The global demand for tele-medicine services has increased rapidly, prompting clinics to adopt virtual care solutions. This shift towards remote medical consultations has become especially crucial for minimizing direct patient contact, particularly in urgent medical situations where receiving professional healthcare in a timely manner is essential, such as during episodes of stroke. Importantly, access to specialist care in regional centres (spokes) is often limited and relies on communication with major centres (hubs) for consultation and treatment support. The technical needs for tele-medicine are well defined, however the tools are often inadequate, requiring multiple parallel applications to be used at a time. For consultations, remote clinicians need to access to patient assessment notes, medical records and often imaging in order to provide treatment recommendations, oversee patient care and make transfer recommendations when the patient requires a higher level of care. The implementation of the hub and spoke tele-medicine model has been highly successful in supporting regional healthcare throughout the world, greatly increasing access to specialist care. In a typical regional centre (spokes), there is often no specialist staff, which is strongly associated with poorer clinical outcomes among patients living in regional areas. We have developed a single, scalable platform to support tele-medicine needs patients in the Australian context. The platform enables healthcare providers to rapidly connect, collaborate, and plan effective treatment pathways for patients. It supports real-time clinical assessment notes sharing, live patient tracking with integrated video conferencing and chat capabilities by facilitating real-time communication and coordination among medical professionals. The Mobile Tele-medicine Platform (MTP) offers patients and especially those in rural areas a greater chance of receiving the vital care from remote specialists - in particular during the episodes of stroke.

Bushfires are particularly prevalent in Australia. They pose significant economic and safety challenges. A key component in the mitigation of these challenges is the precise monitoring of burnt areas. In this paper we evaluate modern computer vision models for detecting burnt areas, especially in the context of noisy data, e.g. where there is a significant amount of clouds and smoke, which traditional methods do not adequately address. We design a bushfire data collection pipeline to establish a dataset covering the 2019 Australian Black Summer bushfire events. Several prominent computer vision models are then explored for burnt area detection including Convolutional Neural Network (CNN)-based models including U-Net [25], Mask R-CNN [10] and YOLOv8 [12], as well as transformer-based models including SAM [16] and SegFormer [31]. We identify that SegFormer-b0 achieves the best performance in the presence of noise such as clouds and smoke with an overall F1-score of 89.7%, IoU of 81.6%, MCC of 89.4% and AIC of 81.7%. This far exceeds traditionally adopted approaches for satellite image analysis dealing with noisy data.

Abstract Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.