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Robotic thoracic surgery is a minimally invasive technique that allows the surgeon to perform delicate, accurate surgical manoeuvres within the chest cavity without rib spreading. Previous studies have suggested potential benefits of the robotic platform in nodal upstaging due to its versatility, seven degrees of freedom of movement, and superior vision. However, there is currently a paucity of robust clinical data from Australia. This study aimed to assess the perioperative safety and oncological efficacy of anatomical pulmonary resections performed using the robotic platform. Endpoints included mortality and major morbidity outcomes according to Clavien–Dindo classification and rate of pathological nodal upstaging compared with preoperative imaging using positron emission tomography. A single-surgeon retrospective analysis was performed using data collected from two institutions from July 2021 to May 2022, after ethics committee approval. Consecutive patients who underwent anatomical robotic resections were included in the study, with subsequent analysis of patients who had confirmed primary lung cancer. A total of 52 patients underwent robotic anatomical pulmonary resection during the study period. Safety was demonstrated by 0% mortality and a 9.6% major complication rate, which was related to chest tube insertion for prolonged air leak or intensive care unit monitoring during treatment of atrial arrhythmia. After excluding patients who did not have primary lung cancer, 48 patients remained for further analysis; pathological nodal upstaging was observed in nine (18.8%) of these patients. On multivariate analysis, the total number of lymph nodes harvested was found to be a statistically significant predictor of nodal upstaging. Complete microscopic resection (R0) was achieved in 100% of patients. This study represents the most extensive documentation of robotic thoracic procedures in Australia in the existing literature. It demonstrated a satisfactory safety profile with a relatively high rate of nodal upstaging, possibly reflecting the ability of the robotic instruments to perform comprehensive and complete nodal resection at the time of anatomical pulmonary resection. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of a social robot intervention on sleep and motor activity in nursing home residents living with dementia and chronic pain.Method: A pilot randomized controlled trial was conducted with 41 residents from three Australian nursing homes. People living with dementia and chronic pain were randomized into either a 30-minute daily social robot (PARO) condition or a usual care condition for six weeks. Sleep and motor activity were assessed by actigraphy at four-time points: week 0 at baseline, week one, week six, and after the intervention. Data were reduced into daytime (8:00am - 7:59pm) and night-time (8:00pm - 7:59am) summaries. Change scores for each time point compared with baseline were computed for data analysis and the generalized estimating equation model with imbalanced baseline values added as covariates were performed.Results: At week one, residents in the PARO group had a greater increase in the night sleep period (1.81, 95 % CI: 0.22-3.84, p = 0.030, Cohen's d = 0.570). At week six, residents in the PARO group showed a greater increase in daytime wakefulness (1.91, 95 % CI: 0.09-3.73, p = 0.042, Cohen's d = 0.655) and a greater reduction in daytime sleep (-1.35, 95 % CI: -2.65 to -0.05, p = 0.040, Cohen's d = 0.664). No significant results were found for motor activity.Conclusion: PARO could improve sleep patterns for nursing home residents living with dementia and chronic pain, but the effect of PARO on motor activity needs further research. Australian New Zealand Clinical Trials Registry (ACTRN12618000082202). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas--or concepts--in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies--such as big data, automation and robotics--is the central community discussion topic. [ABSTRACT FROM AUTHOR]

To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral and psychological symptoms of people with dementia and chronic pain. A parallel pilot randomized controlled trial conducted between January 2018 and January 2019. Three long-term care facilities in Australia. Forty-three participants aged ≥65 years living with dementia and chronic pain. Participants were randomized to the PARO group (individual, nonfacilitated, 30-minute sessions, 5 days per week for 6 weeks) or a usual care group using a computer-generated random number. The primary outcome was researcher-rated observational pain behaviors before and after each session. Secondary outcomes were staff-rated pain level, agitation, depression, and anxiety measured at baseline and the end of week 6. Medications regularly prescribed and as needed were recorded weekly. Analyses followed intention-to-treat, using the generalized estimating equation model. Australian New Zealand Clinical Trials Registry (ACTRN12618000082202). Participants in the PARO group had a significantly lowered level of observed pain [−0.514, 95% confidence interval (CI) −0.774 to −0.254, P <.001] and used fewer pro re nata medications (−1.175, 95% CI −2.205 to −0.145, P =.025) than those in usual care after controlling for age, sex, cognitive function and medications. There were no significant differences in staff-rated pain, agitation, anxiety, and depression, nor regularly scheduled medications between intervention and control group. PARO shows promise in reducing pain and medications for individuals with dementia and chronic pain in long-term care facilities. This intervention might be incorporated into daily practice as an alternative to manage pain in people with dementia. Larger randomized controlled trials with longer time frames are needed to identify further and test the use of PARO in long-term care settings. [ABSTRACT FROM AUTHOR]

This paper describes insights gained from a decade of autonomous marine systems development at the University of Sydney’s Australian Centre for Marine Robotics. Over the course of this time, we have deployed numerous vehicles and imaging platforms in support of applications in engineering science, marine ecology, archaeology and geoscience. We have operated an Australia-wide benthic observing program designed to deliver precisely navigated, repeat imagery of the seafloor. This initiative makes extensive use of Autonomous Underwater Vehicles (AUVs) to collect high-resolution stereo imagery, multibeam sonar and water column measurements on an annual or semi-annual basis at sites around Australia, spanning the full latitudinal range of the continent from tropical reefs in the north to temperate regions in the south. We have also contributed to expeditions to document coral bleaching, cyclone recovery, submerged neolithic settlement sites, ancient shipwrecks, methane seeps and deepwater hydrothermal vents. We briefly consider how automated tools for working with this imagery have facilitated the resulting science outcomes. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel approach for automatic segmentation and object detection of tree crowns in airborne images captured from a low-flying Unmanned Aerial Vehicle (UAV) in ecology monitoring applications. Cost effective monitoring in these applications necessitates the use of vision-band-only imaging on the UAV platform; the reduction in spectral resolution (compared to multi- or hyper-spectral imaging) is balanced by the high spatial resolution available (20cm/pixel) from the low-flying UAV, when compared to existing satellite or manned-aerial survey data. Our approach to object detection thus uses both geometry and appearance information (through the use of tree shape and shadow information) in addition to spectral information to help accurately distinguish tree crowns within our application. A predictive geometric template for tree detection is constructed using on-board UAV navigation data, sun lighting information and information about the geometry of the target crown. A two-stage detection algorithm is then used to segment tree crowns based on spectral (colour) information convolved with information from the predictive template. Results of our approach are presented using airborne image data collected from a fixed-wing UAV during a weed monitoring and mapping mission over farmland in West Queensland, Australia. [Copyright &y& Elsevier]

Objectives To examine the within-trial costs and cost-effectiveness of using PARO, compared with a plush toy and usual care, for reducing agitation and medication use in people with dementia in long-term care. Design An economic evaluation, nested within a cluster–randomized controlled trial. Setting Twenty-eight facilities in South-East Queensland, Australia. Participants A total of 415 residents, all aged 60 years or older, with documented diagnoses of dementia. Intervention Facilities were randomized to 1 of 3 groups: PARO (individual, nonfacilitated 15-minute sessions, 3 afternoons per week for 10 weeks); plush toy (as per PARO but with artificial intelligence disabled); and usual care. Measurements The incremental cost per Cohen-Mansfield Agitation Inventory–Short Form (CMAI-SF) point averted from a provider's perspective. Australian New Zealand Clinical Trials Registry (BLINDED FOR REVIEW). Results For the within-trial costs, the PARO group was $50.47 more expensive per resident compared with usual care, whereas the plush toy group was $37.26 more expensive than usual care. There were no statistically significant between-group differences in agitation levels after the 10-week intervention. The point estimates of the incremental cost-effectiveness ratios were $13.01 for PARO and $12.85 for plush toy per CMAI-SF point averted relative to usual care. Conclusion The plush toy used in this study offered marginally greater value for money than PARO in improving agitation. However, these costs are much lower than values estimated for psychosocial group activities and sensory interventions, suggesting that both a plush toy and the PARO are cost-effective psychosocial treatment options for agitation. [ABSTRACT FROM AUTHOR]