Showing papers by "University of New South Wales published in 2022"

Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection

Abstract: A proportion of patients surviving acute coronavirus disease 2019 (COVID-19) infection develop post-acute COVID syndrome (long COVID (LC)) lasting longer than 12 weeks. Here, we studied individuals with LC compared to age- and gender-matched recovered individuals without LC, unexposed donors and individuals infected with other coronaviruses. Patients with LC had highly activated innate immune cells, lacked naive T and B cells and showed elevated expression of type I IFN (IFN-β) and type III IFN (IFN-λ1) that remained persistently high at 8 months after infection. Using a log-linear classification model, we defined an optimal set of analytes that had the strongest association with LC among the 28 analytes measured. Combinations of the inflammatory mediators IFN-β, PTX3, IFN-γ, IFN-λ2/3 and IL-6 associated with LC with 78.5–81.6% accuracy. This work defines immunological parameters associated with LC and suggests future opportunities for prevention and treatment. Phetsouphanh and colleagues show that individuals with long COVID have persistent activation of the innate and adaptive immune system at 8 months after infection and define a set of analytes associated with long COVID.

Solar cell efficiency tables (Version 60)

Abstract: Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since January 2022 are reviewed. An appendix describing temporary electrical contacting of large-area solar cells approaches and terminology is also included.

Deep learning via LSTM models for COVID-19 infection forecasting in India

Abstract: The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

Self-templating synthesis of heteroatom-doped large-scalable carbon anodes for high-performance lithium-ion batteries

Abstract: We design a simple and cost-effective strategy to construct a large–scalable nitrogen-rich sulfur-doped porous carbon material as a high-performance anode material for lithium-ion batteries.

Clay content mapping and uncertainty estimation using weighted model averaging

Abstract: Accurate prediction of clay is the basis for soil quality assessment and decision making in land use because it governs soil moisture and fertility dynamics. However, using laboratory methods to determine clay across a large district and at multiple depths is tedious and expensive. An alternative is to use proximally and remotely sensed digital data, that can be coupled to laboratory measured clay through models. This study aims to predict topsoil (0–0.3 m) and subsoil (0.9–1.2 m) clay at district scale by comparing; i) importance of proximally (i.e., apparent soil electrical conductivity – ECa) and remotely (i.e., γ-ray spectrometry, digital elevation model – DEM) sensed data, ii) models including a linear mixed model (LMM) and machine learning models (MLs, i.e., Cubist, random forest [RF], support vector machine regression [SVMR], quantile regression forests [QRF], extreme gradient boosting [XGBoost] and bagEarth), iii) two model averaging techniques (i.e., Granger–Ramanathan averaging (GRA) and Lin’s concordance (LCCC) weights) from the top four best models, and iv) uncertainty of the prediction. The results showed that the γ-ray data was most important for topsoil clay prediction, while in the subsoil the slope was most important. Moreover, for topsoil clay prediction the RF was best with fair accuracy (RPD = 1.64), followed by QRF (1.62), Cubist (1.61) and LMM (1.55) which outperformed bagEarth (1.51), SVMR (1.47) and XGBoost (1.47). For the subsoil, all seven models achieved poor accuracy (RPD

Enhanced light–matter interaction in two-dimensional transition metal dichalcogenides

Abstract: Two-dimensional (2D) transition metal dichalcogenide (TMDC) materials, such as MoS2, WS2, MoSe2, and WSe2, have received extensive attention in the past decade due to their extraordinary electronic, optical and thermal properties. They evolve from indirect bandgap semiconductors to direct bandgap semiconductors while their layer number is reduced from a few layers to a monolayer limit. Consequently, there is strong photoluminescence in a monolayer (1L) TMDC due to the large quantum yield. Moreover, such monolayer semiconductors have two other exciting properties: large binding energy of excitons and valley polarization. These properties make them become ideal materials for various electronic, photonic and optoelectronic devices. However, their performance is limited by the relatively weak light-matter interactions due to their atomically thin form factor. Resonant nanophotonic structures provide a viable way to address this issue and enhance light-matter interactions in 2D TMDCs. Here, we provide an overview of this research area, showcasing relevant applications, including exotic light emission, absorption and scattering features. We start by overviewing the concept of excitons in 1L-TMDC and the fundamental theory of cavity-enhanced emission, followed by a discussion on the recent progress of enhanced light emission, strong coupling and valleytronics. The atomically thin nature of 1L-TMDC enables a broad range of ways to tune its electric and optical properties. Thus, we continue by reviewing advances in TMDC-based tunable photonic devices. Next, we survey the recent progress in enhanced light absorption over narrow and broad bandwidths using 1L or few-layer TMDCs, and their applications for photovoltaics and photodetectors. We also review recent efforts of engineering light scattering, e.g., inducing Fano resonances, wavefront engineering in 1L or few-layer TMDCs by either integrating resonant structures, such as plasmonic/Mie resonant metasurfaces, or directly patterning monolayer/few layers TMDCs. We then overview the intriguing physical properties of different van der Waals heterostructures, and their applications in optoelectronic and photonic devices. Finally, we draw our opinion on potential opportunities and challenges in this rapidly developing field of research.

Recent advances on interface engineering of perovskite solar cells

Abstract: Lead halide perovskite solar cells (PSCs) have been rapidly developed in the past decade. Owing to its excellent power conversion efficiency with robust and low-cost fabrication, perovskite quickly becomes one of the most promising candidates for the next-generation photovoltaic technology. With the development of PSCs, the interface engineering has witnessed its increasingly critical role in maximizing the device performance as well as the long-term stability, because the interfaces in PSCs are closely correlated with the defect management, carrier dynamics and surface passivation. This review focuses on interfacial modification between the perovskite active layer and the charge transport layer, as well as the recent advances on high-efficiency and stable PSCs driven by interface engineering strategies. The contributing roles of interface engineering in terms of defect passivation, inhibiting ion migration, optimization of energy band alignment and morphological control are discussed. Finally, based on the latest progress and advances, strategies and opportunities for the future research on interface engineering for PSCs are proposed to promote the development of perovskite photovoltaic technology.

Unsupervised Domain Adaptation for Nonintrusive Load Monitoring Via Adversarial and Joint Adaptation Network

Abstract: Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load consumption from the aggregate load in a house. Monitoring the energy behavior has become increasingly important for home energy management. For many machine learning-based models, model training needs enough, and diverse appliance-level labeled data from different houses, which is very time-consuming, expensive, and unacceptable for users. In this article, we propose an algorithm based on the adversarial network and the joint adaptation network for energy disaggregation to decrease the distribution gaps of both the feature space and the label space between the source and target domains. With only very limited labeled data in the source domain and enough unlabeled data in the target domain, our proposed algorithm can obtain satisfactory accuracy results for NILM. Extensive experiments for intradomain and interdomain demonstrate that the proposed algorithm can significantly improve the domain adaptation. Comparing with the baseline method that without any domain adaptation, the improvement on mean absolute error with the proposed algorithm can reach 67.72%, 67.53%, and 66.56% for the washing machine (W.M), the dishwasher (D.W), and the microwave (M.V), respectively.

Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy

Abstract: Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.

Designing Angstrom‐Scale Asymmetric MOF‐on‐MOF Cavities for High Monovalent Ion Selectivity

Abstract: Biological ion channels feature angstrom-scale asymmetrical cavity structures, which are the key to achieving highly efficient separation and sensing of alkali metal ions from aqueous resources. The clean energy future and lithium-based energy storage systems heavily rely on highly efficient ionic separations. However, artificial recreation of such a sophisticated biostructure has been technically challenging. Here, a highly tunable design concept is introduced to fabricate monovalent ion-selective membranes with asymmetric sub-nanometer pores in which energy barriers are implanted. The energy barriers act against ionic movements, which hold the target ion while facilitating the transport of competing ions. The membrane consists of bilayer metal-organic frameworks (MOF-on-MOF), possessing a 6 to 3.4-angstrom passable cavity structure. The ionic current measurements exhibit an unprecedented ionic current rectification ratio of above 100 with exceptionally high selectivity ratios of 84 and 80 for K+ /Li+ and Na+ / Li+ , respectively (1.14 Li+ mol m-2 h-1 ). Furthermore, using quantum mechanics/molecular mechanics, it is shown that the combined effect of spatial hindrance and nucleophilic entrapment to induce energy surge baffles is responsible for the membrane's ultrahigh selectivity and ion rectification. This work demonstrates a striking advance in developing monovalent ion-selective channels and has implications in sensing, energy storage, and separation technologies.

Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles With Payload Uncertainties

Abstract: A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.

MOOCsChain: A blockchain-based secure storage and sharing scheme for MOOCs learning

Abstract: Massive Open Online Courses (MOOCs) have become a paramount online learning approach for flexible learning methods and extensive learning courses. Different from the traditional learning method, MOOCs advocate completing the learning process through online devices. Electronic Learning Records (ELRs) are vital for learners as compelling evidence of the learning process, generally are stored in a cloud data center. However, with trustless third-party storage, the security and privacy of ELRs cannot be guaranteed. Due to such, we propose a Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems. Designed to take advantage of blockchain, the proposed solution can support efficient conditional anonymity, secure storage, and sharing without the need for sophisticated cryptographic calculations. The experimental results and the security analysis shows that the proposed scheme achieves legitimate security assurance and outperforms other similar works.

Theory of zero-field superconducting diode effect in twisted trilayer graphene

Abstract: In a recent experiment [Lin et al., arXiv:2112.07841], the superconducting phase hosted by a heterostructure of mirror-symmetric twisted trilayer graphene and WSe$_2$ was shown to exhibit significantly different critical currents in opposite directions in the absence of external magnetic fields. We here develop a microscopic theory and analyze necessary conditions for this zero-field superconducting diode effect. Taking into account the spin-orbit coupling induced in trilayer graphene via the proximity effect, we classify the pairing instabilities and normal-state orders and derive which combinations are consistent with the observed diode effect, in particular, its field trainability. We perform explicit calculations of the diode effect in several different models, including the full continuum model for the system, and illuminate the relation between the diode effect and finite-momentum pairing. Our theory also provides a natural explanation of the observed sign change of the current asymmetry with doping, which can be related to an approximate chiral symmetry of the system, and of the enhanced transverse resistance above the superconducting transition. Our findings not only elucidate the rich physics of trilayer graphene on WSe$_2$, but also establish a means to distinguish between various candidate interaction-induced orders in spin-orbit-coupled graphene moir\'e systems, and could therefore serve as a guide for future experiments as well.