62353 research outputs found
Sort by
The Assessment of Physiotherapy Practice is a robust measure of entry-level physiotherapy standards: Reliability and validity evidence from a large, representative sample
No full textThe Assessment of Physiotherapy Practice (APP) is a 20-item assessment instrument used to assess entry-level physiotherapy practice in Australia, New Zealand and other international locations. Initial APP reliability and validity evidence supported a unidimensional or single latent factor as the best representation of entry-level physiotherapy practice performance. However, there remains inconsistency in how the APP is interpreted and operationalised across Australian and New Zealand universities offering entry-level physiotherapy programs. In essence, the presumption that the psychometric integrity of the APP generalises across people, time, and contexts remains largely untested. This multi-site, archival replication study utilised APP assessment data from 8,979 clinical placement assessments, across 19 Australian and New Zealand universities, graduating entry-level physiotherapy students (n=1865) in 2019. Structural representation of APP scores were examined via confirmatory factor analysis and penalised structural equation models. Factor analyses indicated a 2-factor representation, with four items (1–4) for the professional dimension and 16 items (5–20) for the clinical dimension, is the best approximation of entry-level physiotherapy performance. Measurement invariance analyses supported the robustness of this 2-factor representation over time and across diverse practice areas in both penultimate and final years of study. The findings provide strong evidence for the psychometric integrity of the APP, and the 2-factor alternative interpretation and operationalisation is recommended. To meet entry-level standards students should be assessed as competent across both professional and clinical dimensions of physiotherapy practice
Annotating and Inferring Compositional Structures in Numeral Systems Across Languages
No full textNumeral systems across the world’s languages vary in fascinating ways, both regarding their synchronic structure and the diachronic processes that determined how they evolved in their current shape. For a proper comparison of numeral systems across different languages, however, it is important to code them in a standardized form that allows for the comparison of basic properties. Here, we present a simple but effective coding scheme for numeral annotation, along with a workflow that helps to code numeral systems in a computer-assisted manner, providing sample data for numerals from 1 to 40 in 25 typologically diverse languages. We perform a thorough analysis of the sample, focusing on the systematic comparison between the underlying and the surface morphological structure. We further experiment with automated models for morpheme segmentation, where we find allomorphy as the major reason for segmentation errors. Finally, we show that subword tokenization algorithms are not viable for discovering morphemes in low-resource scenarios
Decoding gene essentiality in Streptococcus suis using Tn-seq and genome-scale metabolic modeling
No full textHigh-throughput transposon mutagenesis methods, such as transposon sequencing, are powerful tools for genome-wide identification of essential and conditionally essential genes in bacterial pathogens. In a recent study, Y. Zhang, R.Gong, M.Liang, L.Zhang, et al. (Microbiol Spectr 13:e0279124, 2025, https://doi.org/10.1128/spectrum.02791-24) applied Himar1-based Tn-seq to Streptococcus suis, generating a relatively dense mutant library, in combination with genome-scale metabolic modeling, to identify 244 candidate essential genes. Aside from the well-characterized antibiotic targets, there are several novel candidates currently being explored for drug development against other critical pathogens, and a number of previously uncharacterized potential targets were uncovered in classical model organisms. The study highlights the value of high-throughput transposon mutagenesis and genome-scale metabolic modeling in a less-characterized zoonotic pathogen and contributes important genetic insights that may inform future antimicrobial strategies
Adult migrants urgent need for drowning prevention in Australia: water safety perceptions, attitudes, and behaviours
No full textDrowning is a global public health issue with over 300 000 people fatally drowning annually. Inequities exist, with 90% of drowning concentrated in low- and middle-income countries. Populations more vulnerable to drowning across all countries and contexts include children, males, migrants, and First Nations peoples. In Australia, migrants account for 34% of drowning fatalities, therefore are a priority population for reducing drowning. This study aimed to explore the underlying factors influencing the knowledge, attitudes and behaviour towards water safety and drowning risk among adult migrants in Australia. A qualitative exploratory study was undertaken guided by the theory of planned behaviour. Fifty-seven adults residing across Australia participated in a semi-structured interview (n = 15) or a focus group (n = 42). Data were coded and thematically analysed using a deductive approach, guided by Braun and Clarke's framework. Participants originated from 19 different countries, 54% were female. Residential time in Australia ranged from 18 months to 25+ years. Four key thematic areas were identified: ‘Water Safety experiences; Attitudes, beliefs and behaviour including cultural norms; Motivations and barriers to swimming; and Benefits of learning to swim’. This study highlighted that migrant's awareness, attitudes, and behaviour towards water safety were informed by factors linked to cultural norms and life experiences. Migrant adults perceived swimming as essential for inclusion in the Australian community. Identified risks included limited exposure to the water and a lack of safety knowledge and skills prior to migrating. These findings offer new insights to inform contemporary drowning prevention strategies that respond to changing population demographics, in Australia and globally
Exploring residents' views on festival impacts in Baguio: insights from a pilot study
No full textThe annual Flower Festival, known locally as Panagbenga, in Baguio City, Philippines, has blossomed over 29 years into a significant cultural event. Since its humble beginnings, the festival has evolved from a weekend affair to a month-long celebration, drawing in millions of visitors eager to experience its vibrant activities. While the economic boost from tourism is undeniable, concerns about sociocultural and environmental impacts have surfaced (Palangchao, 2016). Despite its significance, there is a surprising lack of research on the festival's broader impacts and limited information archives. Public opinions on social media and in newspapers are abundant but often lack cohesion, making it difficult for governments to develop effective policies, especially regarding the festival's socio-economic and commercial aspects. To address this gap, the authors are conducting a study to examine how residents and visitors perceive Panagbenga's economic, social, and environmental impacts. The study uses an online survey to measure residents’ perceptions of the festival's benefits and impacts and their expectations for the city’s sustainable future. Lastly, the study aims to provide empirical-based recommendations to organisers, local government, and destination marketing organisations on managing residents’ perceptions and expectations
Nothing Will Come of Nothing
No full textChaos. A strangled cry. Urgent entreaties to push, push. Sweating brow, panting breath, gritted teeth.
A squall breaks the air; the shock of newness.
‘It’s a girl!’
Three times. Three times, Saul gazes in wonder at a small, slimy creature, a screaming sack of fluid and flesh, and thinks, this is mine
Carbon Tracking for Carbon Transparency: A Promising Approach to Decarbonizing the Tropics
No full textThis chapter explores how blockchain-powered emission tracking technology can facilitate the adoption of low-carbon travel behaviors in tropical regions. Grounded in the combined Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), this chapter suggests that factors such as social and financial incentives, personal innovativeness, privacy concerns, perceived usefulness, ease of use, perceived behavioral control, attitudes, and social norms can influence the adoption of this technology. The core features of blockchain-based emission tracking apps, including safety, transparency, user-centricity, and adaptability, can play a crucial role in driving behavioral change. However, perceived complexity and concerns about data reliability may hinder adoption
Validation of the Internet Gaming Disorder Scale–Short‑Form and the Gaming Disorder Test in Singapore
No full textSMOTE-ENN resampling technique with Bayesian optimization for multi-class classification of dry bean varieties
No full textThe imbalanced classification problem poses a significant challenge in machine learning, often resulting in biased models and poor performance for minority classes. This study introduces an innovative hybrid resampling technique combining Synthetic Minority Oversampling Technique and Edited Nearest Neighbours (SMOTE- ENN), optimized using Bayesian Optimization, to address these limitations. The proposed framework integrates advanced feature pre-processing, hybrid resampling, and machine learning models to enhance classification performance. Using the publicly available dry bean dataset containing 16 geometric features of seven seed varieties, the methodology demonstrates remarkable improvements in predictive accuracy and class balance. Employing cutting-edge classifiers, the improved Light Gradient Boosting Machine (LBM) with Bayesian optimization achieved an unprecedented accuracy of 99.59 %, outperforming traditional approaches. Results reveal the potential of hybrid resampling techniques and Bayesian optimization in effectively capturing feature patterns, enhancing model diversity, and ensuring robust classification of imbalanced datasets. This research underscores the application of soft computing methods to real-world multi-class classification challenges, offering practical insights for similar domains
Genetic modeling for enhancing machining performance of high-volume fraction 45% SiCp/Al particle reinforcement metal matrix composite
No full textMetal matrix composites (MMCs) have gained great recognition in recent decades in a wide range of applications, including aerospace, automobiles, engine cylinders, and other sectors. MMCs possess excellent properties including being light in weight, high corrosion resistance, stiffness, and strength. However, they are categorized as difficult-to-cut materials where machining of these materials remains a challenging task. To improve the machining process quality and to avoid unnecessary experiments in a cost-effective manner, this article aims to develop an artificial intelligence model, using the genetic programming (GP) method to predict the cutting force, surface roughness, and tool life during the machining process of SiCp/Al at different cutting parameters including cutting speed, feed rate, and depth of cut. The developed genetic programming-based prediction model is designed and developed using MATLAB software. Meanwhile, the GP parameters including mean square error, root means square error, normalized mean square error, mean error, variation of error, correlation coefficient, and R-square are used for the validating of the proposed model. The GP model results are compared with our previous response surface methodology (RSM) model results that were employed to estimate the machining characteristics of the SiC particle-reinforced metal matrix composites (45% SiCp) with different cutting parameters. The GP results prove the higher efficiency with the prediction of the cutting force, surface roughness, and tool life, 43.07%, 37.82%, and 115.64%, respectively, compared with the previous RSM method