Search

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91

Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.

Coronavirus disease 2019 (COVID-19) is an infectious disease characterised by symptoms that are like the common cold. The current pandemic situation in anticipation of a vaccine has posed serious threats to the health and economic sectors of countries worldwide. To overcome the quick transmission of the virus, the government of Australia has also taken drastic measures to prevent its spread. These policies include an international and interstate travel ban, social distancing rules, lockdown, shutdown of educational institutes and work-from-home policies. Such rules have affected people on both behavioural and psychological levels. This study aims to analyse the effect of COVID-19 on Australian citizens, and therefore, the changed behaviour of citizens concerning their mobility patterns, transport preferences and shopping methods under the pandemic have been studied. A detailed literature search was adopted for gathering data related to the study theme, along with real-time evidence of changes in the behaviour of people following the pandemic. The socioeconomic impact of the pandemic on social inequality and thereby the effect on the vulnerable people of the population are also studied. Authentic surveys and statistical data are consulted to figure out how the new lifestyle choices of people will linger in the post-pandemic era. It was found that people in Australia have adopted the work-from-home regime, and new habits suiting the nationwide restrictions have become routine for many people.

The vestibulo-ocular reflex (VOR) is the main vision-stabilising system during rapid head movements in humans. A visual-vestibular mismatch stimulus can be used to train or adapt the VOR response because it induces a retinal image slip error signal that drives VOR motor learning. The training context has been shown to affect VOR adaptation. We sought to determine whether active (self-generated) versus passive (externally imposed) head rotation vestibular training would differentially affect adaptation and short-term retention of the active and passive VOR responses. Ten subjects were tested, each over six separate 1.5-h sessions. We compared active versus passive head impulse (transient, rapid head rotations with peak velocity ~ 150 °/s) VOR adaptation training lasting 15 min with the VOR gain challenged to increment, starting at unity, by 0.1 every 90 s towards one side only (this adapting side was randomised to be either left or right). The VOR response was tested/measured in darkness at 10-min intervals, 20-min intervals, and two single 60-min interval sessions for 1 h post-training. The training was active or passive for the 10- and 20-min interval sessions, but only active for the two single 60-min interval sessions. The mean VOR response increase due to training was ~ 10 % towards the adapting side versus ~2 % towards the non-adapting side. There was no difference in VOR adaptation and retention between active and passive VOR training. The only factor to affect retention was exposure to a de-adaptation stimulus. These data suggest that active VOR adaptation training can be used to optimally adapt the passive VOR and that adaptation is completely retained over 1 h as long as there is no visual feedback signal driving de-adaptation.

The vestibulo-ocular reflex (VOR) is the main retinal image stabilising mechanism during rapid head movement. When the VOR does not stabilise the world or target image on the retina, retinal image slip occurs generating an error signal that drives the VOR response to increase or decrease until image slip is minimised, i.e. VOR adaptation occurs. Visual target contrast affects the human smooth pursuit and optokinetic reflex responses. We sought to determine if contrast also affected VOR adaptation. We tested 12 normal subjects, each over 16 separate sessions. For sessions 1–14, the ambient light level (lx) during adaptation training was as follows: dark, 0.1, 0.2, 0.3, 0.5, 0.7, 1, 2, 8, 16, 32, 64, 128 and 255 lx (light level for a typical room). For sessions 15–16, the laser target power (related to brightness) was halved with ambient light at 0 and 0.1 lx. The adaptation training lasted 15 min and consisted of left/right active head impulses. The VOR gain was challenged to increment, starting at unity, by 0.1 every 90 s for rotations to the designated adapting side and fixed at unity towards the non-adapting side. We measured active and passive VOR gains before and after adaptation training. We found that for both the active and passive VOR, there was a significant increase in gain only towards the adapting side due to training at contrast level 1.5 k and above (2 lx and below). At contrast level 261 and below (16 lx and above), adaptation training resulted in no difference between adapting and non-adapting side gains. Our modelling suggests that a contrast threshold of ~ 1000, which is 60 times higher than that provided by typical room lighting, must be surpassed for robust active and passive VOR adaptation. Our findings suggest contrast is an important factor for adaptation, which has implication for rehabilitation programs.