Showing papers by "Auckland University of Technology published in 2020"

Global, Regional and Country-Specific Burden of Ischaemic Stroke, Intracerebral Haemorrhage and Subarachnoid Haemorrhage: A Systematic Analysis of the Global Burden of Disease Study 2017.

Abstract: Background: Stroke is a leading cause of death and disability in globally and particularly in low- and middle-income countries, and this burden is increasing. The burden of stroke pathological subtypes varies in terms of incidence, disability and mortality. Previous Global Burden of Diseases, Injuries, and Risk Factors Studies (GBD) reports did not provide separate global burden and trends estimates for haemorrhagic stroke by primary intracerebral haemorrhage (PICH) and subarachnoid haemorrhage (SAH). Aim: To summarise the GBD 2017 findings for the burden and 27-year trends for ischaemic stroke (IS), intracerebral haemorrhage and SAH by age, sex and country income level in 21 world regions and associated risk factors. Methods: Data on stroke incidence, prevalence, mortality and disability-adjusted life-years (DALY) lost and the burden of IS, PICH and SAH were derived from all available datasets from the GBD 2017 studies. Data were analysed in terms of absolute numbers and age-standardised rates per 100,000 (95% uncertainty interval [UI]), with estimates stratified by age, sex and economic development level by the World Bank classification. We also analysed changes in the patterns of incidence, mortality and DALYs estimates between 1990 and 2017. Results: In 2017, there were 11.9 million incident (95% UI 11.1–12.8), 104.2 million prevalent (98.6–110.2), 6.2 million fatal (6.0–6.3) cases of stroke and 132.1 million stroke-related DALYs (126.5–137.4). Although stroke incidence, prevalence, mortality and DALY rates declined from 1990 to 2017, the absolute number of people who developed new stroke, died, survived or remained disabled from stroke has almost doubled. The bulk of stroke burden (80% all incident strokes, 77% all stroke survivors, 87% of all deaths from stroke and 89 of all stroke-related DALYs) in 2017 was in low- to middle-income countries. Globally in 2017, IS constituted 65%, PICH –26% and SAH –9% of all incident strokes. Discussion: The latest GBD estimates of stroke burden in 195 countries supersede previous GBD stroke burden findings and provide most accurate data for stroke care planning and resource allocation globally, regionally and for 195 countries. Stroke remains the second leading cause of deaths and disability worldwide. The increased stroke burden continues to exacerbate a huge pressure on people affected by stroke, their families and societies. It is imperative to develop and implement more effective primary prevention strategies to reduce stroke burden and its impact.

An affordance perspective of team collaboration and enforced working from home during COVID-19

Abstract: COVID-19 has caused unprecedented challenges to our lives. Many governments have forced people to stay at home, leading to a radical shift from on-site to virtual collaboration for many knowledge workers. Existing remote working literature does not provide a thorough explanation of government-enforced working from home situations. Using an affordance lens, this study explores the sudden and enforced issues that COVID-19 has presented, and the technological means knowledge workers use to achieve their team collaboration goals. We interviewed 29 knowledge workers about their experiences of being required to work from home and introduced the term “enforced work from home”. This paper contributes to the affordance theory by providing an understanding of the substitution of affordances for team collaboration during COVID-19. The shifting of affordances results in positive and negative effects on team collaboration as various affordances of technology were perceived and actualised to sustain “business as usual”.

The global burden of neurological disorders: translating evidence into policy.

Abstract: Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. In the past 30 years, the absolute numbers of deaths and people with disabilities owing to neurological diseases have risen substantially, particularly in low-income and middle-income countries, and further increases are expected globally as a result of population growth and ageing. This rise in absolute numbers of people affected suggests that advances in prevention and management of major neurological disorders are not sufficiently effective to counter global demographic changes. Urgent measures to reduce this burden are therefore needed. Because resources for health care and research are already overstretched, priorities need to be set to guide policy makers, governments, and funding organisations to develop and implement action plans for prevention, health care, and research to tackle the growing challenge of neurological disorders.

The burden of neurological diseases in Europe: an analysis for the Global Burden of Disease Study 2017.

Abstract: Summary Background Neurological disorders account for a large and increasing health burden worldwide, as shown in the Global Burden of Diseases (GBD) Study 2016. Unpacking how this burden varies regionally and nationally is important to inform public health policy and prevention strategies. The population in the EU is older than that of the WHO European region (western, central, and eastern Europe) and even older than the global population, suggesting that it might be particularly vulnerable to an increasing burden of age-related neurological disorders. We aimed to compare the burden of neurological disorders in the EU between 1990 and 2017 with those of the WHO European region and worldwide. Methods The burden of neurological disorders was calculated for the year 2017 as incidence, prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability for the countries in the EU and the WHO European region, totally and, separately. Diseases analysed were Alzheimer's disease and other dementias, epilepsy, headache (migraine and tension-type headache), multiple sclerosis, Parkinson's disease, brain cancer, motor neuron diseases, neuroinfectious diseases, and stroke. Data are presented as totals and by sex, age, year, location and socio-demographic context, and shown as counts and rates. Findings In 2017, the total number of DALYs attributable to neurological disorders was 21·0 million (95% uncertainty interval 18·5–23·9) in the EU and 41·1 million (36·7–45·9) in the WHO European region, and the total number of deaths was 1·1 million (1·09–1·14) in the EU and 1·97 million (1·95–2·01) in the WHO European region. In the EU, neurological disorders ranked third after cardiovascular diseases and cancers representing 13·3% (10·3–17·1) of total DALYs and 19·5% (18·0–21·3) of total deaths. Stroke, dementias, and headache were the three commonest causes of DALYs in the EU. Stroke was also the leading cause of DALYs in the WHO European region. During the study period we found a substantial increase in the all-age burden of neurodegenerative diseases, despite a substantial decrease in the rates of stroke and infections. The burden of neurological disorders in Europe was higher in men than in women, peaked in individuals aged 80–84 years, and varied substantially with WHO European region and country. All-age DALYs, deaths, and prevalence of neurological disorders increased in all-age measures, but decreased when using age-standardised measures in all but three countries (Azerbaijan, Turkmenistan, and Uzbekistan). The decrease was mostly attributed to the reduction of premature mortality despite an overall increase in the number of DALYs. Interpretation Neurological disorders are the third most common cause of disability and premature death in the EU and their prevalence and burden will likely increase with the progressive ageing of the European population. Greater attention to neurological diseases must be paid by health authorities for prevention and care. The data presented here suggest different priorities for health service development and resource allocation in different countries. Funding European Academy of Neurology.

System architecture for blockchain based transparency of supply chain social sustainability

Abstract: Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.

Towards a unified study of multiple stressors: divisions and common goals across research disciplines.

Abstract: Anthropogenic environmental changes, or 'stressors', increasingly threaten biodiversity and ecosystem functioning worldwide. Multiple-stressor research is a rapidly expanding field of science that seeks to understand and ultimately predict the interactions between stressors. Reviews and meta-analyses of the primary scientific literature have largely been specific to either freshwater, marine or terrestrial ecology, or ecotoxicology. In this cross-disciplinary study, we review the state of knowledge within and among these disciplines to highlight commonality and division in multiple-stressor research. Our review goes beyond a description of previous research by using quantitative bibliometric analysis to identify the division between disciplines and link previously disconnected research communities. Towards a unified research framework, we discuss the shared goal of increased realism through both ecological and temporal complexity, with the overarching aim of improving predictive power. In a rapidly changing world, advancing our understanding of the cumulative ecological impacts of multiple stressors is critical for biodiversity conservation and ecosystem management. Identifying and overcoming the barriers to interdisciplinary knowledge exchange is necessary in rising to this challenge. Division between ecosystem types and disciplines is largely a human creation. Species and stressors cross these borders and so should the scientists who study them.

Global Stroke Statistics 2019

Abstract: BackgroundData on stroke epidemiology and availability of hospital-based stroke services around the world are important for guiding policy decisions and healthcare planning.AimsTo provide the most ...

Blockchain-based life cycle assessment: An implementation framework and system architecture

Abstract: Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology offers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in conducting LCA for improving supply chain sustainability. This research develops a framework to guide the implementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications.

Teaching in the Age of Covid-19

Abstract: A collection of 84 author's testimonies and workspace photographs between 18 March and 5 May 2020

COVID-19 and Indigenous Peoples: An imperative for action.

Abstract: Every person on Earth has been affected in some way by the coronavirus disease (COVID-19) pandemic. However, there is a marked inequity in the impact and threat of the disease for the 370 million Indigenous Peoples worldwide. While honouring diversity in peoples and cultures, this editorial (written by a collaborative of Indigenous nurses from Australia, Aotearoa (New Zealand), Canada, the United States of America and Central America), explores contemporary issues raised for Indigenous communities by this latest public health emergency. Please note, while we may describe a situation about a specific Indigenous group, readers can be assured that the issues we raise are endemic across colonised Indigenous communities globally.

Computation-Efficient Offloading and Trajectory Scheduling for Multi-UAV Assisted Mobile Edge Computing

Abstract: The emergence of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) is of great significance for the prospective development of Internet of Things (IoT). The additional computation capability and extensive network coverage provide energy-limited smart mobile devices (SMDs) with more opportunities to experience diverse intelligent applications. In this paper, a computation efficiency maximization problem is formulated in a multi-UAV assisted MEC system, where both computation bits and energy consumption are considered. Based on the partial computation offloading mode, user association, allocation of central processing unit (CPU) cycle frequency, power and spectrum resources, as well as trajectory scheduling of UAVs are jointly optimized. Due to the non-convexity of the problem and the coupling among variables, we propose an iterative optimization algorithm with double-loop structure to find the optimal solution. Simulation results demonstrate that the proposed algorithm can obtain higher computation efficiency than baseline schemes while guaranteeing the quality of computation service.

Using soil bacterial communities to predict physico-chemical variables and soil quality

Abstract: Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R2 value = 0.35) to strong (R2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50–95% accuracy. The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality.

An ultra-wide bandwidth (704 to 4 032 MHz) receiver for the Parkes radio telescope

Abstract: We describe an ultra-wide-bandwidth, low-frequency receiver recently installed on the Parkes radio telescope. The receiver system provides continuous frequency coverage from 704 to 4032 MHz. For much of the band ( ), the system temperature is approximately 22 K and the receiver system remains in a linear regime even in the presence of strong mobile phone transmissions. We discuss the scientific and technical aspects of the new receiver, including its astronomical objectives, as well as the feed, receiver, digitiser, and signal processor design. We describe the pipeline routines that form the archive-ready data products and how those data files can be accessed from the archives. The system performance is quantified, including the system noise and linearity, beam shape, antenna efficiency, polarisation calibration, and timing stability.

The Parkes Pulsar Timing Array project: Second data release

Abstract: We describe 14 yr of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately 3 weeks in three observing bands. A comprehensive description of the pulsar observing systems employed at the telescope since 2004 is provided, including the calibration methodology and an analysis of the stability of system components. We attempt to provide full accounting of the reduction from the raw measured Stokes parameters to pulse times of arrival to aid third parties in reproducing our results. This conversion is encapsulated in a processing pipeline designed to track provenance. Our data products include pulse times of arrival for each of the pulsars along with an initial set of pulsar parameters and noise models. The calibrated pulse profiles and timing template profiles are also available. These data represent almost 21 000 h of recorded data spanning over 14 yr. After accounting for processes that induce time-correlated noise, 22 of the pulsars have weighted root-mean-square timing residuals of in at least one radio band. The data should allow end users to quickly undertake their own gravitational wave analyses, for example, without having to understand the intricacies of pulsar polarisation calibration or attain a mastery of radio frequency interference mitigation as is required when analysing raw data files.

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Abstract: Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.

The MeerKAT Telescope as a Pulsar Facility: System verification and early science results from MeerTime

Abstract: We describe system verification tests and early science results from the pulsar processor (PTUSE) developed for the newly commissioned 64-dish SARAO MeerKAT radio telescope in South Africa. MeerKAT is a high-gain (∼2.8 K Jy-1 ) low-system temperature (∼18 K at 20 cm) radio array that currently operates at 580–1 670 MHz and can produce tied-array beams suitable for pulsar observations. This paper presents results from the MeerTime Large Survey Project and commissioning tests with PTUSE. Highlights include observations of the double pulsar J0737-339A , pulse profiles from 34 millisecond pulsars (MSPs) from a single 2.5-h observation of the Globular cluster Terzan 5, the rotation measure of Ter5O, a 420-sigma giant pulse from the Large Magellanic Cloud pulsar PSR J0540-6919, and nulling identified in the slow pulsar PSR J0633–2015. One of the key design specifications for MeerKAT was absolute timing errors of less than 5 ns using their novel precise time system. Our timing of two bright MSPs confirm that MeerKAT delivers exceptional timing. PSR J2241-5236 exhibits a jitter limit a jitter timing of <4 ns h-1 whilst timing of PSR J909-3744 over almost 11 months yields an rms residual of 66 ns with only 4 min integrations. Our results confirm that the MeerKAT is an exceptional pulsar telescope. The array can be split into four separate sub-arrays to time over 1 000 pulsars per day and the future deployment of S-band (1 750–3 500 MHz) receivers will further enhance its capabilities.

AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention:

Abstract: Artificial intelligence (AI) in the context of customer service, we define as a technology-enabled system for evaluating real-time service scenarios using data collected from digital and/or physica...

Non-invasive Biomarkers for Early Detection of Breast Cancer

Abstract: Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of "omics" strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Pothole Detection Using Computer Vision and Learning

Abstract: Techniques for identifying potholes on road surfaces aim at developing strategies for real-time or offline identification of potholes, to support real-time control of a vehicle (for driver assistance or autonomous driving) or offline data collection for road maintenance. For these reasons, research around the world has comprehensively explored strategies for the identification of potholes on roads. This paper starts with a brief review of the field; it classifies developed strategies into several categories. We, then, present our contributions to this field by implementing strategies for automatic identification of potholes. We developed and studied two techniques based on stereo-vision analysis of road environments ahead of the vehicle; we also designed two models for deep-learning-based pothole detection. An experimental evaluation of those four designed methods is provided, and conclusions are drawn about particular benefits of these methods.

Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks

Abstract: Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provides a systematic quantitative and qualitative analysis and guidelines for optimal temporal encoding. It proposes a methodology of a three-step encoding workflow: method selection by signal characteristics, parameter optimization by error metrics between original and reconstructed signals, and validation by comparison of the original signal and the encoded spike train. Four encoding methods are analyzed: one stimulus estimation [Ben’s Spiker algorithm (BSA)] and three temporal contrast [threshold-based, step-forward (SW), and moving-window (MW)] encodings. A short theoretical analysis is provided, and the extended quantitative analysis is carried out applying four types of test signals: step-wise signal, smooth (sinusoid) signal with added noise, trended smooth signal, and event-like smooth signal. Various time-domain and frequency spectrum properties are explored, and a comparison is provided. BSA, the only method providing unipolar spikes, was shown to be ineffective for step-wise signals, but it can follow smoothly changing signals if filter coefficients are scaled appropriately. Producing bipolar (positive and negative) spike trains, SW encoding was most effective for all types of signals as it proved to be robust and easy to optimize. Signal-to-noise ratio (SNR) can be recommended as the error metric for parameter optimization. Currently, only a visual check is available for final validation.

The curious case of the conflicting roles of hydrogen in global energy scenarios

Abstract: As energy systems transition from fossil-based to low-carbon, they face many challenges, particularly concerning energy security and flexibility. Hydrogen may help to overcome these challenges, with potential as a transport fuel, for heating, energy storage, conversion to electricity, and in industry. Despite these opportunities, hydrogen has historically had a limited role in influential global energy scenarios. Whilst more recent studies are beginning to include hydrogen, the role it plays in different scenarios is extremely inconsistent. In this perspective paper, reasons for this inconsistency are explored, considering the modelling approach behind the scenario, scenario design, and data assumptions. We argue that energy systems are becoming increasingly complex, and it is within these complexities that new technologies such as hydrogen emerge. Developing a global energy scenario that represents these complexities is challenging, and in this paper we provide recommendations to help ensure that emerging technologies such as hydrogen are appropriately represented. These recommendations include: using the right modelling tools, whilst knowing the limits of the model; including the right sectors and technologies; having an appropriate level of ambition; and making realistic data assumptions. Above all, transparency is essential, and global scenarios must do more to make available the modelling methods and data assumptions used.