Showing papers by "University of Auckland published in 2022"

Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

Abstract: Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human–machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human–machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human–machine interface in medical robotics.

Toroidal topology of population activity in grid cells

Abstract: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment1. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations2, and are organized in modules3 that collectively form a population code for the animal's allocentric position1. The invariance of the correlation structure of this population code across environments4,5 and behavioural states6,7, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern1,8-11. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models12. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.

Sustainable applications of rice feedstock in agro-environmental and construction sectors: A global perspective

Abstract: Rice is second only to maize among the world's most important cereal crops, with a global harvested area of approximately 158 million hectares and an annual production of more than 700 million tonnes as paddy rice. At this scale, rice production generates vast amounts of waste in the form of straw, husk, and bran. Because of high cellulose, lignin, and silica contents, rice biowaste (RB) can be used to produce rice biochar (RBC) and rice compost (RC). Furthermore, RB can be used as sorbents, soil conditioners, bricks/concrete blocks, flat steel products, and biofuels, all of which make significant contributions to meeting United Nations Sustainable Development Goals (UNSDGs). Although previous reviews have explored individual applications of rice feedstocks, inadequate attention has been paid to multifunctional values and potential multi-utilities. Here, we offer a comprehensive review of RBC and RC with respect to: (1) preparation and characterization; (2) applications as soil conditioners and organic fertilizers and their effects on soil-carbon sequestration; (3) remediation of toxic element–contaminated soils and water; (4) removal of colors, dyes, endocrine-disrupting chemicals, personal-care products, and residual pesticides from water; and (5) applications in the construction industry. Specifically, we describe the opportunities for the sustainable use of RBC and RC in the management of contaminated soils and water as well as the construction industry. Overall, this review is expected to lengthen the list of possible multifunctional applications of RBC and RC.

Service-oriented industrial internet of things gateway for cloud manufacturing

Abstract: Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.

Implications of social isolation, separation, and loss during the COVID-19 pandemic for couples' relationships.

Abstract: The broad isolation, separation, and loss resulting from the COVID-19 pandemic raise risks for couples' relationship quality and stability. Guided by the vulnerability-stress-adaptation model, we suggest that how pandemic-related loss, isolation, and separation impact couples' relationships will vary depending on the amount and severity of pandemic-related stress, together with enduring personal vulnerabilities (e.g. attachment insecurity), both of which can disrupt adaptive dyadic responses to these challenges. A review of emerging research examining relationship functioning before and during the initial stages of the pandemic offers support for this framework. We draw on additional research to suggest pathways for mitigating relationship disruptions and promoting resilience.

An automatic method for constructing machining process knowledge base from knowledge graph

Abstract: The process knowledge base is the key module in intelligent process design, it determines the intelligence degree of the design system and affects the quality of product design. However, traditional process knowledge base construction is non-automated, time consuming and requires much manual work, which is not sufficient to meet the demands of the modern manufacturing mode. Moreover, the knowledge base often adopts a single knowledge representation, and this may lead to ambiguity in the meaning of some knowledge, which will affect the quality of the process knowledge base. To overcome the above problems, an automatic construction framework for the process knowledge base in the field of machining based on knowledge graph (KG) is introduced. First, the knowledge is classified and annotated based on the function-behavior-states (FBS) design method. Second, a knowledge extraction framework based on BERT-BiLSTM-CRF is established to perform the automatic knowledge extraction of process text. Third, a knowledge representation method based on fuzzy comprehensive evaluation is established, forming three types of knowledge representation with the KG as the main, production rules and two-dimensional data linked list as a supplement. In addition, to overcome the redundancy in the knowledge fusion stage, a hybrid algorithm based on an improved edit distance and attribute weighting is built. Finally, a prototype system is developed, and quality analysis is carried out. Compared with the F values of BiLSTM-CRF and CNN-BiLSTM-CRF, that of the proposed extraction method in the machining domain is increased by 7.35% and 3.87%, respectively.

Shear Capacity of Cold-Formed Steel Channels with Edge-Stiffened Web Holes, Unstiffened Web Holes, and Plain Webs

Abstract: In this paper, a total of 254 results comprising 30 shear tests and 224 finite element (FE) analysis results are reported. Simply supported test specimens of cold-formed steel (CFS) channel...

InGaN quantum well with gradually varying indium content for high-efficiency GaN-based green light-emitting diodes

Abstract: High-efficiency GaN-based green LEDs are of paramount importance to the development of the monolithic integration of multicolor emitters and full-color high-resolution displays. Here, the InGaN quantum well with gradually varying indium (In) content was proposed for improving the performance of GaN-based green LEDs. The InGaN quantum well with gradually varying In content not only alleviates the quantum-confined Stark effect (QCSE), but also yields a low Auger recombination rate. Consequently, the gradual In content green LEDs exhibited increased light output power (LOP) and reduced efficiency droop as compared to constant In content green LEDs. At 60 A/cm2, the LOPs of the constant In content green LEDs and the gradual In content green LEDs were 33.9 mW and 55.2 mW, respectively. At 150 A/cm2, the efficiency droops for the constant In content green LEDs and the gradual In content green LEDs were 61% and 37.6%, respectively. This work demonstrates the potential for the gradual In content InGaN to replace constant In content InGaN as quantum wells in LED devices in a technologically and commercially effective manner.

Capacitive Power Transfer System With Double T-Type Resonant Network for Mobile Devices Charging/Supply

Abstract: In the applications of wireless power transfer for mobile devices, the pick-up of the wireless power transfer system often needs to be moved in and moved out. A sudden change in the system structure will cause high transient voltages or currents, which could damage the power electronic device of the capacitive power transfer (CPT). In addition, the standby power of the system will be too high to work after removing the pick-up. A CPT system with double T-type resonant networks has been proposed to address these problems. The steady-state models of the system without the pick-up and normal state conditions of it are established. The relationships between the resonant network parameters are analyzed, and a systematic circuit parameter design method is provided. The proposed CPT topology and the parameter design method have been verified by simulation and experiment. The system not only achieved self-protection under severe load change but also entered standby mode automatically after the pick-up was moved out. Besides, the system is restored to the normal state condition when the pick-up is moved in without additional detection and control circuitry.

Tailoring of Upconversion Emission in Tm³⁺/Yb³⁺-Codoped Y₂Mo₃O₁₂ Submicron Particles Via Thermal Stimulation Engineering for Non-invasive Thermometry

Abstract: To settle the challenges of optical thermometry with high sensitivity, a series of Tm3+/Yb3+-codoped Y2Mo3O12 (YMO:Tm3+/2xYb3+) submicron particles were developed via a sol–gel route. Excited by 980 nm, bright upconversion (UC) emissions of Tm3+ are observed, in which the optimum intensity is realized when the Yb3+ concentration is 13 mol %. Moreover, the UC mechanism of the emissions originating from the 1G4 level is a three-photon absorption process, while that of the emission from the 3F2,3 level is a two-photon absorption process. Furthermore, thermally enhanced emission intensities are realized in the studied compounds due to the negative thermal expansion effect. Notably, owing to the coexistence of improved energy transfer and cross-relaxation processes at elevated temperature, the intensities of the UC emissions from the 1G4 level increase and then decrease with raising the temperature, whereas that of the UC emission from the 3F2,3 level is enhanced monotonously as temperature increases. Via analyzing the inconsistent thermal quenching characteristics of the UC emissions, we explored the thermometric behaviors of the synthesized products and found that their sensitivities are dependent on the spectral mode. Through investigating the dependence of the emission intensity rate of the emissions from 3F2,3 → 3H6 to 1G4 → 3F4 transitions on temperature, one knows that the maximum absolute and relative sensitivities of the resultant submicron particles are 0.198 K–1 and 3.27% K–1, respectively. Additionally, the thermometric behaviors of YMO:Tm3+/2xYb3+ submicron particles can also be manipulated via altering the Yb3+ concentration.

Acoustic regularities in infant-directed speech and song across cultures

Abstract: When interacting with infants, humans often alter their speech and song in ways thought to support communication. Theories of human child-rearing, informed by data on vocal signalling across species, predict that such alterations should appear globally. Here, we show acoustic differences between infant-directed and adult-directed vocalizations across cultures. We collected 1,615 recordings of infant- and adult-directed speech and song produced by 410 people in 21 urban, rural and small-scale societies. Infant-directedness was reliably classified from acoustic features only, with acoustic profiles of infant-directedness differing across language and music but in consistent fashions. We then studied listener sensitivity to these acoustic features. We played the recordings to 51,065 people from 187 countries, recruited via an English-language website, who guessed whether each vocalization was infant-directed. Their intuitions were more accurate than chance, predictable in part by common sets of acoustic features and robust to the effects of linguistic relatedness between vocalizer and listener. These findings inform hypotheses of the psychological functions and evolution of human communication.

Statistical comparison of <scp>DEC</scp> and <scp>DEC</scp> +J is identical to comparison of two <scp>ClaSSE</scp> submodels, and is therefore valid

Abstract: Aim Statistical model comparison has become common in historical biogeography, enabled by the R package BioGeoBEARS, which implements several models in a common framework, allowing models to be compared with standard likelihood-based methods of statistical model comparison. Ree and Sanmartín (Journal of Biogeography, 45, 741–749, 2018) critiqued the comparison of Dispersal–Extinction–Cladogenesis (DEC) and a modification of it, DEC+J, which adds the process of jump dispersal at speciation. DEC+J provides highly significant improvements in model fit on most (although not all) datasets. They claim that the comparison is statistically invalid for a variety of reasons. I analyse the key claims made by the critique. Location Simulated data. Taxon Simulated data. Methods Likelihood calculations are checked by comparison between programs and by-hand calculations, and by summing likelihoods across all possible datasets. Model adequacy of DEC versus DEC+J is checked by a simulation/inference experiment. Results Mistakes in the critique's example likelihood calculations are demonstrated. DEC+J fits better on datasets because the DEC model is statistically inadequate in the common situation when most species have geographical ranges of single areas; the DEC model requires long residence times of multi-area ranges, and when these are not observed, a model that does produce such data patterns, such as DEC+J, prevails. More fundamentally, statistical comparison of DEC and DEC+J produces identical log-likelihood differences to statistical comparison of two submodels of ClaSSE where extinction rates are fixed to 0. Main Conclusions DEC fails a basic model adequacy check for understandable reasons, while DEC+J does not. As Ree and Sanmartín recommend ClaSSE models as valid for comparison, the comparison of DEC and DEC+J is statistically valid according to their own criteria.

A new numerical investigation of fractional order susceptible-infected-recovered epidemic model of childhood disease

Abstract: The susceptible-infected-recovered (SIR) epidemic model of childhood disease is analyzed in the present framework with the help of q -homotopy analysis transform method ( q -HATM). The considered model consists the system of three differential equations having fractional derivative, and the non-linear system exemplifies the evolution of childhood disease in a population and its influence on the community with susceptible, infected and recovered compartment. The projected method is a mixture of q -homotopy analysis method and Laplace transform. Two distinct explanatory cases are considered, and corresponding simulations have been demonstrated in terms of plots for different value of the order. The present investigation elucidates that the projected both derivative and technique play a vital role in the analysis and illustrate the behaviour of diverse mathematical models described with differential equations in human disease.

Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson's disease and COVID-19

Abstract: This study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD.

A new numerical investigation of fractional order susceptible-infected-recovered epidemic model of childhood disease

Abstract: The susceptible-infected-recovered (SIR) epidemic model of childhood disease is analyzed in the present framework with the help of q-homotopy analysis transform method (q-HATM). The considered model consists the system of three differential equations having fractional derivative, and the non-linear system exemplifies the evolution of childhood disease in a population and its influence on the community with susceptible, infected and recovered compartment. The projected method is a mixture of q-homotopy analysis method and Laplace transform. Two distinct explanatory cases are considered, and corresponding simulations have been demonstrated in terms of plots for different value of the order. The present investigation elucidates that the projected both derivative and technique play a vital role in the analysis and illustrate the behaviour of diverse mathematical models described with differential equations in human disease.