Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research

Planning Cultural Infrastructure for the City of Parramatta: A Research Report

Parramatta is dramatically changing, cultural practices are shifting, and the demands on cultural infrastructure are becoming increasingly complex. This report provides the necessary research and information to assist the City of Parramatta in determining its strategic priorities regarding the development of cultural infrastructure in the City. There are three components of the report - Audit, Benchmarking, and Needs Analysis. Part 1 of this report provides an audit of Parramatta’s cultural infrastructure, its patronage and future needs and trends. It provides a realistic assessment of the gaps in existing cultural infrastructure and facilities in Parramatta and of how the cultural needs of its current and future populations are met. Part 2 of this report provides key data regarding a selection of relevant national and international cities for comparison with Parramatta. Part 3 of this report describes the specific short-term and medium-term needs for investment and planning required to bring Parramatta’s cultural infrastructure profile to that of world-class regional cultural capital

Wearable, Integrated EEG-fNIRS Technologies: A Review.

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG-fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG-fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG-fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG-fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG-fNIRS systems

Towards Data-centric Graph Machine Learning: Review and Outlook

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an in-depth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data-the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities. We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance. A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view. Lastly, we pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications.Comment: 42 pages, 9 figure

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

The integrated effects of projected climate change on cotton growth and physiology.

Changes in atmospheric [CO2], temperature, precipitation and consequently atmospheric vapour pressure deficit (VPDa) under projected climate change scenarios present a challenge to crop production. This may have significant impacts on the physiology and yield of cotton and hence the profitability of the Australian cotton industry. Understanding the implications of integrated environmental impacts on cotton is critical for developing cotton systems that are resilient to stresses induced by climate change. Elevated [CO2] generally increases photosynthesis, reduces transpiration and improves leaf- and plant-level water use efficiency (WUE) of well-watered C3 plants , but this effect may be altered by rising temperature and reduced water availability. Cotton responds to changes in vapour pressure deficit (VPD), yet there has been little research on the leaf-level physiological response to altered VPD in field-grown cotton. In addition, a number of studies have investigated the effect of elevated [CO2] and temperature on physiology and growth of a range of cotton cultivars, yet there has not been a comparison between older and current varieties used in Australian production systems to identify if there has been inadvertent selection of beneficial traits for a changing climate. It is important to understand potential interactions as it is likely that multiple variables will be altered with future climatic changes. This thesis aims to investigate the integrated effects of projected climate change (warmer temperature, elevated [CO2], altered VPD and water stress) on physiology, growth and water use of cotton in high-yielding and high-input modern cotton systems in Australia. This will facilitate development of crop management strategies and improve cotton yield and water use efficiencies. This was achieved through a combination of glasshouse and field-based studies. Glasshouse experiments were conducted during 2010 and 2011 at the University of Western Sydney, Richmond, Australia. In these experiments, cotton was grown in sun-lit glasshouse bays in two [CO2] (CA: 400 µL L-1 and CE: 640 µL L-1) and two temperature (TA: 28/17 °C day/night and TE: 32/21 °C day/night) treatments. Field experiments were conducted during the 2011/12 and 2012/13 cotton seasons at the Australian Cotton Research Institute, Narrabri, Australia. The objective of glasshouse experiment I (Chapter 3) was to quantify the physiological and growth capacity of different cotton genotypes to current and future climate regimes. This experiment compared the early-season growth and physiology response of a past (DP 16) and a current (Sicot 71BRF) cotton cultivars grown in ambient and elevated atmospheric [CO2] and two temperature treatments under well-watered conditions. This study demonstrated that elevated [CO2] increased biomass and photosynthetic rates compared with the ambient [CO2] treatment, and that warmer air temperatures (32/21 oC, day/night) also increased plant biomass. Although limited by the comparison of only one older and one modern cultivar, this study indicated that current cultivars may have an advantage over older varieties in future, warmer environments due to smaller, more compact morphology of the modern cultivar. However, no interaction between elevated temperature (TE) and elevated [CO2] (CE) indicated that substantial potential may exist to increase breeding selection of cotton varieties that are responsive to both TE and CE