Multiple Sclerosis Research Australia

Virtual Reality (VR) supported by Artificial Intelligence (AI) offers immersive, data-driven opportunities to improve safety training in high-risk engineering environments. Yet, large-scale corporate deployment remains sporadic. This study applies a three-round Delphi process with 15 academic and industry experts from the University of New South Wales (UNSW), China Railway 25th Bureau, and Zhongyifeng Construction (Suzhou 2nd Bureau) to prioritise the obstacles that prevent VR adoption. Eighteen barriers extracted from Round 1 were rated in Rounds 2 3. Consensus, assessed with Kendall’s coefficient of concordance (W), rose from 0.32 to 0.52, indicating strong convergence. High initial cost, inadequate IT infrastructure, limited management support, scarcity of domain-specific VR content, and lack of integration standards emerged as the top five critical barriers. Secondary constraints included workforce resistance, cybersickness, trainer preparedness, and technical compatibility issues. The paper offers evidence-based recommendations, including financial modelling, staged infrastructure upgrades, executive engagement strategies, content sharing consortia, and standard setting initiatives to accelerate safe, scalable deployment of AI-enhanced VR training in engineering organisations.

Abstract In India, surface water reservoirs are widely harnessed to meet a variety of needs, including domestic, agricultural, and industrial applications. The quality of these resources, particularly in coal mine regions, undergoes substantial deterioration due to the discharge of various wastes (industrial, municipal, and runoff water) and coal ash deposition. The Korba basin, shaped by mining activities, shallow groundwater levels, and the flow of the expansive Hasedo River, features numerous ponds, pit lakes, and canals. A significant health concern in this area is the prevalence of fluorosis disease among the local population. The main aim of this study was to evaluate the water quality of reservoirs, including ponds, pit lakes, canals, and rivers, with a focus on identifying contaminant levels and tracing the sources of chemical species such as carbonate and organic carbons, anions, and metals. During the period from 2012 to 2017, elevated carbon contents (varying from 1010 to 4420 mg·L− 1) markedly contributed to increased total dissolved solids (TDS), with values ranging between 2865 and 5540 mg·L− 1. fluoride (F−), aluminum (Al), iron (Fe), and manganese (Mn) concentrations in all surface water bodies exhibited variations within the ranges of 1.8–4.4, 0.42–1.91, 0.3–1.22, and 1.0–2.1 mg·L− 1, respectively. This study delves into the temporal and seasonal variations, water quality indices, and toxicities associated with the identified contaminants.

ABSTRACT Physical activity (PA) tends to decline during late childhood and adolescence. In Australia, this decline has been shown to particularly occur non-organized PA (e.g., active play, informal sport). This study aimed to explore predictors of non-organized PA at 13y; and decline between 11y and 13y. Data were sourced from the Longitudinal Study of Australian Children. Non-organized PA was measured using time-use diaries. Analyses included Cragg hurdle and binary logistic regressions. The likelihood of non-organized PA at 13y was predicted by PA enjoyment (OR = 1.36, p = 0.007), number of siblings (OR = 1.11, p < 0.001), sex (females) (OR = 0.66, p < 0.001) and home computer use at 11y (OR = 0.98, p = 0.002) (n = 3,193). Females also engaged in less non-organized PA at 13y (β = −77.77, p = 0.048) (n = 3,193). Significant predictors of decline in non-organized PA were socioeconomic position (OR = 0.92, p = 0.047) and sugary drink consumption at 11y (OR = 1.06, p = 0.033) (n = 3,222). Strategies to promote non-organized PA may include targeting females and those of lower socioeconomic position, and focusing on types of PA that may replace the perceived benefits of computer use.

ABSTRACTThe goals of any major business transformation programme in an official statistical agency often include improving data collection efficiency, data processing methodologies and data quality. However, the achievement of such improvements may have transitional statistical impacts that could be misinterpreted as real-world changes if they are not measured and handled appropriately.This paper describes a development work that sought to explore the design and analysis of a times-series experiment that measured the statistical impacts that sometimes occur during survey redesigns. The Labour Force Survey (LFS) of the Australian Bureau of Statistics (ABS) was used as a case study. In the present study:A large-scale field experiment was designed and conducted that allowed the outgoing and the incoming surveys to run in parallel for some periods to measure the impacts of any changes to the survey process; andThe precision of the impact measurement was continuously improved while the new survey design was being implemented.The state space modelling (SSM) technique was adopted as the main approach, as it provides an efficient impact measurement. This approach enabled sampling error structure to be incorporated in the time-series intervention analysis. The approach was also able to be extended to take advantage of the availability of other related data sources (e.g., the data obtained from the parallel data collection process) to improve the efficiency and accuracy of the impact measurement. As stated above, the LFS was used as a case study; however, the models and methods developed in this study could be extended to other surveys.