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A model, mixed-species urinary catheter biofilm derived from spinal cord injury patients.
Get PDFComplex multispecies biofilms consistently colonise urinary catheters, causing persistent asymptomatic bacteriuria and frequent symptomatic episodes in long-term catheterized individuals. Simple single-species models often fail to capture the complexities of mixed-species interactions and lab-based organisms may not reflect the genomic diversity found in real-world infections. Additionally, growth under flow conditions promotes robust, complex-biofilm structures. Therefore, to reflect the dynamics of in vivo infections, biofilm samples from clinical indwelling catheters of spinal cord injury (SCI) participants colonised by 5-10 species were used to establish polymicrobial macro-fluidic models, in catheters. This resulted in final models of 2-4 species biofilms. Metagenomic techniques using short-read Illumina and long-read Oxford Nanopore sequencing was used to assess the taxonomic composition, in vivo to in vitro biofilms diversity shifts, single nucleotide polymorphism (SNP) analysis and complete metagenome-assembled genomes (MAGs). In silico analysis revealed a high number of varied antibiotic resistance genes, virulence factors and biofilm associated factors present in these biofilms. Antibiotic resistance testing using our models highlighted the drastic differences between planktonic bacteria, single-species and multispecies biofilms. While single-species biofilms show considerably increased tolerance to antibiotics compared to their planktonic counterparts, this resistance is even greater in multispecies biofilms. Under flow conditions, all species in the multispecies biofilm showed increased resistance, unlike static conditions where only most did. Models developed and characterised in this study are expected to facilitate testing of effective strategies to prevent and treat catheter-associated infections by enabling more accurate analysis of biofilm inhibition, disruption and microbial interactions
Region-reaching control for multiple underactuated Euler-Lagrange systems based on energy-shaping framework
No full textThis paper presents a first attempt to address the distributed region-reaching control problems of multiple underactuated Euler-Lagrange systems (MUELSs) within an energy-shaping framework. An adaptive gain technique incorporating the region potential function is suitably introduced to design a distributed region-reaching control scheme by the best use of the passivity-based control (PBC) framework with damping injection. Additionally, the underactuated Euler-Lagrange (EL) dynamics are utilized to systematically integrate the energy of the system model and adaptive controller dynamics. Consequently, two independent region-reaching algorithms for MUELSs in the presence or absence of communication delays are analytically derived using appropriate Lyapunov functions. The overall region-reaching tracking cooperative performance, including stability, adaptability, and robustness, is validated through analysis and comparison of simulation examples
Enhancing the wellbeing of refugees living with advanced life-limiting illness in high-income resettlement countries: A systematic review.
Get PDFBACKGROUND: Refugees experience barriers to health care after resettlement and may have distinct palliative care needs. There is no systematic guidance to support person-centred palliative care services that are responsive to refugees' needs and preferences. AIM: To synthesis evidence regarding factors enhancing the wellbeing of refugees with advanced life-limiting illness, and their families, to inform palliative care in high-income resettlement countries. DESIGN: A systematic review of primary research studies. We applied a strength-based assets framework to the data extraction and synthesis and conducted a directed content analysis. DATA SOURCES: We searched nine electronic databases. RESULTS: Ten of the 1006 studies identified were included in the review: two qualitative, one quantitative and seven case studies. We identified 17 assets that enhanced refugees' wellbeing: resilience, religion, spirituality, sense of identity, belonging, community connections, health and death literacy, acculturation, family and social support, social capital, community structures, access to funeral information, access to services, palliative care service approaches, and workforce capacity. Resilience was linked to identity and belonging, connections within cultural and religious networks, social capital and creating meaningful funeral rituals in resettlement. Palliative care workforce capacity, death literacy, acculturation, refugees' grief experiences and willingness to discuss and plan for death, influenced refugees' attitudes to palliative care, communication with staff about treatment, prognosis and spiritual care, and care outcomes. CONCLUSIONS: Further research, co-designed with diverse refugee groups, is needed to inform palliative care service approaches, develop interventions to strengthen key assets and explore the nuanced role of social capital in end-of-life care
Unsupervised and few-shot segmentation in photovoltaic electroluminescence images for defect detection via a novel enhanced iterative autoencoder with simple implementation
No full textPhotovoltaic electroluminescence (PVEL) imaging captures material-level degradation in PV modules and offers high-resolution input for machine learning (ML) models to perform automated fault detection and health evaluation, reducing reliance on manual inspection. It is expected to have a simple and efficient defect detection ML model to achieve accurate segmentation for the fine-featured identification of defects in fabricated PV modules. This study proposes a novel enhanced iterative autoencoder (EI-AE), a completely new model that differs fundamentally from existing approaches which rely directly on classical ML models for defect detection. The proposed EI-AE, which for the first time introduces an iterative mechanism into the traditional AE framework, features a simple yet effective architecture and achieves accurate unsupervised pixel-level segmentation of all defect types using only normal PVEL images. In addition, few-shot learning can be realized by extending the unsupervised EI-AE with a small number of annotated masks, allowing more detailed functional defect detection while mitigating background interference. Theoretical proof demonstrates the benefits of the proposed EI-AE in improving defect detection compared to the conventional AE. Experimental results further validate its superiority, showing consistently better performance across multiple pixel-level metrics and outperforming both widely used unsupervised and few-shot baseline approaches
Everyday multiculturalism on Asian Australian food blogs
No full textWhile mainstream Australian media has consistently done a poor job at representing Asian Australians, food media is a notable exception where Asian Australians are often overrepresented. I explore food blogs as a form of lifestyle journalism that offers new insights into the Asian Australian experience and their status as culinary tastemakers in the hegemonically white Australian cultural context. In particular, I analyse the potential for food blogs by Asian Australians – which require bloggers to integrate narratives about their family lives, experiences and perspectives alongside the publication of their recipes and food photography – to provide narratives of “everyday multiculturalism” that centre the embodied experiences of non-white Australians. Drawing on two blogs, Recipe Tin Eats and Cook Republic, I examine the ways that these texts foster an understanding of nuanced Asian Australian experiences that go beyond what is palatable to ideologies of racial diversity typically represented in mainstream Australian media. In doing so, this study offers an understanding of how lifestyle journalism can challenge dominant state ideologies and contribute to the quality of representational diversity in the media landscape
Energy Management Under Uncertainty for Hybrid Microgrids: From Data to Decision-Making
Get PDFABSTRACT The increasing adoption of distributed energy resources has greatly amplified interest in microgrids, whose effective, reliable and resilient operation relies on the performance of their energy management systems (EMS). These systems ensure the economic operation and maintain load‐generation balance. A practical microgrid EMS (M‐EMS) incorporates data monitoring, variable forecasting, resource allocation and online supervision to optimise the system while interacting with electricity markets. However, in the inherently uncertain environment of both stand‐alone and grid‐connected microgrids, variations in key variables can significantly impact the decision‐making outcomes of M‐EMS. This review paper explores various sources of uncertainties within microgrids, including forecast errors and uncertainties arising from modelling approximations or monitoring inaccuracies. It also provides insights into handling these uncertainties by thoroughly reviewing the pertinent literature and exploring strategies such as analytical methods and AI‐based approaches for capturing them. The eventual goal of handling the uncertainties is to enhance system reliability and security through robust energy management solutions. Furthermore, practical measures to mitigate uncertainties are discussed. The practical implementation of these concepts is illustrated through a review of commercially available M‐EMS solutions and real‐world projects demonstrating their effectiveness in managing energy resources. This paper aims to help both researchers and industry professionals perceive the uncertainties within M‐EMS and how to handle them to achieve accurate, optimal solutions and avoid unexpected costs. Emerging trends and future research directions are also outlined
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