swartzlander, GA 2023, 'Roadmap for optical tweezers', Journal of Physics: Photonics, vol. 5, no. 2, pp. 022501-022501. View/Download from: Publisher's site View description>>
AbstractOptical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.
Vu, HP, Cai, Z, Tra, V-T, Wang, Q & Nghiem, LD 2023, 'Anaerobic co-digestion of expired alcohol-based hand sanitizer with synthetic wastewater for biogas production', Environmental Technology & Innovation, vol. 32, pp. 103319-103319. View/Download from: Publisher's site
Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'Deep Generative Learning Models for Cloud Intrusion Detection Systems', IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 565-577. View/Download from: Publisher's site View description>>
Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K-nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.
Vu, MT, Duong, HC, Wang, Q, Ansari, A, Cai, Z, Hoang, NB & Nghiem, LD 2023, 'Recent technological developments and challenges for phosphorus removal and recovery toward a circular economy', Environmental Technology & Innovation, vol. 30, pp. 103114-103114. View/Download from: Publisher's site
Vu, MT, Duong, HC, Wang, Q, Cai, Z, Hoang, NB, Viet, NTT & Nghiem, LD 2023, 'A low-cost method using steel-making slag to quench the residual phosphorus from wastewater effluent', Environmental Technology & Innovation, vol. 31, pp. 103181-103181. View/Download from: Publisher's site
Wali, SB, Hannan, MA, Abd Rahman, MS, Alghamdi, HA, Mansor, M, Ker, PJ, Tiong, SK & Mahlia, TMI 2023, 'Usage count of hydrogen-based hybrid energy storage systems: An analytical review, challenges and future research potentials', International Journal of Hydrogen Energy, vol. 48, no. 89, pp. 34836-34861. View/Download from: Publisher's site
Wali, SB, Hannan, MA, Ker, PJ, Rahman, MSA, Tiong, SK, Begum, RA & Mahlia, TMI 2023, 'Techno-economic assessment of a hybrid renewable energy storage system for rural community towards achieving sustainable development goals', Energy Strategy Reviews, vol. 50, pp. 101217-101217. View/Download from: Publisher's site
Wambsganss, A, Bröring, S, Salomo, S & Sick, N 2023, 'Technology strategies in converging technology systems: Evidence from printed electronics', Journal of Product Innovation Management, vol. 40, no. 5, pp. 705-732. View/Download from: Publisher's site View description>>
AbstractNovel technology systems, such as “fiber optics” and “printed electronics,” increasingly emerge at the interface of hitherto unrelated technology areas. As such, new technology systems often arise through technology convergence, characterized by integrating technology components and knowledge from different technology systems, resulting in a novel system architecture. This phenomenon is of utmost societal relevancy but simultaneously poses tremendous challenges for firms' technology strategies. Firms must not only cope with unrelated knowledge rooted in hitherto different technologies but also have to decide deliberately how systemic (i.e., complete technology system) versus focused (i.e., single component of the technology system) their engagement in technology development in the converging technology system ought to be. In addition, firms need to decide strategically to what extent to develop specialized or design knowledge. Extant concepts of technology strategy fall short of capturing this complexity inherent in converging technology systems. Therefore, to address how technology strategies co‐evolve along with the emergence of new technology systems, this study adds a systems perspective to technology strategy by developing the concept of technology system coverage. This novel dimension of technology strategy is formed by the scope (i.e., focused vs. systemic coverage of the technology system) and type of technological knowledge (i.e., specialized or design knowledge). We empirically apply this novel angle of technology strategy to the convergence field of printed electronics. Based on a longitudinal set of 828 patents over 30 years, 74 relevant corporate actors are identified. The underlying taxonomy enables us to reveal four technology strategies and develop five propositions. The results indicate that all firms build design knowledge over time, whereas ...
Wan, M, Shukla, N, Li, J & Pradhan, B 2023, 'Data-driven approaches to sustainable referral system design integrating the offline channel and the online channel', Journal of Cleaner Production, vol. 414, pp. 137691-137691. View/Download from: Publisher's site
Wan, M, Shukla, N, Li, J & Pradhan, B 2023, 'Optimization of teleconsultation appointment scheduling in National Telemedicine Center of China', Computers & Industrial Engineering, vol. 183, pp. 109492-109492. View/Download from: Publisher's site
Wan, S, Liu, Y, Ding, G, Runeson, G & Er, M 2023, 'Risk allocation for energy performance contract from the perspective of incomplete contract: a study of commercial buildings in China', International Journal of Climate Change Strategies and Management, vol. 15, no. 4, pp. 457-478. View/Download from: Publisher's site View description>>
PurposeThis article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.Design/methodology/approachThe article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.FindingsThis study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.Originality/valueExisting research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dyn...
Recently, inkjet printing technology has received increasing interests for membrane fabrication and modification. It offers various advantages such as the facile and fast process, minimal chemical consumption and precise chemical deposition. It is essential to have a holistic understanding of the inkjet printing technique used for different kinds for membranes, not only to further accelerate its application in membrane field, but also to prepare more advanced membranes with excellent performance. This review paper introduced the basic inkjet printer types used for membrane preparation such as thermal and piezoelectric drop-on-demand (DOD) inkjet printers. It also provided a comprehensive review of the detailed inkjet printing assisted membrane fabrication and modification processes and their applications in different membrane areas including the membrane-based separation (e.g., reverse osmosis (RO), nanofiltration (NF), organic solvent nanofiltration (OSN), gas separation membranes, and oil/water separation membranes) and fuel cell applications.
Wang, C, Park, MJ, Gonzales, RR, Matsuyama, H, Drioli, E & Shon, HK 2023, 'Graphene oxide-based layer-by-layer nanofiltration membrane using inkjet printing for desalination', Desalination, vol. 549, pp. 116357-116357. View/Download from: Publisher's site
Wang, C, Wang, L, Soo, A, Bansidhar Pathak, N & Kyong Shon, H 2023, 'Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration', Separation and Purification Technology, vol. 304, pp. 122328-122328. View/Download from: Publisher's site View description>>
In this study, machine learning was used to form prediction models for thin film nanocomposite (TFN) organic solvent nanofiltration (OSN) membrane performance evaluation in terms of relative permeability (RP) and relative selectivity (RS). Twenty references including 9252 data points were collected to form four different models: linear, support vector machine (SVM), boosted tree (BT), and artificial neural network (ANN). Among the four models, BT exhibited optimal prediction accuracy in terms of root mean square error (RMSE) and coefficient of determination (R2) values for membrane RP (RMSE: 0.295, R2: 0.918) and RS (RMSE: 0.053, R2: 0.849) performance prediction. Parameter contribution analysis indicated that nanoparticle loading, amine concentration, chloride concentration, water contact angle, solvent viscosity, and molar volume are the main parameters influencing RP performance. For RS performance, nanoparticle loading, amine concentration, chloride concentration, and solute molecular weight play important roles. Partial dependence analysis indicated that the optimal conditions for TFN-OSN membrane fabrication are nanoparticle loading less than 5 wt%, the amine concentration around 2 wt%, and the chloride concentration around 0.15 wt%. In addition, membrane with super-hydrophilic or super-hydrophobic surface property exhibited higher RP performance based on different feed solvent types. Overall, this work introduces new ways both for TFN-OSN membrane performance prediction and for higher performance membrane design and development.
Wang, C, Wang, Y, Chen, Z, Wei, W, Chen, X, Mannina, G & Ni, B-J 2023, 'A novel strategy for efficiently transforming waste activated sludge into medium-chain fatty acid using free nitrous acid', Science of The Total Environment, vol. 862, pp. 160826-160826. View/Download from: Publisher's site View description>>
The global energy crisis is approaching due to rapid population growth and overexploitation of fossil fuels. Therefore, the development and use of new and renewable energy sources is already in the extreme urgency. This work developed a novel technology to efficiently produce renewable liquid bioenergy from discarded wastes, by effectively transforming sewage sludge into high-value medium chain fatty acids (MCFA). The maximum MCFA yield in the anaerobic sludge fermentation was revealed to be 10.6 times of control when utilizing sewage sludge with 1.78 mg-N/L free nitrous acid (FNA) pretreatment. The carbon flow from sewage sludge into MCFA in the fermentation system was significantly enhanced with appropriate levels (0.71-1.78 mg-N/L) of FNA pretreatment. Compared to FNA pretreatment, however, its direct addition severely inhibited total products (i.e., carboxylates and complex alcohols) generation because of the toxicity on live cells (decreasing to 8.3 %-13.9 %) in sludge. Kinetic models (one-substrate and two-substrate) were utilized to investigate the mechanism of MCFA promotion by FNA pretreatment on anaerobic sludge fermentation, in which linear relationship analysis between FNA-derived organic release and the fitted parameters were also performed. The results indicated that the conversion of refractory materials into rapidly bioavailable substrates for MCFA production contributed to increasing MCFA production rate and potential. Moreover, the relative abundances of functional microorganisms related to hydrolysis-acidification and chain elongation process increased under FNA pretreatment, further favoring the MCFA production. This study provides a novel and effective technology of sludge energy recovery that can achieve the next-generation sustainable sewage sludge management.
Wang, C, Wei, W, Zhang, Y-T, Chen, X & Ni, B-J 2023, 'Hydrochar alleviated the inhibitory effects of polyvinyl chloride microplastics and nanoplastics on anaerobic granular sludge for wastewater treatment', Chemical Engineering Journal, vol. 452, pp. 139302-139302. View/Download from: Publisher's site View description>>
The exposure to microplastics (MPs) and nanoplastics (NPs) has been confirmed to exhibit significant inhibitory effects on anaerobic granular sludge (AGS) for wastewater treatment, with effective mitigation strategies being accordingly imperative. Herein, this study innovatively proposed a strategy for mitigating inhibitory effects of MPs/NPs on AGS system based on coconut shell-derived hydrochar through efficiently capturing the existing polyvinyl chloride microplastics and nanoplastics (PVC-MPs and PVC-NPs) from AGS. The hydrochar increased methane production of AGS from 69.4% to 76.2% and from 65.6% to 91.4% of control when the AGSs were exposed to PVC-MPs and PVC-NPs, respectively. More extracellular polymeric substance (EPS) was secreted with the existence of hydrochar, which enhanced the protective capabilities AGS held to against the negative effects from the external toxicity of PVC-MPs and PVC-NPs, thus maintaining better AGS integrity regarding granule size and cell viability (especially for the PVC-NPs affected AGS). The hydrochar showed stronger adsorption capability to PVC-MPs and PVC-NPs than AGS, confirmed by their characteristics and adsorption kinetic tests. As a result, less plastic particles would attach AGS, inducing less oxidative stress to the microbes. Specially, it would also be less likely for PVC-NPs to penetrate through AGS surface and enter the internal core, retaining better richness of bacteria such as Bacteroidales and Syntrophobacterales in AGS. This work demonstrated hydrochar effectively alleviated the suppression on AGS caused by PVC-MPs and PVC-NPs, providing a novel strategy for improving the wastewater treatment performance under the stress of MPs and NPs.
Wang, D, Han, Q, Xu, S, Zheng, Z, Luo, Q & Mao, J 2023, 'Damage and deformation of new precast concrete shear wall with plastic damage relocation', STEEL AND COMPOSITE STRUCTURES, vol. 48, no. 4, pp. 385-403. View/Download from: Publisher's site
Wang, E, Chen, C, Zhang, G, Luo, Q, Li, Q & Sun, G 2023, 'Multiaxial mechanical characterization of additively manufactured open-cell Kelvin foams', Composite Structures, vol. 305, pp. 116505-116505. View/Download from: Publisher's site
Wang, F, Long, G & Zhou, JL 2023, 'Deep insight into green remediation and hazard-free disposal of electrolytic manganese residue-based cementitious material', Science of The Total Environment, vol. 894, pp. 165049-165049. View/Download from: Publisher's site View description>>
This work presents an innovative approach to developing a low-carbon and hazard-free cementitious material (EGC) by activating ground granulated blast-furnace slag (GGBS) with electrolytic manganese residue (EMR), which has an excellent heavy metal solidified capacity. Herein, the multi-step leaching was creatively conducted to investigate the solidified morphology of heavy metals in hazardous EMR. CO2 emission per unit strength factor was calculated to quantitatively analyze the low-carbon degree. The results show that the added hazardous EMR rich in sulfate and the dilution effect caused by the decrease in GGBS lessen the final setting time and fluidity. Low-temperature calcination (200 °C) alters the dissolution rate of ettringite and AFm-like phases by changing the sulfate crystal. Excessive acidic EMR consumes more calcium hydroxide and lowers the pH of the EGC system, resulting in weakened GGBS activity. The formation of jouravskite, thaumasite, and henritermierite are AFm-like hydrated lamellated structures, which provides evidence for the immobilization of Mn2+ in EMR. Vast Mn2+ are embedded in the main interlayer of [Ca2Al(OH)6]+ by substituting Al to form AFm-like phase. The lowest 60d unit compressive strength carbon emission of the EGC system containing 20 % calcinated EMR is 0.78 kg∙MPa-1∙m-3, meaning the substitution barrier is better addressed by adding calcined EMR. This work provides an innovative solution for high value-added and hazard-free utilization for EMR and carbon reduction in the cement industry.
Wang, F, Long, G, Bai, M, Shi, Y & Zhou, JL 2023, 'Feasibility of low-carbon electrolytic manganese residue-based supplementary cementitious materials', Science of The Total Environment, vol. 883, pp. 163672-163672. View/Download from: Publisher's site
Wang, F, Long, G, Bai, M, Wang, J, Shi, Y, Zhou, X & Zhou, JL 2023, 'A new perspective on Belite-ye'elimite-ferrite cement manufactured from electrolytic manganese residue: Production, properties, and environmental analysis', Cement and Concrete Research, vol. 163, pp. 107019-107019. View/Download from: Publisher's site View description>>
In this research, the electrolytic manganese residue (EMR) based belite-ye'elimite-ferrite (E-BYF) clinker composed of 51.6 % belite, 25.6 % iron phase, 15.8 % ye'elimite and 6.5 % over-burned anhydrite phase was manufactured successfully at 1200 °C _ 30 min by utilizing EMR (45.5 %) and additional CaO and Al2O3. The existence of iron-rich and heavy metal phases was a potential driving force for achieving near complete calcination at 1200 °C _30 min. The iron ions (8.4 %) and the over-burned anhydrite phase partially replaced the Al3+ ions by entering the ye'elimite lattice and provided enough SO3 (5.9 %) to help stabilize ye'elimite and β-C2S formation, respectively. Layered double hydroxides such as AFm phase and strätlingite formed dense microstructure and improved pastes strength as a new rigid skeleton. The formation of rigid solid skeleton-flexible gel embedded co-support system provided a reliable solution for eliminating inherent strength plateau (3-day of 45.9 MPa and 60-day of 107.0 MPa). In this system, the solid-phase hydrates as the main skeleton offered sufficient rigid-supporting, while the gels hydrates as the auxiliary fluid that filled into the pores of the formed skeleton gave the adequately flexible-supporting. In addition, the pastes showed an excellent stabilization ability of heavy metals. The E-BYF system has great potential in reducing CO2 emissions (0.58 kg/kg and 1.36 kg·/MPa·m3) and cost. This work provides a new perspective for a high-quality solution to the strength plateau while achieving a high value-added utilization of EMR.
Wang, F, Long, G, Ma, K, Zeng, X, Tang, Z, Dong, R, He, J, Shangguan, M, Hu, Q, Liew, RK, Li, Y & Zhou, J 2023, 'Recyling manganese-rich electrolytic residues: a review', Environmental Chemistry Letters, vol. 21, no. 4, pp. 2251-2284. View/Download from: Publisher's site
Wang, G, Wu, N, Tao, Y, Lee, WH, Cao, Z, Yan, X & Wang, G 2023, 'The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-11. View/Download from: Publisher's site
Wang, H, Ma, J & Zhu, J 2023, 'Identifying household EV models via weighted power recurrence graphs', Electric Power Systems Research, vol. 217, pp. 109121-109121. View/Download from: Publisher's site
Wang, H, Zhang, Q, Li, X, Yi, Y, Wang, Q, Gao, L, Wang, J, He, D & Li, M 2023, 'Surface microrelief induced by tillage management alters the pathway and composition of dissolved organic matter exports from soils to runoff during rainfall', Water Research, vol. 245, pp. 120554-120554. View/Download from: Publisher's site
Wang, J, Li, L & zhang, J 2023, 'Deep Reinforcement Learning for Energy Trading and Load Scheduling in Residential Peer-to-Peer Energy Trading Market', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108885-108885. View/Download from: Publisher's site
Wang, J, Mishra, DK, Li, L & Zhang, J 2023, 'Demand Side Management and Peer-to-Peer Energy Trading for Industrial Users Using Two-Level Multi-Agent Reinforcement Learning', IEEE Transactions on Energy Markets, Policy and Regulation, vol. 1, no. 1, pp. 23-36. View/Download from: Publisher's site
Wang, J, Tian, Y, Hua, L, Shi, K, Zhong, S & Wen, S 2023, 'New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays', Mathematics, vol. 11, no. 3, pp. 684-684. View/Download from: Publisher's site View description>>
In this work, we are concerned with the finite-time synchronization (FTS) control issue of the drive and response delayed memristor-based inertial neural networks (MINNs). Firstly, a novel finite-time stability lemma is developed, which is different from the existing finite-time stability criteria and extends the previous results. Secondly, by constructing an appropriate Lyapunov function, designing effective delay-dependent feedback controllers and combining the finite-time control theory with a new non-reduced order method (NROD), several novel theoretical criteria to ensure the FTS for the studied MINNs are provided. In addition, the obtained theoretical results are established in a more general framework than the previous works and widen the application scope. Lastly, we illustrate the practicality and validity of the theoretical results via some numerical examples.
Wang, J, Wang, K, Li, Z, Lu, H & Jiang, H 2023, 'Short-term power load forecasting system based on rough set, information granule and multi-objective optimization', Applied Soft Computing, vol. 146, pp. 110692-110692. View/Download from: Publisher's site
Wang, J, Zhang, J, Li, L & Lin, Y 2023, 'Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems', IEEE Systems Journal, vol. 17, no. 1, pp. 154-163. View/Download from: Publisher's site View description>>
The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. Facing the increasing complexity of the market structure and decision-making strategies, this article proposes a P2P energy trading model for residential households, and the objective is to help the centralized market coordinator optimize the benefit of participants under such a P2P market. To this end, a new mathematical model, including the rules for buying and selling energy, is presented. In this model, a supply function bidding mechanism is formulated to match the power supply imbalance and calculate the market-clearing price. An optimization problem is formulated to identify the optimal strategies for energy buying and selling, which consists of two parts: the first part is to maximize the social welfare; the second part is to minimize the unfair benefit distribution that participants can gain through P2P energy trading. The case study based on the real data for four different household categories has revealed that households can achieve 26.38% net cost reduction, and the proposed fair benefit distribution function also can fairly allocate the benefit by enforcing households' benefit variance indexes at a low level.
Wang, J, Zhu, S, Liu, X & Wen, S 2023, 'Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with generalized piecewise constant argument', Neural Networks, vol. 162, pp. 175-185. View/Download from: Publisher's site
Bipartite graphs are of great importance in many real-world applications. Butterfly, which is a complete 2 × 2 biclique, plays a key role in bipartite graphs. In this paper, we investigate the problem of efficient counting the number of butterflies. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite graphs. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve the time efficiency while theoretically retaining the time complexity of BFC-VP. We also show that our proposed techniques can work efficiently in external and parallel contexts. Moreover, we study the butterfly counting problem on batch-dynamic graphs. Specifically, given a bipartite graph G and a batch-update of edges B, we aim to maintain the number of butterflies in G. To tackle this problem, fast vertex-priority-based algorithms are proposed with optimizations for reducing the computation of existing wedges in G. Our extensive empirical studies demonstrate that the proposed techniques significantly outperform the baseline solutions on real datasets.
Wang, K, Lu, J, Liu, A & Zhang, G 2023, 'TCR-M: A Topic Change Recognition-based Method for Data Stream Learning', Procedia Computer Science, vol. 225, pp. 3001-3010. View/Download from: Publisher's site
Wang, K, Lu, J, Liu, A, Zhang, G & Xiong, L 2023, 'Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2110-2123. View/Download from: Publisher's site View description>>
In nonstationary environments, data distributions can change over time. This phenomenon is known as concept drift, and the related models need to adapt if they are to remain accurate. With gradient boosting (GB) ensemble models, selecting which weak learners to keep/prune to maintain model accuracy under concept drift is nontrivial research. Unlike existing models such as AdaBoost, which can directly compare weak learners' performance by their accuracy (a metric between [0, 1]), in GB, weak learners' performance is measured with different scales. To address the performance measurement scaling issue, we propose a novel criterion to evaluate weak learners in GB models, called the loss improvement ratio (LIR). Based on LIR, we develop two pruning strategies: 1) naive pruning (NP), which simply deletes all learners with increasing loss and 2) statistical pruning (SP), which removes learners if their loss increase meets a significance threshold. We also devise a scheme to dynamically switch between NP and SP to achieve the best performance. We implement the scheme as a concept drift learning algorithm, called evolving gradient boost (LIR-eGB). On average, LIR-eGB delivered the best performance against state-of-the-art methods on both stationary and nonstationary data.
Wang, K, Zhao, G, Zhang, W, Lin, X, Zhang, Y, He, Y & Li, C 2023, 'Cohesive Subgraph Discovery Over Uncertain Bipartite Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11165-11179. View/Download from: Publisher's site
Wang, L, Deng, X, Gui, J, Zhang, H & Yu, S 2023, 'Computation Placement Orchestrator for Mobile Edge Computing in Heterogeneous Vehicular Networks', IEEE Internet of Things Journal, vol. 10, no. 24, pp. 1-1. View/Download from: Publisher's site
Wang, M, Li, W, Shi, J, Wu, S & Bai, Q 2023, 'DOR: a novel dual-observation-based approach for recommendation systems', Applied Intelligence, vol. 53, no. 23, pp. 29109-29127. View/Download from: Publisher's site View description>>
AbstractAs online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.
Wang, M, Zhu, T, Zuo, X, Yang, M, Yu, S & Zhou, W 2023, 'Differentially Private Crowdsourcing With the Public and Private Blockchain', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8918-8930. View/Download from: Publisher's site View description>>
As a result of the rapid development of IoT systems, an increasing number of academics are focusing on finding new applications for IoT systems. For Internet of Things (IoT) systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilising crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.
Wang, M-M, Liu, L-J, Xi, J-R, Ding, Y, Liu, P-X, Mao, L, Ni, B-J, Wang, W-K & Xu, J 2023, 'Lattice doping of Zn boosts oxygen vacancies in Co3O4 Nanocages: Improving persulfate activation via forming Surface-Activated complex', Chemical Engineering Journal, vol. 451, pp. 138605-138605. View/Download from: Publisher's site View description>>
The presence of oxygen vacancies (OVs) promotes persulfate activation. However, rational modulation of OVs without compromising the inherent structure of catalysts is challenging. Herein, novel OVs-enriched hollow ZnCo2O4 nanocages are synthesized based on a bimetallic ZIF-67@ZIF-8 precursor for efficient peroxydisulfate (PDS) activation. The incorporation of Zn into the lattice of Co3O4 boosts the number of OVs in the catalysts while preserving the morphology of Co3O4 nanocages derived from metal-organic framework (MOFs) templates. As a result, the degradation rate of organic pollutants such as bisphenol A is improved by over 20 times in the developed PDS activation system. OVs promote the formation of a surface-activated complex from PDS onto the catalyst surface, which can subsequently deprive electrons from pollutants. The developed PDS activation system is resistant to Cl−, NO3− and humic acid at environmental concentrations. This system adapts to selectively degrade organic pollutants with low ionic potential, and shows applicable potential in practical packaging wastewater treatment. The decreased catalytic performance of catalysts during utilization can be recovered with a facile thermal treatment. Our work constructs OV active sites on Co3O4 nanocages while preserving their original structural superiorities, providing a new strategy to functionalize MOF-derived materials.
Wang, Q, Chen, G, Jin, X, Ren, S, Wang, G, Cao, L & Xia, Y 2023, 'BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series', Computers in Biology and Medicine, vol. 155, pp. 106586-106586. View/Download from: Publisher's site
Wang, Q, Han, N, Shen, Z, Li, X, Chen, Z, Cao, Y, Si, W, Wang, F, Ni, B-J & Thakur, VK 2023, 'MXene-based electrochemical (bio) sensors for sustainable applications: Roadmap for future advanced materials', Nano Materials Science, vol. 5, no. 1, pp. 39-52. View/Download from: Publisher's site View description>>
MXenes are emerging transition metal carbides and nitrides-based 2D conductive materials. They have found wide applications in sensors due to their excellent valuable properties. This paper reviews the recent research status of MXene-based electrochemical (bio) sensors for detecting biomarkers, pesticides, and other aspects. The first part of this paper introduced the synthesis strategy and the effect of surface modification on various properties of MXenes. The second part of this paper discussed the application of MXenes as electrode modifiers for detecting pesticides, environmental pollutants, and biomarkers such as glucose, hydrogen peroxide, etc. Hope this review will inspire more efforts toward research on MXene-based sensors to meet the growing requirements.
Wang, Q, Zhang, Z, Chen, K, Guan, J, Fang, W, Liu, J & Ying, M 2023, 'Quantum Algorithm for Fidelity Estimation.', IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 273-282. View/Download from: Publisher's site View description>>
For two unknown mixed quantum states ρ and σ in an N-dimensional Hilbert space, computing their fidelity F (ρ, σ) is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in poly(log(N), r, 1/ϵ) time, where r is the lower rank of ρ and σ, and ϵ is the desired precision, provided that the purifications of ρ and σ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in N.
Wang, QY, Teng, JD, Zhong, Y, Zhang, S & Sheng, DC 2023, 'Mesoscale simulation of pore ice formation in saturated frozen soil by using lattice Boltzmann method', Yantu Lixue/Rock and Soil Mechanics, vol. 44, no. 1, pp. 317-326. View/Download from: Publisher's site View description>>
The frost heave of subgrade has an important effect on the operation of high-speed railway in cold regions, while the ice-water phase transition is the key to understanding the mechanism of frost heave. The lattice Boltzmann method is applied in this study, which is a mesoscale numerical method. The modified freezing temperature algorithm of pore water is combined with the enthalpy-based lattice Boltzmann phase transition model. Two freezing processes including the freezing of suspended droplets and the formation of pore water into ice in frozen soil are investigated, which aim to reveal the mesoscopic mechanism of the ice-water phase transition in free state and pore-bound state, respectively. The numerical results show that the process of ice crystals growing from the inside to the outside in the pores is completely opposite to the freezing process of droplets suspended in the air, and the pore water has a lower freezing temperature when it is closer to the surface of the soil particles. The soil freezing characteristic curves (SFCCs) differ obviously for the particles with the same size but in different particle arrangements. Meanwhile, the morphology of SFCC becomes steeper with increasing soil particle size, and the residual water content gradually decreases. The numerical results of the ice-water phase transition process are validated by measured data in the literature, which indicate that the lattice Boltzmann method can provide a new tool to study the water-gas migration and phase transformation process in porous media in mesoscale.
Wang, S, Lyu, B, Wen, S, Shi, K, Zhu, S & Huang, T 2023, 'Robust Adaptive Safety-Critical Control for Unknown Systems With Finite-Time Elementwise Parameter Estimation', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 3, pp. 1607-1617. View/Download from: Publisher's site View description>>
Safety is always one of the most critical principles for a control system. This article investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefiting from the dynamic regressor extension and mixing (DREM), an extended elementwise parameter identification law is utilized to dismiss the uncertainty. It is shown that the proposed control scheme can always ensure safety in the identification process with injected excitation noise. Besides, the elementwise identification process using DREM can minimize the theoretical conservatism of the safe adaptation law compared to other existing adaptive CBF (aCBF) algorithms. The stability of the proposed safe control scheme is proven, where the safety is guaranteed by constructing appropriate forward invariant aCBF. Furthermore, the robustness of our algorithms under bounded disturbances is analyzed. Finally, the proposed framework is tested on two simulation-based examples, including the adaptive cruise control problem where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances, and the potential crash risk is avoided by our safe control scheme. These examples illustrate the effectiveness of our algorithm.
Wang, S, Wang, Y, Hu, L, Zhang, X, Zhang, Q, Sheng, QZ, Orgun, MA, Cao, L & Lian, D 2023, 'Modeling User Demand Evolution for Next-Basket Prediction', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 11, pp. 11585-11598. View/Download from: Publisher's site View description>>
Users' purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, resulting in a basket of items. However, most of the existing works simply predict the next basket from a shallow perspective of (purchase) sequence data modeling without deep insight into the underlying factors which drive user purchase behaviors. In fact, filling a basket with multiple items is a process to incrementally satisfy a user's demand. Therefore, the key challenges to predict a user's next basket lie in (1) how to track the changes of the user's demand, and (2) how to satisfy her demand at a given moment. To this end, we propose an Evolving DEmand SAtisfaction (EvoDESA) model to model a user's demand evolution for next-basket prediction. In EvoDESA, a demand evolution module learns the dynamics of user demand over a sequence of basket-purchase behaviors. Then, a next-basket planning module effectively packs an optimal combination of items to best satisfy the user's current demand. Extensive experiments on three real-world transaction datasets demonstrate the considerable superiority of EvoDESA over the state-of-the-art approaches.
Wang, S, Wang, Y, Liu, C, Lei, G, Guo, Y & Zhu, J 2023, 'Performance Comparison of Tubular Flux-Switching Permanent Magnet Machines Using Soft Magnetic Composite Material and Hybrid Material Magnetic Cores', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1118-1129. View/Download from: Publisher's site View description>>
The magnetic core of a conventional tubular flux-switching permanent magnet machine (TFSPMM) is made of axially laminated silicon steel sheets. The laminated direction is consistent with the motion direction of the mover. According to the flux-switching principle, a large amount of eddy current loss will be generated when the alternating flux passes through the silicon steel sheets vertically. The soft magnetic composite (SMC) material is magneto-thermal isotropy and has low eddy current loss, which can be used in TFSPMM. However, the permeability of SMC is much lower than that of silicon steel sheets, and the hysteresis loss is large at low frequency. When the two materials are used as the hybrid material magnetic core in the machine, their respective advantages can be used to improve the thrust force of the machine and reduce core loss. In order to study the effect of SMC core and hybrid material magnetic core on the machine, the performances of TFSPMM with different kinds of cores are calculated and compared by finite element method (FEM), including magnetic density distribution, permanent magnet (PM) flux linkage, back-electromotive force (EMF), inductance, detent force, thrust force and core loss. Finally, a prototype machine with hybrid material magnetic core is made and experimental verification is carried out.
Wang, S, Wang, Y, Sivrikaya, F, Albayrak, S & Anelli, VW 2023, 'Data science for next-generation recommender systems', International Journal of Data Science and Analytics, vol. 16, no. 2, pp. 135-145. View/Download from: Publisher's site View description>>
Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.
Wang, S, Wen, S, Shi, K, Zhou, X & Huang, T 2023, 'Approximate Optimal Control for Nonlinear Systems With Periodic Event-Triggered Mechanism', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 6, pp. 2722-2731. View/Download from: Publisher's site View description>>
This article investigates the approximate optimal control problem for nonlinear affine systems under the periodic event triggered control (PETC) strategy. In terms of optimal control, a theoretical comparison of continuous control, traditional event-based control (ETC), and PETC from the perspective of stability convergence, concluding that PETC does not significantly affect the convergence rate than ETC. It is the first time to present PETC for optimal control target of nonlinear systems. A critic network is introduced to approximate the optimal value function based on the idea of reinforcement learning (RL). It is proven that the discrete updating time series from PETC can also be utilized to determine the updating time of the learning network. In this way, the gradient-based weight estimation for continuous systems is developed in discrete form. Then, the uniformly ultimately bounded (UUB) condition of controlled systems is analyzed to ensure the stability of the designed method. Finally, two illustrative examples are given to show the effectiveness of the method.
Wang, S, Wen, S, Yang, Y, Shi, K & Huang, T 2023, 'Suboptimal Leader-to-Coordination Control for Nonlinear Systems With Switching Topologies: A Learning-Based Method', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10578-10588. View/Download from: Publisher's site View description>>
In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.
Wang, S, Yang, Z, Tao, J, Qiu, X & Burnett, IS 2023, 'Transmission loss and directivity of sound transmitted through a slit on ground', The Journal of the Acoustical Society of America, vol. 153, no. 1, pp. 224-235. View/Download from: Publisher's site View description>>
An analytical model is proposed for sound transmission through a slit on a rigid ground based on the modal superposition method to investigate the transmission loss (TL). A simple formula is derived for estimation of the TL for plane waves with and without the ground, which gives a more precise prediction than existing approaches. It is found that a larger slit height generally decreases the TL, except at the resonant frequencies of the slit. The slit width has little effect on the TL at high frequencies, and the slit depth affects the resonant frequencies significantly even though it has little effect on the overall TL. Compared with the same size slit in the free field, the rigid ground reduces the TL at most frequencies, and that reduction is a constant between 3 and 9 dB in the low frequency range. It is also found that the sound transmitted through the slit is almost omnidirectional at low frequencies, while most of the sound energy at high frequencies falls within the range where the long side of the slit is located. The experimental results demonstrate the validity of the analytical model and the findings in numerical simulations.
Wang, S, Zhang, X, Peng, A, Liu, Y, Ngo Huu, H, Guo, W & Wen, H 2023, 'Research progress and challenges in recovery of nitrogen and phosphorus nutrients from water by biochar', Huagong Jinzhan/Chemical Industry and Engineering Progress, vol. 42, no. 10, pp. 5459-5469. View/Download from: Publisher's site View description>>
The presence of excessive nitrogen and phosphorus nutrients leads to eutrophication of water. Biochar has the advantages of large specific surface area, high porosity, high thermal stability, and abundant surface functional groups, and presents good performance in adsorption and removal of pollutants in water. In recent years, biochar has received much attention as an economical and efficient adsorbent for the adsorption of nitrogen and phosphorus in water, however, various biochar exhibits the different adsorption performance in recovering nitrogen or phosphorus in water. This paper reviewes the adsorption performance of biochar prepared and modified from various waste biomasses on nitrogen and phosphorus in water, discusses the factors affecting the adsorption of nitrogen and phosphorus in water by different types of biochar, ambient temperature, solution pH and coexisting ions, and summarizes the main mechanisms for the adsorption of nitrogen and phosphorus in water by biochar. In the meantime, the challenges faced in practical applications are pointed out, and further the future research directions of biochar are also prospected, so as to provide a theoretical basis for the practical use of biochar for the adsorption and recovery of nitrogen and phosphorus in water.
Wang, S, Zhang, X, Wang, Y & Ricci, F 2023, 'Trustworthy Recommender Systems', ACM Transactions on Intelligent Systems and Technology. View/Download from: Publisher's site View description>>
Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow and the research must consider other important factors, and in particular, trustworthiness. A trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased, fair, as well as robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.
Wang, S, Zhu, Q, Tao, J & Qiu, X 2023, 'Acoustic contrast control in two regions in car cabins', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, vol. 268, no. 3, pp. 5452-5458. View/Download from: Publisher's site View description>>
Personal audio systems that generate different sound zones in different regions have received much interest recently, especially in car cabins. The feasibility of creating a listening zone at the driver's seat and a quiet zone at the passenger's seat with the headrest speakers, door loudspeakers, and the combination of them is investigated in this manuscript. A finite element model of a car interior is created in COMSOL Multiphysics to obtain the acoustic transfer functions and the acoustic contrast control performances of 4 different speaker configurations are compared with each other. It is found that the 4 headrest speakers achieve better acoustic contrast control performance than the door speakers at low frequencies, while the sound pressure level distribution tends to be more uniform in the bright zone when using the door speakers.
Wang, S-N, Cao, J-S, Zhang, J-L, Luo, J-Y, Ni, B-J & Fang, F 2023, 'Recovery of phosphorus from wastewater containing humic substances through vivianite crystallization: Interaction and mechanism analysis', Journal of Environmental Management, vol. 331, pp. 117324-117324. View/Download from: Publisher's site View description>>
Vivianite crystallization has been regarded as a suitable option for recovering phosphorus (P) from P-containing wastewater. However, the presence of humic substances (HS) would inevitably affect the formation of vivianite crystals. Therefore, the influences of HS on vivianite crystallization and the changes in the harvested vivianite crystals were investigated in this study. The results suggested the inhibition effect of 70 mg/L HS on vivianite crystallization reached 12.24%, while it could be attenuated by increasing the pH and Fe/P ratio of the solution. Meanwhile, the addition of HS altered the size, purity, and morphology of recovered vivianite crystals due to the blockage of the growth sites on the crystal surface. Additionally, the formation of phosphate ester group, hydrogen bonding, and COOH-Fe2+ complexes are the potential mechanisms of HS interaction with vivianite crystals. The results obtained herein will help to elucidate the underlying mechanism of HS on vivianite crystallization from P-containing wastewater.
Wang, S-N, Chen, Y-H, Ge, R, Cao, J-S, Ni, B-J & Fang, F 2023, 'Revealing the hydrodynamic effects on phosphorus recovery as vivianite in stirring and aeration systems through PIV experiments and theoretical calculations', Chemical Engineering Journal, vol. 475, pp. 146454-146454. View/Download from: Publisher's site
Wang, T, Li, B, Chen, M & Yu, S 2023, 'Preface', SpringerBriefs in Computer Science, p. v.
Wang, T, Lu, W, Yu, H & Liu, D 2023, 'Modular transfer learning with transition mismatch compensation for excessive disturbance rejection', International Journal of Machine Learning and Cybernetics, vol. 14, no. 1, pp. 295-311. View/Download from: Publisher's site View description>>
Underwater robots in shallow waters usually suffer from strong wave forces, which may frequently exceed robot’s control constraints. Learning-based controllers are suitable for disturbance rejection control, but the excessive disturbances heavily affect the state transition in Markov Decision Process (MDP) or Partially Observable Markov Decision Process (POMDP). This issue is amplified by training-test model mismatch. In this paper, we propose a transfer reinforcement learning algorithm using Transition Mismatch Compensation (TMC), that learns an additional compensatory policy through minimizing mismatch of transitions predicted by the two dynamics models of the source and target tasks. A modular network of learning policies is applied, composed of a Generalized Control Policy (GCP) and an Online Disturbance Identification Model (ODI). GCP is first trained over a wide array of disturbance waveforms. ODI then learns to use past states and actions of the system to predict the disturbance waveforms which are provided as input to GCP (along with the system state). We demonstrated on a pose regulation task in simulation that TMC is able to successfully reject the disturbances and stabilize the robot under an empirical model of the robot system, meanwhile improve sample efficiency.
Wang, W, Karimi, F, Khalilpour, K, Green, DG & Varvarigos, M 2023, 'Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM).', Int. J. Crit. Infrastructure Prot., vol. 41, pp. 100600-100600. View/Download from: Publisher's site
Wang, X, Chen, H-T, Wang, Y-K & Lin, C-T 2023, 'Implicit Robot Control Using Error-Related Potential-Based Brain–Computer Interface', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 198-209. View/Download from: Publisher's site View description>>
This paper investigates the application of using error-related potential (ErrP) based brain-computer interface (BCI) paradigm to control robot movements with implicit commands. ErrP is a neural signal that is automatically evoked when the machine’s behavior deviates from the observer’s expectations. By continuously monitoring the presence of ErrP, the system infers the observer’s reaction toward robot movements and automatically translates them into control commands, allowing the implicit control of robot movements without interfering the observer’s other tasks. However, ErrP-based BCI has a major limitation: the ErrP is evoked after the robot has committed an error, which might be costly or dangerous in contexts such as assembly line or autonomous driving. To address these limitations, we propose a novel robotic design for ErrP-based BCI that allows humans to continuously evaluate the robot’s intentions and intervene earlier, if necessary before the robot commits an error. We evaluate the proposed robotic design and BCI system via an experiment where a ground robot performs a binary target-reaching task. The high classification accuracy (77.57%) demonstrated that the proposed ErrP-based BCI is feasible for human-robot intention communication before the robot commits an error and has the potential to broaden the range of applications for ErrP-based BCIs.
Wang, X, Chen, W-H, Huang, Y, Wang, L, Zhao, Y & Gao, J 2023, 'Advances in soot particles from gasoline direct injection engines: A focus on physical and chemical characterisation', Chemosphere, vol. 311, no. Pt 2, pp. 137181-137181. View/Download from: Publisher's site View description>>
With an increasing market share of gasoline direct injection (GDI) vehicles, high particulate emissions of GDI engines are of increasing concern due to their adverse impacts on both human health and the ecological environment. A thorough understanding of GDI nanoparticulate properties is required to develop advanced particulate filters and assess the exhaust toxicity and environmental impacts. To this end, this paper aims to provide a comprehensive review of the physical and chemical characteristics of GDI nanoparticles from a distinctive perspective, including soot oxidation reactivity, morphology, nanostructure, surface chemistry, chemical components, and their correlations. This review begins with a brief description of nanoparticle characterisation methods. Then, the nanoparticle characteristics of GDI engines are reviewed with the following aspects: in-cylinder soot, exhaust particulate features, and a comparison between GDI and diesel nanoparticles. Previous studies showed that exhaust nanoparticle presents a more stable nanostructure and is less prone to oxidation if compared with in-cylinder soot. Additionally, GDI particles are less-ordered, more inorganic and metallic containing, and more reactive than diesel particles. Afterwards, the impacts of engine operating parameters and aftertreatments on GDI soot features are discussed in detail. Finally, the conclusions and future research recommendations are presented.
Wang, X, Han, C, Li, H, Su, P, Ta, N, Ma, Y, Huang, Z & Liu, J 2023, 'Fabrication of monodispersed B, N co-doped hierarchical porous carbon nanocages through confined etching to boost electrocatalytic oxygen reduction', Nano Research, vol. 16, no. 1, pp. 290-298. View/Download from: Publisher's site View description>>
Dual heteroatom-doped carbons have attracted widespread research attention as catalysts in the field of energy storage and conversion due to their unique electronic structures and chemical tunability. In particular, boron and nitrogen co-doped carbon (B,N@C) has shown great potential for photo/electrocatalytic applications. However, more needs to be done for rational designing and regulating the structure of these materials to improve their catalytic performance. Herein, monodispersed hierarchical porous B,N@C nanocages were fabricated by pyrolyzing zeolite imidazole framework (ZIF) which was treated with ammonia borane or boric acid via an integrated double-solvent impregnation and nanocofined-etching method. The treated ZIF-8 provided an essential structural template to achieve B, N co-doped hierarchical structures with micro/meso/macro multimodal pore size distributions. The resultant B,N@C nanocages displayed high catalytic activities for electrochemical oxygen reduction reaction (ORR) in alkaline media, outperforming most carbon-based catalysts, particularly from the perspective of the half-wave potentials. Such high catalytic performance is due to the enhanced activity by the coexistence of B and N and the mass transfer promoted by the unique hierarchical porous structure. [Figure not available: see fulltext.].
Wang, X, Li, Q, Yu, D, Cui, P, Wang, Z & Xu, G 2023, 'Causal Disentanglement for Semantic-Aware Intent Learning in Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 9836-9849. View/Download from: Publisher's site View description>>
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model.
Wang, X, Li, W, Huang, Y, Zhang, S & Wang, K 2023, 'Study on shape-stabilised paraffin-ceramsite composites with stable strength as phase change material (PCM) for energy storage', Construction and Building Materials, vol. 388, pp. 131678-131678. View/Download from: Publisher's site
Wang, X, Liu, T, Li, H, Han, C, Su, P, Ta, N, Jiang, SP, Kong, B, Liu, J & Huang, Z 2023, 'Balancing Mass Transfer and Active Sites to Improve Electrocatalytic Oxygen Reduction by B,N Codoped C Nanoreactors', Nano Letters, vol. 23, no. 11, pp. 4699-4707. View/Download from: Publisher's site View description>>
Mass transfer is critical in catalytic processes, especially when the reactions are facilitated by nanostructured catalysts. Strong efforts have been devoted to improving the efficacy and quantity of active sites, but often, mass transfer has not been well studied. Herein, we demonstrate the importance of mass transfer in the electrocatalytic oxygen reduction reaction (ORR) by tailoring the pore sizes. Using a confined-etching strategy, we fabricate boron- and nitrogen-doped carbon (B,N@C) electrocatalysts featuring abundant active sites but different porous structures. The ORR performance of these catalysts is found to correlate with diffusion of the reactant. The optimized B,N@C with trimodal-porous structures feature enhanced O2 diffusion and better activity per heteroatomic site toward the ORR process. This work demonstrates the significance of the nanoarchitecture engineering of catalysts and sheds light on how to optimize structures featuring abundant active sites and enhanced mass transfer.
Wang, X, Qin, P-Y, Song, L-Z, Jin, R & Guo, YJ 2023, 'Tightly Coupled Huygens Element-Based Conformal Transmitarray for E-Band Airborne Communication Systems', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2467-2475. View/Download from: Publisher's site View description>>
In this article, a wideband conformal transmitarray employing dual-layer tightly coupled Huygens elements is proposed at E-band. The element consists of five pairs of partly overlapped metallic strips with different lengths printed on two sides of a dielectric substrate. It can support tightly coupled Huygens resonances with a high transmission efficiency and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. Equivalent circuit models are created to analyze the tightly coupled Huygens element, which has good agreement with that from full-wave simulations. In order to validate the proposed element, a cylindrically conformal transmitarray at 78 GHz is designed, fabricated, and measured. Good agreement between the measured and simulated results has been obtained, showing a peak realized gain of 26.6 dBi with an aperture efficiency of 37.2% from measurement. A measured 3 dB gain bandwidth of 20.4% is achieved from 71 to 87 GHz, fully covering the E-band spectrum.
Wang, X, Thiyagarajan, K, Kodagoda, S & Zhang, M 2023, 'PIPE-CovNet: Automatic In-Pipe Wastewater Infrastructure Surface Abnormality Detection Using Convolutional Neural Network', IEEE Sensors Letters, vol. 7, no. 4, pp. 1-4. View/Download from: Publisher's site
Wang, X, Yao, L, Wang, X, Paik, H-Y & Wang, S 2023, 'Uncertainty Estimation With Neural Processes for Meta-Continual Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6887-6897. View/Download from: Publisher's site View description>>
The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.
Wang, X, Zheng, Z, He, Y, Yan, F, Zeng, Z & Yang, Y 2023, 'Progressive Local Filter Pruning for Image Retrieval Acceleration', IEEE Transactions on Multimedia, vol. 25, pp. 9597-9607. View/Download from: Publisher's site
Wang, X, Zhu, L, Wu, F & Yang, Y 2023, 'A Differentiable Parallel Sampler for Efficient Video Classification', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 19, no. 3, pp. 1-18. View/Download from: Publisher's site View description>>
It is crucial to sample a small portion of relevant frames for efficient video classification. The existing methods mainly develop hand-designed sampling strategies or learn sequential selection policies. However, there are two challenges to be solved. First, hand-designed sampling strategies are intrinsically non-adaptive to different video backbones. Second, sequential frame selection policies ignore temporal relations among all video frames. The sequential selection process also hinders the application of these video samplers in speed-critical systems. In this article, we propose a differentiable parallel video sampling network (PSN) to tackle the aforementioned challenges, First, we optimize the video sampler with a differentiable surrogate loss, allowing to dynamically learn the sampler with the cooperation from the video classification model. Our sampler considers the feedback from all frames jointly, eliminating the learning difficulties of sequential decision making. The learning process is fully gradient-based, making the sampler be learned efficiently. Our video sampler can assess a set of frames swiftly and determine the importance of each frame in parallel. Second, we propose to model the inter-relation among contextual frames, which encourages the sampler to select frames based on a comprehensive inspection of the entire video. We observe that a simple context relation mining instantiation would significantly improve the classification performance. The experimental results on three standard video recognition benchmarks demonstrate the efficacy and efficiency of our framework.
Wang, X, Zhu, L, Wu, Y & Yang, Y 2023, 'Symbiotic Attention for Egocentric Action Recognition With Object-Centric Alignment', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 6605-6617. View/Download from: Publisher's site View description>>
In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.
Wang, X, Zhu, L, Zheng, Z, Xu, M & Yang, Y 2023, 'Align and Tell: Boosting Text-Video Retrieval With Local Alignment and Fine-Grained Supervision', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 6079-6089. View/Download from: Publisher's site View description>>
Text-video retrieval is one of the basic tasks for multimodal research and has been widely harnessed in many real-world systems. Most existing approaches directly compare the global representation between videos and text descriptions and utilize the global contrastive loss to train the model. These designs overlook the local alignment and the word-level supervision signal. In this paper, we propose a new framework, called Align and Tell, for text-video retrieval. Compared to the previous work, our framework contains additional modules, i.e., two transformer decoders for local alignment and one captioning head to enhance the representation learning. First, we introduce a set of learnable queries to interact with both textual representations and video representations and project them to a fixed number of local features. After that, local contrastive learning is performed to complement the global comparison. Moreover, we design a video captioning head to provide additional supervision signals during training. This word-level supervision can enhance the visual presentation and alleviate the cross-modal gap. The captioning head can be removed during inference and does not introduce extra computational costs. Extensive empirical results demonstrate that our Align and Tell model can achieve state-of-the-art performance on four text-video retrieval datasets, including MSR-VTT, MSVD, LSMDC, and ActivityNet-Captions.
Wang, Y, Douville, C, Cohen, JD, Mattox, A, Curtis, S, Silliman, N, Popoli, M, Ptak, J, Dobbyn, L, Nehme, N, Dudley, JC, Summers, M, Zhang, M, Ho-Pham, LT, Tran, BNH, Tran, TS, Nguyen, TV, Bettegowda, C, Papadopoulos, N, Kinzler, KW & Vogelstein, B 2023, 'Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules', Proceedings of the National Academy of Sciences, vol. 120, no. 15. View/Download from: Publisher's site View description>>
The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here, we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung, and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics.
Wang, Y, He, Y, Zheng, K, Wei, W, Ngo, HH, Guo, W, Ni, B-J, Zhu, T, Horn, H & Liu, Y 2023, 'Ferric oxide stimulates medium-chain carboxylic acids synthesis from waste activated sludge via ethanol-driven chain elongation: Mechanisms and implications', Journal of Cleaner Production, vol. 389, pp. 136044-136044. View/Download from: Publisher's site View description>>
Nowadays, conductive iron-containing materials (i.e., Fe3O4 and zerovalent iron) have attracted greatly attention in improving medium-chain carboxylic acids (MCCA) from waste activated sludge (WAS). However, the feasibility and mechanism of semi-conductive iron oxide, i.e., ferric oxide (Fe2O3), in stimulating MCCA synthesis from WAS via ethanol-driven chain elongation (CE) has been unclear. Therefore, this work is aimed to fill up the knowledge gap. Results showed that the MCCA yield in the Fe2O3-supplemented fermenter attained at 9162 mg COD/L (i.e., 6268 mg COD/L caproate and 2895 mg COD/L caprylate), which was 2.4 times that of the control system. Kinetic analysis proved that Fe2O3 enhanced both the potential and rate of MCCA synthesis. Mechanism analysis indicated that Fe2O3 facilitated the individual steps for MCCA production, i.e., hydrolysis, acidification and CE. Further investigation disclosed that the dissimilatory iron reduction (DIR) induced by Fe2O3 corrosion and released ferrous ions improved enzymes activities of hydrolysis and acidification, while the promotion of CE was mainly ascribed to increased electron transfer efficiency by crystalline Fe2O3. Further, Fe2O3 promotes electron transfer through several mechanisms, including stimulating extracellular polymeric substance (EPS) excretion, increasing EPS electroactivity, and enhancing electron transport system activity. Microbial analysis revealed that Fe2O3 induced microflora structure shifting towards substrates transformation, iron reduction and MCCA generation, and up-regulated the key enzymes in CE pathways. This work provides an efficient strategy for MCCA generation and WAS management.
Wang, Y, Mukherjee, A & Castel, A 2023, 'Ultrasonic guided waves for monitoring incipient corrosion in reinforced concrete with top-bar defect', Cement and Concrete Composites, vol. 141, pp. 105116-105116. View/Download from: Publisher's site
Wang, Y, Sun, T, Li, S, Yuan, X, Ni, W, Hossain, E & Vincent Poor, H 2023, 'Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2245-2298. View/Download from: Publisher's site
Wang, Y, Yang, S, Luo, Q, Li, Q & Sun, G 2023, 'Experimental characterization of impact damage in foam-core sandwich structures using acoustic emission, optical scanning and X-ray computed tomography techniques', Composites Part B: Engineering, vol. 265, pp. 110919-110919. View/Download from: Publisher's site
Wang, Y, Zhang, A, Wu, S & Yu, S 2023, 'VOSA: Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 3601-3616. View/Download from: Publisher's site View description>>
Federated learning has emerged as a promising paradigm by collaboratively training a global model through sharing local gradients without exposing raw data. However, the shared gradients pose a threat to privacy leakage of local data. The central server may forge the aggregated results. Besides, it is common that resource-constrained devices drop out in federated learning. To solve these problems, the existing solutions consider either only efficiency, or privacy preservation. It is still a challenge to design a verifiable and lightweight secure aggregation with drop-out resilience for large-scale federated learning. In this paper, we propose VOSA, an efficient verifiable and oblivious secure aggregation protocol for privacy-preserving federated learning. We exploit aggregator oblivious encryption to efficiently mask users' local gradients. The central server performs aggregation on the obscured gradients without revealing the privacy of local data. Meanwhile, each user can efficiently verify the correctness of the aggregated results. Moreover, VOSA adopts a dynamic group management mechanism to tolerate users' dropping out with no impact on their participation in future learning process. Security analysis shows that the VOSA can guarantee the security requirements of privacy-preserving federated learning. The extensive experimental evaluations conducted on real-world datasets demonstrate the practical performance of the proposed VOSA with high efficiency.
Wang, Y, Zhao, L, Gong, L, Chen, X & Zuo, S 2023, 'A monocular SLAM system based on SIFT features for gastroscope tracking', Medical & Biological Engineering & Computing, vol. 61, no. 2, pp. 511-523. View/Download from: Publisher's site
Wang, Z, Gu, X, Zhang, X, Wang, X, Zhang, J, Liu, Y, Tan, X, Zhao, Y, Kang, D, Guo, W & Ngo, HH 2023, 'New easily recycled carrier based polyurethane foam by loading Al-MOF and biochar for selective removal of fluoride ion from aqueous solutions', Science of The Total Environment, vol. 901, pp. 166312-166312. View/Download from: Publisher's site
Wang, Z, Li, J, Teng, J, Zhang, S & Sheng, D 2023, 'THM coupled model for simulating frost heave based on a new water film pressure criterion', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 45, no. 5, pp. 997-1007. View/Download from: Publisher's site View description>>
The frost heave and thaw settlement are the main frost damage in cold areas, which are the complex coupling process of water, temperature and stress fields. In this study, a coupled thermal-hydraulic-mechanical model is developed based on the water film theory, in which the temperature and void ratio of soils are the input variables. The novelty of this model is that the frozen water film pressure is used as the criterion for the generation of ice lens. The driving force of water migration is newly defined, and the frost heave includes the pristine frost heave and the amount of ice segregation. The fully coupled model is numerically solved based on the Matlab and COMSOL Multiphysics, generating the results of soil temperature, moisture, stress and the layered ice lens. The simulated results are then compared with those of the laboratory freezing tests, which shows that they match quite well and verify the validity of the proposed model. The simulation indicates that temperature gradient can promote the frost heave, and the overburden pressure can attract more water to the freezing front but decrease the amount of the frost heave. In addition, both the hydraulic conductivity and the compressive modulus have positive effects on the frost heave. The proposed model provides a new approach to understand the frost heave.
Wang, Z, Li, J, Wang, Y, Su, Z, Yu, S & Meng, W 2023, 'Optimal Repair Strategy Against Advanced Persistent Threats Under Time-Varying Networks', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5964-5979. View/Download from: Publisher's site
Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Siddiqui, MA, Lin, CSK, Rafe Hatshan, M, Huang, S, Cairney, JM & Wang, Q 2023, 'Enhancing methane production from anaerobic digestion of secondary sludge through lignosulfonate addition: Feasibility, mechanisms, and implications', Bioresource Technology, vol. 390, pp. 129868-129868. View/Download from: Publisher's site View description>>
This study explores the feasibility of using lignosulfonate, a byproduct of the pulp and paper industry, to facilitate sludge anaerobic digestion. Biochemical methane potential assays revealed that the maximum methane production was achieved at 60 mg/g volatile solids (VS) lignosulfonate, 22.18 % higher than the control. One substrate model demonstrated that 60 mg/g VS lignosulfonate boosted the hydrolysis rate, biochemical methane potential, and degradation extent of secondary sludge by 19.12 %, 21.87 %, and 21.11 %, respectively, compared to the control. Mechanisms unveiled that lignosulfonate destroyed sludge stability, promoted organic matter release, and enhanced subsequent hydrolysis, acidification, and methanogenesis by up to 31.30 %, 74.42 % and 28.16 %, respectively. Phytotoxicity assays confirmed that lignosulfonate promoted seed germination and root development of lettuce and Chinese cabbage, with seed germination index reaching 170 ± 10 % and 220 ± 22 %, respectively. The findings suggest that lignosulfonate addition offers a sustainable approach to sludge treatment, guiding effective management practices.
Wang, Z, Li, X, Liu, H, Zhou, T, Qin, Z, Mou, J, Sun, J, Huang, S, Chaves, AV, Gao, L & Wang, Q 2023, 'Bioproduction and applications of short-chain fatty acids from secondary sludge anaerobic fermentation: A critical review', Renewable and Sustainable Energy Reviews, vol. 183, pp. 113502-113502. View/Download from: Publisher's site View description>>
Anaerobic fermentation of secondary sludge is a crucial bio-energy tactic for achieving stabilization, reduction, and resource utilization of secondary sludge in wastewater treatment plants. Short-chain fatty acids (SCFAs), the end product of anaerobic fermentation, have received substantial attention owing to shorter fermentation time, higher economic value, and broader application range. This review summarizes the composition and structure of secondary sludge, the main tactics for SCFAs accumulation, the metabolic pathway of anaerobes’ participation in SCFAs production, and the impact of SCFAs composition from the fermented liquid on its subsequent application. It was found that the composition and structure of secondary sludge may limit its decomposition and impede SCFAs production. Diverse technologies adopted can promote SCFAs accumulation to some extent. It was concluded that the application of SCFAs derived from anaerobic fermentation of secondary sludge depends on its individual SCFAs composition. This review would help advance the SCFAs production and specific applications from anaerobic fermentation of secondary sludge.
Wang, Z, Li, X, Siddiqui, MA, Liu, H, Zhou, T, Zheng, L, Huang, S, Gao, L, Lin, CSK & Wang, Q 2023, 'Effect of humic substances on the anaerobic digestion of secondary sludge in wastewater treatment plants: a review', Environmental Chemistry Letters, vol. 21, no. 5, pp. 3023-3040. View/Download from: Publisher's site View description>>
Anaerobic digestion is a promising technology for energy recovery from secondary sludge, yet the presence of humic substances in wastewater limits anaerobic digestion. In particular, humic substances make secondary sludge denser and more compact, reducing the availability of organic matter for biodegradation. Here we review the impact of humic substances on the anaerobic process, with emphasis on humic substances properties, effect on sludge structure and composition, effect on hydrolysis, acidolysis and methanogenesis, evolution of humic substrances, and strategies to counteract negative impacts. Strategies include removing humic substances, pretreatment of secondary sludge prior anaerobic digestion, and addition of metal salts, enzymes and organisms. We observed that humic substances with a high E4/E6 ratio, representing the absorbance determined at 465 nm and 665 nm, with a low carbon/nitrogen ratio, and with a low aromaticity are easier to digest anaerobically. The liquid–solid phases distribution of humic substances influences the efficiency of anaerobic digestion, and the repolymerisation of humic substances during anaerobic digestion reduces sludge degradability.
Wang, Z, Li, X, Xiang, L, Huang, Y, Lang, B, Cheng, X & Zhang, J 2023, 'Potential of kerosene-diesel blends as alternative fuels for diesel engines: Perspectives from spray combustion characteristics', Fuel, vol. 335, pp. 127112-127112. View/Download from: Publisher's site View description>>
To explore the application potential of kerosene (RP-3)/diesel blends as alternative fuels for diesel engines, the spray combustion characteristics of neat diesel, neat RP-3 and RP-3/diesel blends (varied from 25 % to 75 % of RP-3) were systematically compared in an optical constant volume combustion chamber under non-evaporating (0 % O2, 293 K), evaporating (0 % O2, 900 K) and combustion (21 % O2, 900 K) conditions. Their spray, evaporation, combustion and soot emission characteristics were visualized using various high-speed imaging techniques. The results show that RP-3/diesel blending ratio has significant effects on the spray and combustion processes. Under non-evaporating condition, higher RP-3 contents slightly decrease the spray penetration but increase the corresponding spray angle and volume. Under evaporating condition, the spray angle and volume follow the similar trend as those under non-evaporating condition except for slight fluctuations, but the liquid length decreases with the increase of blending ratio. Unexpectedly, the vapor penetrations of neat diesel and RP-3 are similar and longer than those of their blends. Under combustion condition, both the ignition delay and flame lift-off length increase with the RP-3 blending ratio. In addition, RP-3 component reduced the peak soot mass by 9.78 %, 14.7 % and 17.56 % for R25, R50 and R75, respectively. In summary, this study suggests that RP-3/diesel blends are suitable alternative fuels for diesel engines, in terms of faster evaporation and lower soot emissions. We recommend a blending ratio of 25 % as the most promising fuel for further investigations in real engines.
Wang, Z, Zhang, JA, Xu, M & Guo, YJ 2023, 'Single-Target Real-Time Passive WiFi Tracking', IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3724-3742. View/Download from: Publisher's site
Wang, Z-W, WEI, C-H, Yu, H-R, Qu, F-S, Rong, H-W, He, J-G, Liu, G-L, Huang, X & Ngo, HH 2023, 'Preparation and mechanism of carbon felt supported iron trioxide and zero-valent iron for enhancing anaerobic digestion performance', Chemical Engineering Journal, vol. 468, pp. 143565-143565. View/Download from: Publisher's site
Wei, F, Liu, X, Ding, X-Z, Zhao, X-B & Qin, P-Y 2023, 'A Balanced Filtering Antenna Array With High Gain, Steep Selectivity, and Multiradiation Nulls Parallel-Fed by Differential Broadband Network', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9926-9931. View/Download from: Publisher's site View description>>
In this communication, a 2 × 2 balanced filtering antenna array parallel-fed by a differential broadband network is presented. The array has four identical stacked filtering antenna elements, each of which consists of a main patch etched with a folded U-shaped slot, a pair of ear-shaped parasitic patches, a stacked patch, and a folded T-shaped strip. The conceived antenna topology can enhance the gain flatness, improve selectivity, and increase the number of radiation nulls. Meanwhile, by introducing a novel ring-shaped transition structure (RSTS) to the balanced-to-single-ended (BTSE) four-way feeding network, a broad bandwidth, low insertion losses, low phase difference errors, and high common-mode (CM) suppression are realized. To validate the method, an integrated antenna array with a center frequency at 2.5 GHz is fabricated and measured. Experimental results exhibit a boresight gain of 13.7 dBi, four radiation nulls, and a square factor (SF10) of 1.21.
Wei, J, Li, J & Wu, C 2023, 'Study on hybrid fibre reinforced UHPC beams under single and repeated lateral impact loading', Construction and Building Materials, vol. 368, pp. 130403-130403. View/Download from: Publisher's site View description>>
This paper investigates the dynamic response of hybrid fibre reinforced ultra-high performance concrete (UHPC) beams against single and repeated low-velocity impact loads. A brief description of the drop weight impact tests on the UHPC beams was presented first followed by the development of the material and structural model in finite element analysis. A plasticity-based concrete material model with validated compressive and tensile strength surface and damage algorithm was adopted for hybrid fibre reinforced UHPC material. Based on the test results, the bond-slip behaviour between steel rebar and UHPC matrix was developed in an empirical form and incorporated in the model. Compared to the model with the bond-slip definition, the model with perfect bonding was found to underestimate the maximum mid-span deflection, which highlighted the necessity of considering the bond-slip behaviour in dynamic analysis where large deflection occurs. The repeated impact tests were performed numerically, and the results were validated with experimental data. A parametric study was then performed to investigate the effect of key parameters, including different impact energy and the same impact energy but different impact numbers. The results indicated when the total energy increased, the repeated impact loads became more hazardous than the single impact load. With the validated model, the dynamic shear force and bending moment distribution diagrams were compared to study the failure mechanism in single and repeated impact loads.
Wei, J, Li, J, Liu, Z, Wu, C & Liu, J 2023, 'Behaviour of hybrid polypropylene and steel fibre reinforced ultra-high performance concrete beams against single and repeated impact loading', Structures, vol. 55, pp. 324-337. View/Download from: Publisher's site
Wei, J, Li, J, Wu, C, Hao, H & Liu, J 2023, 'Experimental and numerical study on the impact resistance of ultra-high performance concrete strengthened RC beams', Engineering Structures, vol. 277, pp. 115474-115474. View/Download from: Publisher's site View description>>
To enhance the impact resistance of built reinforced concrete (RC) members, the effectiveness of using ultra-high performance concrete (UHPC) for strengthening RC structures was investigated in this study. The mechanical properties of UHPC were evaluated by the uniaxial compression, tension and flexural bending tests. Drop weights with hemispherical and wedge-shaped indenter were adopted to impact the beam specimens with and without UHPC strengthening. A total of six beams, including three control RC beams and three UHPC jacketed RC (RC-UHPC) beams, were tested. The beams were prestressed in the axial direction with a 200 kN force. The test results revealed that the UHPC jackets improved the structural impact resistance. With an impact mass of 411 kg and an impact velocity of 4.95 m/s, the maximum and residual deflection of the RC-UHPC specimen decreased by 15.3 % and 21.1 % as compared to the RC control specimen, and the failure mode shifted from diagonal shear failure to flexural failure. To further investigate the dynamic responses of the beams, a detailed finite element model was established and validated with the test results in terms of the impact force, structural deflection and damage profile. The dynamic shear force and bending moment distribution diagrams were numerically derived to examine the failure mechanism of the test specimens. Finally, a parametric study was conducted to evaluate the effect of different impact locations and UHPC jacket length on the impact resistance of strengthened RC beams.
Wei, Y, Luo, Q, Li, Q & Sun, G 2023, 'On adhesively bonded joints with a mixed failure mode—An experimental and numerical study', Thin-Walled Structures, vol. 192, pp. 110987-110987. View/Download from: Publisher's site
Wei, Z, Qu, H, Wang, Y, Yuan, X, Wu, H, Du, Y, Han, K, Zhang, N & Feng, Z 2023, 'Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey', IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11068-11092. View/Download from: Publisher's site
Wen, J, Gabrys, B & Musial, K 2023, 'Review and Assessment of Digital Twin–Oriented Social Network Simulators', IEEE Access, vol. 11, pp. 97503-97521. View/Download from: Publisher's site
Wen, L, Yang, F, Li, X, Liu, S, Lin, Y, Hu, E, Gao, L & Li, M 2023, 'Composition of dissolved organic matter (DOM) in wastewater treatment plants influent affects the efficiency of carbon and nitrogen removal', Science of The Total Environment, vol. 857, no. Pt 2, pp. 159541-159541. View/Download from: Publisher's site View description>>
Wastewater treatment plants (WWTPs) play a critical role in receiving, removing, and discharging dissolved organic matter (DOM) in aquatic systems. To date, understanding the composition and fate of DOM in different WWTPs with various environmental and socioeconomic conditions is limited. This study analyzed DOM components in the influent and effluent samples from 49 WWTPs in China using EEM-PARAFAC and ESI-FT-ICR-MS methods. The influencing factors of DOM components in the influent were also analyzed. Geographic location and GDP showed significant (p < 0.05) correlations with DOM components in the influent. The removal efficiency of DOM in WWTPs was closely related to the DOM compositions, where carbohydrates, lipids, and protein-like components (removal efficiencies > 75 %) were more readily decomposed than the humic-like components, lignin, and tannin. The relative fraction of humic-like compound C3 in the influent was correlated negatively with total nitrogen (TN) and chemical oxygen demand (COD) removal in WWTPs (p < 0.05). Besides, the relative fraction of DOM containing the element sulfur also showed significant negative correlations with the humification of DOM (p < 0.05). The results from EEM-PARAFAC and ESI-FT-ICR-MS methods showed no obvious correlation for the DOM characterizations except for humic-like fluorescent fraction C3 and lignin, while significant positive correlations (p < 0.05) between the aromatic index (AI_mod) from the ESI-FT-ICR-MS analysis and the humification index (HIX) from spectrofluorimetry. This supports the use of these spectral indexes as simple surrogates to represent part chemical compositions in further research.
Wen, Y, Qin, P-Y, Maci, S & Guo, YJ 2023, 'Low-Profile Multibeam Antenna Based on Modulated Metasurface', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6568-6578. View/Download from: Publisher's site View description>>
Linearly polarized (LP) multiport multibeam antennas (MBAs) based on modulated metasurface (MTS) are presented in this article. A key challenge for MTS to generate multiple independent beams is the mutual interference of different impedance modulations. This interference can lead to reduced directivities of main beams and high sidelobe levels (SLLs). A full-wave-based optimization for a large number of beams is significantly time-consuming and practically ineffective. The source locations are optimized here by employing the aperture field calculated from a zeroth-order approximation (ZOA) of the currents on the surface. This provides a good preliminary accuracy without any full-wave analysis. The relevant design method is verified by two examples of three-beam and seven-beam MTS antennas. Furthermore, the seven-beam MTS antenna is fabricated and successfully measured. The measurements show a measured realized gain of 20 dBi at 14.10 GHz with a beam coverage (10-dB overlap) up to ±36°. It is seen that the same performance requires larger areas with conventional summation of apertures.
West, N, Schlegl, T & Deuse, J 2023, 'Unsupervised anomaly detection in unbalanced time series data from screw driving processes using k-means clustering', Procedia CIRP, vol. 120, pp. 1185-1190. View/Download from: Publisher's site
White, T, Opremcak, A, Sterling, G, Korotkov, A, Sank, D, Acharya, R, Ansmann, M, Arute, F, Arya, K, Bardin, JC, Bengtsson, A, Bourassa, A, Bovaird, J, Brill, L, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Crook, AL, Curtin, B, Demura, S, Dunsworth, A, Erickson, C, Fatemi, R, Burgos, LF, Forati, E, Foxen, B, Giang, W, Giustina, M, Grajales Dau, A, Hamilton, MC, Harrington, SD, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Iveland, J, Jeffrey, E, Kieferová, M, Kim, S, Klimov, PV, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Laws, L, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, McCourt, T, McEwen, M, Mi, X, Miao, KC, Montazeri, S, Morvan, A, Neeley, M, Neill, C, Nersisyan, A, Ng, JH, Nguyen, A, Nguyen, M, Potter, R, Quintana, C, Roushan, P, Sankaragomathi, K, Satzinger, KJ, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Szalay, M, Torres, A, Woo, BWK, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zhu, N, Zobrist, N, Chen, Y, Megrant, A, Kelly, J & Naaman, O 2023, 'Readout of a quantum processor with high dynamic range Josephson parametric amplifiers', Applied Physics Letters, vol. 122, no. 1, pp. 014001-014001. View/Download from: Publisher's site View description>>
We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250–300 MHz with input saturation powers up to −95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimation of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Finally, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.
Wijayaratna, KP, Hossein Rashidi, T & Gardner, L 2023, 'Adapting to the Emergence of Generation Z in Tertiary Education: Application of Blended Learning Initiatives in Transport Engineering', Journal of Civil Engineering Education, vol. 149, no. 3. View/Download from: Publisher's site
Wilkins-Caruana, A, Bandara, M, Musial, K, Catchpoole, D & Kennedy, PJ 2023, 'Inferring Actual Treatment Pathways from Patient Records', J Biomed Inform. 2023 Nov 22:104554. Epub ahead of print. PMID: 38000767. View description>>
Treatment pathways are step-by-step plans outlining the recommended medicalcare for specific diseases; they get revised when different treatments arefound to improve patient outcomes. Examining health records is an importantpart of this revision process, but inferring patients' actual treatments fromhealth data is challenging due to complex event-coding schemes and the absenceof pathway-related annotations. This study aims to infer the actual treatmentsteps for a particular patient group from administrative health records (AHR) -a common form of tabular healthcare data - and address several technique- andmethodology-based gaps in treatment pathway-inference research. We introduceDefrag, a method for examining AHRs to infer the real-world treatment steps fora particular patient group. Defrag learns the semantic and temporal meaning ofhealthcare event sequences, allowing it to reliably infer treatment steps fromcomplex healthcare data. To our knowledge, Defrag is the firstpathway-inference method to utilise a neural network (NN), an approach madepossible by a novel, self-supervised learning objective. We also developed atesting and validation framework for pathway inference, which we use tocharacterise and evaluate Defrag's pathway inference ability and compareagainst baselines. We demonstrate Defrag's effectiveness by identifyingbest-practice pathway fragments for breast cancer, lung cancer, and melanoma inpublic healthcare records. Additionally, we use synthetic data experiments todemonstrate the characteristics of the Defrag method, and to compare Defrag toseveral baselines where it significantly outperforms non-NN-based methods.Defrag significantly outperforms several existing pathway-inference methods andoffers an innovative and effective approach for inferring treatment pathwaysfrom AHRs. Open-source code is provided to encourage further research in thisarea.
OBJECTIVE: Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. The objective of this study is to develop a method for inferring actual treatment steps for a particular patient group from administrative health records - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. METHODS: We introduce Defrag, a method for examining health records to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability, establish benchmarks, and compare against baselines. RESULTS: We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag inference method, and to compare Defrag to several baselines, where it significantly outperforms non-NN-based methods. CONCLUSIONS: Defrag offers an innovative and effective approach for inferring treatment pathways from complex health data. Defrag significantly outperforms several existing pathway-inference methods, but ...
Williams, P, Kirby, R & Karimi, M 2023, 'The effect of axial boundary conditions on breakout noise from finite cylindrical ducts', International Journal of Mechanical Sciences, vol. 242, pp. 107951-107951. View/Download from: Publisher's site View description>>
Ductborne noise within HVAC and exhaust systems may transfer into the surrounding environment through the walls of the duct. This breakout noise normally needs to be lowered in order to meet health and safety guidelines. This means that the ability of the duct walls to lower breakout noise needs to be predicted during the design stage of the duct systems. Significant work has been conducted into the prediction of breakout noise for infinite length ducts, however there is little to be found on finite length ducts. In particular there are very few studies on the difference between finite and infinite length duct breakout, especially where the noise source lies within the internal fluid. The aim of this work is therefore to investigate the difference in breakout noise between finite length and equivalent infinite length ducts when excited by an internal noise source. This is performed through numerical experiments using the semi analytical finite element method which enables the equations of elasticity for the duct wall, as well as a surrounding fluid, to be accommodated. Axial continuity equations at each end of a finite length elastic duct are then enforced through the point collocation method. It is observed that high sound power levels are emitted at axial resonances of the finite length duct. In the low frequency region these resonances have a narrow bandwidth and are expected to be of little practical significance. However, above the critical frequency the resonance bandwidth increases and this is observed to significantly lower the transverse transmission loss of the duct wall when compared to an infinite length duct. This phenomenon is observed for both clamped and simply supported ducts, as well as for two different internal sound sources. It is concluded that breakout noise from axial resonances in finite length ducts should be examined in design calculations in order to avoid excessive breakout noise.
Wilson, S, Hastings, C, Morris, A, Ramia, G & Mitchell, E 2023, 'International students on the edge: The precarious impacts of financial stress', Journal of Sociology, vol. 59, no. 4, pp. 952-974. View/Download from: Publisher's site View description>>
International students are an important global cohort of ‘noncitizens’ whose experiences are central concerns for urban sociologists and migration scholars. Drawing on survey fieldwork conducted among international students in the private rental sector in Sydney and Melbourne during 2019, this article provides new knowledge about the hardships experienced by international students who report financial stress. Using a modified scale developed by the Australian Bureau of Statistics, we highlight the accelerating role of high levels of financial stress in producing disruptive events such as housing evictions and fears of homelessness, as well as reliance on inadequate housing like ‘hot-bedding’. Financial stress is significantly more likely for students from low-GNI (gross national income) countries and higher stress reduces wellbeing. Access to paid employment, however, does not ‘protect’ against higher financial stress. We conclude that higher education policymakers need tools and policies to prevent disruptive life events among international students related to financial stress, particularly those associated with housing.
Wilson, S, Hastings, C, Morris, A, Ramia, G & Mitchell, E 2023, 'International students on the edge: The precarious impacts of financial stress', Journal of Sociology, vol. 59, no. 4, pp. 952-974. View/Download from: Publisher's site View description>>
International students are an important global cohort of ‘noncitizens’ whose experiences are central concerns for urban sociologists and migration scholars. Drawing on survey fieldwork conducted among international students in the private rental sector in Sydney and Melbourne during 2019, this article provides new knowledge about the hardships experienced by international students who report financial stress. Using a modified scale developed by the Australian Bureau of Statistics, we highlight the accelerating role of high levels of financial stress in producing disruptive events such as housing evictions and fears of homelessness, as well as reliance on inadequate housing like ‘hot-bedding’. Financial stress is significantly more likely for students from low-GNI (gross national income) countries and higher stress reduces wellbeing. Access to paid employment, however, does not ‘protect’ against higher financial stress. We conclude that higher education policymakers need tools and policies to prevent disruptive life events among international students related to financial stress, particularly those associated with housing.
Wu, C, Chen, Y, Dong, Y, Zhou, F, Zhao, Y & Liang, CJ 2023, 'VizOPTICS: Getting insights into OPTICS via interactive visual analysis', Computers and Electrical Engineering, vol. 107, pp. 108624-108624. View/Download from: Publisher's site
Wu, C, Luo, J, Zhong, J, Xu, Y, Wan, B, Huang, W, Fang, J, Steven, GP, Sun, G & Li, Q 2023, 'Topology optimisation for design and additive manufacturing of functionally graded lattice structures using derivative-aware machine learning algorithms', Additive Manufacturing, vol. 78, pp. 103833-103833. View/Download from: Publisher's site
Wu, G, Wang, H, Zhang, H, Zhao, Y, Yu, S & Shen, S 2023, 'Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing', IEEE Internet of Things Journal, vol. 10, no. 20, pp. 17620-17634. View/Download from: Publisher's site
Wu, G, Xie, L, Zhang, H, Wang, J, Shen, S & Yu, S 2023, 'STSIR: An individual-group game-based model for disclosing virus spread in Social Internet of Things', Journal of Network and Computer Applications, vol. 214, pp. 103608-103608. View/Download from: Publisher's site
Wu, G, Xu, Z, Zhang, H, Shen, S & Yu, S 2023, 'Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT', Journal of Parallel and Distributed Computing, vol. 176, pp. 80-94. View/Download from: Publisher's site
Wu, H, Wang, Z, Cheng, X, Huang, Y, Chen, J-Y, Liu, C, Wang, Z, Xu, J & Zhang, X 2023, 'Effect of microwave pulse parameters on energy coupling and enhancement of microwave assisted ignition', Proceedings of the Combustion Institute, vol. 39, no. 4, pp. 5531-5539. View/Download from: Publisher's site View description>>
Microwave Assisted Ignition (MAI) is a promising technology to optimize the lean combustion characteristics of internal combustion engines. This research investigated the effects of microwave pulse waveform and delay time on energy coupling and ignition enhancement by using a power diagnostic with high time-resolution. The pulse width ranged from 80 to 200 μs and the corresponding peak power was adjusted between 1000 and 400 W to keep the incident energy of microwave pulse constant. Results showed that a high power and short width pulse waveform was conducive to the microwave energy absorption. The coupled energy generally decreased with delay time. There was a significant ignition enhancement with only 6-7 mJ energy coupled into the flame kernel, which was approximately 1/5 of total spark energy. Moreover, the ignition enhancement was well correlated with the coupled energy, suggesting that ignition enhancement was determined by coupled energy via electron collision reactions. Finally, the main influencing factor of coupled energy was the energy coupling pattern which changed from none-coupling to saturated coupling. A high pulse power and short delay time facilitated this change. This study revealed the saturated coupling pattern independent of waveform and delay time, which is caused by a stable electron number density due to the cut-off effect.
Wu, J, Jiang, C, Fang, H & Ng, C-T 2023, 'Damage detection in the T-welded joint using Rayleigh-like feature guided wave', NDT & E International, vol. 135, pp. 102806-102806. View/Download from: Publisher's site
Wu, J, Li, L & Zhang, J 2023, 'Maximum demand flexibility from the demand response of a big group of residential homes', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108800-108800. View/Download from: Publisher's site
Wu, J, Tang, L, Jin, S, Li, X, Liu, H, Li, D, Liu, Y & Wang, Q 2023, 'Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment', Industrial & Engineering Chemistry Research, vol. 62, no. 41, pp. 16841-16853. View/Download from: Publisher's site
Wu, K & Guo, YJ 2023, 'Deterministic Solutions to Improved Generalized Joined Coupler Matrix for Multibeam Antennas', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9454-9466. View/Download from: Publisher's site
Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical industrial Internet-of-Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme -an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This paper is dedicated to filling this technology gap. We first design a series of echo pre-processing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, like inter-carrier and inter-symbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. Extensive simulations show that the proposed sensing method approaches the maximum likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a two-dimensional Fourier transform.
Wu, K, Zhang, JA, Huang, X, Guo, YJ & Hanzo, L 2023, 'Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1785-1797. View/Download from: Publisher's site
Wu, K, Zhang, JA, Huang, X, Heath, RW & Guo, YJ 2023, 'Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays', IEEE Communications Magazine, vol. 61, no. 7, pp. 172-178. View/Download from: Publisher's site View description>>
Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energyefficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/ utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.
Wu, L, Lee, KMB, Le Gentil, C & Vidal-Calleja, T 2023, 'Log-GPIS-MOP: A Unified Representation for Mapping, Odometry, and Planning', IEEE Transactions on Robotics, vol. 39, no. 5, pp. 4078-4094. View/Download from: Publisher's site View description>>
Whereas dedicated scene representations are required for each different task in conventional robotic systems, this article demonstrates that a unified representation can be used directly for multiple key tasks. We propose the log-Gaussian process implicit surface for mapping, odometry, and planning (Log-GPIS-MOP): a probabilistic framework for surface reconstruction, localization, and navigation based on a unified representation. Our framework applies a logarithmic transformation to a Gaussian process implicit surface (GPIS) formulation to recover a global representation that accurately captures the Euclidean distance field with gradients and, at the same time, the implicit surface. By directly estimating the distance field and its gradient through Log-GPIS inference, the proposed incremental odometry technique computes the optimal alignment of an incoming frame and fuses it globally to produce a map. Concurrently, an optimization-based planner computes a safe collision-free path using the same Log-GPIS surface representation. We validate the proposed framework on simulated and real datasets in 2-D and 3-D, and benchmark against the state-of-the-art approaches. Our experiments show that Log-GPIS-MOP produces competitive results in sequential odometry, surface mapping, and obstacle avoidance.
Wu, L, Wei, W, Chen, Z, Chen, X & Ni, B-J 2023, 'Long-chain alcohol production in open culture anaerobic fermentation', Chemical Engineering Journal, vol. 452, pp. 139225-139225. View/Download from: Publisher's site View description>>
The continuously rising energy prices and the growth in petroleum consumption have induced the pertinent pursuit of producing alternative fuels. Utilizing long-chain alcohols (LCAs) yielded from anaerobic bioprocess as renewable energy is a promising strategy to achieve the sustainable production of biofuels. Anaerobic fermentation is a sustainable process for reclaiming biodiesel from the organic wastes, which has received increasing attention due to its potential in producing renewable energy. Open-culture fermentation is preferred over single-species fermentation to yield the said alcohols because of its lower operating and capital costs. To better explore the LCAs productions from open-culture fermentation, a comprehensive understanding of this subject is currently needed but not available yet. To this end, the formations of LCAs and their possible precursors, medium-chain fatty acids (MCFAs), were systematically evaluated at first time with the focus on metabolic platforms, interspecies interactions, and competing microbial reactions. Suitable operational conditions and challenges were then synthesized, followed by the discussion on the viability of adopting current strategies towards higher alcohols productivities. The potential opportunities for enhancing LCAs outputs via biological processes were then suggested based on the review.
Wu, L, Wei, W, Chen, Z, Shi, X, Wang, D, Chen, X & Ni, B-J 2023, 'Medium chain fatty acids production from anaerobic fermentation of food wastes: The role of fermentation pH in metabolic pathways', Chemical Engineering Journal, vol. 472, pp. 144824-144824. View/Download from: Publisher's site
Wu, L, Wei, W, Wang, C & Ni, B-J 2023, 'Toward high carbon recovery: Novel strategies to hindering the occurrence of competitive reactions during chain elongation process', Journal of Cleaner Production, vol. 419, pp. 138340-138340. View/Download from: Publisher's site
Wu, RMX, Zhang, Z, Zhang, H, Wang, Y, Shafiabady, N, Yan, W, Gou, J, Gide, E & Zhang, S 2023, 'An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework', Scientific Reports, vol. 13, no. 1, p. 9621. View/Download from: Publisher's site View description>>
AbstractAmong all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach—First-round—Second-round—Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications.
Wu, S, Li, W & Bai, Q 2023, 'GAC: A deep reinforcement learning model toward user incentivization in unknown social networks', Knowledge-Based Systems, vol. 259, pp. 110060-110060. View/Download from: Publisher's site
Wu, S, Li, W, Shen, H & Bai, Q 2023, 'Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction', Information Sciences, vol. 624, pp. 128-146. View/Download from: Publisher's site View description>>
In recent years, recommenze the social influence among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world applications, identifying influential users can be challenging because of the unknown network topology. In this paper, we propose a novel algorithm for exploring influential users in unknown networks, estimating the influential relationships among users based on their historical behaviors without knowing the network topology. In addition, we design an adaptive incentive allocation approach that determines incentive values based on each user's preferences and influence ability. We evaluate the performance of the proposed approaches by conducting experiments on synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.
Wu, S-L, Wei, W, Ngo, HH, Guo, W, Wang, C, Wang, Y & Ni, B-J 2023, 'In-situ production of lactate driving the biotransformation of waste activated sludge to medium-chain fatty acid', Journal of Environmental Management, vol. 345, pp. 118524-118524. View/Download from: Publisher's site
Wu, X, Shen, J, Zheng, W, Lin, L, Sui, Y & Semasaba, AOA 2023, 'RNNtcs: A test case selection method for Recurrent Neural Networks', Knowledge-Based Systems, vol. 279, pp. 110955-110955. View/Download from: Publisher's site
Wu, X, Xu, Y, Zhang, W & Zhang, Y 2023, 'Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach', Proceedings of the VLDB Endowment, vol. 17, no. 2, pp. 175-183. View/Download from: Publisher's site View description>>
Bipartite graph embedding (BGE), as the fundamental task in bipartite network analysis, is to map each node to compact low-dimensional vectors that preserve intrinsic properties. The existing solutions towards BGE fall into two groups: metric-based methods and graph neural network-based (GNN-based) methods. The latter typically generates higher-quality embeddings than the former due to the strong representation ability of deep learning. Nevertheless, none of the existing GNN-based methods can handle billion-scale bipartite graphs due to the expensive message passing or complex modelling choices. Hence, existing solutions face a challenge in achieving both embedding quality and model scalability. Motivated by this, we propose a novel graph neural network named AnchorGNN based on global-local learning framework, which can generate high-quality BGE and scale to billion-scale bipartite graphs. Concretely, AnchorGNN leverages a novel anchor-based message passing schema for global learning, which enables global knowledge to be incorporated to generate node embeddings. Meanwhile, AnchorGNN offers an efficient one-hop local structure modelling using maximum likelihood estimation for bipartite graphs with rational analysis, avoiding large adjacency matrix construction. Both global information and local structure are integrated to generate distinguishable node embeddings. Extensive experiments demonstrate that AnchorGNN outperforms the best competitor by up to 36% in accuracy and achieves up to 28 times speed-up against the only metric-based baseline on billion-scale bipartite graphs.
Wu, Y, Chen, M, Li, Y, Liu, J, Li, Z, Li, J & Wu, X 2023, 'ONP-Miner: One-off Negative Sequential Pattern Mining', ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 3, pp. 1-24. View/Download from: Publisher's site View description>>
Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this article discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this article proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.
Wu, Y, Fang, J, Wu, C, Li, C, Sun, G & Li, Q 2023, 'Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption', International Journal of Mechanical Sciences, vol. 246, pp. 108102-108102. View/Download from: Publisher's site
Wu, Y, Jiang, L & Yang, Y 2023, 'Switchable Novel Object Captioner', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1162-1173. View/Download from: Publisher's site
Wu, Z, Liu, H, Xie, J, Xu, G, Li, G & Lu, C 2023, 'An effective method for the protection of user health topic privacy for health information services', World Wide Web, vol. 26, no. 6, pp. 3837-3859. View/Download from: Publisher's site
Wu, Z, Xie, J, Shen, S, Lin, C, Xu, G & Chen, E 2023, 'A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 5, pp. 1-19. View/Download from: Publisher's site View description>>
In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.
Xia, J, Xu, M, Zhang, H, Zhang, J, Huang, W, Cao, H & Wen, S 2023, 'Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10358-10375. View/Download from: Publisher's site
Xia, N, Yu, H, Wang, Y, Xuan, J & Luo, X 2023, 'DAFS: a domain aware few shot generative model for event detection', Machine Learning, vol. 112, no. 3, pp. 1011-1031. View/Download from: Publisher's site
Xiao, D, Zhang, JA, Liu, X, Qu, Y, Ni, W & Liu, RP 2023, 'A Two-Stage GCN-Based Deep Reinforcement Learning Framework for SFC Embedding in Multi-Datacenter Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4297-4312. View/Download from: Publisher's site
Xiao, F, Guan, J, Zhu, Q & Wang, W 2023, 'Graph Attention for Automated Audio Captioning', IEEE Signal Processing Letters, vol. 30, pp. 413-417. View/Download from: Publisher's site
Xiao, J, Guo, X, Li, Y & Wen, S 2023, 'Further Research on the Problems of Synchronization for Fractional-Order BAM Neural Networks in Octonion-Valued Domain', Neural Processing Letters, vol. 55, no. 8, pp. 11173-11208. View/Download from: Publisher's site
Xiao, J, Hu, Y, Zeng, Z, Wu, A & Wen, S 2023, 'Fixed/predefined-time synchronization of memristive neural networks based on state variable index coefficient', Neurocomputing, vol. 560, pp. 126849-126849. View/Download from: Publisher's site View description>>
This paper has investigated the fixed/predefined-time synchronization problem of delayed memristive neural networks(DNNs). Firstly, two novel controllers with state variable index coefficient are presented for achieving fixed-time synchronization of driver-response systems. Then, one effective control scheme is proposed out to realize predefined-time synchronization based on the results of fixed-time synchronization. Meanwhile, some conditions are given to direct how to select suitable controllers’ parameters by employing fixed-time and Lyapunov theory. The established control schemes are shown to have simpler structures and may need lower energy. In addition, the settling time of fixed time synchronization has been estimated independently on initial states while predefined-time can be set by user in advance. At last, numerical experiments are conducted to verify the effectiveness of the results.
Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2023, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-15. View/Download from: Publisher's site View description>>
With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over $20\%$ improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.
Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-18. View/Download from: Publisher's site View description>>
Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristi...
Xing, B & Tsang, IW 2023, 'Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15. View/Download from: Publisher's site
Xiong, J, Guo, L, Shan, M, Liu, B, Yu, P & Guo, L 2023, 'Wireless Resources Cooperation of Assembled Small UAVs for Data Collections of IoT', IEEE Internet of Things Journal, vol. 10, no. 11, pp. 9411-9422. View/Download from: Publisher's site
Xiong, X, Sun, C, Ni, W & Wang, X 2023, 'Three-Dimensional Trajectory Design for Unmanned Aerial Vehicle-Based Secure and Energy-Efficient Data Collection', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 664-678. View/Download from: Publisher's site
AbstractThe nonlinear variation of soil compressibility and permeability with void ratio (i.e., e-log σ′ and e-log k) has been included in the consolidation theory to accurately predict the behavior of soft soil stabilized by vertical drains. However, most current nonlinear consolidation models incorporating the coupled radial-vertical flow are based on some simplified assumptions, while including some features such as the complex implementation of multilayered computations, time-dependent loading and stress distribution with depth. This study hence introduces a novel approach where the spectral method is used to analyze the nonlinear consolidation behavior of multilayered soil associated with coupled vertical-radial drainage. In addition, time- and depth-dependent stress and soil properties at each soil layer are incorporated into the proposed model. Subsequently, the solution is verified against experimental and field data with comparison to previous analytical solutions. The results show greater accuracy of the proposed method in predicting in-situ soil behavior. A parametric study based on the proposed solution indicates that the ratio between the compression and permeability indices (ω = Cc/Ck) has a great impact on the consolidation rate, i.e., the greater the ω, the smaller the consolidation rate. Increasing the load increment ratio and the absolute difference between unity and ω (i.e., |ω − 1|) can exacerbate prediction error if the conventional simplified methods are used.
Xu, C, Jia, W, Cui, T, Wang, R, Zhang, Y-F & He, X 2023, 'Arbitrary-Shape Scene Text Detection via Visual-Relational Rectification and Contour Approximation', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4052-4066. View/Download from: Publisher's site View description>>
One trend in the latest bottom-up approaches for arbitrary-shape scene text detection is to determine the links between text segments using Graph Convolutional Networks (GCNs). However, the performance of these bottom-up methods is still inferior to that of state-of-the-art top-down methods even with the help of GCNs. We argue that a cause of this is that bottom-up methods fail to make proper use of visual-relational features, which results in accumulated false detection, as well as the error-prone route-finding used for grouping text segments. In this paper, we improve classic bottom-up text detection frameworks by fusing the visual-relational features of text with two effective false positive/negative suppression (FPNS) mechanisms and developing a new shape-approximation strategy. First, dense overlapping text segments depicting the ‘`characterness’' and ‘`streamline’' properties of text are constructed and used in weakly supervised node classification to filter the falsely detected text segments. Then, relational features and visual features of text segments are fused with a novel Location-Aware Transfer (LAT) module and Fuse Decoding (FD) module to jointly rectify the detected text segments. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed based on the rectified text segments, instead of the error-prone route-finding process, to generate the final contour of the detected text. Experiments conducted on five benchmark datasets demonstrate that our method outperforms the state-of-the-art performance when embedded in a classic text detection framework, which revitalizes the strengths of bottom-up methods.
Xu, C, Jia, W, Wang, R, He, X, Zhao, B & Zhang, Y 2023, 'Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation', IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 1-17. View/Download from: Publisher's site
Xu, C, Jia, W, Wang, R, Luo, X & He, X 2023, 'MorphText: Deep Morphology Regularized Accurate Arbitrary-Shape Scene Text Detection', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4199-4212. View/Download from: Publisher's site View description>>
Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targeting these two problems, we propose a novel approach, named ``MorphText', to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections.Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches.
Xu, G, Zhang, X, Sun, S, Zhou, Y, Liu, Y, Yang, H, Huang, Z, Fang, F, Sun, W, Hong, Z, Gao, M & Pan, H 2023, 'Synergized Tricomponent All‐Inorganics Solid Electrolyte for Highly Stable Solid‐State Li‐Ion Batteries', Advanced Science, vol. 10, no. 25. View/Download from: Publisher's site View description>>
AbstractGarnet‐type oxide Li6.4La3Zr1.4Ta0.6O12 (LLZTO) features superior ionic conductivity and good stability toward lithium (Li) metal, but requires high‐temperature sintering (≈1200 °C) that induces high fabrication cost, poor mechanical processability, and high interface resistance. Here, a novel high‐performance tricomponent composite solid electrolyte (CSE) comprising LLZTO−4LiBH4/xLi3BN2H8 is reported, which is prepared by ball milling the LLZTO−4LiBH4 mixture followed by hand milling with Li3BN2H8. Green pellets fabricated by heating the cold‐pressed CSE powders at 120 °C offer ultrafast room‐temperature ionic conductivity (≈1.73 × 10−3 S cm−1 at 30 °C) and ultrahigh Li‐ion transference number (≈0.9999), which enable the Li|Li symmetrical cells to cycle over 1600 h at 30 °C with only 30 mV of overpotential. Moreover, the Li|CSE|TiS2 full cells deliver 201 mAh g−1 of capacity with long cyclability. These outstanding performances are due to the low open porosity in the electrolyte pellets as well as the high intrinsic ionic conductivity and easy deformability of Li3BN2H8.
Xu, H, He, X, Pradhan, B & Sheng, D 2023, 'A pre-trained deep-learning surrogate model for slope stability analysis with spatial variability', Soils and Foundations, vol. 63, no. 3, pp. 101321-101321. View/Download from: Publisher's site
Xu, H, Nanda, P, Liang, J & He, X 2023, 'FCH, an incentive framework for data-owner dominated federated learning', Journal of Information Security and Applications, vol. 76, pp. 103521-103521. View/Download from: Publisher's site
Xu, H, Yan, Z, Xuan, J, Zhang, G & Lu, J 2023, 'Improving proximal policy optimization with alpha divergence', Neurocomputing, vol. 534, pp. 94-105. View/Download from: Publisher's site
Xu, J, Wang, H, Zhang, L & Wen, S 2023, 'Robust twin depth support vector machine based on average depth', Knowledge-Based Systems, vol. 274, pp. 110627-110627. View/Download from: Publisher's site
Xu, K, Guo, Y, Lei, G, Sun, X & Zhu, J 2023, 'Electromagnetic Performance Analysis of a Bearingless Permanent Magnet Synchronous Motor by Model Order Reduction', IEEE Transactions on Magnetics, vol. 59, no. 11, pp. 1-5. View/Download from: Publisher's site
Xu, S, Deo, RC, Soar, J, Barua, PD, Faust, O, Homaira, N, Jaffe, A, Kabir, AL & Acharya, UR 2023, 'Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)', Computer Methods and Programs in Biomedicine, vol. 241, pp. 107746-107746. View/Download from: Publisher's site
Xu, S, Zheng, M, Yuan, P, Wu, P, Shao, R, Liu, Z, Liu, J & Wu, C 2023, 'Experimental study of mechanical properties of G-UHPC against sodium sulfate attack at elevated temperature', Construction and Building Materials, vol. 396, pp. 132387-132387. View/Download from: Publisher's site
Xu, T, Li, Y & Leng, D 2023, 'Mitigating jacket offshore platform vibration under earthquake and ocean waves utilizing tuned inerter damper', Bulletin of Earthquake Engineering, vol. 21, no. 3, pp. 1627-1650. View/Download from: Publisher's site View description>>
AbstractThe unwanted vibrations of offshore structures induced by wave or earthquake loads can lead to the reduction of the service life and fatigue failure of the offshore platforms. This paper introduces tuned inerter damper (TID) to a jacket offshore platform as passive control device for mitigating the excessive vibrations of platform structure induced by wave and earthquake loads. An analytical design method is proposed for jacket platforms and the influence of installation location on the modal response is investigated. The proposed design method can determine the optimal installation position and obtain the optimal design parameters by transform the original multi-degree of freedom (MDOF) system to a single DOF (SDOF) modal system. Two sets of closed-form solutions of which corresponding to wave and earthquake excitations are derived based on the $${\mathrm{H}}_{2}$$H2 optimization criterion. Further, a practical 90 (m) high and 80 (m) deep in-water jacket offshore platform is used in numerical simulation and the wave forces are modeled using Morison’s equation. The case study finds that the optimal installation location of TID is deck level for both wave and earthquake loads. The proposed design method is validated by the numerical example and the results demonstrate that TID system can effectively mitigate the maximum, minimum, and RMS responses of jacket platforms. Besides, the TID is more effective when the jacket platform is under the action of waves and the tuning of TID according to earthquake load is more reliable when the jacket platform subjected to both wave and seismic loads.
Xu, T, Li, Y, Lai, T & Li, S 2023, 'H2 and H∞ optimal designs of tuned inerter dampers for base motion excited structures with inherent damping', Journal of Vibration and Control, vol. 29, no. 15-16, pp. 3692-3707. View/Download from: Publisher's site View description>>
Tuned inerter damper (TID) has recently gained increasing attention as a new structural control mechanism for seismic protection of structures. Currently, theoretical investigations are undertaken by researchers to reveal its fundamentals and to understand its underline principles in altering the structural performances of structures against dynamic loadings. However, the comprehensive study of optimization design of TID for undamped structures is lacking and the majority of the research focuses on the optimization of TID for structures without any damping. This research evaluates the [Formula: see text] and [Formula: see text] optimal designs of TIDs on the structures with damping. Using SDOF structure as an example, the frequency response function of the system equipped with TID underground motion excitation is obtained. The [Formula: see text] and [Formula: see text] designs of TID for structures without damping are derived considering various response parameters using analytical method. A numerical search method is utilized for the [Formula: see text] and [Formula: see text] designs of TID for structures with damping; meanwhile, a set of explicit formulae are obtained by curve-fitting for convenience in the application. Finally, the relative motion response of the inerter is explored, and an optimal design formula of TID which can reduce the displacements of primary mass and inerter simultaneously, is proposed.
Xu, Y, Chen, L, Duan, L, Tsang, IW & Luo, J 2023, 'Open Set Domain Adaptation With Soft Unknown-Class Rejection', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1601-1612. View/Download from: Publisher's site View description>>
The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.
Xu, Y, Chen, Y, Zhang, H, Feng, K, Wang, Y, Yang, C & Ni, Q 2023, 'Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions', Mechanical Systems and Signal Processing, vol. 203, pp. 110724-110724. View/Download from: Publisher's site
Xu, Y, Fang, M, Chen, L, Du, Y, Xu, G & Zhang, C 2023, 'Shared dynamics learning for large-scale traveling salesman problem', Advanced Engineering Informatics, vol. 56, pp. 102005-102005. View/Download from: Publisher's site
Xu, Y, Feng, K, Yan, X, Yan, R, Ni, Q, Sun, B, Lei, Z, Zhang, Y & Liu, Z 2023, 'CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery', Information Fusion, vol. 95, pp. 1-16. View/Download from: Publisher's site
Xu, Y, Gu, Y, Peng, L, Wang, N, Chen, S, Liang, C, Liu, Y & Ni, B-J 2023, 'Unravelling ciprofloxacin removal in a nitrifying moving bed biofilm reactor: Biodegradation mechanisms and pathways', Chemosphere, vol. 320, pp. 138099-138099. View/Download from: Publisher's site View description>>
Although moving bed biofilm reactors (MBBRs) have shown excellent antibiotic removal potentials, the information on underlying mechanisms is yet limited. This work assessed the removal of ciprofloxacin in an enriched nitrifying MBBR by clarifying the contribution of adsorption and microbial-induced biodegradation. Results demonstrated the considerable biomass adsorption (55%) in first 30 min. Limiting nitrite oxidizing bacteria growth or inhibiting nitrification would lead to lower adsorption capacities. The highest ciprofloxacin biodegradation rate constant was 0.082 L g SS-1 h-1 in the presence of ammonium, owing to ammonia oxidizing bacteria (AOB)-induced cometabolism, while heterotrophs played an insignificant role (∼9%) in ciprofloxacin biodegradation. The developed model also suggested the importance of AOB-induced cometabolism and metabolism over heterotrophs-induced biodegradation by analyzing the respective biodegradation coefficients. Cometabolic biodegradation pathways of ciprofloxacin mainly involved the piperazine ring cleavage, probably alleviating antimicrobial activities. It implies the feasibility of nitrifying biofilm systems towards efficient antibiotic removal from wastewater.
Xu, Y, Ji, JC, Ni, Q, Feng, K, Beer, M & Chen, H 2023, 'A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems', Mechanical Systems and Signal Processing, vol. 200, pp. 110609-110609. View/Download from: Publisher's site View description>>
Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.
Xu, Y, Zheng, R, Zhang, S, Liu, M & Huang, S 2023, 'CARE: Confidence-Rich Autonomous Robot Exploration Using Bayesian Kernel Inference and Optimization', IEEE Robotics and Automation Letters, vol. 8, no. 10, pp. 6755-6762. View/Download from: Publisher's site View description>>
In this letter, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments.
Xu, Y, Zhou, C, Yu, X & Yang, Y 2023, 'Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 1992-2002. View/Download from: Publisher's site View description>>
Weakly-supervised object detection (WSOD), which requires only image-level annotations for training detectors, has gained enormous attention. Despite recent rapid advance in WSOD, there remains a large performance gap compared with fully-supervised object detection. To narrow the performance gap, we study cross-supervised object detection (CSOD), where existing classes (base classes) have instance-level annotations while newly added classes (novel classes) only need image-level annotations. For improving localization accuracy, we propose a Cyclic Self-Training (CST) method to introduce instance-level supervision into a commonly used WSOD method, online instance classifier refinement (OICR). Our proposed CST consists of forward pseudo labeling and backward pseudo labeling. Specifically, OICR exploits the forward pseudo labeling to generate pseudo ground-truth bounding-boxes for all classes, thus enabling instance classifier training. Then, the backward pseudo labeling is designed to generate pseudo ground-truth bounding-boxes of higher quality for novel classes by fusing the predictions of the instance classifiers. As a result, both novel and base classes will have bounding-box annotations for training, alleviating the supervision inconsistency between base and novel classes. In the forward pseudo labeling, the generated pseudo ground-truths may be misaligned with objects and thus introduce poor-quality examples for training the ICs. To reduce the impacts of these poor-quality training examples, we propose a Proposal Weight Modulation (PWM) module learned in a class-agnostic and contrastive manner by exploiting bounding-box annotations of base classes. Experiments on PASCAL VOC and MS COCO datasets demonstrate the superiority of our proposed method.
Xu, Z, Gao, X, Li, G, Nghiem, LD & Luo, W 2023, 'Microbes from mature compost to promote bacterial chemotactic motility via tricarboxylic acid cycle-regulated biochemical metabolisms for enhanced composting performance', Bioresource Technology, vol. 387, pp. 129633-129633. View/Download from: Publisher's site
Xu, Z, Khabbaz, H, Fatahi, B & Wu, D 2023, 'Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction', Engineering Structures, vol. 274, pp. 115180-115180. View/Download from: Publisher's site View description>>
Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.
Xu, Z, Li, J & Wu, C 2023, 'A numerical study of blast resistance of fire damaged ultra-high performance concrete columns', Engineering Structures, vol. 279, pp. 115613-115613. View/Download from: Publisher's site View description>>
Concrete structures may experience fire and blast during their service life as a result of accidental explosions or vehicular collisions. Both fire and blast can cause severe damage that threatens the structural safety. In the present study, reinforced concrete columns fabricated by ultra-high performance concrete (UHPC) are investigated under coupled fire and blast loads. Strength degradation and damage of UHPC and steel reinforcement after exposure to elevated temperature (up to 800 °C) were established based on the experimental data. In addition to the detrimental effect on individual material, bond-slip behaviour between the UHPC and reinforcement affected by the elevated temperature was considered. The findings revealed that material strength degradation and damage owing to elevated temperature significantly influenced the structural blast resistance, and the degraded bond-slip behaviour had varying impact on the structural response depending on the structural damage mode. Up to 10% mid-span displacement differences were noted in columns with/without the consideration of bond-slip behaviour. Different failure mechanisms pre- and post-fire damage were observed in the numerical simulations. To quickly assess blast induced damage on UHPC columns, Pressure-Impulse (P-I) diagrams of the UHPC columns before and after elevated temperature were established and empirical formulae were proposed to generate the P-I diagrams.
Xu, Z, Sun, M, Xu, X, Cao, X, Ippolito, JA, Mohanty, SK, Ni, B-J, Xu, S & Tsang, DCW 2023, 'Electron donation of Fe-Mn biochar for chromium(VI) immobilization: Key roles of embedded zero-valent iron clusters within iron-manganese oxide', Journal of Hazardous Materials, vol. 456, pp. 131632-131632. View/Download from: Publisher's site
Xue, C, Sirivivatnanon, V, Nezhad, A & Zhao, Q 2023, 'Comparisons of alkali-activated binder concrete (ABC) with OPC concrete - A review', Cement and Concrete Composites, vol. 135, pp. 104851-104851. View/Download from: Publisher's site View description>>
Improving the sustainability of concrete industry by adopting durable concrete with low energy intensity has become one of the major research focus in recent years, leading to the remarkable progress on alkali-activated binder concrete (ABC). To reveal the potentialities yet limitations of ABC towards a wider application, the parameters important to the properties of ABC were comprehensively reviewed in this paper. A systematic comparison with Ordinary Portland Cement (OPC) concrete was provided, intending to assist in the selection of raw materials as well as curing regime for ABC to achieve similar workability, mechanical properties and better durability. However, a broad synthesis of available laboratory and field data highlighted the fact that it is rather complicated to control the performance of ABC which is highly sensitive to both the chemical and physical characteristic of precursors and activators. The lack of standardised procedures for assessing specific performance has further challenged the production of ABC with desirable properties, since some practical and reliable test methods developed for OPC were proved unsuitable for ABC. Nevertheless, given careful mix design with locally available raw materials, ABC is a potential low carbon material, especially for structures in harsh environments like sewage, chemical acid, organic acid and elevated temperature.
Xue, C, Tapas, MJ & Sirivivatnanon, V 2023, 'Cracking and stimulated autogenous self-healing on the sustainability of cement-based materials: a review', Journal of Sustainable Cement-Based Materials, vol. 12, no. 2, pp. 184-206. View/Download from: Publisher's site
Xue, H, Liu, B, Yuan, X, Ding, M & Zhu, T 2023, 'Face image de‐identification by feature space adversarial perturbation', Concurrency and Computation: Practice and Experience, vol. 35, no. 5. View/Download from: Publisher's site View description>>
SummaryPrivacy leakage in images attracts increasing concerns these days, as photos uploaded to large social platforms are usually not processed by proper privacy protection mechanisms. Moreover, with advanced artificial intelligence (AI) tools such as deep neural network (DNN), an adversary can detect people's identities and collect other sensitive personal information from images at an unprecedented scale. In this paper, we introduce a novel face image de‐identification framework using adversarial perturbations in the feature space. Manipulating the feature space vector ensures the good transferability of our framework. Moreover, the proposed feature space adversarial perturbation generation algorithm can successfully protect the identity‐related information while ensuring the other attributes remain similar. Finally, we conduct extensive experiments on two face image datasets to evaluate the performance of the proposed method. Our results show that the proposed method can generate real‐looking privacy‐preserving images efficiently. Although our framework has only been tested on two real‐life face image datasets, it can be easily extended to other types of images.
Yaghoubi Naei, V, Bordhan, P, Mirakhorli, F, Khorrami, M, Shrestha, J, Nazari, H, Kulasinghe, A & Ebrahimi Warkiani, M 2023, 'Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA', Therapeutic Advances in Medical Oncology, vol. 15. View/Download from: Publisher's site View description>>
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient’s disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems’ operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
Yam, AO, Bailey, J, Lin, F, Jakovija, A, Youlten, SE, Counoupas, C, Gunzer, M, Bald, T, Woodruff, TM, Triccas, JA, Goldstein, LD, Gallego-Ortega, D, Grey, ST & Chtanova, T 2023, 'Neutrophil Conversion to a Tumor-Killing Phenotype Underpins Effective Microbial Therapy', Cancer Research, vol. 83, no. 8, pp. 1315-1328. View/Download from: Publisher's site View description>>
AbstractThe inflammatory microenvironment of solid tumors creates a protumorigenic milieu that resembles chronic inflammation akin to a subverted wound healing response. Here, we investigated the effect of converting the tumor microenvironment from a chronically inflamed state to one of acute microbial inflammation by injecting microbial bioparticles directly into tumors. Intratumoral microbial bioparticle injection led to rapid and dramatic changes in the tumor immune composition, the most striking of which was a substantial increase in the presence of activated neutrophils. In situ photoconversion and intravital microscopy indicated that tumor neutrophils transiently switched from sessile producers of VEGF to highly motile neutrophils that clustered to make neutrophil-rich domains in the tumor. The neutrophil clusters remodeled tumor tissue and repressed tumor growth. Single-cell transcriptional analysis of microbe-stimulated neutrophils showed a profound shift in gene expression towards heightened activation and antimicrobial effector function. Microbe-activated neutrophils also upregulated chemokines known to regulate neutrophil and CD8+ T-cell recruitment. Microbial therapy also boosted CD8+ T-cell function and enhanced the therapeutic benefit of checkpoint inhibitor therapy in tumor-bearing mice and provided protection in a model of tumor recurrence. These data indicate that one of the major effector mechanisms of microbial therapy is the conversion of tumor neutrophils from a wound healing to an acutely activated cytotoxic phenotype, highlighting a rationale for broader deployment of microbial therapy in the treatment of solid cancers.Significance:Intratumoral injection of microbial bioparticles stimulates neutrophil antitumor functions, suggesting pathways for optimizing efficacy of microbial therapies and paving the way for th...
YAMADA, K & JI, J 2023, 'Accuracy enhancement of modal analysis using higher-order residual terms', Mechanical Engineering Journal, vol. 10, no. 5. View/Download from: Publisher's site
YAMADA, K & JI, J 2023, 'Substructure elimination method for evaluating bending vibration of beams', Mechanical Engineering Journal, vol. 10, no. 6. View/Download from: Publisher's site
YAMADA, K & JI, J 2023, 'Substructure elimination method for vibration systems governed by a one-dimensional wave equation', Mechanical Engineering Journal, vol. 10, no. 5. View/Download from: Publisher's site
Yan, L, Ge, L, Dong, S, Saluja, K, Li, D, Reddy, KS, Wang, Q, Yao, L, Li, JJ, Roza da Costa, B, Xing, D & Wang, B 2023, 'Evaluation of Comparative Efficacy and Safety of Surgical Approaches for Total Hip Arthroplasty', JAMA Network Open, vol. 6, no. 1, pp. e2253942-e2253942. View/Download from: Publisher's site View description>>
ImportanceEach approach for primary total hip arthroplasty (THA) has a long learning curve, so a surgeon’s choice to change their preferred approach needs to be guided by clear justifications. However, current evidence does not suggest that any of the THA approaches are more beneficial than others, and the choice of approach is mainly based on the knowledge and experience of the surgeon and individual patient characteristics.ObjectiveTo assess the efficacy and safety associated with different surgical approaches for THA.Data SourcesA comprehensive search of PubMed, EMBASE, and Cochrane databases from inception to March 26, 2022; reference lists of eligible trials; and related reviews.Study SelectionRandomized clinical trials (RCTs) comparing different surgical approaches, including the 2-incision approach, direct anterior approach (DAA), direct lateral approach (DLA), minimally invasive direct lateral approach (MIS-DLA), minimally invasive anterolateral approach (MIS-ALA), posterior approach (PA), minimally invasive posterior approach (MIS-PA), and supercapsular percutaneously assisted total hip arthroplasty (SuperPath), for primary THA.Data Extraction and SynthesisFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 2 reviewers independently extracted data on study participants, interventions, and outcomes as well as assessed the risk of bias using the Cochrane risk of bias tool and the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation framework. A frequentist framework was used to infor...
Yan, X, Sun, J, Wang, Y, Zhang, Z, Zhang, C, Li, W, Xu, J, Dai, X & Ni, B-J 2023, 'Low-rate ferrate dosing damages the microbial biofilm structure through humic substances destruction and facilitates the sewer biofilm control', Water Research, vol. 235, pp. 119834-119834. View/Download from: Publisher's site
Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 2023, 'Attentional Gated Res2Net for Multivariate Time Series Classification', Neural Processing Letters, vol. 55, no. 2, pp. 1371-1395. View/Download from: Publisher's site View description>>
AbstractMultivariate time series classification is a critical problem in data mining with broad applications. It requires harnessing the inter-relationship of multiple variables and various ranges of temporal dependencies to assign the correct classification label of the time series. Multivariate time series may come from a wide range of sources and be used in various scenarios, bringing the classifier challenge of temporal representation learning. We propose a novel convolutional neural network architecture called Attentional Gated Res2Net for multivariate time series classification. Our model uses hierarchical residual-like connections to achieve multi-scale receptive fields and capture multi-granular temporal information. The gating mechanism enables the model to consider the relations between the feature maps extracted by receptive fields of multiple sizes for information fusion. Further, we propose two types of attention modules, channel-wise attention and block-wise attention, to better leverage the multi-granular temporal patterns. Our experimental results on 14 benchmark multivariate time-series datasets show that our model outperforms several baselines and state-of-the-art methods by a large margin. Our model outperforms the SOTA by a large margin, the classification accuracy of our model is 10.16% better than the SOTA model. Besides, we demonstrate that our model improves the performance of existing models when used as a plugin. Further, based on our experiments and analysis, we provide practical advice on applying our model to a new problem.
Yang, J & Lin, C-T 2023, 'Multi-View Adjacency-Constrained Hierarchical Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 4, pp. 1126-1138. View/Download from: Publisher's site
Yang, J, Chen, G, Wen, S & Wang, L 2023, 'Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method', Chaos, Solitons & Fractals, vol. 176, pp. 114161-114161. View/Download from: Publisher's site
Yang, L, Zhu, X & Gómez-García, R 2023, 'High-Order Quasi-Elliptic-Type Wideband Bandpass Filter With Ultrabroad Input-Reflectionless Stopband Range', IEEE Microwave and Wireless Technology Letters, vol. 33, no. 6, pp. 655-658. View/Download from: Publisher's site
Yang, M, Du, Z, Bao, H, Zhang, X, Liu, Q, Guo, W, Ngo, H-H & Nghiem, LD 2023, 'Experimental and Theoretical Insight of Perfluorooctanoic Acid Destruction by Alkaline Hydrothermal Treatment Enhanced with Zero-Valent Iron in Biochar', ACS ES&T Water, vol. 3, no. 5, pp. 1286-1293. View/Download from: Publisher's site
Yang, R, Huang, S, Zhang, Y, Zhang, C, Qian, J, Lam, RHW, Lee, JE-Y & Wang, Z 2023, 'Developing a multi-sample acoustofluidic device for high-throughput cell aggregation', Journal of Micromechanics and Microengineering, vol. 33, no. 5, pp. 055003-055003. View/Download from: Publisher's site View description>>
AbstractPlug-and-play acoustofluidic devices are highly promising for dexterously aggregating microparticles owing to the advantages of being contactless, label-free, and cost-efficient. Despite the extensive progress, existing acoustofluidic devices are largely limited to addressing a single sample per device, lacking the ability to address multiple samples for high-throughput operations in a single acoustofluidic device. In this work, we report a high-throughput multi-sample acoustofluidic aggregation device that enables manipulation of up to 12 samples simultaneously using a single reusable acoustic tweezer. The key design of the multi-sample acoustofluidic device lies in the utilization of a polydimethylsiloxane frame as a selective acoustic-absorbing feature to create asymmetric acoustic waves over multiple detachable superstrates in a single device. This approach is distinct from conventional strategies which mostly have involved modifying the superstrates or tuning the settings for individual superstrates. We demonstrate that the proposed acoustofluidic device can efficiently aggregate multiple samples of various compositions ranging from non-bioactive microparticles to bioactive cells, as well as a range of object sizes spanning from 0.6 µm to 13 µm. Given its merits of simplicity, cost-efficiency and high throughput, the proposed platform could be useful for biomedical applications requiring large-scale operations, such as 3D tumor spheroids and bio-sensors.
Yang, S, Verma, S, Cai, B, Jiang, J, Yu, K, Chen, F & Yu, S 2023, 'Variational co-embedding learning for attributed network clustering', Knowledge-Based Systems, vol. 270, pp. 110530-110530. View/Download from: Publisher's site
Yang, S, Wu, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 2023, 'A Parametrical Model for Instance-Dependent Label Noise', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14055-14068. View/Download from: Publisher's site
Yang, T, Miro, JV, Nguyen, M, Wang, Y & Xiong, R 2023, 'Template-Free Nonrevisiting Uniform Coverage Path Planning on Curved Surfaces', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 4, pp. 1853-1861. View/Download from: Publisher's site
Yang, Y, Zhang, X, Zhang, L, Zhang, W, Liu, H, Huang, Z, Yang, L, Gu, C, Sun, W, Gao, M, Liu, Y & Pan, H 2023, 'Recent advances in catalyst-modified Mg-based hydrogen storage materials', Journal of Materials Science & Technology, vol. 163, pp. 182-211. View/Download from: Publisher's site View description>>
The storage of hydrogen in a compact, safe and cost-effective manner can be one of the key enabling technologies to power a more sustainable society. Magnesium hydride (MgH2) has attracted strong research interest as a hydrogen carrier because of its high gravimetric and volumetric hydrogen densities. However, the practical use of MgH2 for hydrogen storage has been limited due to high operation temperatures and sluggish kinetics. Catalysis is of crucial importance for the enhancement of hydrogen cycling kinetics of Mg/MgH2 and considerable work has been focused on designing, fabricating and optimizing catalysts. This review covers the recent advances in catalyzed Mg-based hydrogen storage materials. The fundamental properties and the syntheses of MgH2 as a hydrogen carrier are first briefly reviewed. After that, the general catalysis mechanisms and the catalysts developed for hydrogen storage in MgH2 are summarized in detail. Finally, the challenges and future research focus are discussed. Literature studies indicate that transition metals, rare-earth metals and their compounds are quite effective in catalyzing hydrogen storage in Mg/MgH2. Most metal-containing compounds were converted in situ to elemental metal or their magnesium alloys, and their particle sizes and dispersion affect their catalytic activity. The in-situ construction of catalyzed ultrasmall Mg/MgH2 nanostructures (< 10 nm in size) is believed to be the future research focus. These important insights will help with the design and development of high-performance catalysts for hydrogen storage in Mg/MgH2.
Yang, Z, Li, H, Lin, J, Xing, D, Li, JJ, Cribbin, EM, M. Kim, A, He, Z, Li, H, Guo, W, Zhang, L & Lin, J 2023, 'Research landscape of 3D printing in bone regeneration and bone repair: A bibliometric and visualized analysis from 2012 to 2022', International Journal of Bioprinting, vol. 9, no. 4, pp. 0-0. View/Download from: Publisher's site View description>>
Three-dimensional printing (3DP) is a popular manufacturing technique with versatile potential for materials processing in tissue engineering and regenerative medicine. In particular, the repair and regeneration of significant bone defects remain as substantial clinical challenges that require biomaterial implants to maintain mechanical strength and porosity, which may be realized using 3DP. The rapid progress in 3DP development in the past decade warrants a bibliometric analysis to gain insights into its applications in bone tissue engineering (BTE). Here, we performed a comparative study using bibliometric methods for 3DP in bone repair and regeneration. A total of 2,025 articles were included, and the results showed an increase in the number of publications and relative research interest on 3DP annually worldwide. China was the leader in international cooperation in this field and also the largest contributor to the number of citations. The majority of articles in this field were published in the journal Biofabrication. Chen Y was the author who made the highest contribution to the included studies. The keywords included in the publications were mainly related to BTE and regenerative medicine (including “3DP techniques,” “3DP materials,” “bone regeneration strategies,” and “bone disease therapeutics”) for bone regeneration and repair. This bibliometric and visualized analysis provides significant insights into the historical development of 3DP in BTE from 2012 to 2022, which will be beneficial for scientists to conduct further investigations into this dynamic field.
Yang, Z, Wang, B, Liu, W, Li, X, Liang, K, Fan, Z, Li, JJ, Niu, Y, He, Z, Li, H, Wang, D, Lin, J, Du, Y, Lin, J & Xing, D 2023, 'In situ self-assembled organoid for osteochondral tissue regeneration with dual functional units', Bioactive Materials, vol. 27, pp. 200-215. View/Download from: Publisher's site
Yao, J, Han, B, Zhou, Z, Zhang, Y & Tsang, IW 2023, 'Latent Class-Conditional Noise Model', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 9964-9980. View/Download from: Publisher's site
Yao, L, Kusakunniran, W, Zhang, P, Wu, Q & Zhang, J 2023, 'Improving Disentangled Representation Learning for Gait Recognition Using Group Supervision', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4187-4198. View/Download from: Publisher's site View description>>
In decades, gait has been gathering extensive interest for the advantage that it can be measured from a distance without physical contact. However, for image/video-based gait recognition, its performance can be remarkably influenced by exterior factors, such as viewing angles and clothing changes. Thus, in this paper, a group-supervised disentangled representation learning network is proposed for gait recognition to extract features invariant to these factors. First, sequences are explicitly disentangled into pose, gait, appearance, and view features through a generic encoder-decoder framework. To ensure the feature adaptability and independency, a disentanglement swap module is specifically adopted during our encode-decoder process through a series of swap operations based on the feature attributes. Following the feature disentanglement, a disentanglement aggregation module is also specially proposed for pose, gait, and appearance features to enhance their effectiveness. Finally, the enhanced three features are concatenated together for gait recognition. Relevant experiments certify that compared with other disentangled representation learning-based gait recognition methods, our proposed method enables to obtain a more excellent recognition result, despite fewer gait frames being utilized.
Yao, Q, Lu, DD-C & Lei, G 2023, 'A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1103-1112. View/Download from: Publisher's site View description>>
To monitor the thermal performance of the battery, the surface temperature (ST) of the battery is normally directly measured by temperature sensors. As the number of battery cells or strings increases, the number of temperature sensors increases proportionally. This increases the cost and reduces the reliability of the battery systems. To solve this problem, this article introduces a method to accurately estimate the ST of lithium-ion batteries using a recurrent neural network (RNN) with gated recurrent unit (GRU). First, this article analyzes the battery ST distribution theory and proves that it is a time series task since the present ST is conditioned on the previous state. Second, a GRU-RNN model is adopted to estimate the battery ST as this model has the ability to automatically encode dependencies in time and accurately estimate the battery ST without using any physical battery models or filters. Third, an improved data normalization method is proposed to enhance the estimation accuracy and robustness. Fourthly, the proposed data normalization method is incorporated into the stacked GRU-RNN to estimate the battery ST from compulsory online signals. The proposed method is verified with LiFePO4 using US06 and Federal Urban Driving Schedule (FUDS) profiles under four fixed ambient temperatures and with LiNiCoAIO2 using a mixed dynamic profiles under varying ambient temperature ranges (from 10 °C to 25 °C). The estimation error using mean absolute error (MAE) is less than 0.2 °C over all the fixed ambient temperature conditions and 0.42 °C over the varying ambient temperature conditions.
Yao, R, Pang, T, Zhang, B, Fang, J, Li, Q & Sun, G 2023, 'On the crashworthiness of thin-walled multi-cell structures and materials: State of the art and prospects', Thin-Walled Structures, vol. 189, pp. 110734-110734. View/Download from: Publisher's site
Yao, Y, Liu, Y, Huang, S, Pan, C, Li, X & Yuan, X 2023, 'Federated learning-based user access strategy and energy consumption optimization in cell-free massive MIMO network', Tongxin Xuebao/Journal on Communications, vol. 44, no. 10, pp. 112-123. View/Download from: Publisher's site View description>>
To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed. First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information. Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security. Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system. Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.
Yao, Y, Qu, X, Zhou, L, Liu, Y, Hong, Z, Wu, Y, Huang, Z, Hu, J, Gao, M & Pan, H 2023, 'Rational Design of Robust and Universal Aqueous Binders to Enable Highly Stable Cyclability of High‐Capacity Conversion and Alloy‐Type Anodes', ENERGY & ENVIRONMENTAL MATERIALS, vol. 6, no. 5. View/Download from: Publisher's site View description>>
The development of high‐performance binders is a simple but effective approach to address the rapid capacity decay of high‐capacity anodes caused by large volume change upon lithiation/delithiation. Herein, we demonstrate a unique organic/inorganic hybrid binder system that enables an efficient in situ crosslinking of aqueous binders (e.g., sodium alginate (SA) and carboxymethyl cellulose (CMC)) by reacting with an inorganic crosslinker (sodium metaborate hydrate (SMH)) upon vacuum drying. The resultant 3D interconnected networks endow the binders with strong adhesion and outstanding self‐healing capability, which effectively improve the electrode integrity by preventing fracturing and exfoliation during cycling and facilitate Li+ ion transfer. SiO anodes fabricated from the commercial microsized powders with the SA/0.2SMH binder maintain 1470 mAh g−1 of specific capacity at 100 mA g−1 after 200 cycles, which is 5 times higher than that fabricated with SA binder alone (293 mAh g−1). Nearly, no capacity loss was observed over 500 cycles when limiting discharge capacity at 1500 mAh g−1. The new binders also dramatically improved the performance of Fe2O3, Fe3O4, NiO, and Si electrodes, indicating the excellent applicability. This finding represents a novel strategy in developing high‐performance aqueous binders and improves the prospect of using high‐capacity anode materials in Li‐ion batteries.
Yari, M, He, B, Armaghani, DJ, Abbasi, P & Mohamad, ET 2023, 'A novel ensemble machine learning model to predict mine blasting–induced rock fragmentation', Bulletin of Engineering Geology and the Environment, vol. 82, no. 5. View/Download from: Publisher's site
Yazdani, D, Yazdani, D, Branke, J, Omidvar, MN, Gandomi, AH & Yao, X 2023, 'Robust Optimization Over Time by Estimating Robustness of Promising Regions', IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 657-670. View/Download from: Publisher's site View description>>
Many real-world optimization problems are dynamic. The field of robust optimization over time (ROOT) deals with dynamic optimization problems in which frequent changes of the deployed solution are undesirable. This can be due to the high cost of switching the deployed solutions, the limitation of the needed resources to deploy such new solutions, and/or the system being intolerant towards frequent changes of the deployed solution. In the considered ROOT problems in this article, the main goal is to find solutions that maximize the average number of environments where they remain acceptable. In the state-of-the-art methods developed to tackle these problems, the decision makers/metrics used to select solutions for deployment mostly make simplifying assumptions about the problem instances. Besides, the current methods all use the population control components which have been originally designed for tracking the global optimum over time without taking any robustness considerations into account. In this paper, a multi-population ROOT method is proposed with two novel components: a robustness estimation component that estimates robustness of the promising regions, and a dual-mode computational resource allocation component to manage sub-populations by taking several factors, including robustness, into account. Our experimental results demonstrate the superiority of the proposed method over other state-of-the-art approaches.
Yazdani, D, Yazdani, D, Yazdani, D, Omidvar, MN, Gandomi, AH & Yao, X 2023, 'A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems', ACM Transactions on Evolutionary Learning and Optimization, vol. 3, no. 4, pp. 1-25. View/Download from: Publisher's site View description>>
Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.
Ye, D, Zhu, T, Zhu, C, Zhou, W & Yu, PS 2023, 'Model-Based Self-Advising for Multi-Agent Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7934-7945. View/Download from: Publisher's site View description>>
In multiagent learning, one of the main ways to improve learning performance is to ask for advice from another agent. Contemporary advising methods share a common limitation that a teacher agent can only advise a student agent if the teacher has experience with an identical state. However, in highly complex learning scenarios, such as autonomous driving, it is rare for two agents to experience exactly the same state, which makes the advice less of a learning aid and more of a one-time instruction. In these scenarios, with contemporary methods, agents do not really help each other learn, and the main outcome of their back and forth requests for advice is an exorbitant communications' overhead. In human interactions, teachers are often asked for advice on what to do in situations that students are personally unfamiliar with. In these, we generally draw from similar experiences to formulate advice. This inspired us to provide agents with the same ability when asked for advice on an unfamiliar state. Hence, we propose a model-based self-advising method that allows agents to train a model based on states similar to the state in question to inform its response. As a result, the advice given can not only be used to resolve the current dilemma but also many other similar situations that the student may come across in the future via self-advising. Compared with contemporary methods, our method brings a significant improvement in learning performance with much lower communication overheads.
Ye, K & Ji, JC 2023, 'A Novel Morphing Propeller System Inspired by Origami-Based Structure', Journal of Mechanisms and Robotics, vol. 15, no. 1. View/Download from: Publisher's site View description>>
AbstractFor a standard propeller system, the thrust output and the energy dissipation are proportionally dependent on its rotating speed, as its physical characteristics and working conditions are normally fixed during its operation. In order to improve the system performance and meet special application requirements, this paper presents the design of a novel two-stage propeller system with a morphing blade structure for higher thrust output and energy efficiency in operations. Based on the stacked Miura-ori (SMO) pattern, an origami-based structure is designed to enable a change in blade length for a propeller system and thus improve the system performance. The unique snap-through feature of the proposed origami structure is utilized to provide a two-stage working condition according to its rotating speed. The geometric parameter analysis of the SMO structure is first investigated, specifically focusing on the operating mechanism due to the snap-through behavior. Then, the implementation of the SMO structure into a rotating system is studied. The effects of design parameters on the critical transition points, which correspond to two operating states of the proposed propeller system, are numerically discussed. The simulation results confirm the performance improvement in the thrust output and energy-saving. The feasibility of using origami-based structures provides valuable insights into more applications in similar domains, such as fan system and wind turbine blades.
Ye, K & Ji, JC 2023, 'Dynamic analysis of the effects of self-weight induced structural and damping nonlinearity on the performance of an origami-inspired vibration isolator', Journal of Sound and Vibration, vol. 547, pp. 117538-117538. View/Download from: Publisher's site View description>>
Origami-inspired structure has shown strong nonlinearity on its force response during morphing process between phases. When the origami-inspired structure is applied as vibration isolation system, the structural weight is rare to be considered and discussed in the modelling and analysis of vibration isolation system. The effects of the structural self-weight on the dynamic behaviour of the isolation system is not yet fully understood. Thus, this study aims to investigate the influence of the self-weight induced structural and damping nonlinearity on the dynamic performance of an origami-based vibration isolator. A three-mass body, which includes the payload mass, top facets’ mass and bottom facets’ mass, with multiple degree-of-freedom (DOF) motion is proposed to describe the vibration isolation system. First, a quasi-zero-stiffness feature is designed and its static performance is discussed for a set of specifically selected system parameters. Then, the equation of motion for such three-mass body with spring damping considered is derived by using the harmonic balance method (HBM) on its Lagrange's formulation, where the effects of strong nonlinearity on its dynamic performance can be investigated. The analytical expression is verified with the numerical solutions, which are obtained using the Newmark numerical integration method. The influences of each important system parameter on the dynamic nonlinearity are also discussed. It is expected that this study would provide valuable insights to the effects of structural self-weight in a quasi-zero-stiffness isolation system.
Ye, K, Ji, JC & Fitch, R 2023, 'Further investigation and experimental study of an origami structure-based quasi-zero-stiffness vibration isolator', International Journal of Non-Linear Mechanics, vol. 157, pp. 104554-104554. View/Download from: Publisher's site View description>>
A quasi-zero stiffness (QZS) vibration isolator formed from a truss-spring stacked Miura-ori (TS-SMO) origami structure can provide a desired ultra-low dynamic stiffness for vibration isolation while remaining a high-static stiffness for load supporting capacity. This paper further investigates the design parameters and experimentally studies the dynamic performance of the proposed TS-SMO vibration isolation system. The effects of the spring parameters and the initial setup conditions on its static response are analysed. With the proper parameter selection, the resultant supporting force generated by the origami structure can be expressed as a polynomial containing the static force and dynamic force components which does not have the linear term. The displacement transmissibility of the proposed system is calculated to evaluate its isolation performance. Analytical and numerical results are in good agreement and both demonstrate an ultra-low resonance frequency for vibration isolation. The dynamic behaviour of the proposed system is also investigated under different conditions to enhance the vibration isolation performance. A proof-of-concept prototype is designed, fabricated and tested to verify both static and dynamic performances of the TS-SMO QZS isolator. The comparative experimental results between the corresponding linear isolation system and the proposed nonlinear QZS system validate the design of origami-inspired structure for vibration isolation and further confirm the effectiveness of the QZS vibration isolator. It is hoped that this research would provide a solid foundation for designing and modelling the TS-SMO structure adopted for vibration isolation in practical engineering applications.
Ye, S-Q, Ding, H, Wei, S, Ji, J-C & Chen, L-Q 2023, 'Nonlinear forced vibrations of a slightly curved pipe conveying supercritical fluid', Journal of Vibration and Control, vol. 29, no. 15-16, pp. 3634-3645. View/Download from: Publisher's site View description>>
Vibrations of pipes caused by axially flowing fluids are very common in engineering applications. Due to material imperfections, guide misalignment, and improper supports, the installed pipes are prone to the initial curvature. Though small, the initial curvature can significantly change the dynamic characteristics of the slightly curved pipe system. This study investigates the non-linear forced vibration of a slightly curved pipe conveying supercritical fluid around the curved equilibrium, with the emphasis on amplitude–frequency responses around two asymmetric non-trivial equilibrium configurations. The governing equations for the forced vibration of a slightly curved pipe conveying supercritical fluids are derived using the generalized Hamilton principle. Then, the equations of motion are discretized into a set of coupled ordinary differential equations via the Galerkin truncation method and solved by the harmonic balance method combined with the pseudo-arc length technique. The approximate analytical results are verified by the numerical integration results. The obtained results demonstrate that the initial curvature has a significant effect on the dynamic characteristics of pipes conveying supercritical fluids, and can lead to significant differences in the dynamic response of the pipe system near different equilibrium configurations.
Ye, Y, Wang, H, Xu, B & Zhang, J 2023, 'An imitation learning-based energy management strategy for electric vehicles considering battery aging', Energy, vol. 283, pp. 128537-128537. View/Download from: Publisher's site
Ye, Y, Zhang, J, Pilla, S, Rao, AM & Xu, B 2023, 'Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management', Journal of Energy Storage, vol. 59, pp. 106546-106546. View/Download from: Publisher's site
Ye, Z, Yan, D, Dong, L, Deng, J & Yu, S 2023, 'Stealthy Backdoor Attack Against Speaker Recognition Using Phase-Injection Hidden Trigger', IEEE Signal Processing Letters, vol. 30, pp. 1057-1061. View/Download from: Publisher's site
Yeh, C-K, Tzu, F-M, Chen, P-Y, Shen, H-C, Yuan, C-S, Lin, C, Pu, H-P, Ngo, HH & Bui, X-T 2023, 'Emission characteristics of naphthalene from ship exhausts under global sulfur cap', Science of The Total Environment, vol. 902, pp. 166172-166172. View/Download from: Publisher's site
Yildiz, AM, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Ooi, CP, Fujita, H & Rajendra Acharya, U 2023, 'A novel tree pattern-based violence detection model using audio signals', Expert Systems with Applications, vol. 224, pp. 120031-120031. View/Download from: Publisher's site
Yildiz, AM, Tanabe, M, Kobayashi, M, Tuncer, I, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2023, 'FF-BTP Model for Novel Sound-Based Community Emotion Detection', IEEE Access, vol. 11, pp. 108705-108715. View/Download from: Publisher's site
Yin, H, Sun, Y, Xu, G & Kanoulas, E 2023, 'Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1', ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1-5. View/Download from: Publisher's site
Yin, S, Liu, P, Kong, L, Zhang, X, Qi, Y & Huang, J 2023, 'Accumulated plastic strain behavior of granite residual soil under traffic loading', Soil Dynamics and Earthquake Engineering, vol. 164, pp. 107617-107617. View/Download from: Publisher's site
Yin, W, Liu, L, Zhang, W, Li, M & Guo, Y 2023, 'Performance Improving of Wind Power Generation Systems Through Parameter Optimization and Dynamic Analysis of the Speed-Regulating Differential Transmission', Journal of Energy Resources Technology, vol. 145, no. 12. View/Download from: Publisher's site View description>>
AbstractHybrid drive wind power generation systems (WPGSs) equipped with speed-regulating differential mechanisms (SRDMs) have emerged as a promising solution for integrating large-scale wind energy into the power grid without the need for partially or fully rated converters. This article presents a comprehensive study on the dynamic analysis and parameter optimization of the SRDM-based transmission, with the aim of providing a sound foundation for the design and performance improvement of hybrid drive WPGSs. This study first formulates the kinematics, power flow, and mechanical efficiency of the SRDM and then proposes an effective parameter configuration model for optimizing the speed ratios of the key link units. The objective function is set as the minimum peak power required for speed regulation by the SRDM. Furthermore, to deal with the unique mechanical features such as dual power inputs, continuously variable transmission, and time-varying steering mechanism, an appropriate nonlinear dynamic modeling method of the SRDM transmission is developed. The torsion–translation vibration equations are derived and solved using the Runge–Kutta numerical integral method, considering randomly changing wind speed inputs and time-varying internal/external excitations. Results reveal that the sun gear experiences severe vibrations with the maximal and average vibration displacements of 0.563 mm and 0.112 mm, respectively, in the circumferential direction, while the planet gear exhibits complex frequency responses. Finally, specialized case studies are demonstrated to verify the proposed approaches, showing the satisfactory on-grid operating performance of the proposed SRDM-based WPGSs.
Ying, M & Zhang, Z 2023, 'Quantum Recursive Programming with Quantum Case Statements.', CoRR, vol. abs/2311.01725.
Youssry, A, Paz-Silva, GA & Ferrie, C 2023, 'Noise detection with spectator qubits and quantum feature engineering', New Journal of Physics, vol. 25, no. 7, pp. 073004-073004. View/Download from: Publisher's site View description>>
AbstractDesigning optimal control pulses that drive a noisy qubit to a target state is a challenging and crucial task for quantum engineering. In a situation where the properties of the quantum noise affecting the system are dynamic, a periodic characterization procedure is essential to ensure the models are updated. As a result, the operation of the qubit is disrupted frequently. In this paper, we propose a protocol that addresses this challenge by making use of a spectator qubit to monitor the noise in real-time. We develop a machine-learning-based quantum feature engineering approach for designing the protocol. The complexity of the protocol is front-loaded in a characterization phase, which allow real-time execution during the quantum computations. We present the results of numerical simulations that showcase the favorable performance of the protocol.
Yu, D, Ian, O, Jie, L, Xiaoru, Y & Vinh, NQ 2023, 'User-centered visual explorer of in-process comparison in spatiotemporal space', Journal of Visualization, vol. 26, no. 2, pp. 403-421. View/Download from: Publisher's site View description>>
AbstractWe propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback.Graphical abstract
Yu, D, Li, Q, Wang, X & Xu, G 2023, 'Deconfounded recommendation via causal intervention', Neurocomputing, vol. 529, pp. 128-139. View/Download from: Publisher's site
Yu, G, Wang, X, Ni, W, Lu, Q, Xu, X, Liu, RP & Zhu, L 2023, 'Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 7256-7268. View/Download from: Publisher's site
Yu, G, Wang, X, Sun, C, Yu, P, Ni, W & Liu, RP 2023, 'Obfuscating the Dataset: Impacts and Applications', ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 5, pp. 1-15. View/Download from: Publisher's site View description>>
Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ 2 -distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.
Yu, H, Hossain, SM, Wang, C, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'Selective lithium extraction from diluted binary solutions using metal-organic frameworks (MOF)-based membrane capacitive deionization (MCDI)', Desalination, vol. 556, pp. 116569-116569. View/Download from: Publisher's site
Yu, H, Li, J, Lu, J, Song, Y, Xie, S & Zhang, G 2023, 'Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14. View/Download from: Publisher's site
Yu, H, Liu, W, Lu, J, Wen, Y, Luo, X & Zhang, G 2023, 'Detecting group concept drift from multiple data streams', Pattern Recognition, vol. 134, pp. 109113-109113. View/Download from: Publisher's site
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Low-Resolution Hybrid Beamforming in Millimeter-Wave Multi-User Systems', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8941-8955. View/Download from: Publisher's site
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Regularized Zero-Forcing Aided Hybrid Beamforming for Millimeter-Wave Multiuser MIMO Systems', IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3280-3295. View/Download from: Publisher's site View description>>
This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.
Yu, J, Wu, M, Yang, W & Ji, J 2023, 'A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties', Asian Journal of Control. View/Download from: Publisher's site View description>>
AbstractThe tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.
AbstractIn recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on ($$\alpha $$α, $$\beta $$β)-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.
Yu, N 2023, 'Almost Tight Sample Complexity Analysis of Quantum Identity Testing by Pauli Measurements', IEEE Transactions on Information Theory, vol. 69, no. 8, pp. 5060-5068. View/Download from: Publisher's site
Yu, N 2023, 'Structured Theorem for Quantum Programs and its Applications', ACM Transactions on Software Engineering and Methodology, vol. 32, no. 4, pp. 1-35. View/Download from: Publisher's site View description>>
This article proves a structured program theorem for flowchart quantum programs. The theorem states that any flowchart quantum program is equivalent to a single quantum program that repeatedly executes a quantum measurement and a subprogram, so long as the measurement outcome is true. Moreover, their expected runtime, variance, and general moments are the same. This theorem simplifies the quantum program’s verification significantly.– We derive an analytical characterization of the termination problem for quantum programs in polynomial time. Our procedure is more efficient and accurate with much simpler techniques than the analysis of this problem, as described in [ 29 ]. – We compute the expected runtime analytically and exactly for quantum programs in polynomial time. This result improves the methods based on the weakest precondition calculus for the question recently developed in [ 31 , 34 ]. – We show that a single loop rule is a relatively complete Hoare logic for quantum programs after applying our structured theorem. Although using fewer rules, our method verifies a broad...
Yu, S, Gu, B, Qu, Y & Wang, X 2023, 'Preface', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 489 LNICST, pp. v-vii.
Yu, X, Wang, F, Luo, Z, Kang, Z & Wang, Y 2023, 'Design of hierarchical microstructures with isotropic elastic stiffness', Materials & Design, vol. 229, pp. 111895-111895. View/Download from: Publisher's site View description>>
Elastic stiffness is one of the most fundamental properties of materials. Design of the microstructures with isotropic stiffness has been an attractive area in the field of metamaterials for over three decades. Despite many classes of isotropic microstructures, exploring novel isotropic microstructures based on innovative mechanics principles has attracted great and continuing interests. This paper presents a novel family of isotropic hierarchical microstructures (Iso-HMs). These hierarchical microstructures are modeled by replacing the solid parts of prescribed single-level microstructures with arrayed microstructures in the second level, where the key task is to identify the correct geometries of the second-level microstructures by conducting parameter space exploring. These Iso-HMs realize isotropic stiffness based on synergistic deformations of the members in the two levels, which is essentially different from existing isotropic microstructures replying on deformations of the members in a single level. Two categories of Iso-HMs with rectangular holes and Vidergauze-type struts are designed. Considering the large size difference in the designed Iso-HMs, additive manufacturing becomes a unique technique for manufacturing the designed Iso-HMs, where the size ratio between the 3D-printed specimens and the minimal features reaches 400:1. Both numerical and experimental results validate the isotropic stiffness of the designed Iso-HMs. Furthermore, the results of a microstructural instability analysis show that the designed Iso-HMs can gain improved buckling strength up to a hundred times higher than their single-level counterparts. The hierarchical design provides a new way to identify novel functional microstructures for applications, and the hierarchical configurations expand the space of the already-known families of isotropic microstructures.
Yu, X, Xiao, B, Ni, W & Wang, X 2023, 'Optimal Adaptive Power Control for Over-The-Air Federated Edge Learning Under Fading Channels', IEEE Transactions on Communications, vol. 71, no. 9, pp. 5199-5213. View/Download from: Publisher's site
Yu, Y, Hoshyar, AN, Samali, B, Zhang, G, Rashidi, M & Mohammadi, M 2023, 'Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion', Neural Computing and Applications, vol. 35, no. 25, pp. 18697-18718. View/Download from: Publisher's site
Yu, Y, Li, J, Li, J, Xia, Y, Ding, Z & Samali, B 2023, 'Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion', Developments in the Built Environment, vol. 14, pp. 100128-100128. View/Download from: Publisher's site View description>>
A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.
Yu, Y, Zhang, C, Xie, X, Yousefi, AM, Zhang, G, Li, J & Samali, B 2023, 'Compressive strength evaluation of cement-based materials in sulphate environment using optimized deep learning technology', Developments in the Built Environment, vol. 16, pp. 100298-100298. View/Download from: Publisher's site
Yu, Z, Zhu, Q, Wu, M & Yang, J 2023, 'Exploring the limits of virtual source localization with amplitude panning on a flat panel with actuator array: Implications for future research', The Journal of the Acoustical Society of America, vol. 154, no. 3, pp. 1362-1371. View/Download from: Publisher's site View description>>
Immersive and spatial sound reproduction has been widely studied using loudspeaker arrays. However, flat-panel loudspeakers that utilize thin flat panels with force actuators are a promising alternative to traditional coaxial loudspeakers for practical applications, with benefits in low-visual profiles and diffuse radiation. Literature has addressed the sound quality and applications of flat-panel loudspeakers in three-dimensional sound reproduction, such as wave field synthesis and sound zones. This paper revisits the spatial sound perception of flat-panel loudspeakers, specifically the localization mismatch between the perceived and desired sound directions when using amplitude panning. Subjective tests in an anechoic chamber with 24 subjects result in the mean azimuth direction mismatch within ±6.0° and the mean elevation mismatch within ±10.0°. The experimental results show that the virtual source created by amplitude panning over a flat-panel loudspeaker still achieves spatial localization accuracy close to that of a real sound source, despite not using complex algorithms or acoustic transfer function information. The findings of this study establish a benchmark for virtual source localization in spatial sound reproduction using flat-panel loudspeakers, which can serve as a starting point for future research and optimization of algorithms.
Yuan, B, Wang, Z, Cao, J, Huang, Y, Shen, Y, Song, Z, Zhao, H & Cheng, X 2023, 'Mixture formation characteristics and feasibility of methanol as an alternative fuel for gasoline in port fuel injection engines: Droplet evaporation and spray visualization', Energy Conversion and Management, vol. 283, pp. 116956-116956. View/Download from: Publisher's site View description>>
Under the background of energy saving and emission reduction, methanol is considered an ideal alternative fuel for gasoline because of its renewability, high combustion efficiency and low emissions, while an issue is the cold start of methanol port fuel injection engines. To comprehensively understand the underlying mechanisms and the feasibility of methanol as a substitute of gasoline, a suspension method was first used to investigate the evaporation characteristics of methanol and gasoline droplets at low temperatures. Then an optical intake port was constructed based on the outer contour modification strategy and an optical distortion correction algorithm was proposed. The effects of inlet temperature, inlet flow rate and injection pressure on spray characteristics of methanol and gasoline were comparatively investigated. In addition, the influence of valve temperature on the characteristics of spray impingement on a valve was studied. The results showed that the difference between the evaporation rates of methanol and gasoline was small at 60–90 °C. The difference became obvious when the temperature was below 60 °C or above 90 °C. Reducing the ambient humidity may reduce this difference and improve the cold start performance of methanol engines. A higher inlet temperature slightly increased the spray area. A higher inlet flow rate and injection pressure significantly increased the spray area, but also caused serious wet wall problem. From the point of view of spray in the port, methanol was a feasible substitute for gasoline in the presence of inlet flow and at low injection pressure of 6 bar. The valve temperature should exceed 200 and 230 °C for methanol and gasoline, respectively to form vapor films that prevent fuel adhesion onto the valve. However, methanol may not be a suitable alternative fuel for gasoline when spray directly impacted on the valve in port fuel injection engines due to its much smaller spray area.
Yuan, B, Zhang, F, Wan, J, Zhao, H, Yu, S, Zou, D, Hua, Q & Jin, H 2023, 'Resource investment for DDoS attack resistant SDN: a practical assessment', Science China Information Sciences, vol. 66, no. 7. View/Download from: Publisher's site
Yuan, C, Ni, W, Zhang, K, Bai, J, Shen, J & Jamalipour, A 2023, 'User Pairing and Power Allocation in Untrusted Multiuser NOMA for Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13155-13167. View/Download from: Publisher's site
Yuan, L, Wang, T, Ferraro, G, Suominen, H & Rizoiu, M-A 2023, 'Transfer learning for hate speech detection in social media', Journal of Computational Social Science, vol. 6, no. 2, pp. 1081-1101. View/Download from: Publisher's site View description>>
AbstractToday, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content, such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation—dubbed the Map of Hate—in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.
Yuan, X, Hu, S, Ni, W, Liu, RP & Wang, X 2023, 'Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1631-1645. View/Download from: Publisher's site
Yuan, X, Hu, S, Ni, W, Wang, X & Jamalipour, A 2023, 'Deep Reinforcement Learning-Driven Reconfigurable Intelligent Surface-Assisted Radio Surveillance With a Fixed-Wing UAV', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4546-4560. View/Download from: Publisher's site
Yuan, X, Ni, W, Ding, M, Wei, K, Li, J & Poor, HV 2023, 'Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1884-1897. View/Download from: Publisher's site
Zabed, HM, Akter, S, Yun, J, Zhang, G, Zhao, M, Mofijur, M, Awasthi, MK, Kalam, MA, Ragauskas, A & Qi, X 2023, 'Towards the sustainable conversion of corn stover into bioenergy and bioproducts through biochemical route: Technical, economic and strategic perspectives', Journal of Cleaner Production, vol. 400, pp. 136699-136699. View/Download from: Publisher's site
Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP & Franquelo, LG 2023, 'Long Prediction Horizon FCS-MPC for Power Converters and Drives', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 159-175. View/Download from: Publisher's site
Zanker, J, Scott, D, Alajlouni, D, Kirk, B, Bird, S, DeBruin, D, Vogrin, S, Bliuc, D, Tran, T, Cawthon, P, Duque, G & Center, JR 2023, 'Mortality, falls and slow walking speed are predicted by different muscle strength and physical performance measures in women and men', Archives of Gerontology and Geriatrics, vol. 114, pp. 105084-105084. View/Download from: Publisher's site
Zareei, SM, Sepehrirahnama, S, Jamshidian, M & Ziaei-Rad, S 2023, 'Three-dimensional numerical simulation of particle acoustophoresis: COMSOL implementation and case studies', Engineering with Computers, vol. 39, no. 1, pp. 735-750. View/Download from: Publisher's site
Zavahir, S, Elmakki, T, Gulied, M, Shon, HK, Park, H, Kakosimos, KE & Han, DS 2023, 'Integrated photoelectrochemical (PEC)-forward osmosis (FO) system for hydrogen production and fertigation application', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110525-110525. View/Download from: Publisher's site
Zeng, J, Desmond, P, Ngo, HH, Lin, W, Liu, X, Liu, B, Li, G & Ding, A 2023, 'Membrane modification in enhancement of virus removal: A critical review', Journal of Environmental Sciences. View/Download from: Publisher's site
Zeng, J, Wu, T, Feng, W, Ni, W, Lv, T, Zhou, S, Wang, X & Guo, YJ 2023, 'Analysis of Massive Ultra-Reliable and Low-Latency Communications Over the κ-μ Shadowed Fading Channel', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1798-1813. View/Download from: Publisher's site
Zeng, J, Xiao, C, Wu, T, Ni, W, Liu, RP & Guo, YJ 2023, 'Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness', IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1298-1313. View/Download from: Publisher's site
Zeng, S, Sun, J, Lü, X, Peng, Z, Dong, B, Dai, X & Ni, B-J 2023, 'Impacts of norfloxacin on sewage sludge anaerobic digestion: Bioenergy generation and potential environmental risks', Results in Engineering, vol. 20, pp. 101392-101392. View/Download from: Publisher's site
Zeng, Y-C, Ding, H, Ji, J-C, Jing, X-J & Chen, L-Q 2023, 'A tristable nonlinear energy sink to suppress strong excitation vibration', Mechanical Systems and Signal Processing, vol. 202, pp. 110694-110694. View/Download from: Publisher's site View description>>
As well known, the vibration reduction efficiency of the nonlinear energy sink (NES) is poor under strong excitation. In this paper, a tristable NES (TNES) is proposed. The TNES can degenerate into bistable and mono-stable NES by adjusting the geometric parameters of the springs. The governing equations of a linear oscillator coupled with the TNES under harmonic excitation are derived. The approximate analytical solution of the coupled system is obtained by using the harmonic balance method and verified numerically. The vibration suppression efficiency of TNES and NES is compared. The dynamic behavior of TNES under strong excitation is demonstrated. The results show that the nonlinear restoring force is softened due to the wide distribution of the three stable points of TNES. Therefore, compared with NES, the proposed TNES can suppress stronger excitation vibration. In addition, the low side barrier depth is conducive to TNES to perform chaotic inter-well oscillation, which can effectively suppress the strong excitation vibration and obtain good vibration reduction performance. As a result, the proposed TNES can eliminate the detached resonance curve and enlarge the effective range of the NES. Under relatively weak excitation, the vibration suppression efficiency of TNES is slightly lower than that of NES, although the TNES is also relatively significant. Therefore, this paper reveals the vibration suppression mechanism of TNES, and provides a way to effectively solve the problem of low vibration reduction efficiency of NES under strong excitation.
Zhand, S, Zhu, Y, Nazari, H, Sadraeian, M, Warkiani, ME & Jin, D 2023, 'Correction to “Thiolate DNAzymes on Gold Nanoparticles for Isothermal Amplification and Detection of Mesothelioma-derived Exosomal PD-L1 mRNA”', Analytical Chemistry, vol. 95, no. 32, pp. 12193-12193. View/Download from: Publisher's site
Catalytic DNAzymes have been used for isothermal amplification and rapid detection of nucleic acids, holding the potential for point-of-care testing applications. However, when Subzymes (universal substrate and DNAzyme) are tethered to the polystyrene magnetic microparticles via biotin-streptavidin bonds, the residual free Subzymes are often detached from the microparticle surface, which causes a significant degree of false positives. Here, we attached dithiol-modified Subzyme to gold nanoparticle and improved the limit of detection (LoD) by 200 times compared to that using magnetic microparticles. As a proof of concept, we applied our new method for the detection of exosomal programed cell-death ligand 1 (PD-L1) RNA. As the classical immune checkpoint, molecule PD-L1, found in small extracellular vesicles (sEVs, traditionally called exosomes), can reflect the antitumor immune response for predicting immunotherapy response. We achieved the LoD as low as 50 fM in detecting both the RNA homologous to the PD-L1 gene and exosomal PD-L1 RNAs extracted from epithelioid and nonepithelioid subtypes of mesothelioma cell lines, which only takes 8 min of reaction time. As the first application of isothermal DNAzymes for detecting exosomal PD-L1 RNA, this work suggests new point-of-care testing potentials toward clinical translations.
Zhang, C, Xu, RYD, Zhang, X & Huang, W 2023, 'Capture and control content discrepancies via normalised flow transfer', Pattern Recognition Letters, vol. 165, pp. 161-167. View/Download from: Publisher's site
Zhang, C, Zhang, S, Yu, JJQ & Yu, S 2023, 'SAM: Query-efficient Adversarial Attacks against Graph Neural Networks', ACM Transactions on Privacy and Security, vol. 26, no. 4, pp. 1-19. View/Download from: Publisher's site View description>>
Recent studies indicate that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Particularly, adversarially perturbing the graph structure, e.g., flipping edges, can lead to salient degeneration of GNNs’ accuracy. In general, efficiency and stealthiness are two significant metrics to evaluate an attack method in practical use. However, most prevailing graph structure-based attack methods are query intensive, which impacts their practical use. Furthermore, while the stealthiness of perturbations has been discussed in previous studies, the majority of them focus on the attack scenario targeting a single node. To fill the research gap, we present a global attack method against GNNs, Saturation adversarial Attack with Meta-gradient, in this article. We first propose an enhanced meta-learning-based optimization method to obtain useful gradient information concerning graph structural perturbations. Then, leveraging the notion of saturation attack, we devise an effective algorithm to determine the perturbations based on the derived meta-gradients. Meanwhile, to ensure stealthiness, we introduce a similarity constraint to suppress the number of perturbed edges. Thorough experiments demonstrate that our method can effectively depreciate the accuracy of GNNs with a small number of queries. While achieving a higher misclassification rate, we also show that the perturbations developed by our method are not noticeable.
Zhang, C, Zhang, S, Zou, X, Yu, S & Yu, JJQ 2023, 'Toward Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4506-4519. View/Download from: Publisher's site View description>>
Network partitioning is recognized as an effective auxiliary approach for solving transportation tasks on large-scale traffic networks in a domain-decomposition manner. Most of the existing related partitioning algorithms are explicitly designed to traffic management problems and merely focus on the implied topology of the networks. In this paper, towards the practical problems that happened to traffic forecasting tasks, we propose a network-partitioning-based domain-decomposition framework to improve GCN-based predictors’ performance on large-scale transportation networks. Particularly, we devise a data-driven network-partitioning approach, namely, Speed-Matching-Partitioning, which employs not only the topological features but also the traffic speed observations of traffic networks for partitioning. Additionally, we propose a data-parallel training strategy that feeds partitioned sub-networks into independent predictors for parallel training. The proposed approach is tested by comprehensive case studies on three real-world datasets to evaluate its effectiveness. The results indicate that the proposed approach can help improve GCN-based predictors’ accuracy and training efficiency on both small and relatively large traffic datasets. Furthermore, we investigate the model sensitivity to the selection of graph representations and framework parameters, and the learning efficiency of the data-parallel training strategy.
Zhang, D, Peng, C, Chang, X & Xia, F 2023, 'The Effect of Facial Perception and Academic Performance on Social Centrality', IEEE Transactions on Computational Social Systems, vol. 10, no. 3, pp. 970-981. View/Download from: Publisher's site View description>>
Facial perception is of significant influence on the positions of people in social networks. Particularly, students' facial traits can affect their social centrality in educational settings (e.g., students looking intelligent can attract more friends). However, in educational environments, the social biases associated with appearances have alarming consequences, and little research has been done to investigate the effect of facial perception on social networks. Therefore, it is necessary to comprehensively analyze the influence of perceived facial traits on students' status in social interaction. In this article, we explore the effect of facial perception on the social centrality of students in social networks. Because students' social centrality is based on both their study ability and facial traits, this study does a comparative analysis of how facial perception and academic performance influence the social centrality of students. Subsequently, the experimental results demonstrate that facial perception, as well as academic performance, closely correlates with the social centrality of students. Finally, this study contributes to a comprehensive and deep understanding of social networks by analyzing facial trait-based social biases.
Zhang, G, Feng, S, Chen, Y, Yang, Y, Zhu, H, Zhou, X, Hong, J, Tang, W & Yang, J 2023, '3-D Printed Filtering Rat-Race Couplers Using Hemispherical Cavity Resonator', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4922-4932. View/Download from: Publisher's site
Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2023, 'Label-Only Membership Inference Attacks and Defenses in Semantic Segmentation Models', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1435-1449. View/Download from: Publisher's site
Zhang, G, Sun, J, Xu, F, Sui, Y, Bandara, HMND, Chen, S & Menzies, T 2023, 'A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning', IEEE Internet Computing, vol. 27, no. 6, pp. 13-20. View/Download from: Publisher's site
Zhang, H & Xu, M 2023, 'Multiscale Emotion Representation Learning for Affective Image Recognition', IEEE Transactions on Multimedia, vol. 25, pp. 2203-2212. View/Download from: Publisher's site
Zhang, H & Xu, M 2023, 'Recognition of Emotions in User-Generated Videos through Frame-Level Adaptation and Emotion Intensity Learning', IEEE Transactions on Multimedia, vol. 25, pp. 881-891. View/Download from: Publisher's site
Zhang, H, Li, B, Karimi, M, Saydam, S & Hassan, M 2023, 'Recent Advancements in IoT Implementation for Environmental, Safety, and Production Monitoring in Underground Mines', IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14507-14526. View/Download from: Publisher's site
Zhang, H, Li, H, Wang, H & Zhu, X 2023, 'Numerical investigation of a proposed multidimensional isolator applied to large-scale domes subjected to earthquakes', Soil Dynamics and Earthquake Engineering, vol. 175, pp. 108252-108252. View/Download from: Publisher's site
Zhang, H, Liu, C, Zhang, S, Wang, Y, Lei, G & Zhu, J 2023, 'Analysis and Design Optimization of Axial–Radial Flux Hybrid Excitation Permanent Magnet Eddy Current Coupling', Journal of Electrical Engineering & Technology, vol. 18, no. 6, pp. 4231-4244. View/Download from: Publisher's site
Zhang, H, Song, Y, Zhu, X, Zhang, Y, Wang, H & Gao, Y 2023, 'A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale dome structures', Frontiers of Structural and Civil Engineering, vol. 17, no. 12, pp. 1813-1829. View/Download from: Publisher's site
Zhang, H, Zhang, L, Dong, S, Duan, X, Zhu, D, Ni, B-J & Lyu, C 2023, 'Regulating energy band structures of triazine covalent organic frameworks with electron-donating/withdrawing substituents for visible-light-responsive photocatalytic tetracycline degradation and Cr(VI) reduction', Journal of Hazardous Materials, vol. 446, pp. 130756-130756. View/Download from: Publisher's site View description>>
Environmental contaminations have raised soaring concerns about human health worldwide. Developing metal-free photocatalysts as green agents to solve these problems is urgent. Covalent organic frameworks (COFs) are considered a promising platform for the molecule-level design of visible-light-responsive photocatalysts due to their tailored coordination/electronic structures and excellent charge carrier mobility. However, COFs without substituents (e.g., COFs-H) still suffer from broad bandgaps and low electron-hole separation efficiency. In this work, we introduced electron-donating/withdrawing substituents on COFs-H to fine-tune the bandgap and photocatalytic performance of COFs. Theoretical and experimental studies revealed that all substituents narrowed the bandgap of COFs and enhanced the electron-hole separation efficiency. Electron-withdrawing/donating substituents significantly alter the energy level of COFs-R, improving the redox capacities of photo-generated holes and electrons for tetracycline (TC) degradation and Cr(VI) reduction. The large difference in electrostatic potential between the two monomers in COFs-R enhances the charge carrier generation and intramolecular electron transfer intrinsically. This work unravels how substituents with different electronic effects regulate the energy band structures and photo-redox capacities of COFs. It further provides new insight into the precise regulation of COFs toward highly efficient visible-light-driven photocatalytic remediation of organic contaminants and heavy metal ions.
Zhang, J, Chen, Z, Liu, Y, Wei, W & Ni, B-J 2023, 'Iron-assisted bio-chemical processes in sewer systems: Iron cycle and its role in sewer management', Journal of Cleaner Production, vol. 414, pp. 137707-137707. View/Download from: Publisher's site
Zhang, J, Lei, J, Xie, W, Fang, Z, Li, Y & Du, Q 2023, 'SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery', IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15. View/Download from: Publisher's site
Zhang, J, Liu, Y, Wu, D, Lou, S, Chen, B & Yu, S 2023, 'VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems', Digital Communications and Networks, vol. 9, no. 4, pp. 981-989. View/Download from: Publisher's site
Zhang, J, Zhang, N, Wang, D, Gao, B, Shon, HK, Yang, X, Zhao, H & Wang, Z 2023, 'Polyamidoamine and carboxylated cellulose nanocrystal grafted antifouling forward osmosis membranes for efficient leachate treatment via integrated forward osmosis and membrane distillation process', Journal of Membrane Science, vol. 668, pp. 121241-121241. View/Download from: Publisher's site View description>>
Organic fouling remains an intractable challenge for forward osmosis (FO) and integrated FO-membrane distillation (MD) strategy to treat landfill leachate. To address this challenge, polyamidoamine (PAMAM)/polydopamine (PDA) and carboxylated cellulose nanocrystal (CCN) were grafted gradually on the commercial thin film composite (TFC) FO membranes via chemical coupling to achieve excellent antifouling capacity. PAMAM dendrimers, with abundant terminal amines, hyperbranched structure and open interior cavity, served as a unique intermediate platform for chemically covalent attachment of CCN with high water affinity, then cooperated with CCN to form a hydrophilic and robust antifouling layer. Compared to the raw TFC membrane, the PAMAM/PDA-CCN modified TFC membrane exhibited a similar water flux of 30.6 L m−2 h−1 (LMH) accompanied with a decreased reverse salt flux of 6.9 g m−2 h−1 (gMH) by using 1 M NaCl solution as draw solution (DS) and DI water as feed solution (FS). When actual leachate was used as FS, the modified FO membranes possessed significantly enhanced antifouling capacity with a lower flux decline (≤43.7%) and a higher flux recovery rate (≥94.2%) than the raw TFC membrane (flux decline ≤59.4% and flux recovery rate ≥79.0%, respectively). The improved fouling resistance could be further demonstrated by the reduced thickness of the organic fouling layer. Eventually, the employment of the modified FO membranes improved the compatibility of the FO and MD water fluxes and achieved sustained water production. The construction of synergistic dendritic PAMAM and CCN grafted surface paves a new way for the development of high-performance water treatment membranes.
Zhang, JA, Wu, K, Huang, X & Guo, YJ 2023, 'Beam Alignment for Analog Arrays Based on Gaussian Approximation', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8152-8157. View/Download from: Publisher's site View description>>
Beam alignment is a process for receive and transmit antenna arrays to find the correct beamforming directions. It is typically based on beam scanning and peak-energy searching, which lead to time-consuming beam training process in communication protocols, such as 802.11ad for vehicular networks. In this correspondence, we propose a fast beam alignment method for analog arrays, based on directly estimating the angle-of-arrival (AoA) of the incoming signals. We propose simple and highly efficient AoA estimators, by approximating the power of the array response as a Gaussian function. One estimator is based on the power ratio and can coherently combine multiple measurements scanned at arbitrary intervals and with different beam widths. The other two are based on an innovative idea of Gaussian curve fitting with weighted least square techniques, and one of them can even work without knowing the beam width. Simulation results validate the effectiveness of the proposed scheme.
Zhang, K, Fan, Z, Song, X & Yu, S 2023, 'Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge', IEEE Internet of Things Journal, vol. 10, no. 6, pp. 5583-5594. View/Download from: Publisher's site
Zhang, L, Cao, C, Tay, SS, Chen, C, Macmillan, A, Wen, S, Jin, D, McCarroll, J & Stenzel, MH 2023, 'Exploring the Effect of Drug Loading on the Biological Fate of Polymer-Coated Solid Nanoparticles', Chemistry of Materials, vol. 35, no. 11, pp. 4471-4488. View/Download from: Publisher's site
Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2023, 'Federated Fuzzy Neural Network With Evolutionary Rule Learning', IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1653-1664. View/Download from: Publisher's site View description>>
Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods. Our code is available online1
https://github.com/leijiezhang/FedFNN
Zhang, L, Szmalenberg, A, Cook, K, Liu, B, Ding, L, Wang, F & McGloin, D 2023, 'Trapped aerosol sizes under fiber-based counterpropagation optical trapping', Journal of the Optical Society of America B, vol. 40, no. 2, pp. 460-460. View/Download from: Publisher's site View description>>
Quantifying the size range of aerosols that can be trapped in a counterpropagation dual-fiber trapping configuration is important in understanding how these particles can be manipulated and characterized in such traps. Here, we present simulations and experiments investigating the trapped aerosol size range variations in the intermediate position of two fibers under different fiber separations, aerosol particle sizes, fiber powers, and radial offset. By doing so, we establish a parametric space plot of stable aerosol trapping, and the parametric analysis provides insight into the tolerance of such traps to trapping fluctuations.
Zhang, L, Xiao, F & Cao, Z 2023, 'Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory', Information Sciences, vol. 642, pp. 119107-119107. View/Download from: Publisher's site
Zhang, L, Xing, X, Liu, Y, Shi, W & Wang, M 2023, 'Directional methanolysis of kitchen waste for the co-production of methyl levulinate and fatty acid methyl esters: Catalytic strategy and machine learning modeling', Bioresource Technology, vol. 367, pp. 128274-128274. View/Download from: Publisher's site
Zhang, L, Zhang, X, Wang, Q, Wu, W, Chang, X & Liu, J 2023, 'RPMG-FSS: Robust Prior Mask Guided Few-Shot Semantic Segmentation', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 11, pp. 6609-6621. View/Download from: Publisher's site
Zhang, L, Zhang, X, Zhang, W, Huang, Z, Fang, F, Li, J, Yang, L, Gu, C, Sun, W, Gao, M, Pan, H & Liu, Y 2023, 'Nanoparticulate ZrNi: In Situ Disproportionation Effectively Enhances Hydrogen Cycling of MgH2', ACS Applied Materials & Interfaces, vol. 15, no. 34, pp. 40558-40568. View/Download from: Publisher's site View description>>
High thermal stability and sluggish absorption/desorption kinetics are still important limitations for using magnesium hydride (MgH2) as a solid-state hydrogen storage medium. One of the most effective solutions in improving hydrogen storage properties of MgH2 is to introduce a suitable catalyst. Herein, a novel nanoparticulate ZrNi with 10-60 nm in size was successfully prepared by co-precipitation followed by a molten-salt reduction process. The 7 wt % nano-ZrNi-catalyzed MgH2 composite desorbs 6.1 wt % hydrogen starting from ∼178 °C after activation, lowered by 99 °C relative to the pristine MgH2 (∼277 °C). The dehydrided sample rapidly absorbs ∼5.5 wt % H2 when operating at 150 °C for 8 min. The remarkably improved hydrogen storage properties are reasonably ascribed to the in situ formation of ZrH2, ZrNi2, and Mg2NiH4 caused by the disproportionation reaction of nano-ZrNi during the first de-/hydrogenation cycle. These catalytic active species are uniformly dispersed in the MgH2 matrix, thus creating a multielement, multiphase, and multivalent environment, which not only largely favors the breaking and rebonding of H-H bonds and the transfer of electrons between H- and Mg2+ but also provides multiple hydrogen diffusion channels. These findings are of particularly scientific importance for the design and preparation of highly active catalysts for hydrogen storage in light-metal hydrides.
Zhang, L, Zhu, T, Hussain, FK, Ye, D & Zhou, W 2023, 'A Game-Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Information Forensics and Security, vol. 18, no. 99, pp. 1349-1364. View/Download from: Publisher's site View description>>
Advanced persistent threats (APTs) are one of today's major threats to cyber security. Highly determined attackers along with novel and evasive exfiltration techniques mean APT attacks elude most intrusion detection and prevention systems. The result has been significant losses for governments, organizations, and commercial entities. Intriguingly, despite greater efforts to defend against APTs in recent times, frequent upgrades in defense strategies are not leading to increased security and protection. In this paper, we demonstrate this phenomenon in an appropriately designed APT rivalry game that captures the interactions between attackers and defenders. What is shown is that the defender's strategy adjustments actually leave useful information for the attackers, and thus intelligent and rational attackers can improve themselves by analyzing this information. Hence, a critical part of one's defense strategy must be finding a suitable time to adjust one's strategy to ensure attackers learn the least possible information. Another challenge for defenders is determining how to make the best use of one's resources to achieve a satisfactory defense level. In support of these efforts, we figured out the optimal timings of a player's strategy adjustment in terms of information leakage, which form a family of Nash equilibria. Moreover, two learning mechanisms are proposed to help defenders find an appropriate defense level and allocate their resources reasonably. One is based on adversarial bandits, and the other is based on deep reinforcement learning. Experimental simulations show the rationales behind the game and the optimality of the equilibria. The results also demonstrate that players indeed have the ability to improve themselves by learning from past experiences, which shows the necessity of specifying optimal strategy adjustment timings when defending against APTs.
Zhang, L-C, Chen, L-Y, Zhou, S & Luo, Z 2023, 'Powder bed fusion manufacturing of beta-type titanium alloys for biomedical implant applications: A review', Journal of Alloys and Compounds, vol. 936, pp. 168099-168099. View/Download from: Publisher's site
Zhang, M, Zhou, J, Zhang, G, Cui, L, Gao, T & Yu, S 2023, 'APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks', IEEE Transactions on Network Science and Engineering, vol. 10, no. 2, pp. 922-933. View/Download from: Publisher's site View description>>
In the Big Data era, the wide usage of mobile devices has led to large amounts of information release and sharing through social networks, where sensitive information of the data owners may be leaked. Traditional approaches that provide the identical privacy protection levels for all users result in poor quality of service, thus the concept of personalized privacy has been proposed in recent years. However, existing methods that add different noises to each user will require both high real-time performance and resource consumption. This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. Specifically, we design a new mechanism that defines the privacy protection levels of different users based on their attribute values. In particular, we exploit the TOPSIS method to map the attribute values to the amount of noise required to add. Furthermore, to prevent illegal download of data, the access control is integrated with differential privacy. Compared with traditional attribute-based encryption data publishing schemes, our scheme can get rid of the expensive computation overhead. Theoretical analyses and simulations show that APDP can realize efficient personalized differential privacy data publishing with reasonable data utility.
Zhang, Q, Hu, J, Yang, C, Li, J, Liu, N, Guo, W, Dai, C, Wang, L, Tian, Y & Ngo, HH 2023, 'Preparation of boronate affinity controllable-oriented polysaccharides magnetic molecularly imprinted polymer and its application for membrane flux improvement', Journal of Environmental Chemical Engineering, vol. 11, no. 5, pp. 110370-110370. View/Download from: Publisher's site
Zhang, Q, Liao, W, Zhang, G, Yuan, B & Lu, J 2023, 'A Deep Dual Adversarial Network for Cross-Domain Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3266-3278. View/Download from: Publisher's site View description>>
Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions has been cross-domain recommender systems, which supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation. By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines.
Zhang, S, Chen, X, Wen, S & Li, Z 2023, 'Density-based reliable and robust explainer for counterfactual explanation', Expert Systems with Applications, vol. 226, pp. 120214-120214. View/Download from: Publisher's site
Zhang, S, Ding, L, Xie, M, He, X, Yang, R & Tong, C 2023, 'Reliability analysis of slope stability by neural network (NN), principal component analysis (PCA), and transfer learning (TL) techniques', Journal of Rock Mechanics and Geotechnical Engineering. View/Download from: Publisher's site
Zhang, S, Shi, J, Li, X, Coin, L, O'Brien, JW, Sivakumar, M, Hai, F & Jiang, G 2023, 'Triplex qPCR assay for Campylobacter jejuni and Campylobacter coli monitoring in wastewater', Science of The Total Environment, vol. 892, pp. 164574-164574. View/Download from: Publisher's site
Zhang, S, Shi, J, Li, X, Tiwari, A, Gao, S, Zhou, X, Sun, X, O'Brien, JW, Coin, L, Hai, F & Jiang, G 2023, 'Wastewater-based epidemiology of Campylobacter spp.: A systematic review and meta-analysis of influent, effluent, and removal of wastewater treatment plants', Science of The Total Environment, vol. 903, pp. 166410-166410. View/Download from: Publisher's site View description>>
Campylobacter spp. is one of the four leading causes of diarrhoeal diseases worldwide, which are generally mild but can be fatal in children, the elderly, and immunosuppressed persons. The existing disease surveillance for Campylobacter infections is usually based on untimely clinical reports. Wastewater surveillance or wastewater-based epidemiology (WBE) has been developed for the early warning of disease outbreaks and the detection of the emerging new variants of human pathogens, especially after the global pandemic of COVID-19. However, the WBE monitoring of Campylobacter infections in communities is rare due to a few large data gaps. This study is a meta-analysis and systematic review of the prevalence of Campylobacter spp. in various wastewater samples, primarily the influent of wastewater treatment plants. The results showed that the overall prevalence of Campylobacter spp. was 53.26 % in influent wastewater and 52.97 % in all types of wastewater samples. The mean concentration in the influent was 3.31 ± 0.39 log10 gene copies or most probable number (MPN) per 100 mL. The detection method combining culture and PCR yielded the highest positive rate of 90.86 %, while RT-qPCR and qPCR were the two most frequently used quantification methods. In addition, the Campylobacter concentration in influent wastewater showed a seasonal fluctuation, with the highest concentration in the autumn at 3.46 ± 0.41 log10 gene copies or MPN per 100 mL. Based on the isolates of all positive samples, Campylobacter jejuni (62.34 %) was identified as the most prevalent species in wastewater, followed by Campylobacter coli (30.85 %) and Campylobacter lari (4.4 %). These findings provided significant data to further develop and optimize the wastewater surveillance of Campylobacter spp. infections. In addition, large data gaps were found in the decay of Campylobacter spp. in wastewater, indicating insufficient research on the persistence of Campylobacter spp. in wastewater.
Zhang, S, Shi, J, Sharma, E, Li, X, Gao, S, Zhou, X, O'Brien, J, Coin, L, Liu, Y, Sivakumar, M, Hai, F & Jiang, G 2023, 'In-sewer decay and partitioning of Campylobacter jejuni and Campylobacter coli and implications for their wastewater surveillance', Water Research, vol. 233, pp. 119737-119737. View/Download from: Publisher's site
Zhang, S, Zhang, T, Su, J & Sheng, D 2023, 'Mesoscopic theoretical and numerical study of particle migration in porous media', Journal of Railway Science and Engineering, vol. 20, no. 6, pp. 2103-2111. View/Download from: Publisher's site View description>>
Particle migrations in porous media emerge in many engineering problems, and exert influence phenomena like structure changes of soil and pollutants transportation in soils. There are few theoretical analyses for these migration processes from the mesoscopic aspects of particle motion and porous media structure. Therefore, theoretical methods and simulation were implemented here to study this problem. In the theoretical aspect, the motion of the particle was treated as a random process, and the one-dimensional convection-diffusion equation was derived from the mesoscopic view. Diffusion coefficient and convection coefficient were expressed as functions of the parameters p and α, where p represents particle mesoscopic motion probability and α represents the blocking effect of porous media. In the aspect of simulation, the relation between α and connectivity of porous media gets studied by embedding random walk into percolation configuration. Theoretical results show that convection-diffusion equation can be used to describe the macroscopic transportation of particles, which mesoscopic motions are random. Simulation shows that, when connected probability P is larger than 0.5, convection-diffusion equation can well describe the process of particle migration, and there exists quadratic function relation between α and P. When P is less than 0.5, clogging phenomenon appears and theoretical result becomes not applicable gradually.
Zhang, T, Ye, D, Zhu, T, Liao, T & Zhou, W 2023, 'Evolution of cooperation in malicious social networks with differential privacy mechanisms', Neural Computing and Applications, vol. 35, no. 18, pp. 12979-12994. View/Download from: Publisher's site View description>>
Zhang, W, Pradhan, B, Stuyts, B & Xu, C 2023, 'Application of artificial intelligence in geotechnical and geohazard investigations', Geological Journal, vol. 58, no. 6, pp. 2187-2194. View/Download from: Publisher's site View description>>
The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.
Zhang, X & Far, H 2023, 'Seismic Response of High-Rise Frame–Shear Wall Buildings under the Influence of Dynamic Soil–Structure Interaction', International Journal of Geomechanics, vol. 23, no. 9. View/Download from: Publisher's site View description>>
Frame-shear wall buildings with multiple basements are the most commonly used structural form of high-rise buildings in the world today. In the traditional design method, structures are usually assumed as rigid base structures without considering soil-structure interaction (SSI), since incorporating the dynamic SSI tends to prolong natural periods and increase the damping of the system, which are considered beneficial for the seismic response of structures. However, recent studies exposed a potentially harmful aspect of SSI. In this study, a soil-foundation-structure model developed in finite element software and verified by shaking table tests is used to critically investigate the influence of SSI on high-rise frame-shear wall structures with a series of superstructure and substructure parameters. The beneficial and detrimental impacts of SSI are identified and discussed. Numerical simulation results indicate the rise in the stiffness of subsoil can dramatically amplify the base shear of structures. As the foundation rotation increases, inter-storey drifts are increased and base shears are reduced. In general, SSI amplifies the inter-storey drifts showing detrimental effects of SSI. However, as for the base shear, SSI exerts detrimental effects on most piled foundation cases as well as classical compensated foundation structures founded on Ce soil, whereas, for classical compensated foundation structures founded on soil types De and Ee, effects of SSI are beneficial since the base shear is reduced. Moreover, regarding structures with different foundation types, minimum base shear ratios considering the SSI reduction effect are presented.
Zhang, X, Huang, H, Du, Q, Gao, F, Wang, Z, Wu, G, Guo, W & Hao Ngo, H 2023, 'Performance of a recirculated biogas-sparging anaerobic membrane bioreactor system for treating synthetic swine wastewater containing sulfadiazine antibiotic', Chemical Engineering Journal, vol. 476, pp. 146735-146735. View/Download from: Publisher's site
Zhang, X, Li, Y, Wang, J, Xu, G & Gu, Y 2023, 'A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction', Interdisciplinary Sciences: Computational Life Sciences, vol. 15, no. 4, pp. 696-709. View/Download from: Publisher's site
Zhang, X, Lin, Y, Zhang, L, Huang, Z, Yang, L, Li, Z, Yang, Y, Gao, M, Sun, W, Pan, H & Liu, Y 2023, 'Hydrogen-assisted one-pot synthesis of ultrasmall TiC nanoparticles enhancing hydrogen cycling of sodium alanate', Chemical Engineering Journal, vol. 462, pp. 142199-142199. View/Download from: Publisher's site
Zhang, X, Peng, H, Zhang, J & Wang, Y 2023, 'A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification', Expert Systems with Applications, vol. 213, pp. 119073-119073. View/Download from: Publisher's site
Zhang, X, Wang, H, Yu, J, Chen, C, Wang, X & Zhang, W 2023, 'Bipartite graph capsule network', World Wide Web, vol. 26, no. 1, pp. 421-440. View/Download from: Publisher's site View description>>
AbstractGraphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are preserved in the graphs. Meanwhile, the bipartite graphs that model the complex relationships among different entities with vertices partitioned into two disjoint sets, are becoming increasing popular and ubiquitous in many real life applications. Though several graph classification methods on unipartite and homogenous graphs have been proposed by using kernel method, graph neural network, etc. However, these methods are unable to effectively capture the hidden information in bipartite graphs. In this paper, we propose the first bipartite graph-based capsule network, namely Bipartite Capsule Graph Neural Network (BCGNN), for the bipartite graph classification task. BCGNN exploits the capsule network and obtains information between the same type vertices in the bipartite graphs by constructing the one-mode projection. Extensive experiments are conducted on real-world datasets to demonstrate the effectiveness of our proposed method.
Zhang, X, Wang, X, Wan, C, Yang, B, Tang, Z & Li, W 2023, 'Performance evaluation of asphalt binder and mixture modified by pre-treated crumb rubber', Construction and Building Materials, vol. 362, pp. 129777-129777. View/Download from: Publisher's site View description>>
This work presents a laboratory and field study on the performance of the asphalt binder and mixture modified by pre-treated crumb rubber. Initially, a mechano-chemical method was employed to pre-treat crumb rubber, intending to increase its surface activity and compatibility with asphalt binder. Subsequently, the properties of asphalt binders and mixtures modified by the pre-treated crumb rubber were evaluated systematically. Test results showed that the incorporation of pre-treated crumb rubber resulted in the reduced penetration value, higher softening point, enhanced tensile performance, improved elastic recovery, and greater aging resistance of asphalt binder. Additionally, the asphalt mixtures modified by pre-treated crumb rubber demonstrated excellent road performance. Furthermore, the developed asphalt mixtures were successfully applied in practical engineering, validating the feasibility and practicality of the modified asphalt mixture by pre-treated crumb rubber.
Zhang, X, Xia, W, Cui, Q, Tao, X & Liu, RP 2023, 'Efficient and Trusted Data Sharing in a Sharding-Enabled Vehicular Blockchain', IEEE Network, vol. 37, no. 2, pp. 230-237. View/Download from: Publisher's site
Zhang, X, Xie, W, Li, Y, Lei, J & Du, Q 2023, 'Filter Pruning via Learned Representation Median in the Frequency Domain', IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3165-3175. View/Download from: Publisher's site
Zhang, X, Zhang, X, Zhang, L, Huang, Z, Fang, F, Yang, Y, Gao, M, Pan, H & Liu, Y 2023, 'Remarkable low-temperature hydrogen cycling kinetics of Mg enabled by VH nanoparticles', Journal of Materials Science & Technology, vol. 144, pp. 168-177. View/Download from: Publisher's site View description>>
Nanoscaled catalysts have attracted much more attention due to their more abundant active sites and better dispersion than their bulky counterparts. In this work, VHx nanoparticles smaller than 10 nm in average size are successfully synthesized by a simple solid-state ball milling coupled with THF washing process, which are proved to be highly effective in enhancing the hydrogen absorption/desorption kinetics of MgH2 at moderate temperatures. The nano-VHx-modified MgH2 releases hydrogen from 182 °C, which is 88 °C lower than additive-free MgH2. The release of hydrogen amounts to 6.3 wt% H within 10 min at 230 °C and 5.6 wt% H after 30 min at 215 °C with initial vacuum. More importantly, the dehydrogenated MgH2+10 wt.% nano-VHx rapidly absorbs 5.2 wt% H within 3 min at 50 °C under 50 bar H2. It even takes up 4.3 wt% H within 30 min at room temperature (25 °C) under 10 bar H2, exhibiting superior hydrogenation kinetics to most of the previous reports. Mechanistic analyzes disclose the reversible transformation between V and V-H species during the hydrogen desorption-absorption process. The homogeneously distributed V-based species is believed to act as hydrogen pump and nucleation sites for MgH2 and Mg, respectively, thus triggering fast hydrogenation/dehydrogenation kinetics.
Zhang, X, Zhang, X, Zhang, L, Huang, Z, Yang, L, Gao, M, Gu, C, Sun, W, Pan, H & Liu, Y 2023, 'Nb2O5 Nanostructures as Precursors of Cycling Catalysts for Hydrogen Storage in MgH2', ACS Applied Nano Materials, vol. 6, no. 15, pp. 14527-14539. View/Download from: Publisher's site View description>>
High operating temperatures and sluggish kinetics are major obstacles for practical applications of MgH2 as a solid hydrogen carrier. Introducing nanoscaled high-activity catalysts has been effective in improving the hydrogen cycling of MgH2. However, it remains still unclear that between nanoparticle size and morphology, which one is the decisive factor of the catalytic activity of a given catalyst. In this work, we studied this topic by taking nanostructured niobium oxide (Nb2O5) as a representative sample. Five types of Nb2O5 catalytic additives with different morphologies and nanosizes were synthesized, and their catalytic activities were compared with commercial microparticles. Our results unambiguously demonstrate that the catalytic activity of Nb2O5 is determined by the primary particle size rather than the morphology and structure because the ultrasmall Nb2O5 nanoparticles that measured ∼5 nm in size enable dehydrogenation of MgH2 starting at 165 °C after one-cycle activation. The smaller nanoparticle sizes not only enhance the reactivity of Nb2O5 but also lead to more uniform dispersion when ball-milled with MgH2, which enables in situ formation of more homogeneous and finer Nb-based active species and therefore much higher catalytic activity. This important insight will guide the design and optimization of novel high-activity catalysts for hydrogen cycling of MgH2 and other hydrogen storage materials.
Zhang, X, Zhao, Z & Li, J 2023, 'ARDE-N-BEATS: An Evolutionary Deep Learning Framework for Urban Traffic Flow Prediction', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2391-2403. View/Download from: Publisher's site
Zhang, X, Zuo, S, Li, S, Shang, Y, Du, Q, Wang, H, Guo, W & Ngo, HH 2023, 'Responses of biofilm communities in a hybrid moving bed biofilm reactor-membrane bioreactor system to sulfadiazine antibiotic exposure', Bioresource Technology, vol. 382, pp. 129126-129126. View/Download from: Publisher's site
Zhang, Y, Afroz, S, Nguyen, QD, Kim, T, Castel, A & Xu, T 2023, 'Modeling blended cement concrete tensile creep for standard ring test application', Structural Concrete, vol. 24, no. 2, pp. 2170-2188. View/Download from: Publisher's site View description>>
AbstractThe tensile creep of concrete with supplementary cementitious materials (SCMs) such as fly ash (FA) and ground granulated blast furnace slag (GGBFS) is investigated in this paper. A total of 21 series of tensile creep tests using dog‐bone specimens under uniaxial tensile loading have been carried out. The cement replacement rates considered were 30% fly ash, 40% and 60% GGBFS. The characteristic compressive strength of concrete was ranging from 25 to 100 MPa. The tests were conducted from the age of 2 days until 28 days. It is observed that the tensile creep of fly ash concretes was slightly lower than that of the reference mixtures without SCM. For GGBFS concrete, the higher the GGBFS content, the higher the tensile creep. Existing creep models, originally developed for creep in compression, could not predict the experimental tensile creep results. Thus, a new tensile creep model was proposed including creep prediction for concrete with fly ash and GGBFS. The new model was calibrated only for controlled environmental conditions (23°C and 50% RH) and has been validated by analyzing the development of concrete tensile stress in the restrained ring test.
Zhang, Y, Bai, G, Li, X, Nepal, S, Grobler, M, Chen, C & Ko, RKL 2023, 'Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3341-3357. View/Download from: Publisher's site
Zhang, Y, Falque, R, Zhao, L, Chen, Y, Huang, S & Li, H 2023, 'Structure-to-Shape Aortic 3-D Deformation Reconstruction for Endovascular Interventions', IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2954-2972. View/Download from: Publisher's site View description>>
Fluoroscopy-guided endovascular interventions by using X-ray images are challenging. The catheter needs to be manipulated precisely inside the aorta, while only 2-D views from the X-ray fluoroscopy are currently used to help the surgeons. Because the catheter is operated in a 3-D space, a visualization of the deforming 3-D aorta will be useful as guidance for catheter manipulation. Existing 3-D reconstruction methods fall short in only focusing on the deformation reconstruction of the aortic 3-D centerline, or using additional prior knowledge of 3-D catheter position for estimating the aortic 3-D deformation. In this article, we propose a novel framework that reconstructs the aortic 3-D deformation by fusing a preoperative 3-D model and two intraoperative X-ray images. Different from existing methods, the proposed framework reconstructs aortic deformation using a coarse-to-fine pipeline by first reconstructing the aortic 3-D centerline and then reconstructing the 3-D shape. To obtain the accurate features for the fluoroscopic-based 3-D reconstruction, we extract semantic features from the X-ray images, and compute the distance field to efficiently calculate the 3-D-2-D nonrigid correspondence. Nonlinear least squares optimization is used to solve the deformation of both centerline and shape. The proposed framework is validated using phantom and patient datasets, whose results demonstrate improved efficiency and accuracy compared with the existing methods. This framework provides a valuable clinical tool for endovascular interventions.
Zhang, Y, Feng, K, Ji, JC, Yu, K, Ren, Z & Liu, Z 2023, 'Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 2, pp. 1070-1080. View/Download from: Publisher's site View description>>
Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.
Zhang, Y, Ji, JC, Ren, Z, Ni, Q & Wen, B 2023, 'Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions', Mechanical Systems and Signal Processing, vol. 191, pp. 110172-110172. View/Download from: Publisher's site View description>>
Domain adaptation techniques have the proven ability to deal with fault diagnosis issues under variable operating conditions. They can achieve a superb diagnostic performance in single-sensor monitoring scenarios where the training and test data share the same label space. However, in practical engineering, fault modes are usually mixed with each other and new failure modes may appear during operation, which poses a challenge to the effectiveness of existing cross-domain fault diagnosis methods. Furthermore, with the increasing complexity of modern industrial systems, multi-sensor collaborative monitoring has been increasingly deployed for comprehensive measurement and detection of the complicated system. Unfortunately, there is less attention paid to multi-sensor cross-domain diagnosis in the current literature. To bridge this research gap, this paper aims to develop a novel multi-sensor open-set cross-domain fault diagnosis method. First, a convolutional neural network-based single-sensor feature extraction module and a Transformer-based multi-sensor feature fusion module are constructed for discriminative feature extraction and fusion. Second, a weighted adversarial learning scheme is built to conduct domain-invariant learning of the shared fault modes between the source and target domains. Then, a threshold-based supervised contrastive loss is defined to realize instance-level domain alignment, together with an entropy max–min loss to identify unknown class samples. The effectiveness and practicability of the proposed method are validated by a series of experiments conducted on two different types of gearboxes.
Zhang, Y, Ji, JC, Ren, Z, Ni, Q, Gu, F, Feng, K, Yu, K, Ge, J, Lei, Z & Liu, Z 2023, 'Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing', Reliability Engineering & System Safety, vol. 234, pp. 109186-109186. View/Download from: Publisher's site View description>>
Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings.
Zhang, Y, Liu, H, Zhao, S, Xie, C, Huang, Z & Wang, S 2023, 'Insights into the Dynamic Evolution of Defects in Electrocatalysts', Advanced Materials, vol. 35, no. 9, pp. 2209680-2209680. View/Download from: Publisher's site View description>>
AbstractThis review focuses on the formation and preparation of defects, the dynamic evolution process of defects, and the influence of defect dynamic evolution on catalytic reactions. The summary of the current advances in the dynamic evolution process of defects in oxygen evolution reaction, hydrogen evolution reaction, nitrogen reduction reaction, oxygen reduction reaction, and carbon dioxide reduction reaction, and the given perspectives are expected to provide a more comprehensive understanding of defective electrocatalysts on the structural evolution process during electrocatalysis and the reaction mechanisms, especially for the defect dynamic evolution on the performance in catalytic reactions.
Zhang, Y, Peng, Q, Wang, C, Huang, Y, Zhou, P, Qian, Y, Ye, B, Indra Mahlia, TM & Chyuan Ong, H 2023, 'State-of-the-art modeling of two-stage auto-ignition: Turbulence, evaporation and chemistry effects', Energy Conversion and Management, vol. 291, pp. 117269-117269. View/Download from: Publisher's site View description>>
Internal combustion engines are the dominant power sources in transport, accounting for significant amounts of fuel consumption and pollutant emissions. Low-temperature combustion is a promising technology for engine combustion, whose main challenge is the complex control of two-stage auto-ignition that determines the performance of a low-temperature combustion engine. This paper systematically reviews the state-of-the-art advances in auto-ignition modeling which is an essential tool to understand auto-ignition mechanisms and provides valuable guidance for designing more efficient and cleaner engines. This paper focuses on turbulence, evaporation and chemistry effects without the consideration of inter-droplet interactions. Five models with increasing complexity are discussed and compared, including homogeneous models without and with evaporation (models 1 and 2), droplet simulation in static environments (model 3), and direct numerical simulation without and with evaporation (models 4 and 5). Rapid mixing leads to homogeneous conditions in models 1 and 2, in which two-stage auto-ignition is divided into low-temperature induction, low-temperature auto-ignition, high-temperature induction and high-temperature auto-ignition. Model 1 only considers chemical reactions and auto-ignition is determined for a certain thermal state. Droplet evaporation affects the auto-ignition evolution in model 2 through evaporation-induced changes in the thermal state. Compared with homogeneous models, droplet evaporation in model 3 leads to compositional and temperature stratifications which cause three new phenomena: preferential auto-ignition, reaction front propagation and non-zero scalar dissipation rate. Models 4 and 5 introduce turbulent effects on induction timescale and front propagation. Finally, challenges and future directions in auto-ignition modeling are outlined.
Zhang, Y, Wen, G, Rahmani, A, Peng, Z & Wen, S 2023, 'Observer-Based Intra-Cluster Lag Consensus of Multi-Agent Systems via Intermittent Adaptive Pinning Control', Journal of Systems Science and Complexity, vol. 36, no. 5, pp. 1809-1829. View/Download from: Publisher's site
Zhang, Y, Wu, M, Zhang, G & Lu, J 2023, 'Stepping beyond your comfort zone: Diffusion‐based network analytics for knowledge trajectory recommendation', Journal of the Association for Information Science and Technology, vol. 74, no. 7, pp. 775-790. View/Download from: Publisher's site View description>>
AbstractPredicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter‐/cross‐/multi‐disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion‐based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co‐topic layer and a co‐authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments—one with a local dataset and the other with a global dataset—demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
Zhang, Y, Zhang, H, Qiu, L, Wang, Z, Zhang, S, Qiu, N & Fang, J 2023, 'A stochastic framework for computationally efficient fail-safe topology optimization', Engineering Structures, vol. 283, pp. 115831-115831. View/Download from: Publisher's site
Zhang, Z, Song, Y, Huang, S, Xiong, R & Wang, Y 2023, 'Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design', IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2892-2911. View/Download from: Publisher's site View description>>
This article focuses on designing a consistent and efficient filter for visual-inertial localization given a prebuilt map. First, we propose a new Lie group with its algebra based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of map information, the proposed invariant EKF is able to naturally preserve the correct observability properties of the system. To consider the uncertainty of map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of map information can be taken into consideration to avoid overconfident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.
Zhang, Z, Wang, L, Wang, Y, Zhou, L, Zhang, J & Chen, F 2023, 'Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 247-263. View/Download from: Publisher's site View description>>
Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel '`base-detector repository'' and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.
Zhang, Z, Wei, Y, Zhang, H, Yang, Y, Yan, S & Wang, M 2023, 'Data-Driven single image deraining: A Comprehensive review and new perspectives', Pattern Recognition, vol. 143, pp. 109740-109740. View/Download from: Publisher's site
Zhang, Z, Wu, L, Ma, C, Li, J, Wang, J, Wang, Q & Yu, S 2023, 'LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 365-379. View/Download from: Publisher's site
Zhang, Z, Xu, Z, McGuire, HM, Essam, C, Nicholson, A, Hamilton, TJ, Li, J, Eshraghian, JK, Yong, K-T, Vigolo, D & Kavehei, O 2023, 'Neuromorphic cytometry: implementation on cell counting and size estimation', Neuromorphic Computing and Engineering, vol. 3, no. 4, pp. 044005-044005. View/Download from: Publisher's site View description>>
AbstractImaging flow cytometry (FC) is a powerful analytic tool that combines the principles of conventional FC with rich spatial information, allowing more profound insight into single-cell analysis. However, offering such high-resolution, full-frame feedback can restrain processing speed and has become a significant trade-off during development. In addition, the dynamic range (DR) offered by conventional photosensors can only capture limited fluorescence signals, which compromises the detection of high-velocity fluorescent objects. Neuromorphic photo-sensing focuses on the events of interest via individual-firing pixels to reduce data redundancy and latency. With its inherent high DR, this architecture has the potential to drastically elevate the performance in throughput and sensitivity to fluorescent targets. Herein, we presented an early demonstration of neuromorphic cytometry, demonstrating the feasibility of adopting an event-based resolution in describing spatiotemporal feedback on microscale objects and for the first time, including cytometric-like functions in object counting and size estimation to measure 8 µm, 15 µm microparticles and human monocytic cell line (THP-1). Our work has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting microparticles. Moreover, the capacity of an event-based photosensor in registering fluorescent signals was evaluated by recording 6 µm Fluorescein isothiocyanate-marked particles in different lighting conditions, revealing superior performance compared to a standard photosensor. Although the current platform cannot deliver multiparametric measurements on cells, future endeavours will include further functionalities and increase the measurement parameters (granularity, cell condition, fluorescence analysis) to enrich cell interpretation.
Zhao, F, Cao, S, Luo, Q & Ji, J 2023, 'Enhanced design of the quasi-zero stiffness vibration isolator with three pairs of oblique springs: Theory and experiment', Journal of Vibration and Control, vol. 29, no. 9-10, pp. 2049-2063. View/Download from: Publisher's site View description>>
Quasi-zero stiffness vibration isolators have been extensively studied due to superior passive vibration isolation performance. As the quasi-zero stiffness region of the isolators is generally small, the research on their responses to the excitation with high amplitude is currently quite limited. This paper presents an improved design of the quasi-zero stiffness isolator with three pairs of oblique springs to increase the amplitude of the excitation. Theoretical formulations are derived for stiffness and force, and then the influences of three independent parameters on the quasi-zero stiffness region are studied to obtain optimal design parameters. A prototype is fabricated and tested for displacement excitations with amplitudes of 5 mm, 10 mm, and 15 mm in a frequency range of 1.5–10 Hz. The absolute displacement transmissibility of the enhanced quasi-zero stiffness isolator is theoretically and experimentally compared with that of the corresponding linear isolator and that of the previous isolators with three pairs of oblique springs using the same parameter conditions of the loaded mass, the horizontal length of oblique springs, and the vertical spring. The experimental results show that the enhanced design of the quasi-zero stiffness isolator with three pairs of oblique springs can achieve lower displacement transmissibility and deal with the displacement excitation with higher amplitude.
Zhao, H, Li, W, Gan, Y, Wang, K & Luo, Z 2023, 'Nano/microcharacterization and image analysis on bonding behaviour of ITZs in recycled concrete enhanced with waste glass powder', Construction and Building Materials, vol. 392, pp. 131904-131904. View/Download from: Publisher's site
Zhao, H, Zheng, Z, Zhou, H, Chang, L, Tsoutas, K, Yang, L, Alavi, SKH, Liu, Y, Akhavan, B, Bilek, MM & Liu, Z 2023, 'Cathodic arc deposition of high entropy alloy thin films with controllable microstructure', Surfaces and Interfaces, vol. 37, pp. 102692-102692. View/Download from: Publisher's site
Zhao, J, Hosseini, S, Chen, Q & Jahed Armaghani, D 2023, 'Super learner ensemble model: A novel approach for predicting monthly copper price in future', Resources Policy, vol. 85, pp. 103903-103903. View/Download from: Publisher's site
Zhao, L-H, Wen, S, Guo, Z, Shi, K, Xiao, J, Zhu, S & Huang, T 2023, 'Finite-Time Nonchattering Synchronization of Coupled Neural Networks With Multi-Weights', IEEE Transactions on Network Science and Engineering, vol. 10, no. 4, pp. 2212-2225. View/Download from: Publisher's site
Zhao, L-H, Wen, S, Zhu, S, Guo, Z & Huang, T 2023, 'Robust H∞ Pinning Synchronization for Multiweighted Coupled Reaction–Diffusion Neural Networks', IEEE Transactions on Cybernetics, vol. 53, no. 10, pp. 6549-6561. View/Download from: Publisher's site
Zhao, Y, Wang, S, Wang, Y & Liu, H 2023, 'MbSRS: A multi-behavior streaming recommender system', Information Sciences, vol. 631, pp. 145-163. View/Download from: Publisher's site View description>>
Streaming Recommender Systems (SRSs) have emerged to deliver recommendations based on pervasive data streams, which are a sequence of user-item interactions with multiple behavior types (e.g., purchase, add-to-cart, and view). However, existing SRSs all rely on a single behavior type (e.g., purchase) to make streaming recommendations, and commonly suffer from the data sparsity problem. To address this issue, the relatively more abundant multi-behavior interactions (i.e., interactions with multiple behavior types) could be well leveraged for more accurate streaming recommendations. However, it remains a challenge on how to effectively leverage the commonly-existing and complex multi-behavior interactions for improving the accuracy of streaming recommendations. Targeting at this challenge, we propose the first Multi-behavior Streaming Recommender System in the literature, called MbSRS, to elaborately exploit multi-behavior interactions for delivering accurate recommendations in streaming scenarios. In MbSRS, we first learn instant user preferences and unified item characteristics collaboratively from multi-behavior interactions. Then, we attentively learn long-term user preferences from the historical items interacted by the corresponding users. After that, we wisely fuse the learned instant and long-term user preferences via a gate mechanism. Finally, a novel multi-behavior-specific training process is devised for more effectively learning user preferences towards items from multi-behavior interactions. Extensive experiments on three real-world datasets demonstrate that the proposed MbSRS significantly outperforms the state-of-the-art baselines.
Zhao, YC, Zhang, Y, Wang, Z, Jiang, F, Kyanian, K, Aye, S, Hong, T, Vatankhah, P, Nasser, A, Sun, A, Moldovan, L, Su, QP, Cho, A, Wang, Y, Passam, F, Ang, T & Ju, LA 2023, 'Novel Movable Typing for Personalized Vein‐Chips in Large Scale: Recapitulate Patient‐Specific Virchow's Triad and its Contribution to Cerebral Venous Sinus Thrombosis', Advanced Functional Materials, vol. 33, no. 23. View/Download from: Publisher's site View description>>
AbstractThe Vein‐Chip recapitulates CVST Virchow's triad and enables systematic characterization of venous thrombogenesis with respect to fibrin formation and platelet aggregation. Distinct from the arterial setting, platelets universally adhere across the entire CVS Vein‐Chip independent of stenotic geometry and flow disturbance. Intriguingly, fibrin propagates along with the flow direction, but exclusively deposits to the inner vessel wall. Upon inflammatory endothelial injury, fibrin deposition mirrors to the outer vessel wall, but still not in the lumen. Together, the Vein‐Chip promises future applications for personalized thrombotic assessment and monitoring.
Zhao, YC, Zhang, Y, Wang, Z, Jiang, F, Kyanian, K, Aye, S, Hong, T, Vatankhah, P, Nasser, A, Sun, A, Moldovan, L, Su, QP, Cho, A, Wang, Y, Passam, F, Ang, T & Ju, LA 2023, 'Novel Movable Typing for Personalized Vein‐Chips in Large Scale: Recapitulate Patient‐Specific Virchow's Triad and its Contribution to Cerebral Venous Sinus Thrombosis (Adv. Funct. Mater. 23/2023)', Advanced Functional Materials, vol. 33, no. 23. View/Download from: Publisher's site
Zhao, Z, Cao, L & Lin, K-Y 2023, 'Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 15743-15758. View/Download from: Publisher's site
Zhao, Z-C, Fan, S-Q, Lu, Y, Dang, C-C, Wang, X, Liu, B-F, Xing, D-F, Ma, J, Ren, N-Q, Wang, Q & Xie, G-J 2023, 'Reactivated biofilm coupling n-DAMO with anammox achieved high-rate nitrogen removal in membrane aerated moving bed biofilm reactor', Environmental Research, vol. 220, pp. 115184-115184. View/Download from: Publisher's site
Zhen, J, Oehmen, A, Wei, W, Ni, S-Q & Ni, B-J 2023, 'Synergism and physiological characteristics of glycogen accumulating organisms (GAOs) in anaerobic ammonia oxidation based (anammox-based) systems: Mechanisms and prospects', Chemical Engineering Journal, vol. 478, pp. 147316-147316. View/Download from: Publisher's site
Zheng, B, Ji, J, Miao, Z & Zhou, J 2023, 'Achieving Distributed Consensus in Networked Flexible-joint Manipulator Systems via Energy-shaping Scheme', International Journal of Control, Automation and Systems, vol. 21, no. 7, pp. 2323-2337. View/Download from: Publisher's site View description>>
This paper deals with the distributed consensus problem for networked flexible-joint manipulator systems which are formulated by underactuated Euler-Lagrange (EL) dynamics. Based on the energy-shaping scheme of passivity-based control (PBC) with interconnection and damping assignment, a novel decentralized controller is proposed to solve the leaderless and the leader-follower consensus problems. The main feature of the present work is the systematical integration of the energy of the systems composed of underactuated and actuated components and the energy of the controller as a total energy. Then the total energy is formulated as a suitable Lyapunov function to solve distributed consensus problems for the networked underactuated EL systems. The proposed consensus scheme without the need of velocity measurement possesses a relatively simple structure and good robustness. It is shown that interconnection pattern and damping assignment of the PBC are two key factors to affect the cooperative behavior of networked flexible-joint manipulator systems, which will be used to regulate or improve the cooperative performance of networked flexible-joint manipulator systems in practice. Finally, two numerical examples of networked six flexible-joint manipulator systems are presented to validate the correctness of the proposed algorithms.
Zheng, H, Du, X, Liu, Q, Ou, K, Cao, Y, Fang, X, Fu, Q & Sun, Y 2023, 'Self-healing and wide temperature tolerant flexible supercapacitor based on ternary-network organo-hydrogel electrolyte', International Journal of Hydrogen Energy, vol. 48, no. 35, pp. 13264-13275. View/Download from: Publisher's site
Zheng, J, Hang, T, Sun, Z, Jiang, S, Li, Z, Dong, W, Li, X, Li, Y, Sun, A & Chen, Y 2023, 'Temperature-stimulated composite foams for reversibly switching microwave absorption towards electromagnetic interference shielding capability', Diamond and Related Materials, vol. 140, no. Part B, pp. 110499-110499. View/Download from: Publisher's site
Zheng, J, Ma, F-J, Liao, C, Bing, J, Tang, S, Soufiani, AM, Lee Chin, R, Xue, C, Qu, J, Yang, L, Mahmud, MA, Sun, Z, Leung, TL, Wang, G, Cairney, JM, Bremner, S, McKenzie, DR, Huang, S & Ho-Baillie, AWY 2023, 'Efficient perovskite solar cell on steel enabled by diffusion barrier and surface passivation', Cell Reports Physical Science, vol. 4, no. 9, pp. 101543-101543. View/Download from: Publisher's site
Zheng, J, Ying, W, Pan, H, Tong, J, Liu, Q & Ji, J 2023, 'Improved Holo-Hilbert Spectrum Analysis-Based Fault Diagnosis Method for Rotating Machines', Journal of Mechanical Engineering, vol. 59, no. 1, pp. 162-162. View/Download from: Publisher's site View description>>
Although the time-frequency analysis method can extract both the time and frequency domains information of vibration signal for the faulty equipment simultaneously, its use in reflecting the cross-scale coupling relationship between the amplitude-modulation and frequency-modulation characteristics of the nonlinear vibration signal has so far been hindered, and it is prone to be interfered by noises. On this base, the Holo-Hilbert spectral analysis (HHSA) method is innovatively introduced into mechanical fault diagnosis. The internal modulation characteristics of vibration signals can be completely described by the HHSA method with double-layer empirical mode decomposition (EMD) structure, making it an ideal tool for detecting the local faults of mechanical components. At the same time, to improve the diagnosis accuracy of HHSA and suppress the noise interference and the mode aliasing caused by EMD, an improved HHSA (IHHSA) method based on improved regenerated phase shifted sinusoidal assisted EMD (IRPSEMD) is proposed. The usefulness of the IHHSA method for local fault feature diagnosis are validated by the analysis of simulation signals. Finally, the IHHSA method is applied to the detection of gear crack fault and the diagnosis of rolling bearings with local fault. The results show that the internal modulation relationship of nonlinear fault vibration signal can be reflected by the proposed IHHSA method comprehensively, which shows a better fault identification ability.
Zheng, Z, Zheng, L, Yang, Y & Wu, F 2023, 'U-Turn: Crafting Adversarial Queries with Opposite-Direction Features', International Journal of Computer Vision, vol. 131, no. 4, pp. 835-854. View/Download from: Publisher's site
Zhong, J, Zhuang, T, Li, M, Kirby, R, Karimi, M, Lu, J & Zhang, D 2023, 'Sidelobe Suppression for a Steerable Parametric Source Using the Sparse Random Array Technique', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 3152-3161. View/Download from: Publisher's site
Zhong, L, Fang, Z, Liu, F, Yuan, B, Zhang, G & Lu, J 2023, 'Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 3859-3873. View/Download from: Publisher's site
Zhou, C, Lyu, B, Feng, Y & Hoang, DT 2023, 'Transmit Power Minimization for STAR-RIS Empowered Symbiotic Radio Communications', IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1641-1656. View/Download from: Publisher's site View description>>
In this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered transmission scheme for symbiotic radio (SR) systems to make more flexibility for network deployment and enhance system performance. The STAR-RIS is utilized to not only beam the primary signals from the base station (BS) towards multiple primary users on the same side of the STAR-RIS, but also achieve the secondary transmission to the secondary users on another side. We consider both the broadcasting signal model and unicasting signal model at the BS. For each model, we aim for minimizing the transmit power of the BS by designing the active beamforming and simultaneous reflection and transmission coefficients under the practical phase correlation constraint. To address the challenge of solving the formulated problem, we propose a block coordinate descent based algorithm with the semidefinite relaxation, penalty dual decomposition and successive convex approximation methods, which decomposes the original problem into one sub-problem about active beamforming and the other sub-problem about simultaneous reflection and transmission coefficients, and iteratively solve them until the convergence is achieved. Numerical results indicate that the proposed scheme can reduce up to 150.6% transmit power compared to the backscattering device enabled scheme.
Zhou, G, Duan, X, Chang, J, Bo, Y & Huang, Y 2023, 'Investigation of CH4/CO2 competitive adsorption-desorption mechanisms for enhanced shale gas production and carbon sequestration using nuclear magnetic resonance', Energy, vol. 278, pp. 127964-127964. View/Download from: Publisher's site View description>>
Understanding the competitive adsorption-desorption mechanisms in shale is of fundamental significance for enhancing CH4 recovery and CO2 sequestration. This study adopted nuclear magnetic resonance to reveal the influence of CO2 on adsorption-desorption behaviors of CH4 in plug-sized samples. Three distinctive peaks were observed in the transverse relaxation time (T2) spectrum of a CH4-saturated sample, which indicated the adsorbed CH4 (0.1 ms < T2 < 1 ms), free state CH4 in pores (2 ms < T2 < 30 ms) and free state CH4 in fractures (100 ms < T2 < 1000 ms). When CH4 reached adsorption equilibration under 20 MPa, the total T2 signals of adsorbed CH4, free state CH4 in pores and free state CH4 in fractures were 565.3, 591.5 and 306.6 p. u., respectively. Subsequently, CO2 was pumped into the CH4-saturated sample under 22 MPa. When CO2–CH4 completed the competitive adsorption process, T2 signals decreased from 565.3 to 396.6 p. u. for adsorbed CH4, increased from 591.5 to 707.3 p. u. for free state CH4 in pores, and increased from 306.6 to 359.5 p. u. for free state CH4 in fractures. Afterwards, the desorption of shale sample began. CH4 concentration decreased from 79% to 55% while CO2 concentration increased from 21% to 45%. Finally, the total desorption rate of adsorbed CH4 (65%) was much higher than that without introducing CO2 (25%–40%).
Zhou, H, Long, Y, Gong, S, Zhu, K, Hoang, DT & Niyato, D 2023, 'Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 986-1001. View/Download from: Publisher's site View description>>
Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this paper, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.
Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2023, 'Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data', Environmental Modelling & Software, vol. 165, pp. 105724-105724. View/Download from: Publisher's site
Zhou, J & Chen, F 2023, 'AI ethics: from principles to practice', AI & SOCIETY, vol. 38, no. 6, pp. 2693-2703. View/Download from: Publisher's site
Zhou, J, Duan, Y, Zou, Y, Chang, Y-C, Wang, Y-K & Lin, C-T 2023, 'Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2140-2153. View/Download from: Publisher's site
Zhou, J, Peng, H, Su, S & Song, R 2023, 'Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot', IEEE Transactions on Biomedical Engineering, vol. 70, no. 6, pp. 1858-1868. View/Download from: Publisher's site
Zhou, J, Sheppard-Law, S, Xiao, C, Smith, J, Lamb, A, Axisa, C & Chen, F 2023, 'Leveraging twitter data to understand nurses’ emotion dynamics during the COVID-19 pandemic', Health Information Science and Systems, vol. 11, no. 1, p. 28. View/Download from: Publisher's site View description>>
AbstractThe nursing workforce is the largest discipline in healthcare and has been at the forefront of the COVID-19 pandemic response since the outbreak of COVID-19. However, the impact of COVID-19 on the nursing workforce is largely unknown as is the emotional burden experienced by nurses throughout the different waves of the pandemic. Conventional approaches often use survey question-based instruments to learn nurses’ emotions, and may not reflect actual everyday emotions but the beliefs specific to survey questions. Social media has been increasingly used to express people’s thoughts and feelings. This paper uses Twitter data to describe the emotional dynamics of registered nurse and student nurse groups residing in New South Wales in Australia during the COVID-19 pandemic. A novel analysis framework that considered emotions, talking topics, the unfolding development of COVID-19, as well as government public health actions and significant events was utilised to detect the emotion dynamics of nurses and student nurses. The results found that the emotional dynamics of registered and student nurses were significantly correlated with the development of COVID-19 at different waves. Both groups also showed various emotional changes parallel to the scale of pandemic waves and corresponding public health responses. The results have potential applications such as to adjust the psychological and/or physical support extended to the nursing workforce. However, this study has several limitations that will be considered in the future study such as not validated in a healthcare professional group, small sample size, and possible bias in tweets.
Zhou, J, Wang, Z, Li, C, Wei, W, Wang, S, Armaghani, DJ & Peng, K 2023, 'Hybridized random forest with population-based optimization for predicting shear properties of rock fractures', Journal of Computational Science, vol. 72, pp. 102097-102097. View/Download from: Publisher's site
Zhou, L, Fu, A, Yang, G, Gao, Y, Yu, S & Deng, RH 2023, 'Fair Cloud Auditing Based on Blockchain for Resource-Constrained IoT Devices', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4325-4342. View/Download from: Publisher's site View description>>
Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a Fair Cloud Auditing proposal by employing the Blockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the honest cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.
Zhou, M, Lu, J, Song, Y & Zhang, G 2023, 'Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12828-12841. View/Download from: Publisher's site
Zhou, S, Liu, C, Ye, D, Zhu, T, Zhou, W & Yu, PS 2023, 'Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity', ACM Computing Surveys, vol. 55, no. 8, pp. 1-39. View/Download from: Publisher's site View description>>
The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses.
Zhou, S, Lu, Y, Pan, Y, Li, J, Qu, F, Luo, Z & Li, W 2023, 'Flowability prediction of recycled α-hemihydrate gypsum for 3D powder printing under combined effects of different glidants using response surface methodology', Developments in the Built Environment, vol. 16, pp. 100265-100265. View/Download from: Publisher's site
Zhou, T, Yang, Y & Wang, W 2023, 'Differentiable Multi-Granularity Human Parsing', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-14. View/Download from: Publisher's site
Zhou, T, Zhang, Z, Liu, H, Dong, S, Nghiem, LD, Gao, L, Chaves, AV, Zamyadi, A, Li, X & Wang, Q 2023, 'A review on microalgae-mediated biotechnology for removing pharmaceutical contaminants in aqueous environments: Occurrence, fate, and removal mechanism', Journal of Hazardous Materials, vol. 443, no. Pt A, pp. 130213-130213. View/Download from: Publisher's site View description>>
Pharmaceutical compounds in aquatic environments have been considered as emerging contaminants due to their potential risks to living organisms. Microalgae-based technology showed the feasibility of removing pharmaceutical contaminants. This review summarizes the occurrence, classification, possible emission sources, and environmental risk of frequently detected pharmaceutical compounds in aqueous environments. The efficiency, mechanisms, and influencing factors for the removal of pharmaceutical compounds through microalgae-based technology are further discussed. Pharmaceutical compounds frequently detected in aqueous environments include antibiotics, hormones, analgesic and non-steroidal anti-inflammatory drugs (NSAIDs), cardiovascular agents, central nervous system drugs (CNS), antipsychotics, and antidepressants, with a concentration ranging from ng/L to μg/L. Microalgae-based technology majorly remove the pharmaceutical compounds through bioadsorption, bioaccumulation, biodegradation, photodegradation, and co-metabolism. This review identifies the opportunities and challenges for microalgae-based technology and proposed suggestions for future studies to tackle challenges. The findings of this review advance our understanding of the occurrence and fate of pharmaceutical contaminants in aqueous environments, highlighting the potential of microalgae-based technology for pharmaceutical contaminants removal.
Zhou, W, Wen, S, Liu, Y, Liu, L, Liu, X & Chen, L 2023, 'Forgetting memristor based STDP learning circuit for neural networks', Neural Networks, vol. 158, pp. 293-304. View/Download from: Publisher's site View description>>
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. This paper proposes a new STDP learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDP, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTP phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDP learning rules under different conditions. The application of neural network proves the availability of this circuit.
Zhou, X, Feng, Y & Li, S 2023, 'Supervised Learning Enhanced Quantum Circuit Transformation', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 437-447. View/Download from: Publisher's site View description>>
A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). By inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constraint imposed by the QPU. Due to the nonnegligible gate error and the limited qubit coherence time of the QPU, QCT algorithms that minimize gate number or circuit depth or maximize the fidelity of output circuits are in urgent need. Unfortunately, finding optimized transformations often involve exhaustive searches, which are extremely time consuming and not practical for most circuits. In this article, we propose a framework that uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP gate. ANNs can be trained offline in a distributed way and the trained ANN can be easily incorporated into QCT algorithms to enable them to search deeper without bringing too much overhead in time complexity. Exemplary embeddings of the trained ANNs into target QCT algorithms demonstrate that the transformation performance can be consistently improved on QPUs with various connectivity structures and random or realistic quantum circuits.
AbstractDeep Neural Networks (DNNs) have recently achieved great success in many classification tasks. Unfortunately, they are vulnerable to adversarial attacks that generate adversarial examples with a small perturbation to fool DNN models, especially in model sharing scenarios. Adversarial training is proved to be the most effective strategy that injects adversarial examples into model training to improve the robustness of DNN models against adversarial attacks. However, adversarial training based on the existing adversarial examples fails to generalize well to standard, unperturbed test data. To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples. As opposed to most of the existing methods that generate adversarial examples in the input space, LADDER generates a myriad of high-quality adversarial examples through adding perturbations to latent features. The perturbations are made along the normal of the decision boundary constructed by an SVM with an attention mechanism. We analyze the merits of our generated boundary-guided adversarial examples from a boundary field perspective and visualization view. Extensive experiments and detailed analysis on MNIST, SVHN, CelebA, and CIFAR-10 validate the effectiveness of LADDER in achieving a better trade-off between standard accuracy and adversarial robustness as compared with vanilla DNNs and competitive baselines.
Zhou, Y, Liu, X, Fu, Y, Wu, D, Wang, JH & Yu, S 2023, 'Optimizing the Numbers of Queries and Replies in Convex Federated Learning With Differential Privacy', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 6, pp. 4823-4837. View/Download from: Publisher's site
Zhou, Z, Xu, C, Wang, M, Kuang, X, Zhuang, Y & Yu, S 2023, 'A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4230-4244. View/Download from: Publisher's site View description>>
Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals' wish to “hide in the crowd” for privacy frequently clashes with aggregator' need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.
Zhu, C, Cheng, Z, Ye, D, Hussain, FK, Zhu, T & Zhou, W 2023, 'Time-Driven and Privacy-Preserving Navigation Model for Vehicle-to-Vehicle Communication Systems', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8459-8470. View/Download from: Publisher's site
Zhu, HY, Chen, H-T & Lin, C-T 2023, 'The Effects of Virtual and Physical Elevation on Physiological Stress During Virtual Reality Height Exposure', IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 4, pp. 1937-1950. View/Download from: Publisher's site View description>>
Advances in virtual reality technology have greatly benefited the acrophobia research field. Virtual reality height exposure is a reliable method of inducing stress with low variance across ages and demographics. When creating a virtual height exposure environment, researchers have often used haptic feedback elements to improve the sense of realism of a virtual environment. While the quality of the rendered for the virtual environment increases over time, the physical environment is often simplified to a conservative passive haptic feedback platform. The impact of the increasing disparity between the virtual and physical environment on the induced stress levels is unclear. This paper presents an experiment that explored the effect of combining an elevated physical platform with different levels of virtual heights to induce stress. Eighteen participants experienced four different conditions of varying physical and virtual heights. The measurements included gait parameters, heart rate, heart rate variability, and electrodermal activity. The results show that the added physical elevation at a low virtual height shifts the participant's walking behaviour and increases the perception of danger. However, the virtual environment still plays an essential role in manipulating height exposure and inducing physiological stress. Another finding is that a person's behaviour always corresponds to the more significant perceived threat, whether from the physical or virtual environment.
Zhu, HY, Hossain, SN, Jin, C, Singh, AK, Nguyen, MTD, Deverell, L, Nguyen, V, Gates, FS, Fernandez, IG, Melencio, MV, Bell, J-AR & Lin, C-T 2023, 'An investigation into the effectiveness of using acoustic touch to assist people who are blind', PLOS ONE, vol. 18, no. 10, pp. e0290431-e0290431. View/Download from: Publisher's site View description>>
Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer’s surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as “acoustic touch” to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device’s field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user’s cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.
Zhu, J, Yang, Y, Hou, Z, Liao, S & Xue, Q 2023, 'Aperture-Shared All-Metal Endfire High-Gain Parabolic Antenna for Millimeter-Wave Multibeam and Sub-6-GHz Communication Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2784-2789. View/Download from: Publisher's site
Zhu, J, Yang, Y, Liao, S & Xue, Q 2023, 'Additively Manufactured Metal-Only Waveguide-Based Millimeter-Wave Broadband Achromatic Reflectarrays', IEEE Transactions on Antennas and Propagation, vol. 71, no. 7, pp. 6185-6190. View/Download from: Publisher's site
Zhu, Q & Ha, QP 2023, 'A Bidirectional Self-Rectifying Network With Bayesian Modeling for Vision-Based Crack Detection', IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 3017-3028. View/Download from: Publisher's site
Zhu, Q & Zhao, S 2023, 'Optimizing acoustic contract control target for sound zones in an enclosed space', The Journal of the Acoustical Society of America, vol. 154, no. 4_supplement, pp. A182-A182. View/Download from: Publisher's site View description>>
Acoustic contrast control is fundamental to achieving the highest sound energy difference between listening and quiet zones within a space. While extensively studied in simulations and laboratories, its real-world application has been confined to engineered applications, for example, acoustically treated rooms. However, for a broader application, we notice significant degradation in acoustic contrast performance from the laboratory prototype to the actual room application. To mitigate this disparity, we propose a physically optimized target to enhance room acoustic contrast control. The proposed method is evaluated by offline simulations using real-world room impulse responses.
Zhu, R, Wang, P, Geng, Z, Zhao, Y & Yu, S 2023, 'Double-Agent Reinforced vNFC Deployment in EONs for Cloud-Edge Computing', Journal of Lightwave Technology, vol. 41, no. 16, pp. 5193-5208. View/Download from: Publisher's site
Zhu, S, Chen, C & Wen, S 2023, 'Controller design for finite-time attractive and energy consumption of stochastic nonlinear systems', International Journal of Control, vol. 96, no. 1, pp. 74-81. View/Download from: Publisher's site View description>>
Controller design for finite-time attractive and energy consumption of stochastic nonlinear systems is investigated in this paper. By constructing appropriate controller and using inequality techniques, sufficient conditions for finite-time attractiveness of stochastic nonlinear systems are addressed. To estimate the control energy consumption, this paper first gives the definition of expected energy consumption for stochastic system. Furthermore, the upper bounds of control time and energy consumption are given. Afterwards, the trade-off analysis between control time and energy consumption is carried out to find a optimal control intensity choice. Finally, two numerical examples are presented to verify the validity of our theoretical results.
Zhu, T, Ye, D, Cheng, Z, Zhou, W & Yu, PS 2023, 'Learning Games for Defending Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2410-2422. View/Download from: Publisher's site
Zhu, T, Ye, D, Zhou, S, Liu, B & Zhou, W 2023, 'Label-Only Model Inversion Attacks: Attack With the Least Information', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 991-1005. View/Download from: Publisher's site
Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8869-8885. View/Download from: Publisher's site
Zhu, Y, Li, J, Yang, L, Huang, Z, Yang, X-S, Zhou, Q, Tang, R, Shen, S & Ouyang, L 2023, 'Closed loops for hydrogen storage: Hydrolysis and regeneration of metal borohydrides', Journal of Power Sources, vol. 563, pp. 232833-232833. View/Download from: Publisher's site
Zhu, Y, Lin, Q, Lu, H, Shi, K, Liu, D, Chambua, J, Wan, S & Niu, Z 2023, 'Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4178-4189. View/Download from: Publisher's site
Zoccheddu, F, Gobbetti, E, Livesu, M, Pietroni, N & Cherchi, G 2023, 'HexBox: Interactive Box Modeling of Hexahedral Meshes', Computer Graphics Forum, vol. 42, no. 5. View/Download from: Publisher's site View description>>
AbstractWe introduce HexBox, an intuitive modeling method and interactive tool for creating and editing hexahedral meshes. Hexbox brings the major and widely validated surface modeling paradigm of surface box modeling into the world of hex meshing. The main idea is to allow the user to box‐model a volumetric mesh by primarily modifying its surface through a set of topological and geometric operations. We support, in particular, local and global subdivision, various instantiations of extrusion, removal, and cloning of elements, the creation of non‐conformal or conformal grids, as well as shape modifications through vertex positioning, including manual editing, automatic smoothing, or, eventually, projection on an externally‐provided target surface. At the core of the efficient implementation of the method is the coherent maintenance, at all steps, of two parallel data structures: a hexahedral mesh representing the topology and geometry of the currently modeled shape, and a directed acyclic graph that connects operation nodes to the affected mesh hexahedra. Operations are realized by exploiting recent advancements in grid‐based meshing, such as mixing of 3‐refinement, 2‐refinement, and face‐refinement, and using templated topological bridges to enforce on‐the‐fly mesh conformity across pairs of adjacent elements. A direct manipulation user interface lets users control all operations. The effectiveness of our tool, released as open source to the community, is demonstrated by modeling several complex shapes hard to realize with competing tools and techniques.
Zowghi, D & Bano, M 2023, 'What’s Missing in Requirements Engineering for Responsible AI?', IEEE Software, vol. 40, no. 6, pp. 11-15. View/Download from: Publisher's site
Zuo, W, Chen, Z, E, J, Li, Q, Zhang, G & Huang, Y 2023, 'Effects of structure parameters of tube outlet on the performance of a hydrogen-fueled micro planar combustor for thermophotovoltaic applications', Energy, vol. 266, pp. 126434-126434. View/Download from: Publisher's site View description>>
In order to obtain high energy output power and energy conversion efficiency for micro-thermophotovoltaic system, in this work, a hydrogen-fueled micro planar combustor with tube outlet is designed. Effects of structure parameters of tube outlet (tube distance, tube cross section and tube number) on the performance of micro planar combustor are numerically investigated for thermophotovoltaic applications. Results suggest that the energy output and energy conversion efficiency of MTPV system is slightly increased with the increment of tube distance, and the pressure loss is slightly reduced. Moreover, compare with the micro planar combustor with the circular tube, the micro planar combustor with square tube is more suitable for MTPV system. Furthermore, with the tube number increasing from two to three, the energy output and energy conversion efficiency of MTPV system is increased by about 8.49%–9.38%, and the pressure loss is significantly reduced by about 44.54%–46.78%.
Zuo, W, Luo, Q, Li, Q & Sun, G 2023, 'Effect of thermal and hydrothermal aging on the crashworthiness of carbon fiber reinforced plastic composite tubes', Composite Structures, vol. 303, pp. 116136-116136. View/Download from: Publisher's site
Zuo, Y, Lu, W, Peng, X, Wang, S, Zhang, W & Qiao, X 2023, 'DuCL: Dual-stage contrastive learning framework for Chinese semantic textual matching', Computers and Electrical Engineering, vol. 106, pp. 108574-108574. View/Download from: Publisher's site View description>>
Chinese semantic textual matching is a fundamental yet challenging task in natural language processing (NLP). How to accurately capture the features in a single piece of text and the interactive features between pieces of text is the core problem of the task. Although pretrained language models (PLMs) and contrastive learning (CL) have been applied to address the problem to some extent, the existing works usually just utilize contrastive learning to finetune the PLMs on one single perspective, such as the sentence or pair level, which neglects to capture the semantic features from the other perspective, leading to inefficient learning and suboptimal performance. To tackle the problem, we propose a novel dual-stage contrastive learning framework (DuCL) for Chinese semantic textual matching. Specifically, DuCL consists of two stages sequentially, i.e., CL on the sentence level and CL on the pair level, each of which is responsible to finetune PLMs from the corresponding perspective. Besides, DuCL introduces a block-enhanced interaction module to integrate token-level and block-level interactive features to generate a semantic matching representation for two pieces of text. Extensive experimental results on two real-world public datasets demonstrate that our method can achieve better performance than the representative and state-of-the-art methods.
Abahussein, S, Zhu, T, Ye, D, Cheng, Z & Zhou, W 1970, 'Protect Trajectory Privacy in Food Delivery with Differential Privacy and Multi-agent Reinforcement Learning', Advanced Information Networking and Applications, Springer International Publishing, pp. 48-59. View/Download from: Publisher's site View description>>
Today, multiple food delivery companies work globally in different regions, and this expansion could expose users’ data to danger. This data could be stored by a third party and could be used in further analysis. The stored data needs to be stored in a proper way to prevent any other from identifying the real data if this data is disclosed. This work considers this issue to maintain the data privacy of stored customer data by leveraging differential privacy and multi-agent reinforcement learning. In the beginning, the agent delivers the food to the customer. Then the agent constructs N of obfuscated trajectories with different privacy budgets. The multi-agent reinforcement learning then chooses one trajectory from the constructed trajectories. The trajectory is then evaluated by considering three factors: the similarity between the selected trajectory and the original trajectory, the sensitivity of destination location and the frequency of the number of orders by the customer. We implemented our experiment on meal delivery data sets in Iowa City, USA.
Abbasi, M, Li, L, Aguilera, RP, Lu, D & Wang, F 1970, 'A New Single-Switch Step-Up DC-DC Converter with High Gain, Reduced Voltage Stress, and Continuous Input Current', 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1003-1007. View/Download from: Publisher's site View description>>
Generally, renewable energy sources (RESs) such as solar and wind energy systems have variable output due to their dependence on the weather condition. Hence, numerous converter structures with variable voltage gain have been introduced, which can be used to regulate the output voltage of RESs. Providing a high dynamic voltage gain with even a small input voltage was realized by developing conventional DC-DC converters, such as Zeta, Sepic, and Cuk Converters. But these conventional converters cannot provide high voltage gains with non-extreme duty cycles. In this paper, it is aimed to design a new single-switch DC-DC power converter without employing a large number of components. This topology provides higher dynamic voltage gain with a lower duty cycle, continuous input current, and lower voltage stress on the components. The performance of the converter is proved by comparison and simulation results obtained in MATLAB/Simulink environment.
Abhijith, V, Hossain, MJ, Lei, G, Sreelekha, PA & Kadam, SB 1970, 'High Torque Capability Non-Permanent Magnet Hybrid Excited Switched Reluctance Motor for Electric Vehicle Application', 2023 IEEE Energy Conversion Congress and Exposition (ECCE), 2023 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE. View/Download from: Publisher's site
Abhijith, V, Hossain, MJ, Lei, G, Sreelekha, PA & Kadam, SB 1970, 'High Torque Capability Segmented Hydrid Excited Switched Reluctance Motor for Electric Vehicle Applications', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Abhijith, V, Hossain, MJ, Lei, G, Sreelekha, PA & Kadam, SB 1970, 'Hybrid Topologies of Non-Permanent Magnet-Excited Switched-Reluctance Motors With High Torque Capability For Electric Vehicle Applications', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Aboutorab, H, Saberi, M, Hussain, OK & Hussain, FK 1970, 'POSSUM: PrOactive diSruption riSk identification for sUpply chain Management', 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE. View/Download from: Publisher's site
Aboutorab, H, Yu, R, Dsouza, A, Saberi, M & Hussain, OK 1970, 'A News Recommendation System for Environmental Risk Management', CEUR Workshop Proceedings. View description>>
Environmental risk events, such as flooding, can disrupt freight routes and cause business losses. It is therefore crucial to proactively identify and manage these risks. When identifying environmental risks, it is essential to examine the impact of these events on freight routes. In this paper, we extract knowledge about environmental risk events from the knowledge graph and build a machine-learning model to identify freight routes potentially affected by floods. We propose a news recommendation system, namely the News Recommender for Environmental Risk Identification & Analysis (NR-ERIA), to recommend news related to a location of interest that has the risk of being affected by environmental risk events to support the risk management. We conducted experiments on real-world datasets and achieved an accuracy of 0.908 in proactively detecting disruptions, which is 196% higher than the baseline approach, demonstrating the effectiveness of our proposed system.
Abualhamayl, AJ, Almalki, MA, Al-Doghman, F, Alyoubi, AA & Hussain, FK 1970, 'Towards Fractional NFTs for Joint Ownership and Provenance in Real Estate', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Abualsaud, A, Taghizadeh, F, Deilami, S, Hossain, J & Lu, J 1970, 'Smart Electric Vehicle Charging System with Flexible Charging and Voltage Stabilisation Service for Australian Household Network', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Adak, C, Jaswanth, B, Akhtar, Z, Kåsen, A & Chanda, S 1970, 'Writer Identification from Nordic Historical Manuscripts using Transformer Networks', 2023 IEEE International Joint Conference on Biometrics (IJCB), 2023 IEEE International Joint Conference on Biometrics (IJCB), IEEE. View/Download from: Publisher's site
Adams, C & Oberst, S 1970, 'Modelling of noise due to impulsive excitation using nonlinear time series analysis', Noise and Vibrations Emerging Methods, Auckland, New Zealand.
Aditiya, HB, Sebayang, AH, Silitonga, AS, Mulyaningsih, Y, Mulyaningsih, Y, Theofany, HC & Supriyanto 1970, 'Design of experiment (DoE) in reducing sugar optimization to produce third-generation bioethanol from Chlorella pyrenoidosa: Central composite design vs Box-Behnken design', PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research, PROCEEDINGS OF THE SYMPOSIUM ON ADVANCE OF SUSTAINABLE ENGINEERING 2021 (SIMASE 2021): Post Covid-19 Pandemic: Challenges and Opportunities in Environment, Science, and Engineering Research, AIP Publishing. View/Download from: Publisher's site
Ahmed, F, Singh, K, Esselle, KP & Thalakotuna, D 1970, 'Near-Field Metallic Metasurfaces for Enhancing Antenna Capabilities', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, pp. 53-54. View/Download from: Publisher's site
Akbarzade, M, Oberst, S, Sepehrirahnama, S & Halkon, B 1970, 'Sensitivity and bifurcation analysis of an analytical model of a trapped object in an externally excited acoustic radiation force field', Proceedings of NOVEM23, Noise and Vibration Emerging Methods, Auckland, New Zealand, pp. 1-10. View description>>
Acoustic radiation force (ARF) is a nonlinear acoustic phenomenon for which the acoustic field properties and, to an even greater extent, the explicit dynamics of the object, have received limited attention in the published literature to date. Any oscillations due to the flow field or external perturbations are thereby negligible while the particle is trapped in a stable position. By changing the viewpoint from the acoustic field to the dynamics of a levitated particle, the amplitude and frequency of external oscillation is non-negligible, we ask the question of how external excitation changes the dynamics of the object. We explicitly derive an analytical formulation of a trapped object in the form of a Duffing-like equation with its constants being defined by the object itself, the fluid, the acoustic wave, and the external vibration properties. In this case, the bifurcation behaviour is studied, and we show this together with a sensitivity analysis to represent correct dynamic behaviour in certain regimes of the bifurcation diagram.
Alalmaie, A, Nanda, P & He, X 1970, 'Zero Trust Network Intrusion Detection System (NIDS) using Auto Encoder for Attention-based CNN-BiLSTM', 2023 Australasian Computer Science Week, ACSW 2023: 2023 Australasian Computer Science Week, ACM. View/Download from: Publisher's site
Alalmaie, A, Nanda, P & He, X 1970, 'ZT-NIDS: Zero Trust, Network Intrusion Detection System', Proceedings of the 20th International Conference on Security and Cryptography, 20th International Conference on Security and Cryptography, SCITEPRESS - Science and Technology Publications, Rome, Italy, pp. 99-110. View/Download from: Publisher's site
Alalmaie, AZ, Nanda, P, He, X & Alayan, MS 1970, 'Why Zero Trust Framework Adoption has Emerged During and After Covid-19 Pandemic', Springer International Publishing, pp. 181-192. View/Download from: Publisher's site
Alalyan, MS, Jaafari, NA & Hussain, FK 1970, 'Barriers to Blockchain Adoption by Saudi Higher Education Institutions: A Structural Equation Analysis', Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Switzerland, pp. 52-61. View/Download from: Publisher's site View description>>
Empirical investigations of the factors influencing blockchain adoption by higher education institutions in Saudi Arabia are rare. This study aimed to partially fill this knowledge gap by focusing on barriers to blockchain adoption. The results from a survey of 289 academic and IT professionals in the Saudi higher education system confirmed the negative effects of privacy and security concerns, the association of blockchain with finance only, and language concerns on blockchain adoption. At the same time, the impact of the lack of knowledge was not observed. The theoretical and practical implications of the results are discussed.
Alcaide, AM, Poblete, P, Vazquez, S, Aguilera, RP, Kouro, S, Leon, JI & Franquelo, LG 1970, 'Feed-Forward Technique to Emulate Natural Sampling Method for Cascaded H-Bridge Converters', IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IEEE. View/Download from: Publisher's site
Alfredo, RD, Nie, L, Kennedy, P, Power, T, Hayes, C, Chen, H, McGregor, C, Swiecki, Z, Gašević, D & Martinez-Maldonado, R 1970, ''That Student Should be a Lion Tamer!' StressViz: Designing a Stress Analytics Dashboard for Teachers', LAK23: 13th International Learning Analytics and Knowledge Conference, LAK 2023: 13th International Learning Analytics and Knowledge Conference, ACM, pp. 57-67. View/Download from: Publisher's site View description>>
In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students' states that are hard to see with the 'naked eye', such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students' physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students' cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.
Alghanmi, NA, Alghanmi, N, Alhosaini, H & Hussain, FK 1970, 'Carbon Credits Storage: A Comparative Multifactor Analysis of On-chain vs Off-chain Approaches', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alharbi, S, Alorini, AFA, Alahmadi, KMM, Alhosaini, H, Zhu, Y & Wang, X 1970, 'Exploring Oversampling Techniques for Fraud Detection with Imbalanced Classes', 11th International Conference on Informatics, Electronics & Vision, 11th International Conference on Informatics, Electronics & Vision, London, UK.
Alkhalaf, A & Hussain, FK 1970, 'Optimisation of Volunteer Node Selection for Scalable and Trustworthy Fog Environments', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Allende, F, Choquepuma, A, Aranda, D, Alvarez, JC, Hasan, ASMM & Trianni, A 1970, 'Model to Increase the Productive Efficiency in the Plastic Manufacturing Sector', 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE. View/Download from: Publisher's site
Almadani, MS & Hussain, FK 1970, 'Implementing a Secure Blockchain-Based Wallet System with Multi-Factor Authentication', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alotaibi, S, Alsobhi, H, Zhao, M & Hussain, FK 1970, 'Blockchain for Identity Management: Ensuring Trust and Integrity in the Education Sector', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
AlSmadi, L, Lei, G & Li, L 1970, 'Enhanced Electricity Demand Forecasting in Australia Using a CNN-LSTM Model with Heating and Cooling Degree Days Data', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Al-Soeidat, M, Khawaldeh, H & Lu, DD-C 1970, 'Investigation of Photovoltaic Panel Degradation Affected by Dust in Jordan', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Alsolmy, M & Hussain, FK 1970, 'Blockchain Adoption in Saudi Manufacturing: The Conceptual Model and Research Hypotheses.', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Alsufyani, N & Gill, AQ 1970, 'A Knowledge-Graph Based Integrated Digital EA Maturity and Performance Framework', Lecture Notes in Business Information Processing, International Conference on Enterprise Design, Operations, and Computing (EDOC): EDOC Workshops, Springer International Publishing, Bozen-Bolzano, Italy, pp. 214-229. View/Download from: Publisher's site
Alsulaimani, S, Hussain, F & Hussain, O 1970, 'Digital Asset Ownership based on Blockchain: A Literature Review', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
An, Y, Zhao, S & Zhang, G 1970, 'A Cycle Architecture Based on Policy Gradient for Unsupervised Video Summarization', Proceedings of the 15th International Conference on Digital Image Processing, ICDIP 2023: The 15th International Conference on Digital Image Processing, ACM. View/Download from: Publisher's site
Anaissi, A, D’souza, K, Suleiman, B, Bekhit, M & Alyassine, W 1970, 'Heterogeneous Transfer Learning in Structural Health Monitoring for High Rise Structures', Springer Nature Switzerland, pp. 405-417. View/Download from: Publisher's site
Ansari, M, Zetterstrom, O, Fonseca, NJG, Quevedo-Teruel, O & Guo, YJ 1970, 'A Lightweight Metalized-Insert Luneburg Lens', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 1970, 'Effectiveness of Geosynthetics at Preventing Subgrade Instability under Cyclic Loading', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers. View/Download from: Publisher's site
Ariyarathna, T, Kularatna, N & Gunawardane, K 1970, 'Supercapacitor assisted extra low frequency power converters and surge protectors: Applying Supercapacitor Assisted Loss Management Concept in practical applications', 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE. View/Download from: Publisher's site
Arnaz, A, Lipman, J & Abolhasan, M 1970, 'A Multi-objective Reinforcement Learning Solution for Handover Optimization in URLLC', 2023 28th Asia Pacific Conference on Communications (APCC), 2023 28th Asia Pacific Conference on Communications (APCC), IEEE. View/Download from: Publisher's site
Badar, AQH, Mangipudi, M, Panda, D & Hossain, MJ 1970, 'Optimal Solar Array Configuration for Multiple Shade Patterns', 2023 10th IEEE International Conference on Power Systems (ICPS), 2023 10th IEEE International Conference on Power Systems (ICPS), IEEE. View/Download from: Publisher's site
Bandara, M, Jiang, Y, Gill, A, Rabhi, F & Beydoun, G 1970, 'An Application Ontology for Reproducibility of Machine Learning Solutions', Australasian Conference on Information Systems, Wellington, New Zealand.
Banuelos, DP, Falque, R, Patten, T & Alempijevic, A 1970, 'Skirting Line Estimation Using Sparse to Dense Deformation', 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. View/Download from: Publisher's site
Basheer, A, Feng, Y, Ferrie, C & Li, S 1970, 'Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 6770-6778. View description>>
Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of multiple layers of ansatzes (simpler PQCs, which are an analogy of NN layers) that differ only in selections of parameters. Previous work has identified the alternating layered ansatz as potentially a new standard ansatz in near-term quantum computing. Indeed, shallow alternating layered VQAs are easy to implement and have been shown to be both trainable and expressive. In this work, we introduce a training algorithm with an exponential reduction in training cost of such VQAs. Moreover, our algorithm uses classical shadows of quantum input data, and can hence be run on a classical computer with rigorous performance guarantees. We demonstrate 2–3 orders of magnitude improvement in the training cost using our algorithm for the example problems of finding state preparation circuits and the quantum autoencoder.
Bayat, A, Oberst, S & Lai, JCS 1970, 'NUMERICAL SIMULATION OF HEAT TRANSFER IN TERMITE MOUNDS', Proceeding of International Heat Transfer Conference 17, International Heat Transfer Conference 17, Begellhouse. View/Download from: Publisher's site
Bekhit, M, Fathalla, A, Eldesouky, E & Salah, A 1970, 'Multi-objective VNF Placement Optimization with NSGA-III', Springer Nature Switzerland, pp. 481-493. View/Download from: Publisher's site
Beni, HM, Mortazavi, H, Scataglini, S, Truijen, S, Islam, MS & Paul, G 1970, 'Fluid-Structure Interaction Modeling of Peak Expiratory-Inspiratory Flow in a Stented Upper Airway Using Experimental Data', Springer Nature Switzerland, pp. 106-114. View/Download from: Publisher's site
Bin sawad, A, Alakhtar, R, Alturki, B, Narayan, B, Lin, S, Prasad, M & Kocaballi, B 1970, 'Towards a Design Framework for Conversational Agents for Diabetes Prevention', https://dl.acm.org/conference/ozchi/proceedings, Oz Computer Human Interaction, Wellington, NZ.
Blooma Mohan John, Ramanathan, S & Jayan Chirayath Kurian, J 1970, 'Design and Evaluation of a Virtual Reality Game to Improve Physical and Cognitive Acuity', Hyderabad, India.
Boiar, D, Killich, N, Schulte, L, Hernandez Moreno, V, Deuse, J & Liebig, T 1970, 'Forecasting Algae Growth in Photo-Bioreactors Using Attention LSTMs', Springer International Publishing, pp. 26-37. View/Download from: Publisher's site
Bourahmoune, K, Ishac, K & Carmichael, M 1970, 'A remote training platform for learning physical skills using an AI powered virtual coach and a novel IoT sensing mat', SIGGRAPH Asia 2023 Posters, SA '23: SIGGRAPH Asia 2023, ACM. View/Download from: Publisher's site
Boyd-Weetman, B, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'Comparison of accelerated test methods for ASR reactivity testing', https://ciaconference.com.au/concrete2023/content.html, Conference of the Concrete Institute of Australia, Concrete 2023, Resilient and Sustainable Structures: Breaking Down Barriers, Perth, WA. View description>>
The deleterious alkali silica reaction (ASR) affects the long term durability of concrete by reducing service life. Standardised accelerated test methods are used to rapidly asses reactivity. Rapidly accelerating the ASR reaction process may produce reactivity responses from aggregates that would not occur in field conditions. The environment in which the accelerated test is conducted is crucial in determining the degree to which the ASR reaction and associated deleterious expansion proceeds. In this study, a course reactive Australian aggregate concrete is exposed to standardised, and modified test methods to assess their effect on the observed ASR reaction. Australia has produced AS1141.60.2, a standardised concrete prism test at 38ºC for determining ASR reactivity of aggregates which is deployed in this study alongside a 60ºC concrete prism test and prism immersion test methods. This paper compares deleterious ASR expansion across a number of accelerated test methods and found good correlation between CPT and immersion based test methods.
Braytee, A, Anaissi, A & Naji, M 1970, 'A Comparative Analysis of Loss Functions for Handling Foreground-Background Imbalance in Image Segmentation', Springer International Publishing, pp. 3-13. View/Download from: Publisher's site
Cabezas, V, Acuna, P, Lezana, P, Aguilera, RP & Garcia, C 1970, 'Selective Harmonic Mitigation — Model Predictive Control for a Grid-Connected Seven-Level Cascaded H-Bridge Converter', 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), IEEE. View/Download from: Publisher's site
Caderno, PV, Awaysheh, FM, Colino-Sanguino, Y, Fuente, LRDL, Valdes-Mora, F, Cabaleiro, JC, Pena, TF & Gallego-Ortega, D 1970, 'OPERA-gSAM: Big Data Processing Framework for UMI Sequencing at High Scalability and Efficiency', 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE. View/Download from: Publisher's site
Cai, J, Nguyen, K-N, Shrestha, N, Good, A, Tu, R, Yu, X, Zhe, S & Serra, T 1970, 'Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions', Springer Nature Switzerland, pp. 200-218. View/Download from: Publisher's site
Cao, J, Gowripalan, N, Sirivivatnanon, V & Nairn, J 1970, 'Investigation of ASR Effects on the Load-Carrying Capacity of Reinforced Concrete Elements by Ultra-Accelerated Laboratory Test', Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 43-52. View/Download from: Publisher's site View description>>
AbstractThe alkali–silica reaction (ASR) can cause expansion, cracking, and degradation of the mechanical properties of affected concrete. Concerns about the safety of ASR-damaged reinforced concrete structures have driven the demand for studying the effects of ASR on residual load capacity of the deteriorated structure. Conventionally, field load testing methods are used to assess the residual load capacity of ASR-affected structures. In this study, a novel accelerated laboratory test using the LVSA 50/70 autoclave to accelerate ASR was applied to investigate the flexural and shear behavior of small-scale reinforced concrete beams affected by ASR. The specimens were subjected to three cycles of 80 °C steam curing at atmospheric pressure in the autoclave, with 60 h/cycle. Significant expansion and ASR damage were observed. Load carrying capacity tests on the small-scale reinforced concrete beams showed that, at the expansion levels achieved, the flexural capacity of the reinforced concrete beams was not significantly affected. Shear resistance of the reinforced concrete beams, however, was found to increase compared with their 28-day counterparts, which could be attributed to the prestressing effect due to ASR expansion. It appears that the multicycle 80 °C steam-curing autoclave test is suitable for investigating ASR deterioration of actual concrete mixes within a short period of time. ASR effects on the load carrying capacity of reinforced concrete elements at higher expansion levels, however, need further investigation.
Cao, J, Liu, B, Wen, Y, Xie, R & Song, L 1970, 'Achieving Privacy-Preserving Multi-View Consistency with Advanced 3D-Aware Face De-identification', ACM Multimedia Asia 2023, MMAsia '23: ACM Multimedia Asia, ACM. View/Download from: Publisher's site
Cao, L, Chen, H, Fan, X, Gama, J, Ong, YS & Kumar, V 1970, 'Bayesian Federated Learning: A Survey', IJCAI International Joint Conference on Artificial Intelligence, pp. 7233-7242. View description>>
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.
Cao, Z, Zhang, S & Lin, C-T 1970, 'Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Chaisumdet, D, Tung Le, D, Khoa Nguyen, DD, Sutjipto, S, Rizvi, D & Paul, G 1970, 'Enhancing the Intuitiveness of Remote Mobile Industrial Robots with Haptic Devices', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARRA, Sydney. View description>>
This paper presents the potential integration of Haptic Devices to complement the current Virtual Reality interfaces used for a remote mobile industrial robot. The integration of haptic feedback devices provides proper kinaesthetic awareness to operators, facilitating a feeling of total immersion, as if physically present. The ability to touch and feel, provided by these devices, allows for greater dexterity when operating these robotic systems. It can also solve issues when working within fragile and precision-required environments where current forms of remote teleoperation are lacking. A summary of the teleoperated manipulator, controlled by a haptic device, is outlined in this paper. This system is tested, and the findings from a human user study using the examined control method are presented. The human user study explores the effect of varying environments and modes of visual feedback on the participants’ performance. These results demonstrate the most practical form of visualising an unknown environment when leveraging haptic force feedback.
Chau, QT, Zhu, X & Schwitter, B 1970, 'Design of a 75-85 GHz Driver Amplifier in 0.1-μm Gallium Arsenide pHEMT Technology', 2023 Asia-Pacific Microwave Conference (APMC), 2023 Asia-Pacific Microwave Conference (APMC), IEEE. View/Download from: Publisher's site
Chen, B, Wu, T, Zhang, Y, Chhetri, MB & Bai, G 1970, 'Investigating Users’ Understanding of Privacy Policies of Virtual Personal Assistant Applications', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Chen, C, Liu, Y, Chen, L & Zhang, C 1970, 'RiskContra: A Contrastive Approach to Forecast Traffic Risks with Multi-Kernel Networks', Advances in Knowledge Discovery and Data Mining, Springer Nature Switzerland, pp. 263-275. View/Download from: Publisher's site View description>>
Traffic accident forecasting is of vital importance to the intelligent transportation and public safety. Spatial-temporal learning is the mainstream approach to exploring complex evolving patterns. However, two intrinsic challenges lie in traffic accident forecasting, preventing the straightforward adoption of spatial-temporal learning. First, the temporal observations of traffic accidents exhibit ultra-rareness due to the inherent properties of accident occurrences (Fig. 1(a)), which leads to the severe scarcity of risk samples in learning accident patterns. Second, the spatial distribution of accidents is severely imbalanced from region to region (Fig. 1(b)), which poses a serious challenge to forecast the spatially diversified risks. To tackle the above challenges, we propose RiskContra, a Contra stive learning approach with multi-kernel networks, to forecast the Risk of traffic accidents. Specifically, to address the first challenge (i.e. temporal rareness), we design a novel contrastive learning approach, which leverages the periodic patterns to derive a tailored mixup strategy for risk sample augmentation. This way, the contrastively learned features can better represent the risk samples, thus capturing higher-quality accident patterns for forecasting. To address the second challenge (i.e. spatial imbalance), we design the multi-kernel networks to capture the hierarchical correlations from multiple spatial granularities. This way, disparate regions can utilize the multi-granularity correlations to enhance the forecasting performance across regions. Extensive experiments corroborate the effectiveness of each devised component in RiskContra.
Chen, K, Zhang, JA, Wang, Z & Guo, YJ 1970, 'Development of an Uplink Sensing Demonstrator for Perceptive Mobile Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Chen, M, Zheng, Z, Yang, Y & Chua, T-S 1970, 'PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation', Proceedings of the 31st ACM International Conference on Multimedia, MM '23: The 31st ACM International Conference on Multimedia, ACM. View/Download from: Publisher's site
Chen, S, Long, G, Shen, T & Jiang, J 1970, 'Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data', IJCAI International Joint Conference on Artificial Intelligence, pp. 3532-3540. View description>>
To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urgency, heterogeneous meteorological sensors across countries and regions, inevitably causing multivariate heterogeneity and data exposure, become the main barrier. This paper develops a foundation model across regions capable of understanding complex meteorological data and providing weather forecasting. To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data. Moreover, a novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints. The effectiveness of the proposed method has been demonstrated on classical weather forecasting tasks using three meteorological datasets with multivariate time series.
Chen, S-L & Guo, YJ 1970, 'Beam-Squint Mitigated Transverse-Slot Leaky-Wave Antenna for Wideband Wireless Communications', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE. View/Download from: Publisher's site
Chen, S-L, Qin, P-Y, Liu, Y & Lin, W 1970, 'Recent Advances and Future Scopes of Multi-Linear Polarization Reconfigurable Antennas', 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE. View/Download from: Publisher's site
Chen, X, Long, G, Tao, C, Li, M, Gao, X, Zhang, C & Zhang, X 1970, 'Improving the Robustness of Summarization Systems with Dual Augmentation', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 6846-6857. View description>>
A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. To create semantic-consistent substitutes, we propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models. Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation. Specifically, the first brittleness factor we found is the poor understanding of infrequent words in the input. Correspondingly, we feed the encoder with more diverse cases created by SummAttacker in the input space. The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states. Hence, we construct adversarial decoder input and devise manifold softmixing operation in hidden space to introduce more diversity. Experimental results on Gigaword and CNN/DM datasets demonstrate that our approach achieves significant improvements over strong baselines and exhibits higher robustness on noisy, attacked, and clean datasets.
Chen, Y & Jupp, JR 1970, 'Towards a Requirements Co-engineering Improvement Framework: Supporting Digital Delivery Methods in Complex Infrastructure Projects', Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies, Springer Nature Switzerland, pp. 262-273. View/Download from: Publisher's site View description>>
To support the delivery of cyber-physical systems of complex infrastructure assets, different requirements (e.g., physical system requirements, asset information requirements) must be developed and managed properly during the lifecycle of the assets. However, there is a lack of integrated and continuous approach to support the co-development and co-management of physical systems requirements and asset information requirements. Adopting a design science research methodology, this paper develops the structure of Requirements Co-engineering Improvement Framework for complex infrastructure projects. This framework defines five maturity levels for requirements relevant process, protocol and supporting software tools. Further validation will be conducted using the Delphi Method in future research.
Chen, Y, Ding, C, Zhu, H, Liu, Y & Guo, YJ 1970, 'A Dual-Slant-Polarized In-band Full-duplex (IBFD) Antenna System with Four Isolated Channels', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Chen, Y, Li, Z, Yang, C, Wang, X, Long, G & Xu, G 1970, 'Adaptive Graph Recurrent Network for Multivariate Time Series Imputation', International Conference on Neural Information Processing, International Conference on Neural Information Processing, Springer Nature Singapore, New Delhi, India, pp. 64-73. View/Download from: Publisher's site
Chen, Y, Shi, K, Wang, X & Xu, G 1970, 'MTSTI: A Multi-task Learning Framework for Spatiotemporal Imputation', International Conference on Advanced Data Mining and Applications, International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland, Shenyang, China, pp. 180-194. View/Download from: Publisher's site
Chen, Y-N, Ding, C, Liu, Y & Guo, YJ 1970, 'Four-Port In-Band Full-Duplex Antenna System – Challenges and Solutions (Invited)', 2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation (APCAP), IEEE. View/Download from: Publisher's site
Choong, DSW, Goh, DJ, Liu, J, Sarafianou, M, Merugu, S, Zhang, QX, Chang, P, Leotti, A, Koppisetti, G, Zakiyyan, N, Lin, H, Bhasetti, C, Ghosh, S, Ramegowda, PC, Chen, DS-H, Lee, JE-Y, Prelini, C, Giusti, D, Savoia, A & K, Y 1970, 'DC Bias Effects on Optimizing ScAlN Air-Coupled pMUT Performance Parameters', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE. View/Download from: Publisher's site
Chougule, M, K, P, P. P, A, Viswanathan, S, Ravichandran, KS, Sethumadhavan, M, Rahimi, M & Gandomi, AH 1970, 'Classifying DNS over HTTPS Malicious/Benign Traffic Using Deep Learning Models', 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE. View/Download from: Publisher's site
Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 1970, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6. View/Download from: Publisher's site View description>>
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.
Coluccia, A, Fascista, A, Sommer, L, Schumann, A, Dimou, A, Zarpalas, D & Sharma, N 1970, 'Drone-vs-Bird Detection Grand Challenge at ICASSP2023', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. View/Download from: Publisher's site
Conway, D, Butavicius, M, Yu, K & Chen, F 1970, 'Are People with Cyber Security Training Worse at Checking Phishing Email Addresses? Testing the Automaticity of Verifying the Sender’s Address', Human Aspects of Information Security and Assurance, Springer Nature Switzerland, pp. 310-323. View/Download from: Publisher's site View description>>
Cyber security training emphasises checking the sender’s email address to identify phishing emails. Dual process theories of cognition suggest that with practice such tactics can transition from effortful, analytic processes to involuntary heuristics and become ‘automatic’. We tested the automaticity of this email habit by developing a scale for cyber security experience and then deployed an interference task where participants (n = 61) had to make a decision about text colour and ignore sender’s addresses from either legitimate or phishing emails. A surprising result emerged: the more cyber security training participants had, the less interference they exhibited in the colour selection task and the more they were able to ignore the content of the sender’s addresses. This suggests that evaluating sender’s addresses does not fulfill the criterion for ‘automatic’ processes when practiced and that more experienced people seem to be able to ignore this important cue when extraneous task goals are present.
Cooper-Woolley, B, Darroch, MM, Cong, J, Hendriks, X, Zhu, Q, Xiao, T, Zhao, S & Halkon, B 1970, 'Initial design, development & calibration of MEMS based sound level meter for real-time construction monitoring', 2022 Conference of the Acoustical Society of New Zealand, 2022 Conference of the Acoustical Society of New Zealand, Wellington, New Zealand.
Cortes, CAT, Lin, C-T, Do, T-TN & Chen, H-T 1970, 'An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments', 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), IEEE. View/Download from: Publisher's site
Cowled, C, Slattery, T, Crews, K & Brooke, H 1970, 'INFLUENCE OF PLASTERBOARD ON THE STRUCTURAL PERFORMANCE OF TIMBER-FRAMED SHEAR WALLS', World Conference on Timber Engineering (WCTE 2023), World Conference on Timber Engineering 2023 (WCTE2023), World Conference on Timber Engineering (WCTE 2023), pp. 3417-3422. View/Download from: Publisher's site View description>>
Timber-framed shear walls are commonly used in residential buildings to provide lateral strength and stiffness against wind and earthquake loads. Wood-based panel products, such as plywood and oriented strand board, are typically fixed to timber framing with nails or screws to provide the necessary racking resistance of a shear wall. Plasterboard is a panel product used on walls to achieve a smooth finished surface. Plasterboard provides some strength and stiffness to the wall even though its primary function is architectural; however, most shear wall tests ignore the influence of plasterboard. The aim of this study is to quantify the influence of plasterboard on the structural performance of timber-framed shear walls. To achieve this aim, six (6) timber-framed shear walls (groups P1 and P2) were fabricated with 7mm F8 plywood sheathing on one side and 10mm plasterboard on the other side and tested under a monotonic loading protocol. Results were then compared with previous test results of three (3) similar timber-framed shear walls (group M1) without plasterboard. Results show that plasterboard improved the ultimate racking strength of these shear walls by up to 53%, a statistically significant result. Shear wall stiffness and failure modes were not affected by adding plasterboard.
Cuzmar, RH, Poblete, P, Aguilera, RP, Pereda, J & Lu, DD-C 1970, 'Power Balance of a Delta-Connected CHB Converter with MPC for Photovoltaic Systems', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Dai, R, Zhang, Y, Fang, Z, Han, B & Tian, X 1970, 'Moderately Distributional Exploration for Domain Generalization', Proceedings of Machine Learning Research, International Conference on Machine Learning (ICML 2023), Hawaii, pp. 6786-6817. View description>>
Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches, yet its performance gain depends on the distribution discrepancy between the generated and target domains. Distributionally robust optimization is promising to tackle distribution discrepancy by exploring domains in an uncertainty set. However, the uncertainty set may be overwhelmingly large, leading to low-confidence prediction in DG. It is because a large uncertainty set could introduce domains containing semantically different factors from training domains. To address this issue, we propose to perform a moderately distributional exploration (MODE) for domain generalization. Specifically, MODE performs distribution exploration in an uncertainty subset that shares the same semantic factors with the training domains. We show that MODE can endow models with provable generalization performance on unknown target domains. The experimental results show that MODE achieves competitive performance compared to state-of-the-art baselines.
Dang, A & Beydoun, G 1970, 'Toward Addressing the Software Architecture Blind Spot of Information System Success Factors in the Public Health domain', Australasian Conference on Information Systems, Australasian Conference on Information Systems, New Zealand. View description>>
Context: In the public health domain, there is no shortage of failed Information Systems projects. Aside from overblown budgets and elapsed deadlines (ad nauseam), technical issues exist. These include poor usability, instability, system performance, and data inconsistency issues. These issues relate to software engineering, and specifically software architecture. However, the enquiries and analyses of these failed Information Systems projects have focused on the perspective of stakeholders (e.g., government ministers and health practitioners) and project managers. Project failure or success does not emanate from these roles exclusively. Aim: To bring to bear and address public sector health Information System failure from the software architecture perspective. Method: A literature survey was conducted to ascertain the perceived failure and success factors within the public health domain. Results: We observed that the available literature on health Information Systems appears to lack success factors which have been noted in the software engineering and software architecture literature. Software architecture appears to be an understudied area within the public health domain. Contribution: To bring awareness to the public health domain that the Information System's success requires multi-faceted perspectives and actions. Specifically, perspectives and actions involving software architects and software engineers are required to successfully address the quality attributes of the proposed systems during development.
Dassanayake, C, Kularatna, N, Steyn-Ross, A, Gunawardane, K & Gurusinghe, N 1970, 'Arc Absorption Options Based on Passive Components in DC Circuit Breakers', IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IEEE. View/Download from: Publisher's site
Dassanayake, C, Kularatna, N, Steyn-Ross, A, Gunawardane, K & Gurusinghe, N 1970, 'Plasma Absorption Techniques in Direct Current Circuit Breakers', 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), IEEE. View/Download from: Publisher's site
Davies, J, Thai, MT, Hoang, TT, Nguyen, CC, Phan, PT, Zhu, K, Nhi Tran, DB, Ho, VA, La, HM, Ha, QP, Lovell, NH & Do, TN 1970, 'A Flexible 3D Force Sensor with In-Situ Tunable Sensitivity', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
De Dios, K, Ngoc, H, Tran, TS, Center, JR & Nguyen, TV 1970, 'Contribution of fat mass and obesity-associated (FTO) gene to osteoporosis phenotypes', JOURNAL OF BONE AND MINERAL RESEARCH, WILEY, pp. 256-256.
Deng, H, Wang, X, Yu, G, Dang, X & Liu, RP 1970, 'A Novel Weights-less Watermark Embedding Method for Neural Network Models', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Dhruva, S, Krishankumar, R, Ravichandran, K & Gandomi, AH 1970, 'Fermatean fuzzy-based PCA CoCoSo framework to assess digital technologies in Health 4.0', 2023 IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI), 2023 IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI), IEEE. View/Download from: Publisher's site
Diego Roman Arbelaez, J, Jayan Chirayath Kurian, J & Beydoun, G 1970, 'METAVERSE IN EDUCATION: A DYNAMIC CAPABILITY THEORY APPROACH', Proceedings of the AIS SIGED 2023 Conference, Hyderabad, India.
Doan, QM, Dinh, TH, Trung, NL, Nguyen, DN, Singh, AK & Lin, C-T 1970, 'Extended Upscale and Downscale Representation with Cascade Arrangement', 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023 IEEE Statistical Signal Processing Workshop (SSP), IEEE. View/Download from: Publisher's site
Dong, R, Liu, F, Chi, H, Liu, T, Gong, M, Niu, G, Sugiyama, M & Han, B 1970, 'Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation', Proceedings of Machine Learning Research, pp. 8260-8275. View description>>
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a few labeled target-domain data and a well-trained source-domain classifier (i.e., a source hypothesis), for the additional information of the highly-compatible unlabeled data. However, the generated data of the existing methods are extremely similar or even the same. The strong dependency among the generated data will lead the learning to fail. In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC). Specifically, DEG-Net will generate data via minimizing the HSIC value (i.e., maximizing the independence) among the semantic features of the generated data. By DEG-Net, the generated unlabeled data are more diverse and more effective for addressing the FHA problem. Experimental results show that the DEG-Net outperforms existing FHA baselines and further verifies that generating diverse data plays a vital role in addressing the FHA problem.
Dong, W, Li, W, Liebscher, M & Mechtcherine, V 1970, 'Development of Self-sensing Cementitious Composites with Improved Water and Chloride Resistance', RILEM Bookseries, Springer Nature Switzerland, pp. 499-508. View/Download from: Publisher's site View description>>
The piezoresistivity of cement-based sensors subjected to moisture ambient is changeable due to the porous structures and pore solutions inside of cementitious composites. This study explored the electrical resistivity and self-sensing performance of carbon black (CB) filled cement-based sensors mixed with silicone hydrophobic powder (SHP) and crystalline waterproofing admixture (CWA), especially before and after different durations of immersion in freshwater and 3% sodium chloride solution. The results show that the composites with SHP exhibited the best water impermeability, while the counterpart containing CWA presented the optimal chloride resistance. The piezoresistivity increased in sodium chloride solution because of the increased free ions. The outcomes are expected to illuminate the piezoresistive behavior of hydrophobic cement-based sensors subjected to moisture and chloride environments, thereby promoting structural health monitoring applications in the future.
Du, H, Yu, X, Hussain, F, Armin, MA, Petersson, L & Li, W 1970, 'Weakly-supervised Point Cloud Instance Segmentation with Geometric Priors', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE. View/Download from: Publisher's site
D'urso, G, Sadeghi, A, Yoo, C, Smith, SL & Fitch, R 1970, 'Distributed Multi-Robot Equitable Partitioning Algorithm for Allocation in Warehouse Picking Scenarios', 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), IEEE. View/Download from: Publisher's site
Dzaklo, CK, Rujikiatkamjorn, C, Indraratna, B & Kelly, R 1970, 'Cyclic Wetting and Drying Behaviour of Coal Wash Treated Black Soil', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers. View/Download from: Publisher's site
Eager, D & Zhou, S 1970, 'EXPERIMENTAL ANALYSIS OF GREYHOUND RACING TRACK PADDING IMPACT ATTENUATION PERFORMANCE', Proceedings of the International Congress on Sound and Vibration. View description>>
To reduce injuries to greyhounds caused by collisions with fixed racing track objects such as the outside fence or the catching pen structures, padding systems are widely adopted. However, there are currently neither recognized standards nor minimum performance thresholds for greyhound industry padding systems. This research investigates the impact attenuation characteristics of different padding systems for use within the greyhound racing industry for the enhanced safety and welfare of racing greyhounds. A standard head injury criterion (HIC) meter was used to examine padding impact attenuation performance based on the maximum g-force, HIC level and HIC duration. Since padding impact attenuation characteristics are affected by the installation and substrate, on-site testing was conducted to obtain the padding system impact attenuation performance in actual greyhound racing track applications. The test results confirm that the padding currently used within the greyhound industry is adequate for the fence but inadequate when used for rigid structural members such as the catching pen gate supports. Thus, increasing the padding thickness is strongly recommended if it is used at such locations. More importantly, it is also recommended that, after the installation of padding on the track, its impact attenuation characteristics should be tested according to the methodology developed herein to verify the suitability for protecting greyhounds from injury.
Eklund, M, Khalilpour, K, Voinov, A & Hossain, MJ 1970, 'Barriers to Community Microgrids in Fragmented Communities: Insights from a Case Study', 2023 International Conference on Future Energy Solutions (FES), 2023 International Conference on Future Energy Solutions (FES), IEEE. View/Download from: Publisher's site
Eshan, MSO, Nafi, MNH, Sakib, N, Emon, MH, Reza, T, Parvez, MZ, Barua, PD & Chakraborty, S 1970, 'Byzantine-Resilient Federated Learning Leveraging Confidence Score to Identify Retinal Disease', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Esselle, K, Singh, K, Thalakotuna, D, Koli, MNY & Ahmed, F 1970, 'Beam-Steering Antenna Technologies for Space-Related Applications', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Esselle, KP & Thalakotuna, DN 1970, 'Emerging Technologies for Steering Narrow Antenna Beams in Modern Radio Systems', 2023 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 2023 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), IEEE. View/Download from: Publisher's site
Executive Committee, SIGCHI, Vivacqua, AS, Sturm, C, Rivera-Loaiza, C, Gamage, D, Yafi, E & Dray, S 1970, 'Going Global: A SIG on the Challenges and Perspectives of Internationalization Within and Across the World of HCI', Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI '23: CHI Conference on Human Factors in Computing Systems, ACM. View/Download from: Publisher's site
Falque, R, Le Gentil, C & Sukkar, F 1970, 'Dynamic Object Detection in Range data using Spatiotemporal Normals', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Sydney. View description>>
On the journey to enable robots to interact with the real world where humans, animals, and unpredictable elements are acting as independent agents; it is crucial for robots to have the capability to detect dynamic objects. In this paper, we argue that the detection of dynamic objects can be solved by computing the spatiotemporal normals of a point cloud. In our experiments, we demonstrate that this simple method can be used robustly for LiDAR and depth cameras with performances similar to the state of the art while offering a significantly simpler method.
Falque, R, Vidal-Calleja, T & Alempijevic, A 1970, 'Semantic Keypoint Extraction for Scanned Animals using Multi-Depth-Camera Systems', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Fan, Y & Liu, D 1970, 'An equivalent two section method for calculating the workspace of multi-segment continuum robots', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Fan, Y, Pietroni, N & Ferguson, S 1970, 'A Neural Network-based Low-cost Soft Sensor for Touch Recognition and Deformation Capture', Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS '23: Designing Interactive Systems Conference, ACM. View/Download from: Publisher's site
Farah, N, Lei, G, Zhu, J & Guo, Y 1970, 'Reinforcement Learning for Intelligent Control of AC Machine Drives: A Review', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Farasat, M, Thalakotuna, D, Yang, Y & Hu, Z 1970, 'A Novel Cross-Interspersed Design of Multiband Antennas for Base Station Applications', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE. View/Download from: Publisher's site
Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lu, DD-C & Siwakoti, YP 1970, 'Flexible Active Power Decoupling Control Strategy for A Single-Stage Switched-Boost Grid-Connected Multilevel Inverter', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Faruque, MO, Hossain, MA, Alam, SMM, Islam, MR, Islam, MR & Guo, Y 1970, 'A Hybrid LSTM-LightGBM Model for Precise Short-Term Wind Power Forecasting', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Fathollahzadeh Aghdam, R, Ahmad, N, Naveed, A & Berenjforoush Azar, B 1970, 'On the relationship between energy and development: A comprehensive note on causation and correlation', Proceedings of the 44th IAEE International Conference/Al-Moneef, The Saudi Association for Energy Economics, The 44th IAEE International Conference in Riyadh.
Feng, T, Wang, W, Wang, X, Yang, Y & Zheng, Q 1970, 'Clustering based Point Cloud Representation Learning for 3D Analysis', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Feng, Y, Wang, Q, Wu, D & Gao, W 1970, 'Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures', Springer Nature Singapore, pp. 347-352. View/Download from: Publisher's site View description>>
AbstractA machine learning (ML) technique was used to assist in the dynamic analysis of mixed geometric and material nonlinearities of real-life engineering structures. Various types of inputs of system properties were considered in the 3D dynamic geometric elastoplastic analysis, giving a series of realistic nonlinear descriptions of complex, large deformation structural behaviors. To resolve the numerical challenges of solving the mixed nonlinear problems, a newly established ML technique using a new cluster-based extended support vector regression (X-SVR) algorithm was applied. With this technique, a surrogate model can be built at each time step in the Newmark time integration process, which can then be used to predict the deflection, force and stress of the relevant structural performance at different loading time stages. To demonstrate the accuracy and efficiency of the proposed framework, practical engineering applications with linear and nonlinear properties are fully demonstrated, and the nonlinear behavior of the structure under predicted working conditions in the future was predicted and verified in numerical studies.
Fu, H, Liu, C, Qi, X, Lin, B, Li, L, Zhang, L & Yu, X 1970, 'Sign Spotting via Multi-modal Fusion and Testing Time Transferring', Computer Vision – ECCV 2022 Workshops, Springer Nature Switzerland, pp. 271-287. View/Download from: Publisher's site View description>>
This work aims to locate a query isolated sign in a continuous sign video. In this task, the domain gap between the isolated and continuous sign videos often handicaps the localization performance. To address this issue, we propose a parallel multi-modal sign spotting framework. In a nutshell, our framework firstly takes advantage of multi-modal information (including RGB frames, 2D key-points and 3D key-points) to achieve representative sign features. The multi-modal features are employed to complement each other and thus compensate for the deficiency of a single modality, thus leading to informative representations for sign spotting. Moreover, we introduce a testing time top-k transferring technique into our framework to reduce the aforementioned domain gap. Concretely, we first compare the query sign with extracted sign video clips, and then update the feature of the query sign with the features of the top-k best matching video clips. In this manner, the updated query feature will exhibit a smaller domain gap with respect to continuous signs, facilitating feature matching in the following iterations. Experiments on the challenging OSLWL-Test-Set benchmark demonstrate that our method achieves superior performance (0.559 F1-score) compared to the baseline (0.395 F1-score). Our code is available at https://github.com/bb12346/OpenSLR.
Galat, D & Rizoiu, MA 1970, 'Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training University of Technology Sydney participation in BioASQ Task 11b Phase B', CEUR Workshop Proceedings, pp. 102-113. View description>>
Biomedical summarization requires large datasets to train for text generation. We show that while transfer learning offers a viable option for addressing this challenge, an in-domain pre-training does not always offer advantages in a BioASQ summarization task. We identify a suitable model architecture and use it to show a benefit of a general-domain pre-training followed by a task-specific fine-tuning in the context of a BioASQ summarization task, leading to a novel three-step fine-tuning approach that works with only a thousand in-domain examples. Our results indicate that a Large Language Model without domain-specific pre-training can have a significant edge in some domain-specific biomedical text generation tasks.
Gao, D, Zhou, L, Ji, L, Zhu, L, Yang, Y & Shou, MZ 1970, 'MIST : Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Gao, L, Yin, M, Xiao, F & Cao, Z 1970, 'A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making', 2023 IEEE International Conference on Unmanned Systems (ICUS), 2023 IEEE International Conference on Unmanned Systems (ICUS), IEEE. View/Download from: Publisher's site
Gao, M, Zhu, X & Li, J 1970, 'Time-Varying Analysis of Vehicle-Bridge Interaction System for Bridge Health Monitoring Using Synchrosqueezing Transform', Proceedings of the 12th International Conference on Structural Health Monitoring of Intelligent Infrastructure, International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hangzhou, China, pp. 346-351. View description>>
The passage of the vehicle over the bridge is a time-varying process. The bridge damage will cause a change in thetime-varying characteristics of the vehicle-bridge interaction (VBI) system. This paper presents a synchrosqueenzing transformbased method for extracting the time-varying characteristics from the dynamic response of the bridge subject to a moving vehicle.A numerical and experimental study has been conducted to show the performance of the proposed method. The bridge damage issimulated as a damage zone with three parameters representing the location, range and severity of the damage. The instantaneousfrequency is obtained by the edge detection from the time-frequency representation of bridge dynamic response. The results showthat the time-varying characteristics of the VBI system using the proposed method could be a good indicator of bridge damage
Gavriel, J, Herr, D, Shaw, A, Bremner, MJ, Paler, A & Devitt, SJ 1970, 'Transversal Injection: Using the Surface Code to Prepare Non-Pauli Eigenstates', 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE. View/Download from: Publisher's site
Gentile, C 1970, 'Abstract P1117: Personalized Care Using Patient-derived Cardiac Spheroids For Heart Failure Patients', Circulation Research, Ovid Technologies (Wolters Kluwer Health). View/Download from: Publisher's site View description>>
Preclinical studies using currently available in vitro and in vivo models fail at fully recapitulating the in vivo human heart microenvironment and its pathophysiology, limiting their translation from the bench to the bedside. Our laboratory has developed cardiac spheroids (CSs) as advanced in vitro models of the human heart using cardiac myocytes, endothelial cells and fibroblasts at ratios approximating the ones found in the human heart. Thanks to patient-derived induced pluripotent stem cells (iPSCs), we have quickly moved to personalized CSs to establish advanced pathophysiological in vitro models of heart failure (HF). CSs have been used to evaluate short- and long-term effects of ischaemic/reperfusion (I/R) injury typical of myocardial infarction (MI), drug-induced toxicity, SARS-CoV-2 exposure and pregnancy-induced HF. This was achieved using our established protocols to induce I/R-mimic injury, as well by exposing CSs to doxorubicin (DOX), SARS-CoV-2 virus and pre-eclampsia blood samples, respectively. First, our analyses of cell toxicity ratios using calcein-AM and ethidium homodimer (staining live and dead cells, respectively) demonstrated treatment-specific loss in cell viability and increase in cell death compared to control (untreated, healthy) CSs. Other CSs stained with ethidium homodimer and cell-specific antibodies were imaged and further used for 3D rendering analyses using IMARIS software. This enabled us to identify cell-specific responses in each pathological condition. Then, our measurements of fractional shortening and contraction frequency in CSs showed a statistically significant loss of contractile function in treated CSs compared to controls. Finally, new ...
Gerrard, L, Peng, X, Clarke, A & Long, G 1970, 'Multi-level Transformer for Cancer Outcome Prediction in Large-Scale Claims Data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 63-78. View/Download from: Publisher's site View description>>
Predicting outcomes for cancer patients initiating chemotherapy is essential for care planning and offers potential to support clinical and health policy decision-making. Existing models leveraging deep learning with longitudinal healthcare data have demonstrated the benefits of Transformer-based approaches to learning temporal relationships among medical codes (e.g., diagnoses, medications, procedures). Recent applications have also recognised the benefit of including patient information such as demographics to improve predictions. However, much of the existing work has focused on Electronic Health Record (EHR) data, and applications to administrative claims data, which has a differing temporal structure to EHR, are limited. Furthermore, it is still unclear how to best encode medical data from both EHR and claims data and model it collectively in Transformer models. Motivated by the above, this work proposes a Multi-Level Transformer specifically designed for claims data (Claims-MLT) to enhance cancer outcome prediction. The model uses a dual-level structure to learn effective patient representations by considering the low-level claims item relationships and sequential patterns in patient claim histories. We also integrate patient demographic and clinical features to provide additional information to the model. We evaluate our approach on two tasks from a real-world cancer dataset containing breast and colorectal cancer patients, and demonstrate the proposed model outperforms comparative baselines.
Ghosh, S, Goh, DJ, Hu, Z, Sharma, J, Teo, YS, McCarthy, M, Ramegowda, PC, Toh, WD, Chen, W, Murugan, A, Zhang, Y, Tsuchiya, Y, Lal, A, Lee, JE-Y & Koh, Y 1970, 'Ultra-Low Power MEMS Inertial Switch Based Wake-Up Wireless Sensing Node for Door Lock Monitoring', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE. View/Download from: Publisher's site
Ghosh, S, Goh, DJ, Koh, Y, Sharma, J, Da Toh, W, Chen, W, Zhang, Y, Ng, E, Lal, A & Lee, JE-Y 1970, 'Wake-Up IoT Wireless Sensing Node Based on a Low-G Threshold mems Inertial Switch with Reliable Contacts', 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS), 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS), IEEE. View/Download from: Publisher's site
Ghosh, S, Ramegowda, PC, Jian Goh, D, Sharma, J, Koh, Y & Lee, JE-Y 1970, 'Extraction of Material Properties of A Thin Silicon Membrane Embedded in A Piezoelectric Stack', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE. View/Download from: Publisher's site
Granatosky, M, Young, M, Dickinson, E, Tanis, D, Ratkiewicz, A, Hanna, C, Currier, A, Kong, F & Webster, C 1970, 'The Onset of Beak and Tail Use are Triggered by Changes in Substrate Orientation in Parrots', INTEGRATIVE AND COMPARATIVE BIOLOGY, OXFORD UNIV PRESS INC, pp. S171-S171.
Grigorev, A, Saleh, K & Mihaita, A-S 1970, 'Traffic Accident Risk Forecasting using Contextual Vision Transformers with Static Map Generation and Coarse-Fine-Coarse Transformers', 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), IEEE. View/Download from: Publisher's site
Guan, J, Fang, W, Huang, M & Ying, M 1970, 'Detecting Violations of Differential Privacy for Quantum Algorithms.', CoRR.
Guan, J, Liu, Y, Zhu, Q, Zheng, T, Han, J & Wang, W 1970, 'Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. View/Download from: Publisher's site
Guan, J, Xiao, F, Liu, Y, Zhu, Q & Wang, W 1970, 'Anomalous Sound Detection Using Audio Representation with Machine ID Based Contrastive Learning Pretraining', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. View/Download from: Publisher's site
Guan, L, Abbasi, A & Merigó, JM 1970, 'Applying social network analysis to risk management of complex projects', The 7th Annual Australian Social Network Analysis Conference, Sydney, Australia.
Guan, L, Chakrabortty, RK, Abbasi, A & Merigó, JM 1970, 'An intelligent decision support system for defence supply chain risk management', The 20th ANZAM Symposium 2023 Operations, Supply Chain and Services Management, The 20th ANZAM Operations, Supply Chain and Services Management Symposium, Sydney, Australia.
Guan, L, M. Merigó, J, K. Chakrabortty, R & Abbasi, A 1970, 'A Simulation-Optimisation-Based Decision Support System for Optimising Project Risk Treatment Decisions Considering Risk Interdependencies', Proceedings of the International Conference on Industrial Engineering and Operations Management, 2nd Australian International Conference on Industrial Engineering and Operations Management, IEOM Society International, Melbourne, Australia. View/Download from: Publisher's site
Gunawan, R, Tran, Y, Zheng, J, Nguyen, H & Chai, R 1970, 'Implementing Natural Image Quality Evaluator for Performance Indicator on Noise Artefacts Recovery in CT Scan', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Guo, CA, Guo, YJ, Wu, K & Yuan, J 1970, 'A New Technique to Form Steerable Multibeams for Integrated Communications and Sensing', 2023 IEEE International Conference on Communications Workshops (ICC Workshops), 2023 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE. View/Download from: Publisher's site
Guo, CA, Jay Guo, Y, Zhu, H & Yuanr, J 1970, 'Optimizing Multibeam Feed Networks for Antenna Arrays with Independent Beam Steering and Low Sidelobes', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Guo, K, Liu, C, Li, C & Guo, Y 1970, 'Comparison Analysis of Doubly Salient Permanent Magnet Machine', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Guo, L, Xiong, J, Zhou, J & Liu, B 1970, 'Regional Scanning Strategy of UAV Cluster Platform for Mobile Emergency Broadcasting', 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, pp. 1-6. View/Download from: Publisher's site
Guo, W-J, Xie, W, Jiang, K, Li, Y, Lei, J & Fang, L 1970, 'Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Guo, Y, Zhu, J, Lei, G, Lu, H & Jin, J 1970, 'Characterization of Electromagnetic Materials under 2D/3D Rotational Magnetization', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Guo, YJ 1970, 'A New Circuit-Type Multibem Antenna Array Employing Generalized Joined Coupler Matrix', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE. View/Download from: Publisher's site
Guo, YJ 1970, 'Phased Multibeam Antennas Employing Generalized Joined Coupler Matrix', 2023 International Workshop on Antenna Technology (iWAT), 2023 International Workshop on Antenna Technology (iWAT), IEEE. View/Download from: Publisher's site
Hadgraft, RG, Trede, F & Rummler, M 1970, 'HOW DO TEACHERS RESPOND TO SUSTAINED CHANGE?', SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings, pp. 536-546. View/Download from: Publisher's site View description>>
Higher Education is facing profound shifts. Employers seek graduates who can work effectively with others in rapidly changing contexts, defined by globalisation, diversity, digitalisation, climate change, complexity, a European war, and a recent global pandemic. The latter caused an instantaneous switch to online learning, where academics were forced to conduct their normally face to face classes through video conferencing tools. The calls for sustained change are challenging academics to rethink their traditional teaching roles and to develop new understandings of future-oriented learning methods and goals for their students. This paper describes the research we have conducted into how academics have responded to these challenges, both short term (emergency remote teaching) and the long-term shift to new ways of teaching (e.g., for transdisciplinary learning working with diverse communities on their solutions). The authors have explored this issue over the last two years, using qualitative research methods, through workshops and interviews, which have been analysed for major themes.
Halder, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 1970, 'A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video', Pattern Recognition, Asian Conference on Pattern Recognition, Springer Nature Switzerland, Kitakyushu, Japan, pp. 30-44. View/Download from: Publisher's site View description>>
Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure.
Han, M, Wang, Y, Li, Z, Yao, L, Chang, X & Qiao, Y 1970, 'HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Data-driven computational algorithms for predicting electricity consumption missing values: a comparative study', Australasian Universities Power Engineering Conference, Adelaide, Australia.
Hasan, M, Shah, R, Amjady, N, Hossain, MJ & Islam, S 1970, 'Impact of MMC-HVDC Control on Power System Dynamics: Various Concepts and Parameterization', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Hason Rudd, D, Huo, H & Xu, G 1970, 'An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance', Advances in Knowledge Discovery and Data Mining, Springer Nature, pp. 219-231. View/Download from: Publisher's site View description>>
AbstractEmotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis. Valuable information underlying the emotions are significant for human-computer interactions enabling intelligent machines to interact with sensitivity in the real world. Previous ER studies through speech signal processing have focused exclusively on associations between different signal mode decomposition methods and hidden informative features. However, improper decomposition parameter selections lead to informative signal component losses due to mode duplicating and mixing. In contrast, the current study proposes VGG-optiVMD, an empowered variational mode decomposition algorithm, to distinguish meaningful speech features and automatically select the number of decomposed modes and optimum balancing parameter for the data fidelity constraint by assessing their effects on the VGG16 flattening output layer. Various feature vectors were employed to train the VGG16 network on different databases and assess VGG-optiVMD reproducibility and reliability. One, two, and three-dimensional feature vectors were constructed by concatenating Mel-frequency cepstral coefficients, Chromagram, Mel spectrograms, Tonnetz diagrams, and spectral centroids. Results confirmed a synergistic relationship between the fine-tuning of the signal sample rate and decomposition parameters with classification accuracy, achieving state-of-the-art 96.09% accuracy in predicting seven emotions on the Berlin EMO-DB database.
He, L, Wang, X, Wang, D, Zou, H, Yin, H & Xu, G 1970, 'Simplifying Graph-based Collaborative Filtering for Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM, Singapore. View/Download from: Publisher's site
He, T, Zhang, W, Wu, M, Lu, DD-C & Zhu, J 1970, 'Virtual Vectors Based Model Predictive Control for Single-Phase Cascaded H-Bridge Converters', 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), IEEE. View/Download from: Publisher's site
He, W, Zhao, J, Yang, L & Guo, Y 1970, 'Study on Magnetic Field Coupling in Integrated Magnetic Suspension Spherical Induction Motor', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
He, Y, Wang, J, Su, D, Nakadai, K, Wu, J, Huang, S, Li, Y & Kong, H 1970, 'Observability Analysis of Graph SLAM-Based Joint Calibration of Multiple Microphone Arrays and Sound Source Localization', 2023 IEEE/SICE International Symposium on System Integration (SII), 2023 IEEE/SICE International Symposium on System Integration (SII), IEEE, Atlanta, GA, USA. View/Download from: Publisher's site View description>>
Multiple microphone arrays have many applications in robot audition including sound source localization audio scene perception and analysis etc However accurate calibration of multiple microphone arrays remains a challenge because there are many unknown parameters to be identified including the Euler angles geometry asynchronous factors between the microphone arrays This paper is concerned with joint calibration of multiple microphone arrays and sound source localization using graph simultaneous localization and mapping SLAM By using a Fisher information matrix FIM approach we focus on the observability analysis of the graph SLAM framework for the above mentioned calibration problem We thoroughly investigate the identifiability of the unknown parameters including the Euler angles geometry asynchronous effects between the microphone arrays and the sound source locations We establish necessary sufficient conditions under which the FIM and the Jacobian matrix have full column rank which implies the identifiability of the unknown parameters These conditions are closely related to the variation in the motion of the sound source and the configuration of microphone arrays and have intuitive and physical interpretations We also discover several scenarios where the unknown parameters are not uniquely identifiable All theoretical findings are demonstrated using simulation data
Heon Lee, JJ, Anstee, S & Fitch, R 1970, 'Automated Slocum Mission Pipeline using Slocum Fleet Mission Control', OCEANS 2023 - Limerick, OCEANS 2023 - Limerick, IEEE. View/Download from: Publisher's site
Hieu, NQ, Chu, NH, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'A Unified Resource Allocation Framework for Virtual Reality Streaming over Wireless Networks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Hieu, NQ, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'Toward BCI-Enabled Metaverse: A Joint Learning and Resource Allocation Approach', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE. View/Download from: Publisher's site
Ho-Le, TP, Tran, TS, Nguyen, HG, Center, JR, Eisman, JA & Nguyen, TV 1970, 'Genetic Prediction of Lifetime Risk of Fracture', The Journal of Clinical Endocrinology & Metabolism, WCO-IOF-ESCEO World Congress on Osteoporosis, Osteoarthritis and Musculoskeletal Diseases, The Endocrine Society, Florence, ITALY, pp. e1403-e1412. View/Download from: Publisher's site View description>>
AbstractContextFragility fracture is a significant public health problem because it is associated with increased mortality. We want to find out whether the risk of fracture can be predicted from the time of birth.ObjectiveTo examine the association between a polygenic risk score (PRS) and lifetime fracture risk.MethodsThis population-based prospective study involved 3515 community-dwelling individuals aged 60+ years who have been followed for up to 20 years. Femoral neck bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry. A PRS was created by summing the weighted number of risk alleles for each single nucleotide polymorphism using BMD-associated coefficients. Fragility fractures were radiologically ascertained, whereas mortality was ascertained through a state registry. Residual lifetime risk of fracture (RLRF) was estimated by survival analysis.ResultsThe mortality-adjusted RLRF for women and men was 36% (95% CI, 34%-39%) and 21% (18%-24%), respectively. Individuals with PRS > 4.24 (median) had a greater risk (1.2-fold in women and 1.1-fold in men) than the population average risk. For hip fracture, the average RLRF was 10% (95% CI, 8%-12%) for women and ∼5% (3%-7%) for men; however, the risk was significantly increased by 1.5-fold and 1.3-fold for women and men with high PRS, respectively.Conclusion ...
Hong, X, Huang, W-J, Chien, W-C, Feng, Y, Hsieh, M-H, Li, S, Yeh, C-S & Ying, M 1970, 'Decision Diagrams for Symbolic Verification of Quantum Circuits.', QCE, IEEE, pp. 970-977.
Hossain, MI & Eager, D 1970, 'Predicting greyhound speed in the race by creating a historical data plane of race data', Proceedings of the 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, Paris, France.
Hu, R, Wang, X, Chang, X, Hu, Y, Xin, X, Ding, X & Guo, B 1970, 'RASNet: A Reinforcement Assistant Network for Frame Selection in Video-based Posture Recognition', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Brisbane. View/Download from: Publisher's site
Huang, C, Wang, S, Wang, X & Yao, L 1970, 'Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation', Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Taipei, Taiwan. View/Download from: Publisher's site
Huang, C, Wang, S, Wang, X & Yao, L 1970, 'Modeling Temporal Positive and Negative Excitation for Sequential Recommendation', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM, Austin, TX, USA. View/Download from: Publisher's site
Huang, S, Yang, Z, Li, L, Yang, Y & Jia, J 1970, 'AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion', Proceedings of the 31st ACM International Conference on Multimedia, MM '23: The 31st ACM International Conference on Multimedia, ACM. View/Download from: Publisher's site
Huang, W, Liao, X, Qian, Y & Jia, W 1970, 'Learning Hierarchical Semantic Information for Efficient Low-Light Image Enhancement', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8. View/Download from: Publisher's site View description>>
Low-light environments can cause a variety of complex degradation problems, which result in poor visibility in images. As a classical vision task, low-light image enhancement has attracted an increasing interest in the research community. However, the existing methods tend to require a large number of parameters, making them difficult to implement and optimize, especially on resource-constrained devices. In this paper, we mainly focus on the lightweight of the method and propose a novel end-to-end two-stage CNN-ViT architecture (HSINet) to learn hierarchical semantic information (HSI) from low-light images efficiently. The HSINet consists of two stages: the first stage is a CNN-based low-level semantic (LS) Stage, and the second stage is ViT-based high-level semantic (HS) Stage. The LS Stage contains an efficient multi-scale convolution block, MLS Block, for low-level semantic information extraction. The HS stage, on the other hand, aims to learn the high-level semantic features via ViT's excellent global-learning capability. We propose a hierarchical Swin Transformer-based block, HS Block, to gradually enlarge Swin Transformer's window size as the network becomes deeper, to learn hierarchical high-level semantic information. Benefiting from the efficient architecture, our model only contains 0.6M parameters, far fewer than the existing SOTAs. We evaluated the method on three challenging benchmark datasets: LOL, VE-LOL, and MIT-Adobe FiveK, using three popular evaluation metrics. The quantitative and qualitative results both show that the proposed method not only outperforms the state of the arts in terms of PSNR, SSIM, LPIPS, and visual effects, but also with better efficiency.
Huang, Y, Wen, D, Lai, L, Qian, Z, Qin, L & Zhang, Y 1970, 'Efficient and Effective Path Compression in Large Graphs.', ICDE, pp. 3093-3105.
Huang, Z, Lin, S, Liu, G, Luo, M, Ye, C, Xu, H, Chang, X & Liang, X 1970, 'FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Hull, R, Brian Lee, KM, Wakulicz, J, Yoo, C, McMahon, J, Clarke, B, Anstee, S, Kim, J & Fitch, R 1970, 'Decentralised Active Perception in Continuous Action Spaces for the Coordinated Escort Problem', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Huynh, NV, Quang Hieu, N, Chu, NH, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Eavesdroppers with Ambient Backscatter Communications', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6. View/Download from: Publisher's site View description>>
Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter technology in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., a secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.
Ibanez-Hidalgo, I, Aguilera, RP, Sanchez-Ruiz, A, Perez-Basante, A, Zubizarreta, A & Ceballos, S 1970, 'SHC-PWM Closed-Loop Control Based on PI Controllers for Active Power Filters', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Indrajith, B, Gunawardane, K & Jayasinghe, H 1970, 'A Review: DC Microgrids for Sustainable Power Delivery in Offshore Industries', Volume 10: Professor Ian Young Honouring Symposium on Global Ocean Wind and Wave Climate; Blue Economy Symposium; Small Maritime Nations Symposium, ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, American Society of Mechanical Engineers. View/Download from: Publisher's site View description>>
AbstractLegacy Alternating Current (AC) based power systems, coupled with bulk-generated electrical energy from fossil fuels, are working against the achievement of sustainable development. Worldwide integration of renewable energy in power generation is growing rapidly. This is making a major contribution to the achievement of sustainable development goals and affordable clean energy. Direct Current (DC) operable products are becoming the most common type of internal power architecture in many application domains in both land and marine based systems. In these systems, power supplied from AC form is internally converted to DC within the appliance. Renewable-based power generation, including solar photovoltaics (PVs), wind, wave, and tidal generators, can generate DC power intrinsically or can be converted to DC power. In addition to that the supportive novel technologies developing around renewable energy generation such as hydrogen storages, fuel cells and associated other alternative energy storage technologies such as battery and supercapacitors all are intrinsically in DC form. Therefore, both the supply and the demand sides of power systems are in favor of moving towards DC systems called DC microgrids. With the elimination of DC/AC and AC/DC converter stages in DC grids, they create a highly efficient, low-cost platform. Also impacts such as skin effect, reactive power usage, power quality issues and grid synchronization are comprehensively reduced too. Since renewable sources like photovoltaics generate on-site DC power, eliminating the need for long distance AC transmission facilities, they are particularly attractive for remote communities and industrial and commercial sites. Since the minimum interaction on main power grids, offshore systems are more attractive to be operated as low voltage DC distribution. DC microgrids have the potential to revolutionize power systems by offering a versatile ...
Indraratna, B, Nguyen, TT, Arivalagan, J, Singh, M, Rujikiatkamjorn, C & Doan, T 1970, 'Undrained instability and fluidization of soft subgrade soils under cyclic rail loading', CRC Press, pp. 10-24. View/Download from: Publisher's site
Indraratna, B, Qi, Y, Ngo, T & Rujikiatkamjorn, C 1970, 'Use of Synthetic Energy Absorbing Layer (SEAL) in Rail Substructure to Minimize Track Degradation', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 343-352. View/Download from: Publisher's site View description>>
This paper presents novel solutions for increasing the stability and resiliency of track structures by developing a synthetic energy-absorbing layer (SEAL) using recycled rubber products based on large-scale laboratory tests (i.e., drop-hammer impact tests and cubic triaxial tests). This includes (1) installing recycled rubber mats under ballast, and (2) using SEAL composed of a mixture of rubber crumbs, steel furnace slag, and coal wash with changing amounts of rubber: 0%, 10%, 20%, 30%, and 40% (by weight) to replace traditional rockfill as subballast. The test results confirm that the inclusion of a recycled rubber mat underneath the ballast layer actively reduces ballast deformation and the propagation of impact loading within the depth of the substructure. Also, the SEAL mixture with 10% rubber reduces the track lateral dilation, reduces ballast breakage and load distribution, and also maintains an acceptable settlement.
Indraratna, B, Rujikiatkamjorn, C, Qi, Y & Kulappu Arachchige, CM 1970, 'Assessment of initial compaction characteristics of Rubber Intermixed Ballast System', Proceedings of the 14th Australia and New Zealand Conference on Geomechanics, Cairns, Australia.
Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 1970, 'K-Nearest Neighbours and Ensemble Based Real-Time Hand Gesture Recognition for Powered Wheelchair', 2023 International Conference on Advanced Mechatronic Systems (ICAMechS), 2023 International Conference on Advanced Mechatronic Systems (ICAMechS), IEEE. View/Download from: Publisher's site
Islam, MR, Kowsar Hossain Sakib, M, Prome, SA, Wang, X, Ulhaq, A, Sanin, C & Asirvatham, D 1970, 'Machine Learning with Explainability for Suicide Ideation Detection from Social Media Data', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE, Larnaca, Cyprus. View/Download from: Publisher's site
Islam, MT, Hossain, MJ & Habib, MA 1970, 'Machine Learning-based Hosting Capacity Analysis and Forecasting in Low-Voltage Networks', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Ivanyos, G & Qiao, Y 1970, 'On the orbit closure intersection problems for matrix tuples under conjugation and left-right actions', Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 4115-4126. View description>>
Let G be a linear algebraic group acting on the vector space V. Given v, v′ ∈ V, the orbit closure intersection problem asks to decide if the orbit closures of v and v′ under G intersect. Due to connections with polynomial identity testing, the orbit closure intersection problems for the conjugation and left-right actions on matrix tuples received considerable attention in computational complexity and computational invariant theory, as seen in the works of Forbes-Shpilka (RANDOM 2013), Allen-Zhu-Garg-Li-Oliveira-Wigderson (STOC 2018), and Derksen-Makam (Algebra & Number Theory 2020). In this paper, we present new algorithms for the orbit closure problem for the conjugation and left-right actions on matrix tuples. The main novel feature is that in the case of intersecting orbit closures, our algorithm outputs cosets of one-parameter subgroups that drive the matrix tuples to a tuple in the intersection of the orbit closures.
Jahan, N, Sultana, Z, Chowdhury, F, Ahmed, S, Parvez, MZ, Barua, PD & Chakraborty, S 1970, 'A Comparison of LSTM and GRU for Bengali Speech-to-Text Transformation', Springer Nature Switzerland, pp. 214-224. View/Download from: Publisher's site
Jayan Chirayath Kurian, J, Zeena, A, Simon, T, Luke, N-H & Rosetta, R 1970, 'INDUSTRY-BASED IT CERTIFICATIONS IN HIGHER EDUCATION INSTITUTIONS: A STAKEHOLDER PERSPECTIVE', Proceedings of the AIS SIGED 2023 Conference 2023, Hyderabad, India.
Jefry, W, Al-Doghman, F & Hussain, F 1970, 'Comparison of Artificial Intelligence Models in Cross-lingual Transfer Learning through Sentiment Analysis', 2023 IEEE International Conference on e-Business Engineering (ICEBE), 2023 IEEE International Conference on e-Business Engineering (ICEBE), IEEE. View/Download from: Publisher's site
Jiang, X, Liu, F, Fang, Z, Chen, H, Liu, T, Zheng, F & Han, B 1970, 'Detecting Out-of-distribution Data through In-distribution Class Prior', Proceedings of Machine Learning Research, 2023 International Conference on Machine Learning, Hawaii, pp. 15067-15088. View description>>
Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD) detection aims to recognize OOD data during the inference stage. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., these OOD-to-ID probabilities actually form a uniform distribution. In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and propose two strategies to modify existing inference-time detection methods: 1) replace the uniform distribution with the ID-class-prior distribution if they explicitly use the uniform distribution; 2) otherwise, reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model. Extensive experiments show that both strategies can improve the OOD detection performance when the ID model is pre-trained with imbalanced data, reflecting the importance of ID-class prior in OOD detection. The codes are available at https://github.com/tmlr-group/class_prior.
Jin, P, Wang, W, Guo, Y, Lei, G & Zhu, J 1970, 'Full Predictive Control of Permanent Magnet Synchronous Machine Based on Differential-Free Disturbance Position-Torque Observer and Online Parameter Identification', 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), IEEE. View/Download from: Publisher's site
John, BM, Jose, S, Thomas, J & Jayan Chirayath Kurian, J 1970, 'Designing an Artifact to Empower Chronic Patients for Monitoring Health During a Pandemic: A COVID-19 Screening App', Maui, US.
Karetla, GR, Catchpoole, D, Kennedy, P, Simoff, S & Nguyen, QV 1970, 'IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data', 2023 Australasian Computer Science Week, ACSW 2023: 2023 Australasian Computer Science Week, ACM. View/Download from: Publisher's site
Karmaker, AK, Sturmberg, B, Behrens, S, Hossain, MJ & Pota, H 1970, 'Characterizing Electric Vehicle Plug-in Behaviors Using Customer Classification Approach', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Keshavarz, R, Winson, D, Lipman, J, Abolhasan, M & Shariati, N 1970, 'Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Khan, AF & Nanda, P 1970, 'C-Block: A Secure and Robust Framework for Authentication Handover in 5G HetNets based on Edge-enabled SDN/NFV Environments', 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023 International Wireless Communications and Mobile Computing (IWCMC), IEEE, Marrakesh, Morocco. View/Download from: Publisher's site
Khoa Le, DD, Hu, G, Liu, D, Khonasty, R, Zhao, L, Huang, S, Shrestha, P & Belperio, R 1970, 'The QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction', 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), IEEE. View/Download from: Publisher's site
Khorshidi, MS, Yazdani, D, Mańdziuk, J, Nikoo, MR & Gandomi, AH 1970, 'A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis', 2023 IEEE Congress on Evolutionary Computation (CEC), 2023 IEEE Congress on Evolutionary Computation (CEC), IEEE. View/Download from: Publisher's site
Kim, J, Xuan, J, Liang, C & Hussain, F 1970, 'An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Kridalukmana, R, Naderpour, M, Ramezani, F, Lu, H & Xavier, P 1970, 'HVAC System Air Filter Maintenance: A Fuzzy Machine-Learning-Based System', 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE. View/Download from: Publisher's site
Kulappu Arachchige, CM, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 1970, 'Experimental Study of Rubber Intermixed Ballast Stratum Subjected to Monotonic and Cyclic Loads', Geocongress 2023 Proceedings, ASCE library org, Los Angeles, California, pp. 565-574.
Kulappu Arachchige, CM, Indraratna, B, Qi, Y & Tawk, M 1970, 'A Sustainable Foundation: Recycling Waste Rubber Tyres in the Railway Track', Proceedings of the 9ICEG 9th International Congress on Environmental Geotechnics, Chania, Greece. View/Download from: Publisher's site
Lai, M, Cao, L, Lu, H, Ha, Q, Li, L, Hossain, J & Kennedy, P 1970, 'An Unsupervised Hierarchical Clustering Approach to Improve Hopfield Retrieval Accuracy', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site View description>>
Despite its efficiency, the classical Hopfield network was a highly impractical data-searching solution due to its limited storage capacity. While the recently released modern Hopfield variant has increased its storage capacity, its searching ability is heavily affected by local minima and saddle points, which prevented it from becoming a worthy successor of the classical Hopfield network. We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network. Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over (20%) in general against the Hopfield Update Rule, proving that it is a far superior modelling solution.
Langie, G, Willey, K & Gardner, A 1970, 'COMMUNITY-BUILDING AMONG PHD STUDENTS IN ENGINEERING EDUCATION RESEARCH: THE SEFI SUMMER SCHOOL AND AAEE WINTER SCHOOL', SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings, pp. 720-728. View/Download from: Publisher's site View description>>
Engineering Education Research (EER) is a rapidly evolving and increasingly valued research field. This supports the number of PhD students to grow steadily, but unfortunately, they are often limited to a few within the large engineering faculty/department, having different backgrounds and interests. Additionally, the research methodologies needed by EER researchers are usually different from the classical technical engineering research (TER) methodologies. This translates into a need for specific training and opportunities to get to know each other better in order to promote international collaboration and develop a community of practice. SEFI and the Australasian Association for Engineering Education (AAEE) both organized a summer/winter school for EER PhD students in 2022, attended by 34/14 participants respectively (note: attendance at the AAEE winter school is not limited to PhD students). We have designed a survey to elicit a mixture of background information (facts), perception data (opinions), and evaluative data (evaluation of the school). By using confirmatory factor analysis on half of the items and descriptive statistical analysis of all data, we aim to provide insights into the success factors of these schools. Both schools attracted a diverse group of EER-PhD students in different areas. The SEFI summer school excelled in building an inclusive and international research community, whereas the AAEE winter school was superior in gaining domain-specific knowledge needed for EER research. The results contribute to a more nuanced understanding of the issues experienced by researchers who are beginning their career in EER and support organizers in designing international research schools.
Lee, JJH, Yoo, C, Anstee, S & Fitch, R 1970, 'Efficient Optimal Planning in non-FIFO Time-Dependent Flow Fields', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Lee, SS, Barzegarkhoo, R, Siwakoti, YP, Grigoletto, FB & Lee, K-B 1970, 'Single-Phase 3-Level and 5-Level Boost Inverters without High-Frequency Common-Mode Voltage', 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia), 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia), IEEE. View/Download from: Publisher's site
Leong, D, Do, TT-T & Lin, C-T 1970, 'Distinction of the object recognition and object identification in the brain-computer interfaces applications', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Lestari, NI, Hussain, W, Merigo, JM & Bekhit, M 1970, 'A Survey of Trendy Financial Sector Applications of Machine and Deep Learning', Springer Nature Switzerland, pp. 619-633. View/Download from: Publisher's site
Li, B, Guo, T, Zhu, X, Li, Q, Wang, Y & Chen, F 1970, 'SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM. View/Download from: Publisher's site
Li, B, Zhu, J, Liu, C, Lei, G & Li, Y 1970, 'Impact of Soft Magnetic Material on Electromagnetic Force and Vibration of Permanent Magnet Electrical Machines', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site View description>>
Electromagnetic vibration is excited by the radial electromagnetic force generated by the air-gap magnetic field acting on the stator core. In this paper, an analysis and reduction of electromagnetic force in high-speed permanent magnet electrical machines with different stator cores are investigated. A fast and accurate calculation method of air gap flux density and radial force density distribution are derived. The radial force density including amplitude, frequency, and order is obtained by a two-dimension fast Fourier transform. Then, the effect of the different PM segmentation on radial force is summarized.
Li, C, Guo, K, Liu, C & Guo, Y 1970, 'High-Precision Spiral Motion Control of Linear Rotary Motor With E-shaped Stator', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Li, G, Kang, G, Wang, X, Wei, Y & Yang, Y 1970, 'Adversarially Masking Synthetic to Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Li, G, Wang, Y, McGill, M, Pöhlmann, K, Brewster, S & Pollick, F 1970, 'Resting-state EEG in the Vestibular Region Can Predict Motion Sickness Induced by a Motion-Simulated in-car VR Platform', 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. View/Download from: Publisher's site
Li, H, Wang, X, Yu, G, Ni, W & Liu, RP 1970, 'A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Li, J & Liu, W 1970, 'Summarization Attack via Paraphrasing', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 16250-16251. View description>>
Many natural language processing models are perceived to be fragile on adversarial attacks. Recent work on adversarial attack has demonstrated a high success rate on sentiment analysis as well as classification models. However, attacks to summarization models have not been well studied. Summarization tasks are rarely influenced by word substitution, since advanced abstractive summary models utilize sentence level information. In this paper, we propose a paraphrasing-based attack method to attack summarization models. We first rank the sentences in the document according to their impacts to summarization. Then, we apply paraphrasing procedure to generate adversarial samples. Finally, we test our algorithm on benchmarks datasets against others methods. Our approach achieved the highest success rate and the lowest sentence substitution rate. In addition, the adversarial samples have high semantic similarity with the original sentences.
Li, J, Pang, G, Chen, L & Namazi-Rad, M-R 1970, 'HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks', 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE. View/Download from: Publisher's site
Li, K, Lu, J, Zuo, H & Zhang, G 1970, 'Attention-Bridging TS Fuzzy Rules for Universal Multi-Domain Adaptation Without Source Data', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Li, K, Ni, W, Yuan, X, Noor, A & Jamalipour, A 1970, 'Exploring Graph Neural Networks for Joint Cruise Control and Task Offloading in UAV-enabled Mobile Edge Computing', 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), IEEE. View/Download from: Publisher's site
Li, K, Yang, Z, Chen, L, Yang, Y & Xiao, J 1970, 'CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation', Proceedings of the 31st ACM International Conference on Multimedia, MM '23: The 31st ACM International Conference on Multimedia, ACM. View/Download from: Publisher's site
Li, K, Yuan, X, Zheng, J, Ni, W & Guizani, M 1970, 'Exploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things', 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023 International Wireless Communications and Mobile Computing (IWCMC), IEEE. View/Download from: Publisher's site
Li, L, Chen, G, Xiao, J, Yang, Y, Wang, C & Chen, L 1970, 'Compositional Feature Augmentation for Unbiased Scene Graph Generation', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Li, L, Miao, J, Shi, D, Tan, W, Ren, Y, Yang, Y & Pu, S 1970, 'Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Li, L, Wang, W, Zhou, T, Li, J & Yang, Y 1970, 'Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Li, M, Lin, B, Chen, Z, Lin, H, Liang, X & Chang, X 1970, 'Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Li, Q, Wu, D & Gao, W 1970, 'Insights into the Size Effect of the Dynamic Characteristics of the Perovskite Solar Cell', Springer Nature Singapore, pp. 353-357. View/Download from: Publisher's site View description>>
AbstractDriven by government policy and incentives, solar power production has soared in the past decade and become a mainstay during the worldwide clean-power transition process. Among the various next-generation photovoltaic technologies, perovskite solar cells (PSCs) are the most important emerging area of research due to their outstanding power conversion efficiency and affordable scale-up operation. We adopted the nonlocal strain gradient theory and the first-order shear deformation plate theory to investigate the size-dependent free vibration behavior of PSCs. The size-dependency in the nanostructure of the PSCs was captured by coupling the nonlocal and strain gradient parameters. In accordance with the Hamilton principle, the governing equations set was derived. Subsequently, the Galerkin procedure was applied to address the dynamic characteristics analysis of PSCs with simply supported and clamped edges. Compared with the size-insensitive traditional continuum plate model, the current multiscale framework revealed a size effect on the free vibration of the PSC. Moreover, some parametric experiments were conducted to explore the impacts of scale length parameter, nonlocal parameter, and boundary conditions on the natural frequency of the PSC.
Li, S, Nguyen, KD, Clare, Z & Feng, Y 1970, 'Single-Qubit Gates Matter for Optimising Quantum Circuit Depth in Qubit Mapping', 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), IEEE. View/Download from: Publisher's site
Li, S, Unanue, IJ & Piccardi, M 1970, 'Improving Machine Translation and Summarization with the Sinkhorn Divergence', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 149-161. View/Download from: Publisher's site View description>>
Important natural language processing tasks such as machine translation and document summarization have made enormous strides in recent years. However, their performance is still partially limited by the standard training objectives, which operate on single tokens rather than on more global features. Moreover, such standard objectives do not explicitly consider the source documents, potentially affecting their alignment with the predictions. For these reasons, in this paper, we propose using an Optimal Transport (OT) training objective to promote a global alignment between the model’s predictions and the source documents. In addition, we present an original implementation of the OT objective based on the Sinkhorn divergence between the final hidden states of the model’s encoder and decoder. Experimental results over machine translation and abstractive summarization tasks show that the proposed approach has been able to achieve statistically significant improvements across all experimental settings compared to our baseline and other alternative objectives. A qualitative analysis of the results also shows that the predictions have been able to better align with the source sentences thanks to the supervision of the proposed objective.
Li, T, Song, Y, Walker, P, Pan, K, van de Graaf, VA, Zhao, L & Huang, S 1970, 'A Closed-Form Solution to Electromagnetic Sensor Based Intraoperative Limb Length Measurement in Total Hip Arthroplasty', Springer Nature Switzerland, pp. 365-375. View/Download from: Publisher's site
Li, Y, Quan, R, Zhu, L & Yang, Y 1970, 'Efficient Multimodal Fusion via Interactive Prompting', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Li, Y, Zhou, J, Dong, Y, Shafiabady, N & Chen, F 1970, 'ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks', Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management, ACM, pp. 1259-1267. View/Download from: Publisher's site
Li, Z, Wang, X, Yang, C, Yao, L, McAuley, J & Xu, G 1970, 'Exploiting Explicit and Implicit Item relationships for Session-based Recommendation', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM, Singapore. View/Download from: Publisher's site
Liang, Y, Wang, X, Zhu, L & Yang, Y 1970, 'MAAL: Multimodality-Aware Autoencoder-based Affordance Learning for 3D Articulated Objects', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Lidfors Lindqvist, A, Willey, K, Lidfors, L & Francis, B 1970, 'Formative Sprints to improve feedback, learning, and fidelity inpractice-based activities', https://www.aaee2023.org/fullpapers/AAEE_2023_final_paper_65.pdf, 34th Australasian Association for Engineering Education Conference, Gold Coast.
Lin, J-Y, Yang, Y, Zhang, T & Wong, S-W 1970, 'A 250 GHz Low-Loss Inline Waveguide Bandpass Filter Using Bandstop Resonator Pairs', 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023, 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023, IEEE. View/Download from: Publisher's site
Lin, K, Wang, X, Zhu, L, Sun, K, Zhang, B & Yang, Y 1970, 'Gloss-Free End-to-End Sign Language Translation', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 12904-12916. View description>>
In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss annotations are hard to acquire, especially in large quantities. This limits the domain coverage of translation datasets, thus handicapping real-world applications. To mitigate this problem, we design the Gloss-Free End-to-end sign language translation framework (GloFE). Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation. Common concepts are extracted from the text and used as a weak form of intermediate representation. The global embedding of these concepts is used as a query for cross-attention to find the corresponding information within the learned visual features. In a contrastive manner, we encourage the similarity of query results between samples containing such concepts and decrease those that do not. We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign.
Lin, X, Sun, X, Yang, Z, Lei, G, Guo, Y & Zhu, J 1970, 'Torque Enhancement Scheme Based on Deadbeat Harmonic Current Control with Extended State Observer for Dual Three-Phase PMSM', 2023 26th International Conference on Electrical Machines and Systems (ICEMS), 2023 26th International Conference on Electrical Machines and Systems (ICEMS), IEEE. View/Download from: Publisher's site
Lindeck, J, Farmer, I, Miao, G & Machet, T 1970, 'AI’ming for success: How can students leverage AI in project-based learning?', Goldcoast.
Liu, A, Han, Q, Xia, L & Yu, N 1970, 'Accelerating Voting by Quantum Computation', Proceedings of Machine Learning Research, pp. 1274-1283. View description>>
Studying the computational complexity and designing fast algorithms for determining winners under voting rules are classical and fundamental questions in computational social choice. In this paper, we accelerate voting by leveraging quantum computation: we propose a quantum-accelerated voting algorithm that can be applied to any anonymous voting rule. We show that our algorithm can be quadratically faster than any classical algorithm (based on sampling with replacement) under a wide range of common voting rules, including positional scoring rules, Copeland, and single transferable voting (STV). Precisely, our quantum-accelerated voting algorithm outputs the correct winner with high probability in (Equation presented) time, where n is the number of votes and MoV is margin of victory, the smallest number of voters to change the winner. In contrast, any classical voting algorithm based on sampling with replacement requires (Equation presented) time under a large class of voting rules. Our theoretical results are supported by experiments under plurality, Borda, Copeland, and STV.
Liu, B, Liu, B, Ding, M, Zhu, T & Yu, X 1970, 'TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4680-4689. View/Download from: Publisher's site
Liu, J, Zhang, Q, Shi, C, Naseem, U, Wang, S, Hu, L & Tsang, IW 1970, 'Causal Intervention for Abstractive Related Work Generation', Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, pp. 2148-2159. View description>>
Abstractive related work generation has attracted increasing attention in generating coherent related work that helps readers grasp the current research. However, most existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models' generation quality and generalizability. In this study, we argue that causal intervention can address such limitations and improve the quality and coherence of generated related work. To this end, we propose a novel Causal Intervention Module for Related Work Generation (CaM) to effectively capture causalities in the generation process. Specifically, we first model the relations among the sentence order, document (reference) correlations, and transitional content in related work generation using a causal graph. Then, to implement causal interventions and mitigate the negative impact of spurious correlations, we use do-calculus to derive ordinary conditional probabilities and identify causal effects through CaM. Finally, we subtly fuse CaM with Transformer to obtain an end-to-end related work generation framework. Extensive experiments on two real-world datasets show that CaM can effectively promote the model to learn causal relations and thus produce related work of higher quality and coherence.
Liu, K, Liu, F, Wang, H, Ma, N, Bu, J & Han, B 1970, 'Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Liu, K, Zhao, F, Xu, G & Wu, S 1970, 'IE-Evo: Internal and External Evolution-Enhanced Temporal Knowledge Graph Forecasting', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE, Shanghai, China. View/Download from: Publisher's site
Liu, K, Zhao, F, Xu, G, Wang, X & Jin, H 1970, 'RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE, Anaheim, CA, USA. View/Download from: Publisher's site
Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Comparative Iron Loss Analysis of the Interior PMSMs for Electric Vehicles Considering the Effects of Temperature Variation', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Review of Data-Driven Artificial Intelligence Applications in Electric Machines and Drive Systems', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Liu, L, Guo, Y, Lei, G, Yin, W & Zhu, J 1970, 'Iron Loss Analytical Prediction of IPMSMs Considering Multi-factor Effects Over the Drive Cycle of Electric Vehicles', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Liu, L, Guo, Y, Lei, G, Yin, W & Zhu, J 1970, 'Multi-objective Design Optimization of an IPMSM Drive System Based on Loss Minimization Control Strategy', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Liu, X, Cheng, X, Yang, Y, Huo, H, Liu, Y & Nielsen, PS 1970, 'Understanding crowd energy consumption behaviors', Advances in Database Technology - EDBT, pp. 799-802. View/Download from: Publisher's site View description>>
Understanding crowd behavior is crucial for energy demand-side management. In this paper, we employ the fluid dynamics concept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.
Liu, Y, Qi, M, Wu, Q, Yang, Y, Li, X & Zhang, J 1970, 'Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE. View/Download from: Publisher's site
Liu, Y, Zhang, H, Sun, Z, Wang, S & Zhang, J 1970, 'WSDM 2023 Workshop on Interactive Recommender Systems', Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining, ACM, pp. 1275-1276. View/Download from: Publisher's site View description>>
Interactive recommender systems have attracted increasingly research attentions from both academia and industry. This workshop is a half-day event, which provides a forum for researchers and practitioners to discuss recent research progress and novel research directions about interactive recommender systems. The program will include two keynotes and 6 to 8 research paper presentations. The objective of this workshop is to consolidate the recent technical progresses about interactive recommendation, which will be a promising research and development direction for future recommendation technologies. This workshop will attract the attention of researchers from both academia and industry. It aligns with WSDM's spirit of promoting the collaborations between academia and industry.
Lowe, DB, Willey, K & Tilley, E 1970, 'STUDENTS' VIEWS OF TAUGHT PROFESSIONAL COMPETENCIES: INVESTIGATING THE IMPACT OF PREVIOUS WORK EXPERIENCE', SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings, pp. 802-809. View/Download from: Publisher's site View description>>
The ability of Engineering graduates to function as successful professionals depends not only on technical disciplinary knowledge but also on a wide range of professional competencies. Students' reactions to the teaching and assessment of these competencies are often negative. An ongoing study by the authors has been exploring the nature of these reactions and in particular, the various factors that contribute to students' views on the teaching of professional competencies. A preliminary factor analysis showed that students' level of professional experience was a key factor in shaping variations in their views. In this paper, we explore this issue in more depth. For example, when asked on the pair of survey questions “do you agree or disagree that each competency type [professional / technical] should be a core component of your Engineering degree program”, the impact of increasing professional experience on the average response was only marginally greater for professional competencies than for technical competencies. In contrast to this, when asked the pair of questions “for each competency type [professional / technical] indicate whether it is easier to learn it at University or at work”, the analysis of the responses shows that as the level of experience increases, there is a small shift for technical competencies towards being taught at University, whereas for professional competencies, there is a significantly greater shift towards being taught in work environments. We explore these, and other related findings, and consider their implications for the design and delivery of engineering degree programs.
Lu, K, Zhang, Q, Zhang, G & Lu, J 1970, 'BERT-RS: A neural personalized recommender system with BERT', Machine Learning, Multi Agent and Cyber Physical Systems, Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), WORLD SCIENTIFIC. View/Download from: Publisher's site
Luo, Y, Chen, Z, Fang, Z, Zhang, Z, Baktashmotlagh, M & Huang, Z 1970, 'Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Pairs. View/Download from: Publisher's site
Luo, Y, Lu, L, Cui, X, Du, Y, Bi, Y, Zhu, L & Liang, CJ 1970, 'Novel few-shot learning based fuzzy feature detection algorithms', 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE. View/Download from: Publisher's site
Luo, Z, Jia, W & Perry, S 1970, 'Transformer-based Geometric Point Cloud Compression with Local Neighbor Aggregation', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Lyv, P, Leng, D, Li, Y, Xu, T, Huixing, W & Xu, H 1970, 'Semi-active magnetorheological suspension of a full-vehicle model based on combined vertical and attitude control', 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE. View/Download from: Publisher's site
Ma, G, Lu, J & Zhang, G 1970, 'Interval-Valued Observations-Based Multi-Source Domain Adaptation Using Fuzzy Neural Networks', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Ma, M, Zhang, Y, Arachchige, PCM, Zhang, LY, Chhetri, MB & Bai, G 1970, 'LoDen: Making Every Client in Federated Learning a Defender Against the Poisoning Membership Inference Attacks', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Machet, T, Leigh, E, Tumpa, R, Willey, K & Moslemi Naeni, L 1970, 'Towards a Complexity Literacy Model.', Australasian Association for Engineering Education Conference, Gold Coast.
Mahajan, A, Wittwer, C & Siwakoti, Y 1970, 'Ultra-Wide Voltage Gain Hybrid Modulated CLLC Converter for Electric Vehicle', 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), IEEE. View/Download from: Publisher's site
Makhalfih, A, Hossain, MJ, Macana, C & Pota, HR 1970, 'Distributed Energy Resources Hosting Capacity Assessment - An Industry Perspective', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Malek, S, Cheraghi-Shirazi, N, Crews, K, Parra, R, Creagh, A & Khoshkbari, P 1970, 'A COMPARATIVE STUDY OF DESIGN STANDARDS FOR ASSESSMENT OF LONG-SPAN STEEL-TIMBER COMPOSITE FLOORS UNDER HUMAN-INDUCED VIBRATION', World Conference on Timber Engineering (WCTE 2023), World Conference on Timber Engineering 2023 (WCTE2023), World Conference on Timber Engineering (WCTE 2023), pp. 1936-1942. View/Download from: Publisher's site View description>>
Steel-timber composite (STC) floors are gaining popularity for residential and commercial buildings worldwide. Adding steel joists to wood-based panels is an attractive option for some designers to increase the span of timber floors. However, there is often a serviceability (vibration) concern with timber composite floors. It is well-known from the literature that human comfort due to vibration is subjective, and people's perception of comfort vanes. Nevertheless, structural engineers still need to consider vibration in designing composite timber floors according to various standards and guidelines, especially when comparing two alternative designs. This study investigates the vibration behavior of STC floors under footfall force using a numerical model validated by experimental data. Transient finite element (FE) analysis is conducted to simulate human walking on the floor. The study also discusses acceptability of STC floors according to guidelines and building codes (e.g., AISC Design Guide 11, ATC, CLT Handbook, Eurocode 5). Lastly, the effects of vanous design parameters, such as CLT thickness, damping ratio, and CLT-to-CLT connection, on the vibration behavior of the composite timber floor are assessed.
Malisetty, RS, Indraratna, B & Kelly, R 1970, 'Displacement of weak rock joints under railway loading using numerical modelling', Australia and New Zealand Conference on Geomechanics,, Cairns, Australia.
Manchanda, C, Hussain, W, Rabhi, L & Rabhi, F 1970, 'Towards an API Marketplace for an e-Invoicing Ecosystem', Springer International Publishing, pp. 82-96. View/Download from: Publisher's site
Manitta, M, Jayasuriya, M & Liu, D 1970, 'A Vector Field-Based Method for Human Action Representation and Recognition During Human-Robot Collaboration.', CASE, 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 1-7. View/Download from: Publisher's site
Mannan, M, Roslan, MF, Reza, MS, Mansor, M, Jern, KP, Hossain, MJ & Hannan, MA 1970, 'An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Manoharan, P, Pranata, A, Tse, KM & Chai, R 1970, 'Estimation of Lumbar Spine Loading of Low Back Pain Participant During Lifting Using an Open Source Musculoskeletal Model', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Martin, L, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'Durability Loss in Concrete Due to ASR-DEF, The Role of Aggregate Reactivity in Deleterious Delayed Ettringite Formation', Concrete 2023 Resilient and Sustainable Concrete: Breaking Down Barriers, Concrete Institute of Australia Biennial National Conference, Perth. View description>>
Durability of concrete and cementitious materials is important to their worldwide usage in housing and infrastructure. Two known causes of durability loss in concrete are the alkali-silica reaction (ASR) and delayed ettringite formation (DEF), which are chemical processes with the potential for expansion, cracking, and strength loss in affected elements. There is significant overlap in the contributing factors for ASR and DEF, in particular pore solution alkalinity. DEF is of most concern for large, precast concrete structures, although in Australia reported cases of deleterious DEF have been in conjunction with mild or moderate ASR. Current guidelines regarding DEF are based on laboratory experiments in mortar specimens, and the role of the aggregate and ASR in concrete has been overlooked. Mitigation strategies for DEF involve temperature thresholds during curing, chemical limits for the binder, and the use of supplementary cementitious materials (SCMs) in the mix design. This study investigates the role of aggregate reactivity, curing temperature, and cement composition in the susceptibility of concrete elements to deleterious DEF, and the efficacy of fly-ash as an SCM in preventing deleterious ASR-DEF. Concrete specimens containing ASR-reactive aggregates were made with different binder and curing conditions, then monitored for expansion and strength loss over two years. Contributions towards the understanding of ASR-DEF mechanisms and the development of industry risk assessments in Australia are presented in this paper.
Martin, L, Thomas, P, De Silva, P & Sirivivatnanon, V 1970, 'Role of Aggregate Reactivity, Binder Composition, and Curing Temperature on the Delayed Ettringite Formation and Associated Durability Loss in Concrete', Springer Nature Singapore, pp. 83-91. View/Download from: Publisher's site View description>>
AbstractThe durability of concrete is critical to its worldwide use as a structural material for buildings and infrastructure, with the lifetime service of concrete greatly affecting its economic, environmental, and social costs. Causes of durability loss in some concrete structures can be attributed to the alkali–silica reaction (ASR) and delayed ettringite formation (DEF). Both are chemical reactions that have the potential to cause expansion and strength loss in affected elements. Significant overlap exists in the factors contributing to ASR and DEF in concrete structures, with widely reported evidence of deleterious DEF frequently occurring in conjunction with mild or moderate ASR. For precast concrete, experiments in mortars have provided limits in the alkali and sulfate content of the binder and maximum curing temperatures used to minimize DEF risk. The role of other constituents in concrete specimens, notably the aggregate, has been overlooked. We investigated the role of reactive aggregates and ASR in the susceptibility of concrete to deleterious DEF.
Matin, A, Islam, MR, Zhu, Y, Wang, X, Huo, H & Xu, G 1970, 'Hybrid Deep Learning for Assembly Action Recognition in Smart Manufacturing', 11th International Conference on Informatics, Electronics & Vision, 11th International Conference on Informatics, Electronics & Vision, London, UK.
Mehdizadeh Miyandehi, B, Vessalas, K, Castel, A & Mortazavi, M 1970, 'Investigation of Carbonation Behaviour in High-Volume GGBFS Concrete for Rigid Road Pavements', ASCP( Australian Society for Concrete Pavements), Wollongong.
Mehdizadeh, B, Vessalas, K, Ben, B, Castel, A, Deilami, S & Asadi, H 1970, 'Advances in Characterization of Carbonation Behavior in Slag-Based Concrete Using Nanotomography', International Symposium on Nanotechnology in Construction, Springer Nature Singapore, melbourne, pp. 297-308. View/Download from: Publisher's site View description>>
AbstractExposure of concrete to the atmosphere causes absorption of CO2and carbonation via a chemical reaction between the CO2and calcium hydroxide and calcium-silicate-hydrate reaction products inside the concrete. A greater understanding of carbonation behavior and its micro- and nanoscale impacts is needed to predict and model concrete durability, cracking potential and steel depassivation behaviors. New and sophisticated techniques have emerged to analyze the microstructural behavior of concrete subjected to carbonation. High-resolution full-field X-ray imaging is providing new insights to nanoscale behavior. Full-field nano-images provide significant insight into 3D structural identification and mapping. Nanotomographic modeling of an accelerated carbonated test specimen can also provide a 3D view of the pore structure that resides inside slag-based concrete. This is critical for better understanding of the capillary porosity and pore solution behaviors of concrete in situ. We investigated the analysis of durability properties, including the carbonation behavior of slag-based concrete, by evaluating microstructural and nanotomographic identification techniques.
Mei, G, Tang, H, Huang, X, Wang, W, Liu, J, Zhang, J, Van Gool, L & Wu, Q 1970, 'Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Meng, MH, Zhang, Q, Xia, G, Zheng, Y, Zhang, Y, Bai, G, Liu, Z, Teo, SG & Dong, JS 1970, 'Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards of User-unresettable Identifiers', Proceedings 2023 Network and Distributed System Security Symposium, Network and Distributed System Security Symposium, Internet Society. View/Download from: Publisher's site
Mian, A, Gill, AQ & Sharma, N 1970, 'Towards the Development of a Security Threat Identification Framework for UAV Information Infrastructure.', SmartIoT, IEEE, pp. 305-309.
Mikolajczyk, K, Bown, O & Ferguson, S 1970, 'A Study of Creative Development with an IoT-based Audiovisual System: Creative Strategies and Impacts for System Design', Creativity and Cognition, C&C '23: Creativity and Cognition, ACM. View/Download from: Publisher's site
Mikolajczyk, K, Trolland, S, Ilsar, A, McCormack, J, Ferguson, S & Bown, O 1970, 'Gestural Interactions with Object-Based Audio in an Internet of Sounds Ecosystem', 2023 4th International Symposium on the Internet of Sounds, 2023 4th International Symposium on the Internet of Sounds, IEEE. View/Download from: Publisher's site
Mitchell, C, Best, G & Hollinger, G 1970, 'Sequential Stochastic Multi-Task Assignment for Multi-Robot Deployment Planning', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Mousavi, M & Gandomi, AH 1970, 'Output-Only Versus Direct Input-Output Structural Condition Monitoring Methods', Springer Nature Switzerland, pp. 1-3. View/Download from: Publisher's site
Murad, MAU, Ahmad, F & Cetindamar, D 1970, 'Critical Success Factors of Technology Transfer: An Investigation into the Health Sector of Bangladesh Using ISM-DEMATEL Approach', 2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE. View/Download from: Publisher's site
Murad, MAU, Cetindamar, D & Chakraborty, S 1970, 'Big Data Analytics Capability and Sustainability: A Systematic Literature Review', 2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE. View/Download from: Publisher's site
Nabeel, MI, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'Improving Performance of Metasurfaces-based Beam-Steering 2D-Leaky Wave Resonant-Cavity Antennas', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Nabeel, MI, Singh, K, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'A Thin Transparent Phase Correction Surface for Patch Antenna Gain Enhancement', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE. View/Download from: Publisher's site
Nawer, N, Khan, MSI, Reza, MT, Parvez, MZ, Barua, PD & Chakraborty, S 1970, 'Improving Non-Invasive Brain Tumor Categorization using Transformers on MRI Data', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Nawer, N, Parvez, MZ, Hossain, MI, Barua, PD, Rahim, M & Chakraborty, S 1970, 'CNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorder', Springer Nature Switzerland, pp. 165-174. View/Download from: Publisher's site
Nerse, C, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Casacci, LP & Barbero, F 1970, 'PROPENSITY TO EFFICIENTLY TRANSMIT VIBRATIONS IN SNAPDRAGONS IN RESPONSE TO VIBROACOUSTIC SIGNALLING', Proceedings of the International Congress on Sound and Vibration, Proceedings of the 29th International Congress on Sound and Vibration, Prague, Czech Republic. View description>>
The coevolution of angiosperms and pollinating insects has drawn a diverse repertoire of plant-pollinator interactions leading to different mechanisms of pollen transfer. Buzz pollination is a dynamic pollen removal process between plants and insects that is thought to have emerged due to ecological and evolutionary factors. The floral morphology in buzz-pollinated flowers restricts the pollen access to non-pollinating insects to increase the conditions that favour fertilisation. Efficient pollinators such as bumblebees use sonication to vibrate the anthers of the flower, thereby causing pollen grains to be expelled through the apical pores of the anthers. Recent studies have shown that the conditions in which buzz pollination occurs vary with respect to the floral morphology and vibration signals produced by the bees mainly determined by the duration, amplitude, and frequency of the vibration. The structural topology and material properties of the flower also induce resonance and damping behaviour, thus mediating the transmission of substrate-borne vibrations. Along with the best-studied mechanism of buzz pollination, it is increasingly clear that vibroacoustic (VA) signals may have played a role in co-evolutionary responses that significantly impact several aspects of plant and insect ecology. However, studies on the material properties in terms of VA transmission are still in their infancy. Therefore, in this study, we numerically investigate the sensitivity of the floral topology on transmission of vibrations from a source signal. For this purpose, stamen structures at different scales and loading conditions are modelled and analysed in a finite element software package. A representative VA signal is implemented as the excitation at locations impacting the flower during feeding events. The results demonstrate that natural frequencies and mode shapes of the stamen may influence the conditions in which the vibrational energy is scattered across the fl...
Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 1970, 'Machine Learning-Based Cyclostationary Spectrum Sensing in Cognitive Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Ngo, T, Indraratna, B & Rujikiatkamjorn, C 1970, 'Modelling of Stone Columns Reinforces Railway Embankments: Coupled DEM-FDM Analysis', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 235-245. View/Download from: Publisher's site View description>>
Stone columns have been increasingly adopted as an environmentally friendly and cost-effective method for stabilizing and reinforcing soft ground embankments. This paper presents a coupled modelling using the discrete element method and continuum modeling approach to study the load-deformation responses of stone columns. In the coupled discrete-continuum model, a soft soil domain under an embankment is simulated by the continuum method (i.e., finite difference method-FDM), while a stone column is simulated by the discrete element method (DEM). A series of interface elements are introduced to facilitate the force-displacement transmission of both domains. The DEM transfers moment and contact forces to the FDM, and then the FDM moves displacements back to the DEM. The model is calibrated and further validated by experimental data. Contact force distributions and shear stress contours developed in the stone column and surrounding clay are captured to provide a better understanding of the load-deformation responses of the stone column from a micromechanical perspective.
Nguyen, HAD, Le, TH, Ha, QP & Azzi, M 1970, 'Deep learning for construction emission monitoring with low-cost sensor network', Proceedings of the 40th International Symposium on Automation and Robotics in Construction, 40th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC). View/Download from: Publisher's site
Nguyen, HM, Chu, NH, Nguyen, DN, Hoang, DT, Hà, MH & Dutkiewicz, E 1970, 'Optimal Privacy Preserving in Wireless Federated Learning Over Mobile Edge Computing', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Nguyen, LV, Le, TH & Ha, QP 1970, 'Prototypical digital twin of multi-rotor UAV control and trajectory following', Proceedings of the 40th International Symposium on Automation and Robotics in Construction, 40th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC). View/Download from: Publisher's site
Nguyen, M, Zhu, H, Sun, H, Nguyen, V, Deverell, L, Singh, A, Jin, C & Lin, C-T 1970, 'An Evaluation of the Presentation of Acoustic Cues for Shorelining Techniques', 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA), 2023 Immersive and 3D Audio: from Architecture to Automotive (I3DA), IEEE. View/Download from: Publisher's site
Nguyen, QD, De Carvalho Gomes, S, Alnahhal, MF, Li, W, Kim, T & Castel, A 1970, 'Testing Geopolymer Concrete Performance in Chloride Environment', RILEM Bookseries, Springer Nature Switzerland, pp. 1197-1203. View/Download from: Publisher's site View description>>
The major barriers to geopolymer concrete widespread adoption by the construction industry are concerns about durability and exclusion from current standards. Chemical reactions characterizing alkali-activated binder systems differ drastically from conventional hydration process of Portland cement. Thus, the mechanisms by which concrete achieves potential durability are different between the two types of binders. As a result, testing methods and performance-based requirements for geopolymer must be developed to be incorporated in a performance base standard. The standard ASTM C1202 RCPT fails to measure the charges passed through most of geopolymer concretes. A modified version of RCPT using 10V (as opposed to 60V specified by standard ASTM C1202) is considered, allowing to successfully complete the tests for all geopolymer concretes. Various precursors are investigated including fly ash, GGBFS, calcined clay and ferronickel slag. Different activators are also considered. A good correlation is observed between modified ASTM C1202 and Standard ASTM C1556 bulk diffusion test results. Performance-based specifications are proposed.
Nguyen, TN, Li, J, Sirivivatnanon, V & Sanchez, L 1970, 'Modeling the Alkali–Silica Reaction and Its Impact on the Load-Carrying Capacity of Reinforced Concrete Beams', Nanotechnology in Construction for Circular Economy, Springer Nature Singapore, pp. 365-372. View/Download from: Publisher's site View description>>
AbstractThe alkali–silica reaction (ASR) is one of the most harmful distress mechanisms affecting concrete infrastructure worldwide. The reaction leads to cracking, loss of material integrity, and consequently compromises the serviceability and capacity of the affected structures. In this study, a modeling approach was proposed to simulate ASR-induced expansion considering three-dimensional stress/restraint conditions, and its impact on the structural capacity of reinforced concrete members. Both the losses in concrete mechanical properties and prestressing effects induced by the expansion under restraints are taken into account in the model. Validation of the developed model is conducted using reliable experimental datasets derived from different laboratory testings and field exposed sites. With the capability of modelling both ASR-induced expansion and its impact on structural capacity, the model provides valuable results to specify effective repair and/or mitigation strategies for concrete structures affected by ASR.
Nguyen, TT, Indraratna, B, Rujikiatkamjorn, C & Haq, S 1970, 'A Comparative Study on the Performance of CFD/LBM-DEM Coupling in Predicting Soil Fluidization', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 51-61. View/Download from: Publisher's site View description>>
Coupling discrete element method (DEM) with computational fluid dynamics including Navier-Stokes theories (CFD-DEM) and Lattice Botlzmann method (LBM-DEM) has been used widely to model the response of soil foundation under increasing seepage flows; however, a comparison of these methods in predicting soil and fluid behaviours during fluidization has not been carried out in a rigorous way. The current paper will hence provide an evaluation on their performance by applying them to model a laboratory test where a sandy soil is subjected to fluidization process under increasing hydraulic gradient. A brief discussion about the differences in their theories and numerical algorithm is made before the DEM is used to simulate a representative soil element while NS-based CFD and LBM are used separately to model upward fluid flows. The mutual interactions between fluid and solid phases are carried out and update to each other through third-party platforms. The results show relatively similar hydraulic conductivity predicted by the two methods, which agrees well with the experimental data; however, the critical hydraulic gradient estimated by LBM-DEM coupling is found closer to the experimental value. The CFD-DEM coupling provides more stable computation through its averaged fluid variables, whereas LBM-DEM coupling can provide more detailed interactions between fluid and soil particles at micro-scale due to its high resolution. The study then suggests several conditions which can optimize the efficiency of using these methods in practical applications.
Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 1970, 'Enabling Intelligent Traffic Steering in A Hierarchical Open Radio Access Network', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE. View/Download from: Publisher's site
Nii, Y, Raj, C, Tiwana, MS, Samarawickrama, M, Simoff, S, Jan, T & Prasad, M 1970, 'Understanding Social Media Engagement in Response to Disaster Fundraising Attempts During Australian Bushfires', Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022), Springer Nature Switzerland, pp. 277-289. View/Download from: Publisher's site
Niu, C, Pang, G & Chen, L 1970, 'Graph-Level Anomaly Detection via Hierarchical Memory Networks', Springer Nature Switzerland, pp. 201-218. View/Download from: Publisher's site
Novakovic, A, Marshall, AH, McGregor, C, Bressan, N, McAllister, K & Courcier, E 1970, 'Using machine learning to improve bovine tuberculosis control in herd level outbreaks', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Nugroho, KA, Amirul Haq, M, Wang, C-K, Ruan, S-J, Polikarpov, M, Wagstyl, D & Deuse, J 1970, 'Towards Smart Manufacturing using Reinforcement Learning in a Sparse-Rewarded Environment for Through-Hole Technology', 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), IEEE. View/Download from: Publisher's site
Nuñez, A, Kong, FH, González-Cantos, A & Fitch, R 1970, 'Risk-Aware Stochastic Ship Routing Using Conditional Value-at-Risk', 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. View/Download from: Publisher's site
Nuñez, A, Kong, FH, Seiler, KM, Cantos, AG & Fitch, R 1970, 'Optimal ship routing via Spherical Visibility Graphs', OCEANS 2023 - Limerick, OCEANS 2023 - Limerick, IEEE. View/Download from: Publisher's site
Oberst, S, Sepehrirahnama, S, Halkon, B, Lai, JCS, Atkinson, T & Evans, TAE 1970, 'A microactuator device for the detection of termite damage in timber poles', International Union of Forest Research Organisations, Cairns.
Oberst, S, Sepehrirahnama, S, Nerse, C, Brodzeli, Z, Lai, JCS, Mankowski, M, Atkinson, T, Arango, R, Kirker, G & Evans, T 1970, 'Towards a microactuator-sensing network for structural health monitoring of timber poles', Proceedings IRG Annual Meeting, International Research Group on Wood Protection, The International Research Group on Wood Protection, Cairns, Queensland, Australia, pp. 1-9.
Olszak, CM, Zurada, J & Kozanoglu, DC 1970, 'Introduction to the Business Intelligence for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era Mini-track', Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 257-258.
Ordibazar, AH, Hussain, O, Chakrabortty, RK, Saberi, M & Irannezhad, E 1970, 'Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation', Springer Nature Switzerland, pp. 53-65. View/Download from: Publisher's site
Ostermeier, F, West, N & Deuse, J 1970, 'How Moderator Variables Affect Scheduling Objectives in Unpaced Mixed-Model Assembly Lines', Lecture Notes in Production Engineering, Springer International Publishing, pp. 419-431. View/Download from: Publisher's site View description>>
Besides the sequence itself also additional factors serving as moderator variables affect the value of scheduling objectives. For mixed-model assembly lines, especially number and heterogeneity of different products, their volume mix proportions, average workload of the jobs to process and the degree of grouping of identical jobs within the sequence play a major role. By means of a simulation study based on data from a real unpaced mixed-model assembly line in the automotive industry, this work analyzes the impact of these moderating variables on various scheduling objectives. The analyzed scheduling objectives encompass flow-related objectives like mean flow time, productivity-related objectives like makespan, customer-related objectives like mean earliness, the supplier-related objective part usage rate variation and the human-related objectives mean learning effect and mean deterioration effect per job. Simulation scenarios are defined that differ regarding number and heterogeneity of products from three homogeneous to seven more heterogeneous products. Within every simulation scenario the volume mix proportions of the products, and inherently also the average workload of jobs, are systematically varied. Every simulation scenario is analyzed for five sequence types differing in the degree of grouping of identical jobs. For almost all scheduling objectives, strong dependencies on the volume mix proportions can be perceived, particularly for mean flow time. Homogeneous volume mixes with a dominating product in the mix often lead to other objective values compared to heterogeneous volume mixes that allow using alternation effects between various products in a sequence. Concerning degree of grouping, while some scheduling objectives like part usage rate variation are always strongly affected by the degree of grouping for every volume mix, other objectives like throughput show strong dependence only for some mixes and makespan does not even show any tende...
Ou, L, Chang, Y-C, Wang, Y-K & Lin, C-T 1970, 'Explain Reinforcement Learning Agents Through Fuzzy Rule Reconstruction', 2023 IEEE International Conference on Fuzzy Systems (FUZZ), 2023 IEEE International Conference on Fuzzy Systems (FUZZ), IEEE. View/Download from: Publisher's site
Overdevest, N, Patibanda, R, Saini, A, Van Den Hoven, E & Mueller, FF 1970, 'Towards Designing for Everyday Embodied Remembering: Findings from a Diary Study', Proceedings of the 2023 ACM Designing Interactive Systems Conference, DIS '23: Designing Interactive Systems Conference, ACM. View/Download from: Publisher's site
P, V, Kar, H, Gautam, S, Mekala, MS, Rahimi, M & Gandomi, AH 1970, 'Sign Language Translator for Dumb and Deaf', 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE. View/Download from: Publisher's site
Pan, K, Zhang, S, Zhao, L, Huang, S, Zhang, Y, Wang, H & Luo, Q 1970, '3D Reconstruction of Tibia and Fibula using One General Model and Two X-ray Images', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 4732-4738. View/Download from: Publisher's site View description>>
The 3D reconstruction of patient specific bone models plays a crucial role in orthopaedic surgery for clinical evaluation, surgical planning and precise implant design or selection. This paper considers the problem of reconstructing a patient-specific 3D tibia and fibula model from only two 2D X-ray images and one 3D general model segmented from the lower leg CT scans of one randomly selected patient. Currently, the bone 3D reconstruction mainly relies on computed tomography (CT) and magnetic resonance imaging (MRI) scanning-based mode segmentation which result in high radiation exposure or expensive costs. While, the proposed algorithm can accurately and efficiently deform a 3D general model to achieve a patient-specific 3D model that matches the patient's tibia and fibula projections in two 2D X-rays. The algorithm undergoes a preliminary deformation, 2D contour registration, and opti-misation based on the deformation graph that represents the shape deformation of models. Evaluations using simulations, cadaver and in-vivo experiments demonstrate that the proposed algorithm can effectively reconstruct the patient's 3D tibia and fibula surface model with high accuracy.
Pan, X, Yang, Z, Ma, J, Zhou, C & Yang, Y 1970, 'TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Pang, X, Xie, K, Zhang, Y, Fleming, M, Xu, DC & Liu, W 1970, 'Adversarial Active Learning with Guided BERT Feature Encoding', Springer Nature Switzerland, pp. 508-520. View/Download from: Publisher's site
Park, E, Wong, RK & Chu, VW 1970, 'Story Ending Generation using Commonsense Casual Reasoning and Graph Convolutional Networks', European Conference on Artificial Intelligence, Kraków.
Parnell, J 1970, 'Challenges in predicting and managing construction noise impacts in urban environments. Case studies from Sydney, Australia', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Institute of Noise Control Engineering (INCE), pp. 6844-6852. View/Download from: Publisher's site View description>>
Approval of major infrastructure projects in Australia is contingent on a vigorous environmental assessment process demonstrating that the impacts from such proposals are acceptable and can be satisfactorily managed. As part of this process, an assessment of construction noise is undertaken by predicting the level and duration of impact. Given the scale, complexity and programs for delivery of major infrastructure projects such as the Sydney Metro, the approach to assessment of environmental impacts is typically undertaken based on early concept designs, and assumptions which enable potential worst-case impacts to be considered. Ongoing design development and construction methodologies continue to occur, and are finalised by the contractor responsible for delivering the package of works for the project. Layers of conservatism could therefore be built into the process to ensure that environmental impacts are understood, and appropriate mitigation measures are considered. Moreover, common predictive noise modelling tools are believed to under-estimate the level of barrier insertion loss provided by residential buildings in urban scenarios, further exacerbating the over-prediction of construction noise impacts. By use of case studies, the present paper examines the challenges of assessing and managing construction noise associated with a major infrastructure project in Sydney, Australia.
Peng, Y, Ma, Z, Zhang, W, Lin, X, Zhang, Y & Chen, X 1970, 'Efficiently Answering Quality Constrained Shortest Distance Queries in Large Graphs', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Peng, Y, Song, A, Ciesielski, V, Fayek, H & Chang, X 1970, 'Fast Evolutionary Neural Architecture Search by Contrastive Predictor with Linear Regions', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '23: Genetic and Evolutionary Computation Conference, ACM, pp. 1257-1266. View/Download from: Publisher's site View description>>
Evolutionary neural architecture search (ENAS) has emerged as a promising approach to finding high-performance neural architectures. However, widespread application has been limited by the expensive computational costs due to the nature of evolutionary algorithms. In this study, we aim to significantly reduce the computational costs of ENAS by involving a training-free performance metric. Specifically, the network performance can be estimated by the training-free metric with only a single forward pass. However, training-free metrics have their own challenges, in particular, an insufficient correlation with ground-Truth performance. We adopt a Graph Convolutional Network (GCN) based contrastive predictor which can leverage the low cost of the training-free performance metric yet improve the correlation between the estimated performance and the true performance of the candidate architectures. Combining a training-free metric-the number of linear regions with the GCN-based contrastive predictor and an active learning scheme, we propose Fast-ENAS which can achieve superior search efficiency and performance on the benchmark NAS-Bench-201 and DARTS search spaces. Furthermore, with a single GPU searching on the DARTS space, Fast-ENAS requires only 0.02 (29 minutes) and 0.026 (37 minutes) GPU days to achieve test error rates of 2.50% and 24.30% on CIFAR-10 and ImageNet respectively.
Petrovich, M, Liang, C, Sato, R, Liu, Y, Tsai, Y-HH, Zhu, L, Yang, Y, Salakhutdinov, R & Yamada, M 1970, 'Feature-Robust Optimal Transport for High-Dimensional Data', Springer Nature Switzerland, pp. 291-307. View/Download from: Publisher's site
Phan, TC, Pranata, A, Farragher, JB, Bryant, AL, Nguyen, HT & Chai, R 1970, 'Machine Learning Derived Lifting Technique in People without Low Back Pain', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Piao, J, Wei, Z, Yuan, X, Yang, X, Wu, H & Feng, Z 1970, 'Mutual Information Metrics for Uplink MIMO-OFDM Integrated Sensing and Communication System', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE. View/Download from: Publisher's site
Pöhlmann, KMT, Al Taie, AJS, Li, G, Dam, A, Wang, Y-K, Wei, C-S & Papaioannou, G 1970, '2nd Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys - Finding Consensus in the Field', Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '23: 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM. View/Download from: Publisher's site
Powell, K, Kodagoda, S & Vidal-Calleja, T 1970, 'Towards Context Aware Emotion Recognition in HRI for Social Robots', Australasian Conference on Robotics and Automation, ACRA. View description>>
Social robots are becoming more prevalent in our daily environments but continue to struggle communicating in human-robot interactions, often misunderstanding people and thus making the interaction uncomfortable. Many attempts have been made to improve their understanding of people and their emotions but they still lack the socio-emotional intelligence humans often use in human-human interactions. A new approach previously explored in computer science is using context emotion recognition to interpret a scene for clues to a person’s emotional state. In this paper, we state that context emotion recognition will benefit the fields of human-robot interaction and social robotics. Further, we extend upon the EMOTIC model successfully adding a graphical representation of the emotion probabilities over time to the model output and with the addition of a facial feature extractor module that obtains an encouraging improvement over the original model. The algorithm was validated through data coming from two robotic platforms, namely PR2 and Pepper. The results show a promising way towards context aware emotion recognition in human-robot interactions with social robots, with 88% accuracy when comparing with 66% accuracy of the base model.
Prazeres, J, Luo, Z, Pinheiro, AMG, da Silva Cruz, LA & Perry, S 1970, 'JPEG Pleno Call for Proposals Responses Quality Assessment', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. View/Download from: Publisher's site
Qaiser, G, Chandrasekaran, S, Chai, R & Zheng, J 1970, 'Classification of DDoS traffic for Industrial Internet of Services using Deep learning approaches', 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE. View/Download from: Publisher's site
Qaiser, G, Chandrasekaran, S, Chai, R & Zheng, J 1970, 'Classifying DDoS Attack in Industrial Internet of Services Using Machine Learning', 2023 15th International Conference on Computer and Automation Engineering (ICCAE), 2023 15th International Conference on Computer and Automation Engineering (ICCAE), IEEE. View/Download from: Publisher's site
Radfar, P, Ding, L, Oh, S & Warkiani, ME 1970, 'BIODEGRADABLE AND CUSTOMISED MICROCARRIERS FOR EFFICIENT AND SCALABLE CULTURE OF ADHERENT CELLS', CYTOTHERAPY, ELSEVIER SCI LTD, pp. S198-S199.
Radhakrishnan, M, Kandappu, T, Gulati, M & Misra, A 1970, 'Wearables for In-Situ Monitoring of Cognitive States: Challenges and Opportunities', 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE. View/Download from: Publisher's site
Rahimi, I, Gandomi, AH, Nikoo, MR & Chen, F 1970, 'Extending Boundary Updating Approach for Constrained Multi-objective Optimization Problems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 102-117. View/Download from: Publisher's site View description>>
To date, several algorithms have been proposed to deal with constrained optimization problems, particularly multi-objective optimization problems (MOOPs), in real-world engineering. This work extends the 2020 study by Gandomi & Deb on boundary updating (BU) for the MOOPs. The proposed method is an implicit constraint handling technique (CHT) that aims to cut the infeasible search space, so the optimization algorithm focuses on feasible regions. Furthermore, the proposed method is coupled with an explicit CHT, namely, feasibility rules and then the search operator (here NSGA-II) is applied to the optimization problem. To illustrate the applicability of the proposed approach for MOOPs, a numerical example is presented in detail. Additionally, an evaluation of the BU method was conducted by comparing its performance to an approach without the BU method while the feasibility rules (as an explicit CHT) work alone. The results show that the proposed method can significantly boost the solutions of constrained multi-objective optimization.
Ramegowda, PC, Choong, DSW, Goh, DJ, Jihang, L, Merugu, S, Chang, P, Leotti, A, Giusti, D, Prelini, C, Ghosh, S, Lee, JE-Y & Koh, Y 1970, 'Iterative Analysis Approach using interactive Python-FEM to estimate the residual stresses in PMUTs', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE. View/Download from: Publisher's site
Ranasinghe, U, Abeyrathne, S, Samaranayake, L, Weerakoon, T, Harischandra, N & Dissanayake, G 1970, 'Enhanced Frequency Domain Analysis for Detecting Wild Elephants in Asia using Acoustics', 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), IEEE, pp. 140-145. View/Download from: Publisher's site View description>>
Human-elephant conflict in Asia and Africa calls for an early warning system to reduce risks and harm for both elephants and humans. Acoustic based warning systems offer a promising solution due to their non-invasive and cost-effective nature. In this paper, we propose a novel approach for detecting wild elephants using acoustic signals, targeting the Asian elephant population in Sri Lanka. The proposed method introduces a unique data preprocessing technique, followed by feature extraction using a deep convolutional neural network followed by fully connected layers for classification. Spectro-grams are used as input data, and transfer learning is employed with YAMNet model layers. Additionally, we have developed a hardware system capable of capturing infra sound signals, although a detailed description of the system is beyond the scope of this paper as it is crucial for detecting elephant activity. The proposed method is evaluated on a large data set recorded under natural field conditions in Sri Lanka, and it demonstrates 97.77% accuracy in detecting elephants and robustness to noise sources. Proposed approach has the potential to develop into a non-invasive early warning system for elephant detection in the wild, contributing to the mitigation of human-elephant conflict and wildlife preservation.
Rao, Q, Yu, X, Navasardyan, S & Shi, H 1970, 'Sim2RealVS: A New Benchmark for Video Stabilization with a Strong Baseline', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE. View/Download from: Publisher's site
Rathnayake, D, Radhakrishnan, M, Hwang, I & Misra, A 1970, 'LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments using LiDARs', Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI '23: International Conference on Internet-of-Things Design and Implementation, ACM. View/Download from: Publisher's site
Raza, MR & Hussain, W 1970, 'Preserving Academic Integrity in Teaching with ChatGPT: Practical Strategies', 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE. View/Download from: Publisher's site
Reza, MS, Hannan, MA, Mansor, M, Ker, PJ, Tiong, SK & Hossain, MJ 1970, 'Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE. View/Download from: Publisher's site
Reza, MT, Dipto, SM, Parvez, MZ, Barua, PD & Chakraborty, S 1970, 'A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet', Springer Nature Switzerland, pp. 246-256. View/Download from: Publisher's site
Richards, C, Rad, DM, Zhand, S, Warkiani, M & McClements, L 1970, 'EVALUATING GENE DELIVERY TECHNOLOGIES TO INVESTIGATE THE ROLE OF FKBPL IN TROPHOBLAST FUNCTION AND THE THERAPEUTIC POTENTIAL OF MESENCHYMAL STEM CELL-DERIVED EXTRACELLULAR VESICLES', PLACENTA, Annual Meeting of the International-Federation-of-Placenta-Associations (IFPA), W B SAUNDERS CO LTD, NEW ZEALAND, Rotorua, pp. E49-E49.
Röhrl, K, Bauer, D, Patten, T & Vincze, M 1970, 'TrackAgent: 6D Object Tracking via Reinforcement Learning', Computer Vision Systems, Springer Nature Switzerland, pp. 323-335. View/Download from: Publisher's site View description>>
Tracking an object’s 6D pose, while either the object itself or the observing camera is moving, is important for many robotics and augmented reality applications. While exploiting temporal priors eases this problem, object-specific knowledge is required to recover when tracking is lost. Under the tight time constraints of the tracking task, RGB(D)-based methods are often conceptionally complex or rely on heuristic motion models. In comparison, we propose to simplify object tracking to a reinforced point cloud (depth only) alignment task. This allows us to train a streamlined approach from scratch with limited amounts of sparse 3D point clouds, compared to the large datasets of diverse RGBD sequences required in previous works. We incorporate temporal frame-to-frame registration with object-based recovery by frame-to-model refinement using a reinforcement learning (RL) agent that jointly solves for both objectives. We also show that the RL agent’s uncertainty and a rendering-based mask propagation are effective reinitialization triggers.
Rudd, DH, Huo, H, Islam, MR & Xu, G 1970, 'Churn Prediction via Multimodal Fusion Learning: Integrating Customer Financial Literacy, Voice, and Behavioral Data', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE. View/Download from: Publisher's site
Rujikiatkamjorn, C & Indraratna, B 1970, 'Application of vertical drains and vacuum preloading for stabilising soft ground for transport infrastructure', CRC Press, pp. 269-277. View/Download from: Publisher's site
Sadaf, A, Mathieson, L, Bródka, P & Musial, K 1970, 'Maximising Influence Spread in Complex Networks by Utilising Community-Based Driver Nodes as Seeds', Springer Nature Switzerland, pp. 126-141. View/Download from: Publisher's site
Saini, A, Sridhar, S, Raheja, A, Patibanda, R, Overdevest, N, Wang, P-YC, Van Den Hoven, E & Mueller, FF 1970, 'Pneunocchio: A playful nose augmentation for facilitating embodied representation', Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST '23: The 36th Annual ACM Symposium on User Interface Software and Technology, ACM. View/Download from: Publisher's site
Sajjad, MB, Indraratna, B, Ngoc Ngo, T & Rujikiatkamjorn, C 1970, 'Modelling of Tracks at Transition Zones: Analytical and Numerical Modelling Approach', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers, pp. 22-29. View/Download from: Publisher's site View description>>
Rail track transitions are the discontinuities along the railway lines where conventional track adjoins the tunnels and bridges and are responsible for a sudden change in track structural properties. Frequent movement of fast passenger trains and heavy freights cause enhanced impact loads and differential settlements at these transitions that further lead to accelerated track degradation and increased maintenance costs. This paper presents a brief introduction to track transitions, the associated problems, and their causes and explains the occurrence of differential settlement at track transitions through analytical and numerical (2D FEM) modelling approaches. The stiffness effect on track settlement response under train loading is investigated, considering an Euler-Bernoulli beam on elastic foundation (BOEF). Vertical displacements of the track are calculated for various track stiffness values under train wheel loading (P = 10 tonnes). The results proved that the stiffer tracks undergo lesser settlements than the softer tracks. The effect of sudden stiffness variation is investigated by considering a one-step track transition where the stiffness suddenly changes from 80 MN/m (slab track) to 5 MN/m (ballast track). The results show that the settlement on ballasted track is almost 12 times greater than those on the slab track resulting in a large differential settlement at track transition.
Salah, A, Bekhit, M, Alkalbani, AM, Mohamed, MA, Lestari, NI & Fathalla, A 1970, 'Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems', Springer Nature Switzerland, pp. 72-85. View/Download from: Publisher's site
Saleh, K, Mihaita, A-S & Ou, Y 1970, 'Metro Ridership Forecasting using Inter-Station-Aware Transformer Networks', 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), IEEE. View/Download from: Publisher's site
Samal, PB, Chen, SJ & Fumeaux, C 1970, 'Time-Domain Performance of a Directional Wearable UWB Antenna under Bending', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE. View/Download from: Publisher's site
Sarafianou, M, Chen, DS-H, Sze Wai Choong, D, Goh, DJ, Liu, J, Lee, JE-Y, Merugu, S, Zhang, QX, Chang, PHK, Giusti, D, Castoldi, L, Pedrini, C, Barabani, L, Leotti, A, Hur, YJ & Lung Lee, Y 1970, 'A Feasibility Study on Underwater 2D Localization Using PZT pMUTs', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE. View/Download from: Publisher's site
Sarafianou, M, Wai Choong, DS, Jian Goh, D, Liu, J, Merugu, S, Zhang, QX, Chang, PHK, Giusti, D, Castoldi, L, D’Ercoli, F, Tacchini, R, Leotti, A, Sim, DH, Stuart Savoia, A & Lee, JE-Y 1970, 'DC-Bias Effects on Sputtered PZT pMUTs with High Transmit and Receive Sensitivities in Immersion for Imaging applications', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE. View/Download from: Publisher's site
Savoia, AS, Giusti, D, Prelini, C, Chang, P, Leotti, A, Lee, J, Koh, Y & Ferrera, M 1970, 'Performance Analysis of Wideband PMUTs: a Comparative Study Between Sol-Gel PZT, PVD PZT, and 15% ScAlN-Based Arrays Through Experimental Evaluation', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE. View/Download from: Publisher's site
Schwenken, J, Klupak, C, Syberg, M, West, N, Walker, F & Deuse, J 1970, 'Development of a Transdisciplinary Role Concept for the Process Chain of Industrial Data Science', Lecture Notes in Networks and Systems, Springer Nature Singapore, pp. 81-88. View/Download from: Publisher's site View description>>
In recent years, there has been an increasing interest in using industrial data science (IDS) in manufacturing companies. Structured IDS projects proceed according to process models such as the cross industry standard process for data mining (CRISP-DM), knowledge discovery in databases (KDD), or the process chain of industrial data science. Because of the process Chain’s transdisciplinary procedure, the participation of different people in the analysis with different tasks and competencies is necessary. Therefore, a concept to define specific roles is required, since it provides unequivocal descriptions of the respective tasks. As no role concept for the process chain of IDS exists, this paper develops and presents a transdisciplinary role concept including the four essential roles: Data engineer, analyst, user, and project manager. These roles are described in terms of their characteristics to enable structured cooperation between them. In addition, this paper presents the AKKORD platform for learning and collaboration, which especially should make an important contribution for small and medium-sized enterprises (SME) to develop knowledge in the usage of the process chain of IDS. The platform provides the opportunity for the users to train the essential roles with their characteristics in the company through targeted competence development.
Sepehrirahnama, S 1970, 'Elastic wave reflection in beams with acoustic black hole termination under axial excitations', INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Noise and Vibration: Emerging Methods, Institute of Noise Control Engineering (INCE), Auckland, New Zealand, pp. 186-195. View/Download from: Publisher's site View description>>
The wave speed in structures can be controlled by altering their stiffness through variations of the geometry and dimensions of the cross-section along its length. Theoretically, slowing down the elastic waves can lead to a zero-reflection effect, which is referred to as acoustic black hole (ABH). ABH has been demonstrated in metallic structures with isotropic-like material properties; while the effects of material anisotropy such as wood mechanical properties on the ABH effect are yet to be explored. We demonstrate numerically and experimentally the ABH effect in wood beams (Pinus radiata) of different lengths by applying an exponential reduction of the circular cross-section. The beams are knot-free and made of cuts of 40mm, 80mm, 100mm, 120mm and 160mm length along the grain, which could lead to a small Poisson ratio by about one order of magnitude. An increase in moisture content of the ABH wood beams by about 8-10% of the dry weights(N=5 samples) and a higher volume fraction of fibrous grains have been shown to reduce the wave reflection, as the drier and lower volume fraction the fibre, the higher the reflection becomes. This work can lead to design of novel elastic wave-guides for timber structures as used in buildings and wooden infrastructure or construction processes
Sepehrirahnama, S, Sansom, T, Lai, JCS & Oberst, S 1970, 'DESIGN OF A MINIATURISED MICRO-FORCE PLATE TO STUDY THE LOCOMOTION OF SMALL ARTHROPODS', Proceedings of the International Congress on Sound and Vibration, 29, International Institute of Acoustics and Vibrations (IIAV) - IIAV CZECH s.r.o., Prague, Czech Republic. View description>>
Walking insects exhibit various types of locomotory gaits to adjust to their environment, for foraging activities, defence, building, courtship and communication. Among hexapods, the walking gaits of ants had been studied by measuring the ground reaction force from their steps with a Micro-Force Plate, which is a bespoke and sensitive thin plate mounted on a structure similar to the suspension system of a vehicle. Compared to ants, termites have quieter footsteps on the same substrate (peak velocity of 0.004 ms-1 compared to 0.4 ms-1 for ants). To study common gaits of small arthropods, we designed a miniaturised micro-force plate capable of measuring forces in the order of 1 μN in the z-direction (out-of-plane); an order of magnitude smaller than the one used for study of ants walking gaits. This is achieved by more compliant beams (halved width, at least 50% longer in the x and y-directions) from 3D-printed resin with minimum required curing; compared to the one for ants from the completed stereolithography. The improved design is assessed numerically by characterising its vibration response in terms of settling time due to an axial force excitation. Our micro-force plate can be used to increase the signal-to-noise ratio, as measured for a 3D-printed prototype, and better resolved force (approximately 1 μN without using any MEMS component). Results presented here indicate our micro-force plate is suitable for studying quiet tiny hexapods to provide insights for bio-inspired engineering of robotic locomotion systems.
Shafei, H, Li, L & Aguilera, RP 1970, 'Oberver-based Attack Detection and Mitigation in DC Microgrid Systems', 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE. View/Download from: Publisher's site
Shahsavari, M, Hussain, OK, Saberi, M & Sharma, P 1970, 'A lightweight and unsupervised approach for identifying risk events in news articles', 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2023 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE. View/Download from: Publisher's site
Shams, RA, Bano, M, Zowghi, D, Lu, Q & Whittle, J 1970, 'Human Value Requirements in AI Systems: Empirical Analysis of Amazon Alexa.', REW, 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), IEEE, pp. 138-145. View/Download from: Publisher's site
Shao, J, Wang, X, Quan, R, Zheng, J, Yang, J & Yang, Y 1970, 'Action Sensitivity Learning for Temporal Action Localization', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Sharma, S, Brian Lee, KM, Brown, M & Best, G 1970, 'Instructing Robots with Natural Language via Bi-RNNs for Temporal Logic Translation', Australasian Conference on Robotics and Automation, ACRA. View description>>
We consider the problem of planning trajectories that satisfy natural language instruction. We explore translating natural language commands to temporal logic formulae to resolve ambiguities for planning. Our main contribution is a new bi-directional recurrent neural network (Bi-RNN) architecture for this translation task. We experimentally show that the proposed Bi-RNN architecture achieves 1.6% better accuracy, 20% faster inference time, and 98% faster training time compared to leading models owing to bidirectional processing. The overall system, including a planning algorithm, exhibits useful diverse behaviours that satisfy given instructions.
Shen, C, Lin, B, Zhang, S, Yu, X, Huang, GQ & Yu, S 1970, 'Gait Recognition with Mask-based Regularization', 2023 IEEE International Joint Conference on Biometrics (IJCB), 2023 IEEE International Joint Conference on Biometrics (IJCB), IEEE. View/Download from: Publisher's site
Shen, J, Xuan, J & Liang, C 1970, 'A Determinantal Point Process Based Novel Sampling Method of Abstractive Text Summarization', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Shen, T, Geng, X, Tao, C, Xu, C, Long, G, Zhang, K & Jiang, D 1970, 'UnifieR: A Unified Retriever for Large-Scale Retrieval', Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM. View/Download from: Publisher's site
Sheng, J, Xiong, J & Liu, B 1970, 'Federated Learning Technology in Serial Topology for IoT Networks', 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE. View/Download from: Publisher's site
Shi, C, Yin, Y, Zhang, Q, Xiao, L, Naseem, U, Wang, S & Hu, L 1970, 'Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference', Findings of the Association for Computational Linguistics: EMNLP 2023, Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore. View/Download from: Publisher's site
Shi, K, Sun, X, He, L, Wang, D, Li, Q & Xu, G 1970, 'AMR-TST: Abstract Meaning Representation-based Text Style Transfer', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 4231-4243. View description>>
Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the ex-plainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.
Shi, L, Zhang, Z, Wang, S, Zhang, Q, Wu, M, Yang, C & Li, S 1970, 'Session-based Interactive Recommendation via Deep Reinforcement Learning', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Shi, L, Zhang, Z, Wang, S, Zhou, B, Wu, M, Yang, C & Li, S 1970, 'Efficient Interactive Recommendation via Huffman Tree-based Policy Learning', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, pp. 1495-1503. View description>>
Interactive recommender systems (IRSs) are an essential part of our daily life, as they can suggest items to persistently satisfy our demands. Due to the interactive nature, conventional static recommendation methods such as matrix factorization, and content-based filtering are ineffective to capture the dynamic preferences of users. Recently, reinforcement learning (RL) has shown great potential in addressing the challenges in IRSs, since it can capture users' dynamic preferences and model the long-term profit of user-item interactions. However, millions of items in real-world IRSs lead to a large discrete action space in the RL setting, rendering RL-based IRSs inefficient and hindering their widespread application. Such an inefficiency issue has not been well addressed in the literature. In order to address this issue, we propose a novel Huffman Tree Policy Recommendation (HTPR) framework. Specifically, a novel policy learning network based on a newly designed Huffman tree is proposed for policy representation learning, which effectively improves the learning efficiency. Moreover, a novel parameter-sharing scheme is devised to further reduce unnecessary computations. Extensive experiments on two real-world benchmark datasets demonstrate the superiority of HTPR over the state-of-the-art IRS methods in terms of both recommendation accuracy and efficiency.
Shi, T, Liu, B, Lei, J, Wang, F, Xiang, X, Li, L & Li, W 1970, 'Virtual Damping Stabilizing for DC Microgrid with Current Disturbance of Selected Frequency Islanding Detection Method', 2023 IEEE PELS Students and Young Professionals Symposium (SYPS), 2023 IEEE PELS Students and Young Professionals Symposium (SYPS), IEEE. View/Download from: Publisher's site
Shi, Y, Yu, X, Wang, S & Li, H 1970, 'CVLNet: Cross-view Semantic Correspondence Learning for Video-Based Camera Localization', Computer Vision – ACCV 2022, Springer Nature Switzerland, pp. 123-141. View/Download from: Publisher's site View description>>
This paper tackles the problem of Cross-view Video-based camera Localization (CVL). The task is to localize a query camera by leveraging information from its past observations, i.e., a continuous sequence of images observed at previous time stamps, and matching them to a large overhead-view satellite image. The critical challenge of this task is to learn a powerful global feature descriptor for the sequential ground-view images while considering its domain alignment with reference satellite images. For this purpose, we introduce CVLNet, which first projects the sequential ground-view images into an overhead view by exploring the ground-and-overhead geometric correspondences and then leverages the photo consistency among the projected images to form a global representation. In this way, the cross-view domain differences are bridged. Since the reference satellite images are usually pre-cropped and regularly sampled, there is always a misalignment between the query camera location and its matching satellite image center. Motivated by this, we propose estimating the query camera’s relative displacement to a satellite image before similarity matching. In this displacement estimation process, we also consider the uncertainty of the camera location. For example, a camera is unlikely to be on top of trees. To evaluate the performance of the proposed method, we collect satellite images from Google Map for the KITTI dataset and construct a new cross-view video-based localization benchmark dataset, KITTI-CVL. Extensive experiments have demonstrated the effectiveness of video-based localization over single image-based localization and the superiority of each proposed module over other alternatives.
Shi, Z, Xu, Y, Fang, M & Chen, L 1970, 'Self-imitation Learning for Action Generation in Text-based Games', EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 703-726. View description>>
In this work, we study reinforcement learning (RL) in solving text-based games. We address the challenge of combinatorial action space, by proposing a confidence-based self-imitation model to generate action candidates for the RL agent. Firstly, we leverage the self-imitation learning to rank and exploit past valuable trajectories to adapt a pre-trained language model (LM) towards a target game. Then, we devise a confidence-based strategy to measure the LM's confidence with respect to a state, thus adaptively pruning the generated actions to yield a more compact set of action candidates. In multiple challenging games, our model demonstrates promising performance in comparison to the baselines.
Shrestha, S, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'Three Dimensional Printable Prototype to Realize Uniformity in Aperture Phase Distribution', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE. View/Download from: Publisher's site
Shubho, FH, Chowdhury, TF, Cheraghian, A, Saberi, M, Mohammed, N & Rahman, S 1970, 'ChatGPT-guided Semantics for Zero-shot Learning', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Sia, J, Chang, Y-C, Lin, C-T & Wang, Y-K 1970, 'EEG-BASED TNN for Driver Vigilance Monitoring', 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. View/Download from: Publisher's site
Singh, K, Ahmed, F & Esselle, KP 1970, 'Beam-Steering Antennas Using Semi-Periodic Metasurfaces', 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE. View/Download from: Publisher's site
Slack-Smith, D, De Silva Wijayaratna, K & Zeibots, M 1970, 'Enhancing the modelling of shared spaces: Evolution of the Social Force Model', Australasian Transport Research Forum 2023, Perth, Western Australia.
Song, B, Hui-Ming, Song, Y-R, Jiang, G-P, Wang, X & Xia, L-L 1970, 'Identifying Influential Nodes in Community Networks', 2023 42nd Chinese Control Conference (CCC), 2023 42nd Chinese Control Conference (CCC), IEEE. View/Download from: Publisher's site
Song, L, Qin, P, Du, J & Guo, YJ 1970, 'Multi-beam Conformal Transmitarray Synthesis for Advanced Wireless Systems', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Song, L, Qin, P, Zhang, T, Du, J & Jay Guo, Y 1970, 'High-Efficiency Multi-Beam GRIN Lens with 2-D Wide-Angle Coverages', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 51-52. View/Download from: Publisher's site View description>>
Multi beam gradient index GRIN lenses are investigated to enable high aperture efficiencies and 2 D wide angular coverages Metasurface composed of engineered elements with varied refractive indices is employed to implement the lens Innovative methodologies are developed to calculate refractive index distributions on the lens as well as the corresponding feed positions for multiple beams A three metal layer element is developed to enable the variation of effective refractive index Microstrip patch array antennas are designed as feed sources Thirteen feed arrays are arranged on two focal arcs in xoz and yoz planes A GRIN lens prototype is constructed radiating multiple beams in a wide range of 45 in two orthogonal planes with low scanning losses The peak realized gain is 21 8 dBi with an aperture efficiency of 62 8 The operating bandwidth is 22 2 from 12 GHz to 15 GHz
Song, W, Wang, S, Wang, Y, Liu, K, Liu, X & Yin, M 1970, 'A Counterfactual Collaborative Session-based Recommender System', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM, pp. 971-982. View/Download from: Publisher's site View description>>
Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user's selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.
Soo, Z, Lin, H, Yang, Y, Grosser, M, Zhang, Y & Lu, J 1970, 'Deep Neural Network-Empowered Polygenic Disease Prediction on Cardiovascular Diseases', 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE. View/Download from: Publisher's site
Stephen, B, Kacprzak, M, Li, B, Guo, T, Wang, Y, Viswanathan, V, Kodagoda, S, Thiyagarajan, K & Vitanage, D 1970, 'Use of Machine Learning and Robotics To Target Renewals in Concrete Gravity Sewers', OzWater'23, OzWater'23, Sydney.
Stewart, MG 1970, 'Risk and decision-making for extreme events: What terrorism and climate change have in common', CRC Press, pp. 109-116. View/Download from: Publisher's site
Stratton, PG, Hamilton, TJ & Wabnitz, A 1970, 'Unsupervised Feature Vector Clustering Using Temporally Coded Spiking Networks', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Suchi, M, Neuberger, B, Salykov, A, Weibel, J-B, Patten, T & Vincze, M 1970, '3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. View/Download from: Publisher's site
Sukkar, F, Moreno, VH, Vidal-Calleja, T & Deuse, J 1970, 'Guided Learning from Demonstration for Robust Transferability', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, London, United Kingdom, pp. 5048-5054. View/Download from: Publisher's site View description>>
Learning from demonstration LfD has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness The latter is particularly important to consider when the target system reproducing the motion is structurally different to the demonstration system as some demonstrated motions may not be reproducible In light of this this paper introduces a new guided learning from demonstration paradigm where an interactive graphical user interface GUI guides the user during demonstration preventing them from demonstrating non reproducible motions The key aspect of our approach is determining the space of reproducible motions based on a motion planning framework which finds regions in the task space where trajectories are guaranteed to be of bounded length We evaluate our method on two different setups with a six degree of freedom DOF UR5 as the target system First our method is validated using a seven DOF Sawyer as the demonstration system Then an extensive user study is carried out where several participants are asked to demonstrate with and without guidance a mock weld task using a hand held tool tracked by a VICON system With guidance users were able to always carry out the task successfully in comparison to only 44 of the time without guidance
Learning from demonstration (LfD) has the potential to greatly increase theapplicability of robotic manipulators in modern industrial applications. Recentprogress in LfD methods have put more emphasis in learning robustness than inguiding the demonstration itself in order to improve robustness. The latter isparticularly important to consider when the target system reproducing themotion is structurally different to the demonstration system, as somedemonstrated motions may not be reproducible. In light of this, this paperintroduces a new guided learning from demonstration paradigm where aninteractive graphical user interface (GUI) guides the user duringdemonstration, preventing them from demonstrating non-reproducible motions. Thekey aspect of our approach is determining the space of reproducible motionsbased on a motion planning framework which finds regions in the task spacewhere trajectories are guaranteed to be of bounded length. We evaluate ourmethod on two different setups with a six-degree-of-freedom (DOF) UR5 as thetarget system. First our method is validated using a seven-DOF Sawyer as thedemonstration system. Then an extensive user study is carried out where severalparticipants are asked to demonstrate, with and without guidance, a mock weldtask using a hand held tool tracked by a VICON system. With guidance users wereable to always carry out the task successfully in comparison to only 44% of thetime without guidance.
Sukkar, F, Savery, R, Haque, N, Le Gentil, C, Falque, R & Vidal-Calleja, T 1970, 'A Robotic System For Imitating Human Percussionists', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Sydney. View description>>
Robot musicians have the potential to revolutionise the way humans perceive and create music. Recent breakthroughs in this field have tended to focus more on the digital generation of music. Instead, we address how a musician’s physical embodiment can be translated to a robotic arm. Robots endowed with human-like musical capability open the possibility for wider applications such as human-robot bands, musical education and musical art. Prior work in this area tends to rely on pre-programmed actuation which is limited to simple motion and sound. In this paper, we propose a robotic system capable of imitating a human musician, with a focus on percussion instruments. Our system consists of a method for recording the human demonstration, a compact continuous representation of the demonstrated motion and a motion reproduction method which considers the dynamic constraints of the robot. We present results of our system and show that it is capable of closely reproducing the motion of the human percussionist.
Sun, Q, Lin, X, Zhang, Y, Zhang, W & Chen, C 1970, 'Towards Higher-order Topological Consistency for Unsupervised Network Alignment', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Sun, R, Chen, C, Wang, X, Zhang, W, Zhang, Y & Lin, X 1970, 'Efficient Maximum Signed Biclique Identification', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Susantha, K, Lu, D & Wang, X 1970, 'Lessons Learned from Previous Cyberattacks on Energy Systems – Global and Australian Context', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Talhouk, R, Alabdulqader, E, Kutay, C, Awori, K, Wong-Villacres, M, Kumar, N, Zaman, T, Wulf, V, Almeraj, Z & Lazem, S 1970, 'Re-articulating North-South Collaborations in HCI', Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI '23: CHI Conference on Human Factors in Computing Systems, ACM. View/Download from: Publisher's site
Tan, Y, Liu, Y, Long, G, Jiang, J, Lu, Q & Zhang, C 1970, 'Federated Learning on Non-IID Graphs via Structural Knowledge Sharing', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 9953-9961. View description>>
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to train strong GNN models in a distributed manner without sharing their private data. A core challenge in federated systems is the non-IID problem, which also widely exists in real-world graph data. For example, local data of clients may come from diverse datasets or even domains, e.g., social networks and molecules, increasing the difficulty for FGL methods to capture commonly shared knowledge and learn a generalized encoder. From real-world graph datasets, we observe that some structural properties are shared by various domains, presenting great potential for sharing structural knowledge in FGL. Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks. To explicitly extract the structure information rather than encoding them along with the node features, we define structure embeddings and encode them with an independent structure encoder. Then, the structure encoder is shared across clients while the feature-based knowledge is learned in a personalized way, making FedStar capable of capturing more structure-based domain-invariant information and avoiding feature misalignment issues. We perform extensive experiments over both cross-dataset and cross-domain non-IID FGL settings, demonstrating the superiority of FedStar.
Tang, H, Wu, S, Xu, G & Li, Q 1970, 'Dynamic Graph Evolution Learning for Recommendation', Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM. View/Download from: Publisher's site
Tang, J, Li, L, Hou, J, Xin, H & Yu, X 1970, 'A Divide-and-conquer Solution to 3D Human Motion Estimation from Raw MoCap Data', 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE. View/Download from: Publisher's site
Tapas, MJ, Thomas, P & Sirivivatnanon, V 1970, 'Effect of supplementary cementitious materials on carbonation rate and CO2 uptake', https://ciaconference.com.au/concrete2023/content.html, Conference of the Concrete Institute of Australia, Concrete 2023, Resilient and Sustainable Structures: Breaking Down Barriers, Perth, WA. View description>>
Carbonation, which pertains to the reaction of carbon dioxide (CO2) with the calcium-bearing phases of the binder, is a natural process that leads to the modification of the pore solution, microstructure as well as various properties of the concrete. This process, although traditionally perceived as undesirable due to its role in increasing the susceptibility of the concrete steel reinforcement to corrosion, is gaining global interest as a possible means to reduce the carbon footprint of the construction industry. CO2 sequestration in concrete is perceived to play a key role in improving the sustainability of concrete production. The carbonation process however remains not fully understood, particularly in binder systems with supplementary cementitious materials. This paper discusses the chemistry of carbonation, the change in phases due to carbonation and the influence of binder type on the carbonation rate and CO2 uptake.
AbstractThis study investigated the composition of alkali–silica reaction (ASR) products formed in mortar and concrete that underwent accelerated ASR testing using two test methods: the accelerated mortar bar test (AMBT) and the simulated pore solution immersion test (SPSM). The composition of the ASR products formed in the accelerated tests was compared with those in a 25-year old bridge in New South Wales demolished due to ASR. Results showed that the ASR products inside an aggregate contained calcium (≈20%), silicon (≈60%), and alkalis (≈20%) regardless of the ASR test method used. The ASR products in the AMBT sample only contained sodium, whereas the ASR products in the SPSM test and the demolished bridge both contained significant amounts of sodium and potassium, which indicated that the type of alkali in the ASR product is largely affected by the dominant alkali in the pore solution. However, considering that the total alkali content (Na + K) in the ASR products was similar regardless of the ASR test method used, this suggests that the total alkali content has more influence on the rate of expansion than the type of alkali. The composition of the ASR products also notably varied depending on the location in the concrete. ASR products closer to the cement paste had a higher calcium and lower alkali content than those inside an aggregate, which suggests that the calcium as well as the alkali content of the ASR products plays a significant role in the degree of ASR expansion.
Thanh, HT, Tapas, MJ, Chandler, J & Sirivivatnanon, V 1970, 'Creep of Slag Blended Cement Concrete with and Without Activator', Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 177-185. View/Download from: Publisher's site View description>>
AbstractPartly replacing Portland cement (PC) with lower carbon footprint cementitious materials such as ground granulated blast furnace slag (slag) is considered as a practical method for reducing CO2 emissions in the cement concrete industry. To mitigate the slow reactivity of slag in a cementitious system and enhance early-age strength, the addition of a chemical activator is a solution. However, the effect of the activator on creep behaviour of slag-blended cement concretes remains unclear. This work presents the effect of sodium sulfate (Na2SO4) activator on the compressive creep of PC concrete blended with 50 and 70 wt% slag. Four concrete mixes (with and without 2.5% Na2SO4 activator) containing 395 kg of cementitious material were prepared. The creep strain measurements were conducted on 150 × 300 mm cylindrical specimens for 140 days under sustained compressive load. The results showed that the 70% slag concrete had lower creep strain than 50% slag-blended cement concrete. The presence of Na2SO4 helped reduce the creep strain of 50% slag concrete but slightly increased that of 70% slag-blended cement concrete. In addition, the applicability of the predictive model in AS3600:2018 for the creep behaviour of high slag content concrete was assessed.
Tian, Y, Zhang, L, Do, TT-T, Liu, J, Wang, Y-K & Lin, C-T 1970, 'Classification of inattentional blindness using brain dynamics of ERPs', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Tian, Z, Zhang, C, Sood, K & Yu, S 1970, 'Inferring Private Data from AI Models in Metaverse through Black-box Model Inversion Attacks', 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom), 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom), IEEE. View/Download from: Publisher's site
Tomidei, L, Sick, N, Guertler, M, Schallow, J, Lenze, D & Deuse, J 1970, 'Dynamic value stream mapping: How Industry 4.0 can help us to learn to see better', 9th Changeable, Agile, Reconfigurable and Virtual Production Conference, Bologna, Italy.
Tomidei, L, Sick, N, Guertler, M, Schallow, J, Lenze, D & Deuse, J 1970, 'Dynamic Value Stream Mapping—How Industry 4.0 Can Help Us to Learn to See Better', Springer International Publishing, Daejeon City, South Korea, pp. 753-762. View/Download from: Publisher's site
Tran, X-T, Do, TT-T & Lin, C-T 1970, 'Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Trung Le, NT, Bray, E, Brian Lee, KM & Best, G 1970, 'Adaptive Trajectory Library Planner for Fast Outdoor Robots', Australasian Conference on Robotics and Automation, ACRA. View description>>
High-speed autonomous operation in outdoor environments requires fast computation of dynamically feasible, collision-free paths. To this end, we propose a new local path planning algorithm called Adaptive Trajectory Library. This approach relies on the selection of a suitable trajectory from a precomputed library to reduce online computation. A subset of trajectories that are dynamically feasible are considered, from which one is chosen to best move the robot towards a goal location while avoiding obstacles. In simulated experiments, the proposed algorithm significantly outperforms the Dynamic Window Approach [Fox et al., 1997] with 84.2 % less travel time and 39.5 % shorter path length. Hardware experiments in outdoor environments show that the proposed planner is able to reliably compute paths for a robot to follow to reach goals at high speeds in off-road terrain while avoiding obstacles.
Tung Le, D, Sutjipto, S, Khoa Nguyen, DD, Long Vu, T, Thong Vu, PD, Munasinghe, N & Paul, G 1970, 'HALO: a Rock Scaling Mobile Manipulator with Interactive Virtual Reality Live Digital Twin', Australasian Conference on Robotics and Automation, ACRA, Sydney. View description>>
The High Access Localised Operations (HALO) system is a mobile manipulator that amalgamates advancements in digital twins, and virtual reality (VR) to enable safer rock scaling operations in the mining industry. Currently, the essential geotechnical activity of rock scaling is performed by certified workers suspended on the side of the rock wall, who perform the physically demanding task of removing loose rock debris. The HALO system enables a remote operator immersed in a VR environment that visualises the digital twin of the robot and its sensor data in real-time to interact with the robot intuitively. This eliminates the need for people to be exposed to the hazards associated with performing manual rock scaling, while enabling them to apply their existing expertise when operating the robot in VR. In this work, we present a summary of the HALO hardware and its interaction architecture, encompassing a framework for real-time remote scene reconstruction and natural interaction. Findings from preliminary site trials are also be presented to provide preliminary evaluation of the system.
Van Nguyen, K, Islam, MR, Huo, H, Tilocca, P & Xu, G 1970, 'Explainable exclusion in the life insurance using multi-label classifier', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Van Nguyen, K, Tilocca, P, Huo, H & Xu, G 1970, 'Underwriting Knowledge Graph Construction, Maintenance and Its Application on Explainable Exclusion', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE. View/Download from: Publisher's site
Waheed, N, Khan, F, Mastorakis, S, Jan, MA, Alalmaie, AZ & Nanda, P 1970, 'Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home Data', 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), IEEE, Berlin, Germany. View/Download from: Publisher's site
Waheed, N, Ur Rehman, A, Nehra, A, Farooq, M, Tariq, N, Jan, MA, Khan, F, Alalmaie, AZ & Nanda, P 1970, 'FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare Through Federated Learning and Blockchain', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE, Kuala Lumpur, pp. 3855-3860. View/Download from: Publisher's site View description>>
The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.
Wakulicz, J, Brian Lee, KM, Vidal-Calleja, T & Fitch, R 1970, 'Topological Trajectory Prediction with Homotopy Classes', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 6930-6936. View/Download from: Publisher's site View description>>
Trajectory prediction in a cluttered environment is key to many important robotics tasks such as autonomous navigation. However, there are an infinite number of possible trajectories to consider. To simplify the space of trajectories under consideration, we utilise homotopy classes to partition the space into countably many mathematically equivalent classes. All members within a class demonstrate identical high-level motion with respect to the environment, i.e., travelling above or below an obstacle. This allows high-level prediction of a trajectory in terms of a sparse label identifying its homotopy class. We therefore present a light-weight learning framework based on variable-order Markov processes to learn and predict homotopy classes and thus high-level agent motion. By informing a Gaussian mixture model (GMM) with our homotopy class predictions, we see great improvements in low-level trajectory prediction compared to a naive GMM on a real dataset.
Wambsganss, A, Tomidei, L, Sick, N, Bröring, S, Salomo, S & Schultz, C 1970, 'Machine-based anticipation of converging technology systems: The case of printed electronics', International Society for Professional Innovation Management, Ljubljana, Slovenia.
Wang, B, Qin, AK, Shafiei, S, Dia, H, Mihaita, A-S & Grzybowska, H 1970, 'Training Physics- Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, AUSTRALIA, Broadbeach. View/Download from: Publisher's site View description>>
Physics-informed neural networks (PINN s) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set. In PINN s, the NN acts as the solution approximator for the PDE while the PDE acts as the prior knowledge to guide the NN training, leading to the desired generalization performance of the NN when facing the limited availability of training data. However, training PINNs is a non-trivial task largely due to the complexity of the loss composed of both NN and physical law parts. In this work, we propose a new PINN training framework based on the multi-task optimization (MTO) paradigm. Under this framework, multiple auxiliary tasks are created and solved together with the given (main) task, where the useful knowledge from solving one task is transferred in an adaptive mode to assist in solving some other tasks, aiming to uplift the performance of solving the main task. We implement the proposed framework and apply it to train the PINN for addressing the traffic density prediction problem. Experimental results demonstrate that our proposed training framework leads to significant performance improvement in comparison to the traditional way of training the PINN.
Wang, D, Liu, S, Wang, H, Grau, BC, Song, L, Tang, J, Song, L & Liu, Q 1970, 'An Empirical Study of Retrieval-Enhanced Graph Neural Networks', IOS Press. View/Download from: Publisher's site View description>>
Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Motivated by this, we want to explore how the retrieval idea can help augment the useful information learned in the graph neural networks, and we design a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. In GRAPHRETRIEVAL, for each input graph, similar graphs together with their ground-true labels are retrieved from an existing database. Thus they can act as a potential enhancement to complete various graph property predictive tasks. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs. Moreover, our empirical study also illustrates that retrieval enhancement is a promising remedy for alleviating the long-tailed label distribution problem.
Wang, K, Ling, Y, Zhang, Y, Yu, Z, Wang, H, Bai, G, Ooi, BC & Dong, JS 1970, 'Characterizing Cryptocurrency-themed Malicious Browser Extensions', Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS '23: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM. View/Download from: Publisher's site
Wang, M, Lin, B, Guo, X, Li, L, Zhu, Z, Sun, J, Zhang, S, Liu, Y & Yu, X 1970, 'GaitStrip: Gait Recognition via Effective Strip-Based Feature Representations and Multi-level Framework', Computer Vision – ACCV 2022, Springer Nature Switzerland, pp. 711-727. View/Download from: Publisher's site View description>>
Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance is often affected by partitioning strategies, which are empirically chosen in different datasets. However, we observe that strips as the basic component of parts are agnostic against different partitioning strategies. Motivated by this observation, we present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the strip-based feature representations by directly taking each strip of the human body as the basic unit. Moreover, we propose a novel multi-branch structure, called Enhanced Convolution Module (ECM), to extract different representations of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network. Specifically, ST aims to extract spatial-temporal features of gait sequences, while FL is used to generate the feature representation of each frame. Second, the parameters of the ECM can be reduced in test by introducing a structural re-parameterization technique. Extensive experimental results demonstrate that our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions. The source code is published at https://github.com/M-Candy77/GaitStrip.
Wang, Q, Fang, Z, Zhang, Y, Liu, F, Li, Y & Han, B 1970, 'Learning to Augment Distributions for Out-of-Distribution Detection', Advances in Neural Information Processing Systems. View description>>
Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection. Advanced works, despite their promising progress, may still fail in the open world, owing to the lack of knowledge about unseen OOD data in advance. Although one can access auxiliary OOD data (distinct from unseen ones) for model training, it remains to analyze how such auxiliary data will work in the open world. To this end, we delve into such a problem from a learning theory perspective, finding that the distribution discrepancy between the auxiliary and the unseen real OOD data is the key to affecting the open-world detection performance. Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution. We justify that the predictor trained over the worst OOD data in the ball can shrink the OOD distribution discrepancy, thus improving the open-world detection performance given only the auxiliary OOD data. We conduct extensive evaluations across representative OOD detection setups, demonstrating the superiority of our DAL over its advanced counterparts. The code is publicly available at: https://github.com/tmlr-group/DAL.
Wang, Q, Feng, Y, Wu, D & Gao, W 1970, 'Non-probabilistic Informed Structural Health Assessment with Virtual Modelling Technique', Springer Nature Singapore, pp. 359-364. View/Download from: Publisher's site View description>>
AbstractIn real-life engineering, non-probabilistic structural information is very common in many and varied disciplines. This class of information is characterized by incompleteness and imprecision, such as interval, fuzzy sets, etc. Non-probabilistic structural information can be reflected in the structural performance and cause it to fluctuate within a specific range, instead of being deterministic. Thus, without appropriate consideration of non-probabilistic information, serious or even disastrous accidents may occur. Therefore, fully estimating the structural health status using non-probabilistic information, especially detecting the lower and upper bounds of the concerned structural response, is extremely significant in uncertainty-sensitive fields. To conquer this challenge, a virtual modeling technique underpinning a structural health assessment framework is introduced. The twin extended support vector regression (T-X-SVR) approach is embedded for virtual model construction. Continuous, differentiable expression of the established virtual model allows the optimal solutions for each interval analysis to be easily achieved. Information update is another inherent feature, which enables structural health assessment to be implemented with updated conditions without rebuilding the virtual model. To demonstrate the applicability of the proposed virtual modeling technique underpinned structural health assessment framework, the non-probabilistic informed elastoplastic nonlocal damage analysis was investigated for engineering structures.
Wang, Q, Liu, D, Carmichael, MG & Lin, C-T 1970, 'Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, ENGLAND, London, pp. 9896-9902. View/Download from: Publisher's site View description>>
Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.
Wang, R, Liu, Y, Gong, Y, Liu, W, Chen, M, Yin, Y & Zheng, Y 1970, 'Fine-grained Urban Flow Inference with Unobservable Data via Space-Time Attraction Learning', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Wang, R, Wang, S, Lu, W, Peng, X, Zhang, W, Zheng, C & Qiao, X 1970, 'Intention-Aware User Modeling for Personalized News Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 179-194. View/Download from: Publisher's site View description>>
Although tremendous efforts have been made in the field of personalized news recommendations, how to accurately model users’ reading preferences to recommend satisfied news remains a critical challenge. In fact, users’ reading preferences are often driven by his/her high-level goal-oriented intentions. For example, in order to satisfy the intention of traveling, a user may prefer to read news about national parks or hiking activities. However, existing methods for news recommendations often focus on capturing users’ low-level preferences towards specific news only, neglecting to model their intrinsic reading intentions, leading to insufficient modeling of users and thus suboptimal recommendation performance. To address this problem, in this paper, we propose a novel intention-aware personalized news recommendation model (IPNR), to accurately model both a user’s reading intentions and his/her preference for personalized next-news recommendations. In addition to modeling users’ reading preferences, our proposed model IPNR can also capture users’ reading intentions and the transitions over intentions for better predicting the next piece of news which may interest the user. Extensive experimental results on real-world datasets demonstrate that IPNR outperforms the state-of-the-art news recommendation methods in terms of recommendation accuracy (The source code is available at: https://github.com/whonor/IPNR ).
Wang, W, Shen, T, Blumenstein, M & Long, G 1970, 'Improving Open-Domain Answer Sentence Selection by Distributed Clients with Privacy Preservation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 15-29. View/Download from: Publisher's site View description>>
Open-domain answer sentence selection (OD-AS2), as a practical branch of open-domain question answering (OD-QA), aims to respond to a query by a potential answer sentence from a large-scale collection. A dense retrieval model plays a significant role across different solution paradigms, while its success depends heavily on sufficient labeled positive QA pairs and diverse hard negative sampling in contrastive learning. However, it is hard to satisfy such dependencies in a privacy-preserving distributed scenario, where in each client, fewer in-domain pairs and a relatively small collection cannot support effective dense retriever training. To alleviate this, we propose a brand-new learning framework for Privacy-preserving Distributed OD-AS2, dubbed PDD-AS2. Built upon federated learning, it consists of a client-customized query encoding for better personalization and a cross-client negative sampling for learning effectiveness. To evaluate our learning framework, we first construct a new OD-AS2 dataset, called Fed-NewsQA, based on NewsQA to simulate distributed clients with different genre/domain data. Experiment results show that our learning framework can outperform its baselines and exhibit its personalization ability.
Wang, W, Tian, Z, Zhang, C, Liu, A & Yu, S 1970, 'BFU: Bayesian Federated Unlearning with Parameter Self-Sharing', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Wang, W, Zhang, C, Liu, S, Tang, M, Liu, A & Yu, S 1970, 'FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Wang, X, Qin, P-Y, Guo, YJ & Gupta, K 1970, 'Reconfigurable Dual-Layer Unit Cell Based Beam Steering Transmitarray', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE, Florence, Italy, pp. 1-4. View/Download from: Publisher's site View description>>
A beam steering transmitarray utilizing reconfigurable dual layer unit cells is presented in this paper Two PIN diodes are used on each array unit cell to achieve a 1 bit phase change with a high transmission Compared to other electronically reconfigurable transmitarrays employing multilayer unit cells with metal vias the developed transmitarray has a much simpler configuration which is beneficial to larger aperture designs in high frequency bands To validate the unit cell design concept a transmitarray prototype at 13 GHz is designed The measured peak gain is 18 4 dBi and 2 D beam steering performances are 50 and 40 in E and H planes respectively
Wang, X, Qin, P-Y, Jin, R & Guo, YJ 1970, 'Wideband Conformal Transmitarray Employing Tightly Coupled Huygens Element at E Band', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE, pp. 485-486. View/Download from: Publisher's site View description>>
A new method to achieve wideband conformal transmitarrays is presented by employing tightly coupled Huygens element in this paper. The element is composed of five pairs of metallic strips which are printed on two sides of a dielectric substrate. The tightly coupled elements can achieve a high transmission and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. To further analyze the element, equivalent circuit models are developed. Good agreement is achieved between circuit and full-wave simulations. A cylindrical conformal transmitarray at 78 GHz is designed, fabricated and measured. The measured results have good agreement with the simulated ones. The measured peak realized gain is 26.6 dBi with an aperture efficiency of 37.2%. The measured 3-dB gain bandwidth is 20.4% from 71 to 87 GHz, which can fully cover the E-band spectrum.
Wang, X, Wang, W, Shao, J & Yang, Y 1970, 'LANA: A Language-Capable Navigator for Instruction Following and Generation', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Wang, X, Wen, D, Zhang, W, Zhang, Y & Qin, L 1970, 'Distributed Near-Maximum Independent Set Maintenance over Large-scale Dynamic Graphs.', ICDE, pp. 2538-2550.
Wang, Y, Peng, X, Shen, T, Clarke, A, Schlegel, C, Martin, P & Long, G 1970, 'Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding', Advanced Data Mining and Applications, Springer Nature Switzerland, pp. 18-32. View/Download from: Publisher's site View description>>
Electronic Health Records (EHRs) are a rich source of information that can be leveraged for various medical applications, such as disease inference, treatment recommendation, and outcome analysis. However, the complexity and heterogeneity of EHR data, along with the limited availability of well-labeled samples, present significant challenges to the development of efficient and adaptable models for EHR tasks (such as rare or novel disease prediction or inference). In this paper, we propose Soft prompt transfer for Electronic Health Records (SptEHR), a novel pipeline designed to address these challenges. Specifically, SptEHR consists of three main stages: (1) self-supervised pre-training on raw EHR data for an EHR-centric transformer-based foundation model, (2) supervised multi-task continual learning from existing well-labeled tasks to further refine the foundation model and learn transferable task-specific soft prompts, and (3) further improve zero-shot and few-shot ability via prompt transfer. Specifically, the transformer-based foundation model learned from stage one captures domain-specific knowledge. Then the multi-task continual training in stage two improves model adaptability and performance on EHR tasks. Finally, stage three leverages soft prompt transfer which is based on the similarity between the new and the existing tasks, to effectively address new tasks without requiring additional/extensive training. The effectiveness of the SptEHR has been validated on the benchmark dataset - MIMIC-III.
Wen, Y, Liu, B, Cao, J, Xie, R & Song, L 1970, 'Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Wen, Y, Qin, P-Y & Guo, YJ 1970, 'A Low Profile Modulated Metasurface Antenna for Multi-Beam Applications', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Wen, Y, Qin, P-Y & Jay Guo, Y 1970, 'A Multi-Beam Antenna Based on Modulated Metasurface', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 55-56. View/Download from: Publisher's site View description>>
In this paper modulated metasurface based multibeam antenna is presented The impedance superposition method of all impedance modulations for different beams is utilized in the design The current reported multi beam modulated metasurface has a very small number of beams due to the mutual interference of different impedance modulations It is found that the source locations for each beam play a key role in interference suppression Instead of using the time consuming full wave simulation based optimization in this paper the optimal source locations are obtained by using the aperture fields calculated from the zeroth order approximation of the currents on the metasurface A five beam modulated metasurface antenna has been designed providing an angular coverage up to 30 The peak realized gain for the broadside beam is 21 3 dBi at 14 30 GHz
Weththasinghe, K, Clark, N, Ngo, QT, Jayawickrama, B, He, Y, Dutkiewicz, E & Liu, RP 1970, 'L-Band Spectral Opportunities for Cognitive GEO-LEO Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Willey, K & Gardner, A 1970, 'STUDENTS' EXPERIENCE OF FEEDBACK PRACTICES AND RECOMMENDATIONS FOR IMPROVEMENT', SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings, pp. 1471-1478. View/Download from: Publisher's site View description>>
There have been numerous research studies and recommendations as to what feedback should look like to improve student learning and the learning experience. These recommendations include being timely, fed forward, provided using different modes and sources and to support students to know how to best use the feedback they are given. The Faculty of Engineering and IT (FEIT) at The University of Technology Sydney (UTS) is currently focusing on improving the quality, effectiveness and delivery of feedback provided to their students on their learning and demonstrated achievement in a variety of settings. This paper reports the first stage of this project where students were asked about their previous experience of receiving feedback, how they are able to use it and their preference as to the type and timing of the feedback they prefer. Students reported feedback was often was non-existent, extremely limited, nonspecific, or too late to be useful. They found feedback was most useful when it was specific, could be used for improvement and was not just focused on correction.
Wu, H, Hu, Z, Li, L, Zhang, Y, Fan, C & Yu, X 1970, 'NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Wu, H, Zhao, G, Wang, S, Xu, C & Yu, S 1970, 'A Location Privacy and Query Privacy Joint Protection Scheme for POI Query in Vehicular Networks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Wu, J, Yang, F, Zheng, J, Nguyen, HT & Chai, R 1970, 'Microwave Imaging Based on a Subspace-based Two-step Iterative Shrinkage/Thresholding Method', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. View/Download from: Publisher's site
Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'A Low-Complexity CSI-Based Wifi Sensing Scheme for LoS-Dominant Scenarios', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Wu, L, Gentil, CL & Vidal-Calleja, T 1970, 'Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields', 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE. View/Download from: Publisher's site
Wu, M, Zhang, Y, Lin, H, Grosser, M, Zhang, G & Lu, J 1970, 'BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Singapore, pp. 187-198. View/Download from: Publisher's site View description>>
Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiting these AI tools and conducting customized analyses. This paper introduces BiblioEngine, a literature analysis platform that helps researchers profile the research landscape and gain genetic insights into diseases. BiblioEngine integrates multiple AI-empowered data sources and employs heterogeneous network analysis to identify and emphasize genes and other biomedical entities for further investigation. Its effectiveness is demonstrated through a case study on stroke-related genetic research. Analysis with BiblioEngine uncovers valuable research intelligence and genetic insights. It provides a profile of leading research institutions and the knowledge landscape in the field. The gene co-occurrence map reveals frequent research of NOTCH3, prothrombotic factors, inflammatory cytokines, and other potential risk factors. The heterogeneous biomedical entity network analysis highlights infrequently studied genes and biomedical entities with potential significance for future stroke studies. In conclusion, BiblioEngine is a valuable tool enabling efficient navigation and comprehension of expanding biomedical knowledge from scientific literature, empowering researchers in their pursuit of disease-specific genetic knowledge.
Wu, S, Xu, G & Wang, X 1970, 'SOAC: Supervised Off-Policy Actor-Critic for Recommender Systems', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Wu, W, Li, B, Chen, L, Gao, J & Zhang, C 1970, 'A Review for Weighted MinHash Algorithms (Extended abstract)', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE, Anaheim, CA, USA, pp. 2553-2573. View/Download from: Publisher's site View description>>
Data similarity computation is a fundamental research topic which underpins many high level applications based on similarity measures However the exact similarity computation has become daunting in large scale real world scenarios Currently MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore weighted MinHash is utilized to estimate the generalized Jaccard similarity of weighted sets This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms Also we have developed a Python toolbox for the algorithms and released it in our github
Wu, W, Wang, X, Luo, H, Wang, J, Yang, Y & Ouyang, W 1970, 'Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. View/Download from: Publisher's site
Wu, X, Lu, J, Fang, Z & Zhang, G 1970, 'Meta OOD Learning For Continuously Adaptive OOD Detection', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Paris. View/Download from: Publisher's site
Wu, Y, Yu, L & Zhang, J 1970, 'Adaptive Local Feature Matching for Few-shot Fine-grained Image Recognition', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. View/Download from: Publisher's site
Xiang, S, Zhu, M, Cheng, D, Li, E, Zhao, R, Ouyang, Y, Chen, L & Zheng, Y 1970, 'Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 14557-14565. View description>>
Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.
Xie, H, Huang, Z, Leung, FHF, Ju, Y, Zheng, Y-P & Ling, SH 1970, 'A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment.', BIBM, IEEE, pp. 1567-1574.
Xie, M, MA, J, Long, G & Zhang, C 1970, 'Robust Clustered Federated Learning with Bootstrap Median-of-Means', Springer Nature Switzerland, pp. 237-250. View/Download from: Publisher's site
Xing, B & Tsang, IW 1970, 'Co-Evolving Graph Reasoning Network for Emotion-Cause Pair Extraction', Springer Nature Switzerland, pp. 305-322. View/Download from: Publisher's site
Xing, V, Zhao, L & Best, G 1970, 'CoordLight-YOLOv5: A Lightweight Object Detection Algorithm for Autonomous Motorsport', Australasian Conference on Robotics and Automation, ACRA. View description>>
Autonomous motorsport is a rapidly developing field, among which autonomous racing based on cone tracks is a challenging testing and racing environment. Object detection is one of its indispensable technologies. We propose an improved lightweight model, CoordLightYOLOv5, motivated by autonomous racing projects that require a high frame rate and detection accuracy by onboard embedded computing. In the proposed method, we deleted large object detection feature maps from the backbone and neck network in order to reduce model training and detection time and reduce model computational costs. Furthermore, we introduce CoordConv layer in the neck network to enhance object localisation ability in Cartesian coordinates without using spatial transformations. Our experiments with five autonomous racing datasets show that compared to YOLOv5s, the average number of model parameters is reduced by 91.5%, the average floating-point operation is reduced by 78%, the detection speed has been increased from the original 94 frames per second to 117 frames per second, and the average detection accuracy decreased by only 2.5%.
Xu, B, Guertler, M & Sick, N 1970, 'Analyzing Industry 4.0 Adoption Barriers of Small and Medium-sized Enterprises and Existing Support', IEEE Conference on Engineering Informatics, Melbourne, pp. 1-10.
Xu, D, Yang, H, Rizoiu, M-A & Xu, G 1970, 'From Occupations to Tasks: A New Perspective on Automatability Prediction Using BERT', 2023 10th International Conference on Behavioural and Social Computing (BESC), 2023 10th International Conference on Behavioural and Social Computing (BESC), IEEE. View/Download from: Publisher's site
Xu, H, Nanda, P & Liang, J 1970, 'Designing Incentive Mechanisms for Fair Participation in Federated Learning', 2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), 2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), IEEE, Melbourne, Australia. View/Download from: Publisher's site
Xu, K, Guo, Y, Lei, G & Zhu, J 1970, 'A Data-Driven Method for Iron Loss Estimation in Bearingless Permanent Magnet Synchronous Motors', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE. View/Download from: Publisher's site
Xu, K, Guo, Y, Lei, G, Liu, L & Zhu, J 1970, 'Calculation of Iron Loss in Permanent Magnet Synchronous Motors Based on PSO-RNN', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Xu, K, Guo, Y, Lei, G, Zhu, J & Sun, X 1970, 'Electromagnetic Performance Analysis of a Bearingless Permanent Magnet Synchronous Motor by Model Order Reduction', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Xu, T, Wang, H, Li, Y, Leng, D & Xu, H 1970, 'Full Vehicle Experimental Testing of Semi-active Suspension Equipped with Magnetorheological Dampers', 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE. View/Download from: Publisher's site
Xu, W, Huang, H, Cheng, M, Yu, L, Wu, Q & Zhang, J 1970, 'Masked Cross-image Encoding for Few-shot Segmentation', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE. View/Download from: Publisher's site
Xu, Y, Yang, Z & Yang, Y 1970, 'Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Xu, Y, Yang, Z & Yang, Y 1970, 'Video Object Segmentation in Panoptic Wild Scenes', IJCAI International Joint Conference on Artificial Intelligence, pp. 1604-1612. View description>>
In this paper, we introduce semi-supervised video object segmentation (VOS) to panoptic wild scenes and present a large-scale benchmark as well as a baseline method for it. Previous benchmarks for VOS with sparse annotations are not sufficient to train or evaluate a model that needs to process all possible objects in real-world scenarios. Our new benchmark (VIPOSeg) contains exhaustive object annotations and covers various real-world object categories which are carefully divided into subsets of thing/stuff and seen/unseen classes for comprehensive evaluation. Considering the challenges in panoptic VOS, we propose a strong baseline method named panoptic object association with transformers (PAOT), which associates multiple objects by panoptic identification in a pyramid architecture on multiple scales. Experimental results show that VIPOSeg can not only boost the performance of VOS models by panoptic training but also evaluate them comprehensively in panoptic scenes. Previous methods for classic VOS still need to improve in performance and efficiency when dealing with panoptic scenes, while our PAOT achieves SOTA performance with good efficiency on VIPOSeg and previous VOS benchmarks. PAOT also ranks 1st in the VOT2022 challenge. Our dataset and code are available at https://github.com/yoxu515/VIPOSeg-Benchmark.
Xue, C & Sirivivatnanon, V 1970, 'Chloride Penetration in Low-Carbon Concrete with High Volume of SCM: A Review Study', Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 141-149. View/Download from: Publisher's site View description>>
AbstractLow-carbon concrete (LCC) uses supplementary cementitious material (SCM) to partially replace cement as a method for reducing its carbon footprint. Previous laboratory and field studies had provided substantial support and experience for using LCC in marine structures, which are the most susceptible to chloride-induced corrosion. Some short-term test methods have provided reliable assessment of the ability of LCC to resist chloride penetration, but the long-term chloride penetration depends on a great many factors and thus could differ from the results obtained from laboratory tests. However, the lack of a correlation between the data from short-term and long-term tests has limited the use of abundant laboratory results for service life design of LCC. This study presents an overview of results obtained when LCCs were exposed to chlorides. The key outcome of this study is a broader synthesis of the available data regarding the relationship between the mix design and the performance of LCCs in various chloride environments, which helps find the possible correlation and fully appreciate the value of the short-term tests.
Yan, P & Long, G 1970, 'Personalization Disentanglement for Federated Learning', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE. View/Download from: Publisher's site
Yang, C, Wang, X, Yao, L, Long, G & Xu, G 1970, 'From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations', International Conference on Advanced Data Mining and Applications, International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland, Shenyang, China, pp. 19-33. View/Download from: Publisher's site
Yang, G, Lei, J, Fang, Z, Li, Y, Zhang, J & Xie, W 1970, 'HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks', 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), IEEE. View/Download from: Publisher's site
Yang, H, Chen, H, Zhang, S, Sun, X, Li, Q, Zhao, X & Xu, G 1970, 'Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM. View/Download from: Publisher's site
Yang, S, Xu, Z, Wang, K, You, Y, Yao, H, Liu, T & Xu, M 1970, 'BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Vancouver Convention Center. View/Download from: Publisher's site
Yang, X, Liu, W & Liu, W 1970, 'Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract)', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Yang, Y & Cao, L 1970, 'MTSNet: Deep Probabilistic Cross-multivariate Time Series Modeling with External Factors for COVID-19', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Yang, Y, Guo, K, Fang, Z, Lin, H, Grosser, M & Lu, J 1970, 'Multi-model Transfer Learning and Genotypic Analysis for Seizure Type Classification', Springer Nature Singapore, pp. 223-234. View/Download from: Publisher's site
Yang, Y, Zhao, Z & Cao, L 1970, 'Deep Spectral Copula Mechanisms Modeling Coupled and Volatile Multivariate Time Series', 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE. View/Download from: Publisher's site
Yang, Y, Zhou, T, Jiang, J, Long, G & Shi, Y 1970, 'Continual Task Allocation in Meta-Policy Network via Sparse Prompting', Proceedings of Machine Learning Research, pp. 39623-39638. View description>>
How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by “Continual Task Allocation via Sparse Prompting (CoTASP)”, which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. CoTASP trains a policy for each task by optimizing the prompts and the sub-network weights alternatively. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing tasks' semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network due to similar prompts while cross-task interference causing forgetting is effectively restrained. Given a meta-policy and dictionaries trained on previous tasks, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences. It outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks. Our code is available at https://github.com/stevenyangyj/CoTASP.
Yang, Z, Zhang, W, Lin, X, Zhang, Y & Li, S 1970, 'HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs', 2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE. View/Download from: Publisher's site
Ye, F, Wang, X, Zhang, Y & Tsang, IW 1970, 'Multi-Task Learning via Time-Aware Neural ODE', Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}, International Joint Conferences on Artificial Intelligence Organization. View/Download from: Publisher's site View description>>
Multi-Task Learning (MTL) is a well-established paradigm for learning shared models for a diverse set of tasks. Moreover, MTL improves data efficiency by jointly training all tasks simultaneously. However, directly optimizing the losses of all the tasks may lead to imbalanced performance on all the tasks due to the competition among tasks for the shared parameters in MTL models. Many MTL methods try to mitigate this problem by dynamically weighting task losses or manipulating task gradients. Different from existing studies, in this paper, we propose a Neural Ordinal diffeRential equation based Multi-tAsk Learning (NORMAL) method to alleviate this issue by modeling task-specific feature transformations from the perspective of dynamic flows built on the Neural Ordinary Differential Equation (NODE). Specifically, the proposed NORMAL model designs a time-aware neural ODE block to learn task-specific time information, which determines task positions of feature transformations in the dynamic flow, in NODE automatically via gradient descent methods. In this way, the proposed NORMAL model handles the problem of competing shared parameters by learning task positions. Moreover, the learned task positions can be used to measure the relevance among different tasks. Extensive experiments show that the proposed NORMAL model outperforms state-of-the-art MTL models.
Yi, K, Zhang, Q, Fan, W, Wang, S, Wang, P, He, H, Lian, D, An, N, Cao, L & Niu, Z 1970, 'Frequency-domain MLPs are More Effective Learners in Time Series Forecasting', Advances in Neural Information Processing Systems, New Orleans. View description>>
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, another kind of approaches based on multi-layer perceptrons (MLPs) are proposed with simple structure, low complexity, and superior performance. However, most MLP-based forecasting methods suffer from the point-wise mappings and information bottleneck, which largely hinders the forecasting performance. To overcome this problem, we explore a novel direction of applying MLPs in the frequency domain for time series forecasting. We investigate the learned patterns of frequency-domain MLPs and discover their two inherent characteristic benefiting forecasting, (i) global view: frequency spectrum makes MLPs own a complete view for signals and learn global dependencies more easily, and (ii) energy compaction: frequency-domain MLPs concentrate on smaller key part of frequency components with compact signal energy. Then, we propose FreTS, a simple yet effective architecture built upon Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components. The above stages operated on both inter-series and intra-series scales further contribute to channel-wise and time-wise dependency learning. Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods. Code is available at this repository: https://github.com/aikunyi/FreTS.
Yin, Z & Yang, Y 1970, 'Low-Profile Dual-Polarization Patch Antenna for Mm-Wave Applications', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE. View/Download from: Publisher's site
You, F, Yuan, X, Ni, W & Jamalipour, A 1970, 'Learning-Based Privacy-Preserving Computation Offloading in Multi-Access Edge Computing', GLOBECOM 2023 - 2023 IEEE Global Communications Conference, GLOBECOM 2023 - 2023 IEEE Global Communications Conference, IEEE. View/Download from: Publisher's site
Yu, G, Wang, Q, Altaf, T, Wang, X, Xu, X & Chen, S 1970, 'Predicting NFT Classification with GNN: A Recommender System for Web3 Assets', 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), IEEE. View/Download from: Publisher's site
Yu, J, Wang, H, Wang, X, Li, Z, Qin, L, Zhang, W, Liao, J & Zhang, Y 1970, 'Group-based Fraud Detection Network on e-Commerce Platforms', Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM. View/Download from: Publisher's site
Yu, L, Xu, W, Wu, Q & Zhang, J 1970, 'Automated Flock Density and Activity Recognition for Welfare Monitoring on Commercial Egg Farms', 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), IEEE. View/Download from: Publisher's site
Yue, Z, Zhang, Y & Liang, J 1970, 'Learning Conflict-Noticed Architecture for Multi-Task Learning', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 11078-11086. View description>>
Multi-task learning has been widely used in many applications to enable more efficient learning by sharing part of the architecture across multiple tasks. However, a major challenge is the gradient conflict when optimizing the shared parameters, where the gradients of different tasks could have opposite directions. Directly averaging those gradients will impair the performance of some tasks and cause negative transfer. Different from most existing works that manipulate gradients to mitigate the gradient conflict, in this paper, we address this problem from the perspective of architecture learning and propose a Conflict-Noticed Architecture Learning (CoNAL) method to alleviate the gradient conflict by learning architectures. By introducing purely-specific modules specific to each task in the search space, the CoNAL method can automatically learn when to switch to purely-specific modules in the tree-structured network architectures when the gradient conflict occurs. To handle multi-task problems with a large number of tasks, we propose a progressive extension of the CoNAL method. Extensive experiments on computer vision, natural language processing, and reinforcement learning benchmarks demonstrate the effectiveness of the proposed methods. The code of CoNAL is publicly available.1
Zakia, MA, Akter, S, Rony, ZI, Rahaman, M, Ahmed, SF, Vo, D-VN & Mofijur, M 1970, 'Environmental and human health impact of single-use plastic-made personal protective equipment used to limit the spread of SARS-CoV-2', 7TH INTERNATIONAL CONFERENCE ON ENVIRONMENT 2021 (ICENV2021), 7TH INTERNATIONAL CONFERENCE ON ENVIRONMENT 2021 (ICENV2021), AIP Publishing. View/Download from: Publisher's site
Zaqumi, MN, Lalbakhsh, A, Moloudian, G & Esselle, K 1970, 'Design of a Multi-layer Dual-band Frequency Selective Surface Bandpass Filter', 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE. View/Download from: Publisher's site
Zeng, F, Ding, C & Guo, YJ 1970, 'A New Form of Polarization Diversity Using A Pair of Spatially-Variable-Orthogonal-Polarizations', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE. View/Download from: Publisher's site
Zeng, G, Fang, Z, Zhang, G & Lu, J 1970, 'One-step Domain Adaptation Approach with Partial Label', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE. View/Download from: Publisher's site
Zhang, C, Long, G, Zhou, T, Yan, P, Zhang, Z, Zhang, C & Yang, B 1970, 'Dual Personalization on Federated Recommendation', Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}, International Joint Conferences on Artificial Intelligence Organization. View/Download from: Publisher's site View description>>
Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available.
Zhang, C, Tian, Z, Yu, JJQ & Yu, S 1970, 'Construct New Graphs Using Information Bottleneck Against Property Inference Attacks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE. View/Download from: Publisher's site
Zhang, C, Wang, W, Yu, JJQ & Yu, S 1970, 'Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Zhang, C, Zhang, Y, Mayr, P, Lu, W, Suominen, A, Chen, H & Ding, Y 1970, 'JCDL2023 Workshop: Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023) and the 3rd AI + Informetrics (AII2023)', 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL), IEEE. View/Download from: Publisher's site
Zhang, C, Zhang, Y, Mayr, P, Lu, W, Suominen, A, Chen, H & Ding, Y 1970, 'Preface to Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023) and the 3rd AI + Informetrics (AII2023) at JCDL 2023', CEUR Workshop Proceedings, pp. 1-5. View description>>
The Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023; https://eeke-workshop.github.io/) and the 3rd AI + Informetrics (AII2023; https://ai-informetrics.github.io/) was held at Santa Fe, New Mexico, USA and online, co-located with the ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2023. The two workshop series aim to engage the communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and the modeling and applications of AI + Informetrics for broad interests in science of science, science, technology, & innovation, etc. This joint workshop comprises keynote speeches, oral presentations, and poster sessions. The main topics of the proceedings include entity extraction and its applications, along with the integration of Artificial Intelligence + Informetrics.
Zhang, H, Huang, X & Zhang, JA 1970, 'Fine Doppler Resolution Channel Estimation and Offset Gradient Descent Equalization for OTFS Transmission over Doubly Selective Channels', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Zhang, H, Liu, C, Zang, S, Lei, G & Wang, Y 1970, 'Design Optimization of an Interior Permanent Magnet Synchronous Machine with Asymmetric and Auxiliary Slot Structure', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Zhang, H, Liu, C, Zang, S, Lei, G & Wang, Y 1970, 'Parameter Analysis and Multilevel Design Optimization of a Permanent Magnet Claw Pole Machine with Hybrid Cores', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Zhang, H, Liu, C, Zang, S, Lei, G & Wang, Y 1970, 'Rotor Auxiliary Slot Design Optimization for Permanent Magnet Synchronous Motor with Double-layer Rotor Structure for Electric Vehicle', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Zhang, H, Liu, C, Zang, S, Lei, G, Wang, Y & Zhu, J 1970, 'Rotor Auxiliary Slot Design Optimization for Interior Permanent Magnet Synchronous Machine for Electric Vehicle', 2023 IEEE International Magnetic Conference (INTERMAG), 2023 IEEE International Magnetic Conference (INTERMAG), IEEE. View/Download from: Publisher's site
Zhang, N, Guo, K & Guo, Y 1970, 'Research on Stable Operation of PMSM at Low Speed Based on Improved Reaching Law', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE. View/Download from: Publisher's site
Zhang, S, Liu, C, Lei, G, Wang, Y & Zhu, J 1970, 'Design and Performance Analysis of a New Synchronous Reluctance Machine with Hybrid Cores', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE. View/Download from: Publisher's site
Zhang, S, Liu, F, Yang, J, Yang, Y, Li, C, Han, B & Tan, M 1970, 'Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score', Proceedings of Machine Learning Research, pp. 41429-41451. View description>>
Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPS-AD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD.
Zhang, S, Liu, J, Bao, Z, Yu, S & Lin, Y 1970, 'Adversarial Domain Generalization Defense for Automatic Modulation Classification', 2023 IEEE/CIC International Conference on Communications in China (ICCC), 2023 IEEE/CIC International Conference on Communications in China (ICCC), IEEE. View/Download from: Publisher's site
Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link with 30 Gbps Data Rate', 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE. View/Download from: Publisher's site
Zhang, T, Zhang, H, Zhu, H, Huang, X, Suzuki, HH, Pathikulangara, J, Smart, KW, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE. View/Download from: Publisher's site
Zhang, W, Yang, Z, Wen, D & Wang, X 1970, 'Efficient Distributed Core Graph Decomposition', 2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2023 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE. View/Download from: Publisher's site
Zhang, X, Peng, X, Guan, H, Zhao, L, Qiao, X & Lu, W 1970, 'Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction', 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, Hainan, China. View/Download from: Publisher's site
Zhang, X, Zhang, Z, Zhong, Q, Zheng, X, Zhang, Y, Hu, S & Zhang, LY 1970, 'Masked Language Model Based Textual Adversarial Example Detection', Proceedings of the ACM Asia Conference on Computer and Communications Security, ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security, ACM. View/Download from: Publisher's site
Zhang, X, Zhu, X, Yu, Y & Li, J 1970, 'Unsupervised Knowledge Transfer for Structural Damage Detection with Limited Data', The twelfth International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hangzhou, China.
Zhang, Y, Bai, G, Chamikara, MAP, Ma, M, Shen, L, Wang, J, Nepal, S, Xue, M, Wang, L & Liu, J 1970, 'AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning', Proceedings of the ACM Web Conference 2023, WWW '23: The ACM Web Conference 2023, ACM. View/Download from: Publisher's site
Zhang, Y, Cheng, C, Falque, R, Zhao, L, Huang, S & Chen, Y 1970, '3D Intra-articular Dense Reconstruction from Arthroscopic Images', 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE. View/Download from: Publisher's site
Zhang, Y, Dong, L, Yang, G, Li, JJ & Wang, B 1970, 'Accelerated Aging Test Method of Lithium-Ion Batteries Featured with Aging Feature Reconstruction', 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), IEEE. View/Download from: Publisher's site
Zhang, Y, Mao, J, Cai, Y, Ye, C & Zhu, Q 1970, 'Broadband Frequency-Invariant Broadside Beamforming with a Differential Loudspeaker Array', 2023 31st European Signal Processing Conference (EUSIPCO), 2023 31st European Signal Processing Conference (EUSIPCO), IEEE, Helsinki. View/Download from: Publisher's site
Zhang, Y, Wang, Z, Luo, Y, Yu, X & Huang, Z 1970, 'Learning Efficient Unsupervised Satellite Image-based Building Damage Detection', 2023 IEEE International Conference on Data Mining (ICDM), 2023 IEEE International Conference on Data Mining (ICDM), IEEE. View/Download from: Publisher's site
Zhang, Y, Zhou, F, Li, Z, Wang, Y & Chen, F 1970, 'Fair Representation Learning with Unreliable Labels', Proceedings of Machine Learning Research, pp. 4655-4667. View description>>
In learning with fairness, for every instance, its label can be systematically flipped to another class due to the practitioner's prejudice, namely, label bias. The existing well-studied fair representation learning methods focus on removing the dependency between the sensitive factors and the input data, but do not address how the representations retain useful information when the labels are unreliable. In fact, we find that the learned representations become random or degenerated when the instance is contaminated by label bias. To alleviate this issue, we investigate the problem of learning fair representations that are independent of the sensitive factors while retaining the task-relevant information given only access to unreliable labels. Our model disentangles the dependency between fair representations and sensitive factors in the latent space. To remove the reliance between the labels and sensitive factors, we incorporate an additional penalty based on mutual information. The learned purged fair representations can then be used in any downstream processing. We demonstrate the superiority of our method over previous works through multiple experiments on both synthetic and real-world datasets.
Zhang, Z, Fang, M, Chen, L & Namazi-Rad, M-R 1970, 'CITB: A Benchmark for Continual Instruction Tuning', Findings of the Association for Computational Linguistics: EMNLP 2023, Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics. View/Download from: Publisher's site
Zhang, Z, Fang, M, Chen, L, Namazi-Rad, MR & Wang, J 1970, 'How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances', EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 8289-8311. View description>>
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field.
Zhang, Z, Fang, M, Ye, F, Chen, L & Namazi-Rad, M-R 1970, 'Turn-Level Active Learning for Dialogue State Tracking', Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. View/Download from: Publisher's site
Zhang, Z, Wang, Y, Yu, G, Wang, X, Zhang, M, Ni, W & Liu, RP 1970, 'A Community-based Strategy for Blockchain Sharding: Enabling More Budget-friendly Transactions', 2023 IEEE International Conference on Blockchain (Blockchain), 2023 IEEE International Conference on Blockchain (Blockchain), IEEE. View/Download from: Publisher's site
Zhao, H, Wei, S, Shi, D, Tan, W, Li, Z, Ren, Y, Wei, X, Yang, Y & Pu, S 1970, 'Learning Symmetry-Aware Geometry Correspondences for 6D Object Pose Estimation', 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE. View/Download from: Publisher's site
Zhao, H, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Does Continual Learning Equally Forget All Parameters?', Proceedings of Machine Learning Research, pp. 42280-42303. View description>>
Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of previously learned knowledge. Although it can be alleviated by repeatedly replaying buffered data, the every-step replay is time-consuming. In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL. Our proposed metrics show that only a few modules are more task-specific and sensitive to task change, while others can be shared across tasks as common knowledge. Hence, we attribute forgetting mainly to the former and find that finetuning them only on a small buffer at the end of any CL method can bring non-trivial improvement. Due to the small number of finetuned parameters, such “Forgetting Prioritized Finetuning (FPF)” is efficient in computation. We further propose a more efficient and simpler method that entirely removes the every-step replay and replaces them by only k-times of FPF periodically triggered during CL. Surprisingly, this “k-FPF” performs comparably to FPF and outperforms the SOTA CL methods but significantly reduces their computational overhead and cost. In experiments on several benchmarks of class- and domain-incremental CL, FPF consistently improves existing CL methods by a large margin, and k-FPF further excels in efficiency without degrading the accuracy. We also empirically studied the impact of buffer size, epochs per task, and finetuning modules on the cost and accuracy of our methods.
Zhao, H, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks', Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, pp. 52-68. View/Download from: Publisher's site View description>>
As large-scale pre-trained models have become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scratch for downstream tasks? How to reuse the pruning results of previous tasks to accelerate the pruning for new tasks? To address these challenges, we create a small model for a new task from the pruned models of similar tasks. We show that a few fine-tuning steps on this model suffice to produce a promising pruned model for the new task. We study this “meta-pruning” from nearest tasks on two major classes of pre-trained models, convolutional neural network and vision transformer, under a limited budget of pruning iterations. Our study begins by investigating the overlap of pruned models for similar tasks and how the overlap changes over different layers and blocks. Inspired by these discoveries, we develop a simple but effective “Meta-Vote Pruning” method that significantly reduces the pruning iterations for a new task by initializing a sub-network from the pruned models of its nearest tasks. In experiments, we demonstrate MVP’s accuracy, efficiency, and generalization advantages through extensive empirical studies and comparisons with popular pruning methods over several datasets.
Zhao, J, Fang, M, Shi, Z, Li, Y, Chen, L & Pechenizkiy, M 1970, 'CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models', Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics. View/Download from: Publisher's site
Zhao, P, Wang, S, Lu, W, Peng, X, Zhang, W, Zheng, C & Huang, Y 1970, 'News Recommendation via Jointly Modeling Event Matching and Style Matching', ECML PKDD 2023: Machine Learning and Knowledge Discovery in Databases: Research Track, Springer Nature Switzerland, pp. 404-419. View/Download from: Publisher's site
Zhao, Y, Liu, B, Ding, M, Liu, B, Zhu, T & Yu, X 1970, 'Proactive Deepfake Defence via Identity Watermarking', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4591-4600. View/Download from: Publisher's site
Zheng, H, Luo, X, Wei, P, Song, X, Li, D & Jiang, J 1970, 'Adaptive Policy Learning for Offline-to-Online Reinforcement Learning', Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp. 11372-11380. View description>>
Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the offline datasets is poor. In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data. Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, that is, a pessimistic update strategy for the offline dataset and an optimistic/greedy update scheme for the online dataset. Such a simple and effective method provides a way to mix the offline and online RL and achieve the best of both worlds. We further provide two detailed algorithms for implementing the framework through embedding value or policy-based RL algorithms into it. Finally, we conduct extensive experiments on popular continuous control tasks, and results show that our algorithm can learn the expert policy with high sample efficiency even when the quality of offline dataset is poor, e.g., random dataset.
Zheng, H, Zhang, Z, Fan, J, Hong, R, Yang, Y & Yan, S 1970, 'Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in the Dark', Proceedings of the 31st ACM International Conference on Multimedia, MM '23: The 31st ACM International Conference on Multimedia, ACM. View/Download from: Publisher's site
Zheng, J, Li, K, Mhaisen, N, Ni, W, Tovar, E & Guizani, M 1970, 'Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE. View/Download from: Publisher's site
Zhong, Z, Liu, J, Wu, D, Di, P, Sui, Y, Liu, AX & Lui, JCS 1970, 'Scalable Compositional Static Taint Analysis for Sensitive Data Tracing on Industrial Micro-Services', 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), IEEE. View/Download from: Publisher's site
Zhou, D, Yang, Z & Yang, Y 1970, 'Pyramid Diffusion Models for Low-light Image Enhancement', IJCAI International Joint Conference on Artificial Intelligence, pp. 1795-1803. View description>>
Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement for recovering realistic details. However, we found two problems when doing this, i.e., 1) diffusion models keep constant resolution in one reverse process, which limits the speed; 2) diffusion models sometimes result in global degradation (e.g., RGB shift). To address the above problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light image enhancement. PyDiff uses a novel pyramid diffusion method to perform sampling in a pyramid resolution style (i.e., progressively increasing resolution in one reverse process). Pyramid diffusion makes PyDiff much faster than vanilla diffusion models and introduces no performance degradation. Furthermore, PyDiff uses a global corrector to alleviate the global degradation that may occur in the reverse process, significantly improving the performance and making the training of diffusion models easier with little additional computational consumption. Extensive experiments on popular benchmarks show that PyDiff achieves superior performance and efficiency. Moreover, PyDiff can generalize well to unseen noise and illumination distributions. Code and supplementary materials are available at https://github.com/limuloo/PyDIff.git.
Zhou, J, Zheng, B & Chen, F 1970, 'Effects of Uncertainty and Knowledge Graph on Perception of Fairness', 28th International Conference on Intelligent User Interfaces, IUI '23: 28th International Conference on Intelligent User Interfaces, ACM. View/Download from: Publisher's site
Zhou, K, Ming, J & Gabrys, B 1970, 'GeAE: GAE-Embedded Autoencoder Based Causal Representation for Robust Domain Adaptation', 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE. View/Download from: Publisher's site
Zhou, P, Gao, J, Xie, Y, Ye, Q, Hua, Y, Kim, J, Wang, S & Kim, S 1970, 'Equivariant Contrastive Learning for Sequential Recommendation', Proceedings of the 17th ACM Conference on Recommender Systems, RecSys '23: Seventeenth ACM Conference on Recommender Systems, ACM, pp. 129-140. View/Download from: Publisher's site
Zhou, P, Ye, Q, Xie, Y, Gao, J, Wang, S, Kim, JB, You, C & Kim, S 1970, 'Attention Calibration for Transformer-based Sequential Recommendation', Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management, ACM, pp. 3595-3605. View/Download from: Publisher's site
Zhou, X, Yin, J & Tsang, IW 1970, 'Edge but not Least: Cross-View Graph Pooling', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer International Publishing, Grenoble, France, pp. 344-359. View/Download from: Publisher's site View description>>
Graph neural networks have emerged as a powerful representation learning model for undertaking various graph prediction tasks. Various graph pooling methods have been developed to coarsen an input graph into a succinct graph-level representation through aggregating node embeddings obtained via graph convolution. However, because most graph pooling methods are heavily node-centric, they fail to fully leverage the crucial information contained in graph structure. This paper presents a cross-view graph pooling method (Co-Pooling) that explicitly exploits crucial graph substructures for learning graph representations. Co-Pooling is designed to fuse the pooled representations from both node view and edge view. Through cross-view interaction, edge-view pooling and node-view pooling mutually reinforce each other to learn informative graph representations. Extensive experiments on one synthetic and 15 real-world graph datasets validate the effectiveness of our Co-Pooling method. Our results and analysis show that (1) our method is able to yield promising results over graphs with various types of node attributes, and (2) our method can achieve superior performance over state-of-the-art pooling methods on graph classification and regression tasks.
Zhou, Y & Long, G 1970, 'Improving Cross-modal Alignment for Text-Guided Image Inpainting', EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 3437-3448. View description>>
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions. Moreover, they take cross-modal fusion for depth features, which ignores a fine-grained alignment between text and image. Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have advanced in most multimodal tasks. In this work, we propose a novel model for TGII by improving cross-modal alignment (CMA). CMA model consists of a VLPM as a vision-language encoder, an image generator and global-local discriminators. To explore cross-modal alignment knowledge for image restoration, we introduce cross-modal alignment distillation and in-sample distribution distillation. In addition, we employ adversarial training to enhance the model to fill the missing region in complicated structures effectively. Experiments are conducted on two popular vision-language datasets. Results show that our model achieves state-of-the-art performance compared with other strong competitors.
Zhou, Y & Long, G 1970, 'Multimodal Event Transformer for Image-guided Story Ending Generation', EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, pp. 3426-3436. View description>>
Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending image. To tackle this drawback, we propose a multimodal event transformer, an event-based reasoning framework for IgSEG. Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality. Next, we connect visual and semantic event graphs and utilize cross-modal fusion to integrate different-modality features. In addition, we propose a multimodal injector to adaptive pass essential information to decoder. Besides, we present an incoherence detection to enhance the understanding context of a story plot and the robustness of graph modeling for our model. Experimental results show that our method achieves state-of-the-art performance for the image-guided story ending generation.
Zhou, Y & Long, G 1970, 'Style-Aware Contrastive Learning for Multi-Style Image Captioning', Findings of the Association for Computational Linguistics: EACL 2023, Findings of the Association for Computational Linguistics: EACL 2023, Association for Computational Linguistics, pp. 2212-2222. View/Download from: Publisher's site View description>>
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods overlook the relationship between linguistic style and visual content. To overcome this drawback, we propose style-aware contrastive learning for multi-style image captioning. First, we present a style-aware visual encoder with contrastive learning to mine potential visual content relevant to style. Moreover, we propose a style-aware triplet contrast objective to distinguish whether the image, style and caption matched. To provide positive and negative samples for contrastive learning, we present three retrieval schemes: object-based retrieval, RoI-based retrieval and triplet-based retrieval, and design a dynamic trade-off function to calculate retrieval scores. Experimental results demonstrate that our approach achieves state-of-the-art performance. In addition, we conduct an extensive analysis to verify the effectiveness of our method.
Zhou, Y, Shen, T, Geng, X, Tao, C, Xu, C, Long, G, Jiao, B & Jiang, D 1970, 'Towards Robust Ranker for Text Retrieval', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 5387-5401. View description>>
A neural ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind due to the weak negative mining during contrastive learning. Compared to retrievers boosted by self-adversarial (i.e., in-distribution) negative mining, the ranker's heavy structure suffers from query-document combinatorial explosions, so it can only resort to the negative sampled by the fast yet out-of-distribution retriever. Thereby, the moderate negatives compose ineffective contrastive learning samples, becoming the main barrier to learning a robust ranker. To alleviate this, we propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker, where i) diverse hard negatives from a joint distribution are prone to fool the ranker for more effective adversarial learning and ii) involving extensive out-of-distribution label noises renders the ranker against each noise distribution, leading to more challenging and robust contrastive learning. To evaluate our robust ranker (dubbed R2ANKER), we conduct experiments in various settings on the passage retrieval benchmarks, including BM25-reranking, full-ranking, retriever distillation, etc. The empirical results verify the new state-of-the-art effectiveness of our model.
Zhou, Z, Kanwal, A, Chaturvedi, K, Raza, R, Prakash, S, Jan, T & Prasad, M 1970, 'Deep Learning-Based Classification of Neurodegenerative Diseases Using Gait Dataset: A Comparative Study', Proceedings of the 2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023: 2023 International Conference on Robotics, Control and Vision Engineering, ACM. View/Download from: Publisher's site
Zhu, F, Lee, VCS, Chang, X & Liang, X 1970, 'Vision Language Navigation with Knowledge-driven Environmental Dreamer', Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}, International Joint Conferences on Artificial Intelligence Organization. View/Download from: Publisher's site View description>>
Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a method that leverages the knowledge of the embodied environment and generates unseen scenes for a navigation agent to learn. Generating an unseen environment with texture consistency and structure consistency is challenging. To address this problem, we incorporate three knowledge-driven regularization objectives into the KED and adopt a reweighting mechanism for self-adaptive optimization. Our KED method is able to generate unseen embodied environments without extra annotations. We use KED to successfully generate 270 houses and 500K instruction-trajectory pairs. The navigation agent with the KED method outperforms the state-of-the-art methods on various VLN benchmarks, such as R2R, R4R, and RxR. Both qualitative and quantitative experiments prove that our proposed KED method is able to high-quality augmentation data with texture consistency and structure consistency.
Zhu, H & Jay Guo, Y 1970, 'Single-Ended-to-Balanced Hybrid Coupler for In-Band Full-Duplex Transceivers', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE. View/Download from: Publisher's site
Zhu, H, Guo, CA & Jay Guo, Y 1970, 'Planar Beamforming Networks for Producing Multiple Independently Steerable Beams', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE. View/Download from: Publisher's site
Zhuo, TY, Liao, Y, Lei, Y, Qu, L, de Melo, G, Chang, X, Ren, Y & Xu, Z 1970, 'ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities', EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023, pp. 2147-2162. View description>>
We introduce ViLPAct, a novel vision-language benchmark for human activity planning. It is designed for a task where embodied AI agents can reason and forecast future actions of humans based on video clips about their initial activities and intents in text. The dataset consists of 2.9k videos from Charades extended with intents via crowdsourcing, a multi-choice question test set, and four strong baselines. One of the baselines implements a neurosymbolic approach based on a multi-modal knowledge base (MKB), while the other ones are deep generative models adapted from recent state-of-the-art (SOTA) methods. According to our extensive experiments, the key challenges are compositional generalization and effective use of information from both modalities1
Zhuo, W, Sun, Y, Wang, X, Zhu, L & Yang, Y 1970, 'WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings', Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 12135-12148. View description>>
This paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE), which combines contrastive learning with a novel shuffled group whitening. Generally, contrastive learning pulls distortions of a single sample (i.e., positive samples) close and push negative samples far away, correspondingly facilitating the alignment and uniformity in the feature space. A popular alternative to the “pushing” operation is whitening the feature space, which scatters all the samples for uniformity. Since the whitening and the contrastive learning have large redundancy w.r.t. the uniformity, they are usually used separately and do not easily work together. For the first time, this paper integrates whitening into the contrastive learning scheme and facilitates two benefits. 1) Better uniformity. We find that these two approaches are not totally redundant but actually have some complementarity due to different uniformity mechanism. 2) Better alignment. We randomly divide the feature into multiple groups along the channel axis and perform whitening independently within each group. By shuffling the group division, we derive multiple distortions of a single sample and thus increase the positive sample diversity. Consequently, using multiple positive samples with enhanced diversity further improves contrastive learning due to better alignment. Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art, e.g., 78.78% (+2.53% based on BERTbase) Spearman correlation on STS tasks.
Awadallah, M University of Technology Sydney 2023, Developing a robotic arm for space applications, pp. 1-73, Sydney.
Halkon, B UTS 2023, Space Machines Co. SmallSat vibration testing research project – Final Report Phase II, UTS.
Jazbec, M, Liu, A, Rutovitz, J, Nghiem, D & Turner, A University of Technology Sydney 2023, Unlocking the value of food waste: A case study of co-digestion in the Western Parkland City, pp. 1-36, University of Technology Sydney.
Knight, S, Heggart, K, Dickson-Deane, C, Ford, H, Hunter, J, Johns, A, Kitto, K, Cetindamar Kozanoglu, D, Maher, D & Narayan, B House Standing Committee on Employment, Education and Training 2023, UTS:CREDS member submission in response to the House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system, House Standing Committee on Employment, Education and Training’s inquiry into the use of generative artificial intelligence in the Australian education system, no. sub019, pp. 1-20, Australia.
This report, supported by Investment NSW, shows the state of NSW’s ecosystem of coworking spaces, accelerators, incubators, and startup hubs and outlines how policymakers and partners in the innovation ecosystem can more robustly measure the impact of these entities on the wider economy.
Tovey, A, Heydon, G, Phansalkar, A, Ruoso, L-E, Alexandra, B, Qureshi Atif, M, Gill, A, Siddiqui, S, Perry, C, Liu, B, Runcie, P, McIntyre, E, Goodman, N, Vardoulakis, S, Hu, T, Izzo, K, Surawski, N, Kulkarni, Y, Liyanage, L & Barns, S Operational Network of Air Quality Impact Resources (OPENAIR) 2023, The OPENAIR Best Practice Guide for smart air quality monitoring, pp. 1-9, Australia. View description>>
The OPENAIR Best Practice Guide for smart air quality monitoring has been developed to help local governments implement smart air quality monitoring projects. It has been developed through collaboration between five NSW universities, the NSW Government, and the NSW Smart Sensing Network (NSSN). It contains world-leading best practice guidance for smart air quality monitoring and we believe it to be the most comprehensively broad and simultaneously in-depth practical resource on this topic in the world.The Guide is divided into sections that reflect the six stages of the OPENAIR Impact Planning Cycle. Each section is organised into topic areas, with a suite of associated resources.Each factsheet, Best Practice Guide chapter, and supplementary resource is available as an individual download. You can also download the entire Best Practice Guide as a single PDF document, with all chapters combined.
Abboodi, B, Pileggi, SF & Bharathy, G 2023, 'Social Networks in Crisis Management: a Concise Literature Review', MDPI AG. View/Download from: Publisher's site
Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2023, 'Adaptive Identification of Supply Chain Disruptions Through Reinforcement Learning', Elsevier BV. View/Download from: Publisher's site
Abtahi, H, Karimi, M & Maxit, L 2023, 'Estimation of the low-wavenumber component of turbulent boundary layer pressure field using vibration data', Acoustical Society of America (ASA), pp. A55-A55. View/Download from: Publisher's site View description>>
In structures subjected to a high-speed flow, the convective region of the wall pressure field (WPF) beneath a turbulent boundary layer (TBL) plays a crucial role in their vibration behaviour. However, when it comes to underwater structures experiencing low-speed flow, they effectively filter out the convective domain of WPF and the bending wavenumber of structures align with the low-wavenumber domain of the WPF. As a result, the primary cause of vibration in this case is the low-wavenumber components of the WPF. Thus, accurate estimation of the WPF at low-wavenumber domain is crucial for predicting vibration responses of these structures. Existing models for WPF accurately predict the convective region but differ significantly in predicting the low-wavenumber levels. This numerical study aims to investigate the feasibility of estimating the low-wavenumber WPF by analysing measured vibration data from a flat plate excited by a TBL. The WPF's cross spectrum in the wavenumber domain can be linked to the cross spectrum of the plate's acceleration. By employing regularization techniques and solving an inverse problem, the low-wavenumber components of the WPF can be then estimated. Virtual experiments are performed to evaluate the accuracy of the studied process by comparing its prediction to the input WPF model.
Adak, C, Karkera, T, Chattopadhyay, S & Saqib, M 2023, 'Detecting Severity of Diabetic Retinopathy from Fundus Images using Ensembled Transformers'.
Allcock, J, Bao, J, Belovs, A, Lee, T & Santha, M 2023, 'On the quantum time complexity of divide and conquer'.
Almalki, R, Khaki, M, Saco, P & Rodriguez, J 2023, 'The Impact of Dam Construction on Downstream Vegetation Area in Dry Areas Using Satellite Remote Sensing', MDPI AG. View/Download from: Publisher's site
Andersen, JP, Di Nota, PM, Alavi, N, Anderson, G, Bennell, C, McGregor, C, Ricciardelli, R, Scott, SC, Shipley, P & Vincent, ML 2023, 'A Biological Approach to Building Resilience and Wellness Capacity Among Police Exposed to Posttraumatic Stress Injuries: Protocol for a Randomized Controlled Trial (Preprint)', JMIR Publications Inc.. View/Download from: Publisher's site
Arachchige, CMK, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2023, 'Experimental Study of Rubber Intermixed Ballast Stratum Subjected to Monotonic and Cyclic Loads'.
Aryal, A, Hossain, J & Khalilpour, K 2023, 'State of Charge Estimation Using Deep Neural Networks for Lithium-Ion Batteries', MDPI AG. View/Download from: Publisher's site
Aryal, A, Hossain, J & Khalilpour, K 2023, 'State of Charge Estimation Using Deep Neural Networks for Lithium-Ion Batteries', MDPI AG. View/Download from: Publisher's site
Aryal, A, Hossain, J & Khalilpour, K 2023, 'State of Charge Estimation Using Deep Neural Networks for Lithium-Ion Batteries', MDPI AG. View/Download from: Publisher's site
Aryal, A, Hossain, J & Khalilpour, K 2023, 'State of Charge Estimation Using Deep Neural Networks for Lithium-Ion Batteries', MDPI AG. View/Download from: Publisher's site
ashtarinakhaei, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'A Location-Based Multi-Hop Routing Protocol for Future Wireless Cellular Networks', Elsevier BV. View/Download from: Publisher's site
Beigi, S & Tomamichel, M 2023, 'Lower Bounds on Error Exponents via a New Quantum Decoder'.
Berta, M & Tomamichel, M 2023, 'Entanglement monogamy via multivariate trace inequalities'.
Bérubé, C, Maritsch, M, Lehmann, VF, Kraus, M, Feuerriegel, S, Züger, T, Wortmann, F, Stettler, C, Fleisch, E, Kocaballi, AB & Kowatsch, T 2023, 'Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving (Preprint)', JMIR Publications Inc.. View/Download from: Publisher's site
Brahmachari, S, Rubboli, R & Tomamichel, M 2023, 'A fixed-point algorithm for matrix projections with applications in quantum information'.
Braytee, A, He, S, Tang, S, Sun, Y, Jiang, X, Yu, X, Khatri, I, Prasad, M & Anaissi, A 2023, 'Identification of Cancer Risk Groups through Multi-Omics Integration using Autoencoder and Tensor Analysis', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Bremner, MJ, Cheng, B & Ji, Z 2023, 'IQP Sampling and Verifiable Quantum Advantage: Stabilizer Scheme and Classical Security'.
Burdon, S, Stewart, C, John, C, Bajada, C, Kang, K & Abedin, B 2023, 'Powering National Outcomes from New Digital Technologies: An analysis of government policies to maximize the economic and social benefits', Asian Productivity Organization. View description>>
The accelerating advancement of digital technologies is providing a myriad of opportunities for nations, both economically and socially. This study was a collaboration between the Asian Productivity Organization (APO) and the University of Technology Sydney (UTS). It considered how APO member governments might approach the advancement of digital technologies to maximize benefits for their nations.Specifically, the study considered which digital technologies, industry sectors, and regulatory and policy initiatives had the greatest potential to deliver national outcomes in areas of economic growth, productivity, and social impact.Overall, the research found that virtually all countries believed that the latest wave of digital technologies would deliver significant economic and productivity growth and that having policies and regulations in place to support a digital economy was important. It also highlighted the different opportunities, constraints, and challenges that individual APO nations faced, which meant that a one-size approach would be inappropriate. So, this report outlines a range of approaches that can be employed by nations, depending on where a country is currently positioned on the spectrum of digital economic development. It provides an initial framework that can be applied to any country, and offers some useful generic approaches, while also proposing specific recommendations for countries at different stages of digital readiness.The study led us to segment APO member economies into four ‘digital economy’ groups: the embryonic, the nascent, the emergent, and the leaders. In addition, we paid particular attention to three exemplars or ‘case study countries,’ each representing a different level of economic development and digital maturity. These were: Indonesia (nascent), Malaysia (emergent), and the Republic of Korea (leaders). Individual countries can get feedback from this report, in the first instance, by considering their relative st...
Cai, J, Nguyen, K-N, Shrestha, N, Good, A, Tu, R, Yu, X, Zhe, S & Serra, T 2023, 'Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions'.
Cao, H, Karimi, M, Williams, P & Dylejko, P 2023, 'Vibration control of a cantilever plate immersed in water using shunted piezoelectric patches', Acoustical Society of America (ASA), pp. A55-A55. View/Download from: Publisher's site View description>>
Minimizing structural vibrations is an important engineering requirement in many applications. This study theoretically investigates the vibration control of an in-water cantilever plate using piezoelectric patches and resonant shunt circuits (including a resistance R and an inductance L in series or parallel). To do this, an analytical model for predicting the point-forced vibration response of a fluid-loaded cantilever plate with two piezoelectric patches is developed. The electromechanical coupling factor and optimum values for R and L are then estimated by considering the dynamics of the system in short-circuit and open-circuit conditions. The effect of piezoelectric patch size, location and electromechanical properties on the vibration response of the plate is also investigated through numerical examples. The results from this study show that optimized piezoelectric patches could provide a significant vibration reduction for underwater applications.
Cartland, SP, Patil, M, Boccanfuso, L, Kelland, E, Patrick, R, Manuneedhi Cholan, P, Su, P, Alwis, I, Ganss, R, Harvey, RP, Griffith, TS, Powell, J, Patel, S & Kavurma, MM 2023, 'Abstract 597: TRAIL-TRAIL-R Ligation Regulates EC-pericyte Crosstalk To Generate Stable Microvessel Networks In Ischemia', Ovid Technologies (Wolters Kluwer Health). View/Download from: Publisher's site View description>>
Endothelial cell (EC)-pericyte crosstalk is essential for generating stable capillary networks. Capillary function and development is disrupted in CVD, and processes mediating this are poorly understood. TNF-related apoptosis-inducing ligand (TRAIL) stimulates blood vessel development in pre-clinical models, while circulating levels are suppressed in CVD patients. The contribution of EC-specific TRAIL to angiogenesis in ischemia is unknown. To address this, an EC-specific TRAIL knockout ( Trail EC-/- ) was generated. Compared to Trail EC+/+ , Trail EC-/- mice had ~60% reduction in plasma TRAIL, revealing the endothelium as a significant source of TRAIL in the healthy circulation. Angiogenesis was quantified in the Matrigel plug, aortic sprouting and hindlimb ischemia (HLI) models. EC/pericyte content in plugs were ~50-60% less in Trail EC-/- than Trail EC+/+ mice, with a ~50% reduction in mRNA expression of angiogenesis/pericyte markers. Trail EC-/- aortic segments had reduced microvascular sprouts in hypoxia, and ECs lacking TRAIL had an impaired ability to form tubules and recruit pericytes. CD31 + SMA + microvessel numbers (measure of EC-pericyte interaction) were signif...
Cervero-Martín, E & Tomamichel, M 2023, 'Device independent security of quantum key distribution from monogamy-of-entanglement games'.
Chakrabortty, R, Pal, SC, Ghosh, M, Arabameri, A, Saha, A, Roy, P, Pradhan, B, Mondal, A, Ngo, PTT, Chowdhuri, I, Yunus, AP, Sahana, M, Malik, S & Das, B 2023, 'Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India', Springer Science and Business Media LLC, pp. 11067-11068. View/Download from: Publisher's site
Cham, B, Islam, SU & Saha, SC 2023, 'Entropy Generation Analysis of Natural Convection in a Porous Medium with Casson Fluid using Finite Element Method', MDPI AG. View/Download from: Publisher's site
Chapman, A, Elman, SJ & Mann, RL 2023, 'A Unified Graph-Theoretic Framework for Free-Fermion Solvability'.
Chaturvedi, K, Braytee, A, Li, J & Prasad, M 2023, 'SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation'.
Chen, J, Hao, D, Chen, W, Liu, Y, Yin, Z, Hsu, H-Y, Ni, B-J, Wang, A, Lewis, S & Jia, G 2023, 'One-dimensional semiconductor-metal heterostructures for solar-to-fuel conversion through water splitting', Authorea, Inc.. View/Download from: Publisher's site
Chen, K-C, Apers, S & Hsieh, M-H 2023, '(Quantum) complexity of testing signed graph clusterability'.
Cheng, MH, Chen, Y-C, Wang, Q, Bartsch, V, Kim, MS, Hu, A & Hsieh, M-H 2023, 'Unleashing Quantum Simulation Advantages: Hamiltonian Subspace Encoding for Resource Efficient Quantum Simulations'.
Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2023, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning'.
Colino-Sanguino, Y, de la Fuente, LR, Gloss, B, Law, AMK, Handler, K, Pajic, M, Salomon, R, Gallego-Ortega, D & Valdes-Mora, F 2023, 'Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Cortes, CAT, Lin, C-T, Do, T-TN & Chen, H-T 2023, 'An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments'.
Deng, F, Sang, R, Li, Y, Deng, W & Goldys, E 2023, 'Bifunctional circular DNA amplifier transforms a classic CRISPR/Cas sensor into an ultrasensitive autocatalytic sensor', Research Square Platform LLC. View/Download from: Publisher's site
Dinh, PV, Nguyen, QU, Hoang, DT, Nguyen, DN, Bao, SP & Dutkiewicz, E 2023, 'Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems'.
Elgharabawy, A, Prasad, M, Lin, C-T & Elgharabawy, A 2023, 'Preference Neural Network', MDPI AG. View/Download from: Publisher's site
Elia, D, Chen, F, Zowghi, D & Rizoiu, M-A 2023, 'The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills'.
Entezari, A, Wu, Q, Mirkhalaf, M, Lu, Z, Roohani, I, Li, Q, Dunstan, C, Jiang, X & Zreiqat, H 2023, 'Unraveling the Influence of Pore Size and Shape in 3D Printed Ceramic Scaffolds on Osteogenesis', Elsevier BV. View/Download from: Publisher's site
Farooq, MU, Fritz, T, Haapasalo, E & Tomamichel, M 2023, 'Matrix majorization in large samples'.
Feng, C, Cao, L, Wu, D, Zhang, E, Wang, T, Jiang, X, Zhou, C, Chen, J, Wu, H, Lin, S, Hou, Q, Lin, C-T, Zhu, J, Yang, J, Sawan, M & Zhang, Y 2023, 'Acoustic inspired brain-to-sentence decoder for logosyllabic language', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Figueroa-Romero, P, Papič, M, Auer, A, Hsieh, M-H, Modi, K & Vega, ID 2023, 'Operational Markovianization in Randomized Benchmarking'.
Galat, D & Rizoiu, M-A 2023, 'Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training'.
Gandomi, AH, Yazdani, D, Omidvar, MN & Deb, K 2023, 'GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization'.
Gao, C, Yang, Q, Kim, ME, Khairi, NM, Cai, LY, Newlin, NR, Kanakaraj, P, Remedios, LW, Krishnan, AR, Yu, X, Yao, T, Zhang, P, Schilling, KG, Moyer, D, Archer, DB, Resnick, SM & Landman, BA 2023, 'Characterizing patterns of DTI variance in aging brains', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Ghorbanpour, S, Richards, C, Pienaar, D, Sesperez, K, Aboulkheyr Es, H, Nikolic, V, Karadzov-Orlic, N, Mikovic, Z, Stefanovic, M, Cakic, Z, Alqudah, A, Cole, L, Gorrie, C, Mcgrath, K, Kavurma, MM, Warkiani, ME & McClements, L 2023, 'SC3_3. FKBPL signalling in placental development and preeclampsia', Elsevier BV, pp. e77-e78. View/Download from: Publisher's site
Giarmatzi, C, Jones, T, Gilchrist, A, Pakkiam, P, Fedorov, A & Costa, F 2023, 'Multi-time quantum process tomography of a superconducting qubit'.
Gill, A 2023, 'Adaptive Architecture for Data, Analytics, and AI: Government's Navigation of Digital Disruption', 9th Annual FST Government Australia Summit 2023, Canberra, Australia.
Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.
Gill, A 2023, 'Adaptive Data Architecture For Responsible & Safe AI Adoption in Government: Reflections & Learnings from 2023', Public Sector Network.
Gill, A 2023, 'ADAPTIVE ENTERPRISE ARCHITECTURE WORKSHOP FOR DECISION MAKERS'.
Gill, A 2023, 'Data Sharing: Architecture, Patterns and Technology Solutions'.
Gill, A 2023, 'The ArcOps: Connected Enterprise Architecture-Operations Pipeline', Enterprise Architecture Professional Journal.
Gill, A & Fitzgibbon, T 2023, 'Why trusted digital identity verification platform is critical for supporting student lifecycle journey?', Australasian Higher Education Cyber Security Service, Canberra, Australia.
Gong, X, McFarland, C, McCarthy, P, Griffith, C & Rizoiu, M-A 2023, 'Informing Innovation Management: Linking Leading R&D Firms and Emerging Technologies'.
Goss, DM, Vasilescu, SA, Vasilescu, PA, Cooke, S, Kim, SHK, Sacks, GP, Gardner, DK & Warkiani, ME 2023, 'AI facilitated sperm detection in azoospermic samples for use in ICSI', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Guivant, J, Kim, J, Narula, K, Li, X & Khan, S 2023, 'Compressed Gaussian Estimation under Low Precision Numerical Representation', MDPI AG. View/Download from: Publisher's site
Hashmi, A, Hutvagner, G & Sidhu, S 2023, 'B-288 The Beginning of the End: AGO2 Protein Identified and Validated as a Novel Biomarker for Multi-cancer Diagnosis Through Bioinformatics Analysis and Immunoassay', Oxford University Press (OUP). View/Download from: Publisher's site View description>>
AbstractBackgroundDespite significant progress in cancer research, the complexity and heterogeneity of the disease still pose major challenges to accurate diagnosis and effective treatment. Recent advancements in biomedical technologies have led to the identification of reliable potential cancer biomarkers. In this sense, the link between microRNAs (master regulators of gene expression) & cancer is well-established, but there has been little research on the microRNA machinery itself. We aim to investigate whether changes in the expression of genes & proteins involved in the miRNA pathway can be used for multi-cancer detection.MethodWe analyzed RNASeq data from the TCGA (The Cancer Genome Atlas) and GTEX (The Genotype-Tissue Expression) projects to compare the expression of core components in the miRNA biogenesis pathway, including AGO2, DGCR8, XPO5, RAN, DROSHA, DICER, and TARBP2, in normal and tumorous samples across 22 tissue types, including adrenocortical, AML, breast, colon, lung, liver, esophageal, prostate, pancreas, stomach, thyroid, rectum, uterus and ovarian cancer. We accessed and analyzed the TCGA and GTEx datasets using the UCSC Xena Browser. We also measured the protein concentrations of all these microRNA regulators in 15 normal adrenal cortex, 15 benign adenoma, and 15 adrenocortical cancer tissue homogenate samples using commercial ELISA kits. The Human Protein Argonaute-2 (EIF2C2) ELISA Kit-AE45910HU (Abebio-Co.Ltd) was used for the assay procedure, which was performed according to the manufacturer’s instructions. Statistical analysis was performed using GraphPad Prism, Version 9 (GraphPad Software, CA, USA). A P-value of less than 0.05 was considered statistically significant....
Hassan, MA, Jamshidi, MB, Manh, BD, Chu, NH, Nguyen, C-H, Hieu, NQ, Nguyen, CT, Hoang, DT, Nguyen, DN, Huynh, NV, Alsheikh, MA & Dutkiewicz, E 2023, 'Enabling Technologies for Web 3.0: A Comprehensive Survey'.
Heusdens, R & Zhang, G 2023, 'Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM'.
Hieu, NQ, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2023, 'Enhancing Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface'.
Hirche, C & Tomamichel, M 2023, 'Quantum Rényi and $f$-divergences from integral representations'.
Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE). View/Download from: Publisher's site
Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE). View/Download from: Publisher's site
Horry, MJ, Chakraborty, S, Pradhan, B, Paul, M, Zhu, J, Barua, PD, Acharya, UR, Chen, F & Zhou, J 2023, 'Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks'.
Hossain, MS, Bacaoco, M, Mai, TNA, Ponchon, G, Chen, C, Ding, L, Chen, Y, Ekimov, E, Xu, H, Solntsev, AS & Tran, TT 2023, 'Fiber-based Ratiometric Optical Thermometry with Silicon-Vacancy in Microdiamonds'.
Howell, N, Middleton, RJ, Sierro, F, Wyatt, NA, Chacon, A, Fraser, BH, Bambery, K, Livio, E, Dobie, C, Bevitt, JJ, Davies, J, Dosseto, A, Franklin, DR, Garbe, U, Guatelli, S, Hirayama, R, Matsufuji, N, Mohammadi, A, Mutimer, K, Rendina, LM, Rosenfeld, AB & Safavi-Naeini, M 2023, 'Neutron capture enhances dose and reduces cancer cell viability in and out of beam during helium and carbon ion therapy', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Hu, Y & Tomamichel, M 2023, 'Fundamental limits on quantum cloning from the no-signalling principle'.
Hussein, A, Al-Humairi, ST, AlMukhtar, RS, Sulyman, M, Fattah, IMR, Dawood, A, AlJaberi, FY, Al-Mayyahi, MA & Le, PC 2023, 'Methylene Blue Removal in Airlift Absorbent Packed with Green Algae Coelastrella Sp: Equilibrium, Kinetics, Thermodynamics and Mass Transfer', Elsevier BV. View/Download from: Publisher's site
Huynh, NV, Wang, J, Du, H, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Letaief, KB 2023, 'Generative AI for Physical Layer Communications: A Survey'.
Ihsan, A, Muttaqin, K, Fajri, R, Mursyidah, M & Fattah, IMR 2023, 'Innovative Bacteria Colony Detection: Leveraging Multi Feature Selection with the Improved Salp Swarm Algorithm', MDPI AG. View/Download from: Publisher's site
Jakubowski, K, Chacon, A, Tran, LT, Garbe, U, Bevitt, J, Olsen, S, Franklin, DR, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2023, 'A Monte Carlo model of the Dingo thermal neutron imaging beamline', Research Square Platform LLC. View/Download from: Publisher's site
In the educational ecosystem, problem-solving, critical thinking, and teamwork skills play acritical role and remain as main concerns in graduate employability (Bhatti et al., 2023). Thepurpose of this study is to examine the employability skills perceived by students across UTSand increase the employability skills of students from low-socioeconomic (LSE) backgroundstudying at UTS. To accomplish this objective, we will use the UTS Social Impact Dashboard(2023) which represents the opinion of students from LSE background on course learningoutcomes with respect to problem-solving, critical thinking, and teamwork skills. According tothe Dashboard, over 73% of students from LSE background in a graduate school at UTS perceivethat they acquired a more developed and advanced set of skills (Problem solving – 73%, Criticalthinking – 73% and Teamwork skills – 80%) useful in future employability opportunities.Furthermore, comparing all faculties, minimum scores recorded for problem solving (44%),critical thinking (54%), and teamwork skills (59%) require further investigation. The UTS SocialImpact Dashboard indicates that a 3% increase from the previous year in attrition rates and1.7% decrease in student success, for students from an LSE background. With respect tograduate outcomes, the gap between UTS domestic students from high-socioeconomic (HSE)background and those from low-socioeconomic (LSE) background is 6.7%. This clear gaprequires an urgent examination of factors that could influence the perception of graduateemployability skills of UTS students compared to those of an LSE background. This will likelybenefit UTS students from an LSE background to gain employment. Furthermore, we canreduce the gap between Australians from the lowest socio-economic backgrounds and highestsocio-economic backgrounds who are not in employment (Strawa, 2022), which is the targetedsocial change that this study intends to address.
Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science perspective of Graph Convolutional Networks: A survey', arXiv.
Ju, L, Wang, H, Vatankhah, P, Wang, Y, Russel, B, Su, Q, Zhou, Z, Cox, C & Jin, J 2023, 'Microscale geometrical modulation of PIEZO1 mediated cell mechanosensing via cytoskeletal redistribution buckle', Research Square Platform LLC. View/Download from: Publisher's site
Kedziora, DJ, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2023, 'Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality'.
Khoa, TV, Son, DH, Nguyen, C-H, Hoang, DT, Nguyen, DN, Trung, NL, Quynh, TTT, Hoang, T-M, Ha, NV, Dutkiewicz, E & Alsheikh, MA 2023, 'Securing Blockchain Systems: A Novel Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts'.
Khuat, TT, Bassett, R, Otte, E, Grevis-James, A & Gabrys, B 2023, 'Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities'.
Kielly, M, Caracciolo, A, Chacon, A, Vita, DD, Mohammadi, A, Tashima, H, Franklin, DR, Yamaya, T, Rosenfeld, A, Carminati, M, Fiorini, C, Guatelli, S & Safavi-Naeini, M 2023, 'First experimental demonstration of real-time neutron capture discrimination in helium and carbon ion therapy', Research Square Platform LLC. View/Download from: Publisher's site
Kocaballi, AB 2023, 'Conversational AI-Powered Design: ChatGPT as Designer, User, and Product'.
Kronowetter, F, Maeder, M, Chiang, Y, Huang, L, Schmid, J, Oberst, S, Powell, D & Marburg, S 2023, 'Direct visualization of a Friedrich-Wintgen quasi-bound state in the continuum', Research Square Platform LLC. View/Download from: Publisher's site
Kumar, A, Esmaili, N & Piccardi, M 2023, 'The Contextualized Regressive Topic Model', Elsevier BV. View/Download from: Publisher's site
Lee, HH, Liu, Q, Bao, S, Yang, Q, Yu, X, Cai, LY, Li, T, Huo, Y, Koutsoukos, X & Landman, BA 2023, 'Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation'.
Leon-Castro, E, Sahni, M, Blanco-Mesa, F, Alfaro-Garcia, V & Merigo, J 2023, 'Innovation and sustainability in governments and companies: A perspective to the new realities', pp. 1-331. View description>>
Innovation and sustainability are issues that have become very relevant in recent years. This book presents a compilation of investigations on these topics, divided into those applied in government or enterprises. The objective is to demonstrate to the audience how these issues have been worked around the world and in different scenarios. Among the papers, there are works related to economic variables, imports, exports, and analysis in different sectors such as tourism, agriculture, education, and even in countries in general.
Li, Z, Chen, Y, Wang, X, Yao, L & Xu, G 2023, 'Multi-view GCN for Loan Default Risk Prediction', Research Square Platform LLC. View/Download from: Publisher's site
Linklater, DP, Vailionis, A, Ryu, M, Kamegaki, S, Morikawa, J, Mu, H, Smith, D, Maasoumi, P, Ford, R, Katkus, T, Blamires, S, Kondo, T, Nishijima, Y, Moraru, D, Shribak, M, O’Connor, A, Ivanova, E, Ng, S, Hideki, M & Juodkazis, S 2023, 'Structure and Optical Anisotropy of Spider Scales and Silk: Use of Chromaticity and Azimuth Colors', MDPI AG. View/Download from: Publisher's site
Lipka-Bartosik, P, Chubb, CT, Renes, JM, Tomamichel, M & Korzekwa, K 2023, 'Quantum dichotomies and coherent thermodynamics beyond first-order asymptotics'.
Liu, C, Zhang, H, Lei, G, Zhang, S, Wang, Y & Zhu, J 2023, 'Ultra-High-Dimensional Multi-Level Optimization Strategies for Electrical Machines', Research Square Platform LLC. View/Download from: Publisher's site
Liu, J, Wu, K, Su, T & Zhang, JA 2023, 'Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment'.
Lo, Y-F, Lee, Y-C & Hsieh, M-H 2023, 'Capacity Bounds for Vertically-Drifted First Arrival Position Channels under a Covariance Constraint'.
Mai, TNA, Ali, S, Hossain, MS, Chen, C, Ding, L, Chen, Y, Solntsev, AS, Mou, H, Xu, X, Medhekar, N & Tran, TT 2023, 'Cryogenic Thermal Shock Effects on Optical Properties of Quantum Emitters in Hexagonal Boron Nitride'.
Maier, M & Hoang, DT 2023, '6G and Onward to Next G: the Road to the Multiverse', Institute of Electrical and Electronics Engineers (IEEE), pp. 20-20. View/Download from: Publisher's site
Mandal, S, Oberst, S & Lai, JCS 2023, 'Modelling termites’ tunnelling and decision-making behaviors', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
McCarthy, PX, Gong, X, Stephany, F, Braesemann, F, Rizoiu, M-A & Kern, ML 2023, 'The Science of Startups: The Impact of Founder Personalities on Company Success'.
Mei, F, Li, JJ, Lin, J, Xing, D & Dong, S 2023, 'Multidimensional characteristics of musculoskeletal pain and risk of hip fractures among elderly adults: The first longitudinal evidence from CHARLS', Research Square Platform LLC. View/Download from: Publisher's site
Mishra, DK, Abbasi, MH, Eskandari, M, Paduel, S, Sahoo, SK, Zhang, J & Li, L 2023, 'State-Space Modelling and Stability Analysis of Solid-State Transformers for Resilient Distribution Systems', MDPI AG. View/Download from: Publisher's site
Nareti, UK, Adak, C & Chattopadhyay, S 2023, 'Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification'.
Nerse, C, Mohapatra, AR, Oberst, S, Navarro-Payá, D, Etxeberria, J, Matus, JT, Bianco, L, Tucci, MR, Cumino, E, Casacci, LP & Barbero, F 2023, 'Model updating of flowering snapdragon (Antirrhinum litigiosum) biomechanical responses to vibro-acoustic stimuli', Acoustical Society of America (ASA), pp. A172-A172. View/Download from: Publisher's site View description>>
The combined variation in gene expression and environmental conditions during flower development can result in phenotypic differences in shape, size, and material composition. Biomechanical responses in flower organs due to external stimuli can be mechanically measured at various levels. Here, we investigate snapdragon (Antirrhinum litigiosum) response to vibro-acoustic stimuli by an interdisciplinary model updating framework. In a climate-controlled setup, sweep signals and artificial signals representative of plant pollinator species were given as excitation input through a loudspeaker to a set of plants; vibrations of the flower organs were measured by laser Doppler vibrometry. Geometric features of the plants were identified using LiDAR combined with photogrammetry, while the density distribution in the flower organs and internal dimensions were estimated using micro-computed tomography scans. A computer model using finite element method was used to identify material properties of the flower organs by combining time domain measurements and dimensional classification. Results demonstrate density and stiffness gradient in the corolla contributing to a modal activity that is adaptive to local conditions and pollinators, but resilient against external noise. The framework outlined herein may give clues to which pollinators induce early-plant responses. [The authors acknowledge the support of the Human Frontier Science Program (HFSP) grant RGP0003/2022.]
Ngo, Q, Phan, TK, Mahmood, A & Xiang, W 2023, 'Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach', Institute of Electrical and Electronics Engineers (IEEE). View/Download from: Publisher's site
Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2023, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications'.
Nguyen, DT, Ho-Le, TP, Pham, L, Ho-Van, VP, Hoang, TD, Tran, TS, Frost, S & Nguyen, TV 2023, 'BONEcheck: a digital tool for personalized bone health assessment', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Nguyen, H, Saputra, YM, Hoang, D, Nguyen, D, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2023, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-based Mobile Edge Computing', Institute of Electrical and Electronics Engineers (IEEE). View/Download from: Publisher's site
Nguyen, HD, Phung, AHT, Do, TC, Nguyen, QHN, Tran, TS, Nguyen, TV & Ho-Pham, LT 2023, 'Association of lifestyle factors and breast cancer risk in Vietnamese women: A matched case-control study', Research Square Platform LLC. View/Download from: Publisher's site
Ouyang, Y, Goswami, K, Romero, J, Sanders, BC, Hsieh, M-H & Tomamichel, M 2023, 'Approximate reconstructability of quantum states and noisy quantum secret sharing schemes'.
Owen, B, Kechagidis, K, Bazaz, SR, Enjalbert, R, Essmann, E, Mallorie, C, Mirghaderi, F, Schaaf, C, Thota, K, Vernekar, R, Zhou, Q, Warkiani, ME, Stark, H & Krüger, T 2023, 'Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Peng, M, She, Z, Yazdani, D, Yazdani, D, Luo, W, Li, C, Branke, J, Nguyen, TT, Gandomi, AH, Jin, Y & Yao, X 2023, 'Evolutionary Dynamic Optimization Laboratory: A MATLAB Optimization Platform for Education and Experimentation in Dynamic Environments'.
Peng, X, Long, G, Yan, P, Tang, W & Clarke, A 2023, 'COVID-19 Impact Analysis on Patients with Complex Health Conditions: A Literature Review', MDPI AG. View/Download from: Publisher's site
Qu, Z, Nguyen, QV, Lau, CW, Johnston, A, Kennedy, PJ, Simoff, S & Catchpoole, D 2023, 'Understanding Cancer Patient Cohorts in Virtual Reality Environment for Better Clinical Decisions: A Usability Study', Research Square Platform LLC. View/Download from: Publisher's site
Ram, R & Rizoiu, M-A 2023, 'Can ideology-detecting algorithms catch online extremism before it takes hold?', The Conversation. View description>>
Ideology has always been a critical element in understanding how we view the world, form opinions and make political decisions.However, the internet has revolutionised the way opinions and ideologies spread, leading to new forms of online radicalisation. Far-right ideologies, which advocate for ultra-nationalism, racism and opposition to immigration and multiculturalism, have proliferated on social platforms.
Richards, C, Rad, DM, Zhand, S, Warkiani, M & McClements, L 2023, 'Evaluating gene delivery technologies to investigate the role of FKBPL in trophoblast function and the therapeutic potential of mesenchymal stem cell-derived extracellular vesicles', Elsevier BV, pp. e48-e49. View/Download from: Publisher's site
Roy, AK, Srivastava, V, Mahanti, S, Giarmatzi, C & Gilchrist, A 2023, 'Semi-device-independent certification of quantum non-Markovianity using sequential Random Access Codes'.
Rubboli, R, Takagi, R & Tomamichel, M 2023, 'Mixed-state additivity properties of magic monotones based on quantum relative entropies for single-qubit states and beyond'.
Rudd, DH, Huo, H & Xu, G 2023, 'An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance'.
Rudd, DH, Huo, H & Xu, G 2023, 'Causal Analysis of Customer Churn Using Deep Learning'.
Rudd, DH, Huo, H & Xu, G 2023, 'Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions'.
Rudd, DH, Huo, H & Xu, G 2023, 'Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition'.
Rudd, DH, Huo, H & Xu, G 2023, 'Predicting Financial Literacy via Semi-supervised Learning'.
Rudd, DH, Huo, H, Islam, MR & Xu, G 2023, 'Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data'.
Acoustic Black Hole (ABH), as a non-reflecting wave effect, has been realised in beams and plates by locally changing their thickness. Geometrical ABH designs stem from their transverse-load bearing properties, assuming isotropic material properties associated with engineering materials (e.g., steels). The underlying physics is to manipulate stiffness locally for a transversely-loaded structure, leading to a change in the elastic wave speed. However, the same ABH would have limitations for axial loading, i.e., slender beams for which longitudinal waves dominate. Here, we present a wave propagation approach using wavefront tracking to identify a potential ABH for axially-loaded circular beams. We study wave propagation in three exponential designs of finite length, monitoring the wave-travel time. Indicative of an effective ABH in finite length sections, the wave-travel time increases compared to the case of a beam without ABH. By employing the wave front tracking method for the design of an ABH with axial loading, it is possible to verify the effectiveness of ABHs. Also, various material models, e.g., orthotropic materials such as wood, and different loading conditions can be considered, which opens a new avenue in applications of ABH phenomena beyond conventional vibro-acoustic control problems.
Sepehrirahnama, S, Oberst, S, Croft, BE & Hanson, D 2023, 'Investigating sound absorption in rail tunnels using wave decomposition', Acoustical Society of America (ASA), pp. A180-A180. View/Download from: Publisher's site View description>>
Acoustic noise in trains is a more prevalent problem in tunnels as compared to open track scenarios. This is mainly due to less acoustic radiation relative to increasing contributions of reflections and reverberation. Sound absorbing panels on a tunnel wall and in the track four-foot perform better above 1 kHz with absorption coefficients larger than 0.8. To investigate sound absorption below 1 kHz, we employ wave decomposition into incidence, reflection and absorption components for a section of a given underground tunnel design. A Finite Element (FE) 2D model of a carriage and tunnel is developed, representing a tangent portion of a rail track and including the noise power spectra from the rail-wheel interactions for three different roughness scenarios. The FE model, compared to a Ray Tracing one, provides precisely imposed boundary conditions and the pressure field of the entire tunnel interior. Our results can identify the performance of current panels, absorbing significantly less noise power in the lower frequency range, especially within the 0.3–0.5 kHz interval. The insights from wave decomposition analysis can lead to solutions to increase absorption by changing the reflection pattern below 1 kHz band, improving the passenger comfort during a longer train ride.
Sezgin, E, Kocaballi, AB, Dolce, M, Skeens, M, Militello, L, Huang, Y, Stevens, J & Kemper, AR 2023, 'Chatbot for social needs screening and resource sharing with vulnerable families: Iterative design and evaluation study', Research Square Platform LLC. View/Download from: Publisher's site
Sheng, H, Yu, X, Wang, F, Khan, MDW, Weng, H, Shariflou, S & Golzan, SM 2023, 'Autonomous Stabilization of Retinal Videos for Streamlining Assessment of Spontaneous Venous Pulsations'.
Shiri, F, Wang, T, Pan, S, Chang, X, Li, Y-F, Haffari, R, Nguyen, V & Yu, S 2023, 'Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain'.
Singh, A, Liu, D & Lin, C-T 2023, 'Neuroadaptation in Physical Human-Robot Collaboration'.
Singh, K & Esselle, K 2023, 'Suppressing Sidelobes in Metasurface-Based Antennas Using Cross-Entropy Method Variant and Full Wave Electromagnetic Simulations', MDPI AG. View/Download from: Publisher's site
Soar, J, Lih, OS, Hui Wen, L, Ward, A, Sharma, E, Deo, R, Barua, PD, Tan, R-S, Rinen, E & Acharya, UR 2023, 'Deep Image Analysis Methods for Microalgae Identification', MDPI AG. View/Download from: Publisher's site
Su, SW 2023, 'Special Stable Matrices and Their Non-square Counterpart'.
Sulimani, H, Sulimani, R, Ramezani, F, Naderpour, M, Huo, H, Jan, T & Prasad, M 2023, 'HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks', Research Square Platform LLC. View/Download from: Publisher's site
Sun, H, Nguyen, M, Zhu, H, Nguyen, V, Lin, C-T & Jin, C 2023, 'A binaural room impulse response dataset and Shorelining psychophysical task for the evaluation of auditory sensory augmentation', Acoustical Society of America (ASA), pp. A195-A195. View/Download from: Publisher's site View description>>
Sensory augmentation using spatial sound presented in augmented-reality (AR) can assist people with low vision or blindness in navigating their environment (Katz et al., 2012). Nonetheless, in many situations, the poor quality of the binaural sound rendered using current tool sets limits the potential capability of the assistive technology. In particular, acoustic environments with near-field sources and reflections pose significant challenges. In this work, we provide a reference binaural room impulse response (BRIR) dataset with near-field sources and reflections and an associated shorelining psychophysical task that is useful for the evaluation of AR spatial audio. The dataset consists of 12∼small loudspeakers arranged on a 3-by-4 grid in a complex acoustic environment. BRIR measurements are recorded using the Head and Torso Simulator (HATS) for 17∼different receiver positions with a 5o angular resolution. Room impulse response measurements are also recorded using the Eigenmike for each of the 17 receiver positions. Using the Razer Anzu smart glasses to render the binaural AR spatial audio, we compare psychophysical performance on the shorelining navigation task using the recorded dataset and various existing binaural AR tool sets.
Sun, Z, Tavakoli, S, Khalilpour, K, Voinov, A & Marshall, J 2023, 'Barriers of Peer-to-Peer Energy Trading Networks: A Multi-dimensional PESTLE Analysis', MDPI AG. View/Download from: Publisher's site
Tan, Z, Leung, LR, Liao, C, Carniello, L, Rodriguez, JF, Saco, PM & Sandi, SG 2023, 'A multi-algorithm approach for modeling coastal wetland eco-geomorphology', Authorea, Inc.. View/Download from: Publisher's site
Tong, A, Kuchroo, M, Gupta, S, Venkat, A, San Juan, BP, Rangel, L, Zhu, B, Lock, JG, Chaffer, CL & Krishnaswamy, S 2023, 'Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Tuan, BM, Nguyen, DN, Trung, NL, Nguyen, V-D, Huynh, NV, Hoang, DT, Krunz, M & Dutkiewicz, E 2023, 'Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI'.
Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification'.
Vachon, P, Merugu, S, Sharma, J, Lal, A, Ng, E, Koh, Y, Lee, J & Lee, C 2023, 'Cavity-Agnostic Acoustofluidic Functions Enabled by Guided Flexural Waves on a Membrane Acoustic Waveguide Actuator', Research Square Platform LLC. View/Download from: Publisher's site
Waheed, N, Khan, F, Mastorakis, S, Jan, MA, Alalmaie, AZ & Nanda, P 2023, 'Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home Data'.
Waheed, N, Rehman, AU, Nehra, A, Farooq, M, Tariq, N, Jan, MA, Khan, F, Alalmaie, AZ & Nanda, P 2023, 'FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain'.
Wang, S, Xu, Q, Zhu, Q, Tao, J & Qiu, X 2023, 'Improving the acoustic contrast control performance in car cabins', Acoustical Society of America (ASA), pp. A270-A270. View/Download from: Publisher's site View description>>
The car cabin is an enclosure where personal audio is very important because different occupants may have individual listening requirements. Potential approaches to improve the acoustic contrast control performance in enclosures are discussed in this paper, including the configuration of loudspeakers and the sound absorption of sidewalls. The effect of the number of loudspeakers on the acoustic contrast control performance is first investigated, and the locations of a fixed number of loudspeakers are then optimized to maximize the acoustic contrast achieved with the system. The sound absorption of sidewalls also has an effect on the acoustic contrast control performance and it is discussed in detail. The simulations are based on the acoustic transfer functions obtained with a finite element model of a car cabin. The findings in this paper may serve as a guide to future designs of personal audio systems in car cabins.
Wills, A, Hsieh, M-H & Strelchuk, S 2023, 'Efficient Algorithms for All Port-Based Teleportation Protocols'.
Wills, A, Lin, T-C & Hsieh, M-H 2023, 'General Distance Balancing for Quantum Locally Testable Codes'.
Wills, A, Lin, T-C & Hsieh, M-H 2023, 'Tradeoff Constructions for Quantum Locally Testable Codes'.
Wu, RMX, Shafiabady, N, Zhang, H, Lu, HH, Gide, E, Liu, J & Charbonnier, CFB 2023, 'Ten Machine Learning Algorithms for Short-Term Forecasting: A Comparative Study in Gas Warning Systems', Research Square Platform LLC. View/Download from: Publisher's site
Xu, J & Cao, L 2023, 'Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling'.
Xu, M, Chen, S, Huang, S, Zhao, L & Hao, Q 2023, 'Invariant EKF based 3D Active SLAM with Exploration Task', Research Square Platform LLC. View/Download from: Publisher's site
Xu, Y, Li, Y, Zhang, JA, Renzo, MD & Quek, TQS 2023, 'Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems'.
Xu, Y, Zheng, R, Zhang, S, Liu, M & Huang, S 2023, 'CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization'.
Yazdani, D, Omidvar, MN, Yazdani, D, Deb, K & Gandomi, AH 2023, 'GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization'.
Yu, H, Hossain, SM, Wang, C, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'Selective Lithium Extraction from Diluted Binary Solutions Using Metal-Organic Frameworks (Mof)-Based Membrane Capacitive Deionization (Mcdi)', Elsevier BV. View/Download from: Publisher's site
Yuan, X, Chen, K, Chu, Q, Sun, H, Song, Y, Liu, S, Feng, W, Wang, X, Wang, S, Wang, L, Wang, X, Xu, F, Wang, Y, Zhao, Y & Hu, S 2023, 'Low Surgical Skill Rating of Surgeons Increased Complication Rates of Patients after Coronary Artery Bypass Graft', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Yuan, Y, Zhu, X & Li, J 2023, 'Moving Principal Component Analysis Based Structural Damage Detection for Highway Bridges Under Moving Vehicles', MDPI AG. View/Download from: Publisher's site
Zhang, G 2023, 'On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance'.
Zhang, G, Kenta, N & Kleijn, WB 2023, 'Lookahead Diffusion Probabilistic Models for Refining Mean Estimation'.
Zhang, G, Kenta, N & Kleijn, WB 2023, 'On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation'.
Zhang, J, Lei, J, Xie, W & Li, D 2023, 'Invariant Attribute-driven Binary Bi-branch Classification for Hyperspectral and LiDAR Images', MDPI AG. View/Download from: Publisher's site
Zhang, Z, Xu, Z, McGuire, HM, Essam, C, Nicholson, A, Hamilton, TJ, Li, J, Eshraghian, JK, Yong, K-T, Vigolo, D & Kavehei, O 2023, 'Neuromorphic Cytometry: Implementation on cell counting and size estimation', Cold Spring Harbor Laboratory. View/Download from: Publisher's site
Zhu, HY, Hieu, NQ, Hoang, DT, Nguyen, DN & Lin, C-T 2023, 'A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey'.
Zhu, Q & Williams, P 2023, 'Experimental study on the sound transmission loss suite at the University of Technology Sydney', Acoustical Society of America (ASA), pp. A127-A127. View/Download from: Publisher's site View description>>
Sound transmission loss suites are essential testing facilities for measuring the sound insulation properties of building elements and assessing noise attenuation. However, inconsistencies in test results can arise due to variations in the size, shape, and construction of test rooms across different laboratories, with biases introduced by the room acoustics or acoustical environment of the facility itself. To evaluate the reliability of such testing, we conducted an experimental study on the sound transmission loss suite at the University of Technology Sydney. Our investigation focused on three key factors: estimating the maximum achievable sound reduction using a heavyweight wall installed at the test aperture on the source room side, testing the effectiveness of vibration isolation between reverberation rooms, and assessing the decoupling of the sound field within the reverberation rooms.
Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink'.
