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Concrete using polypropylene fibers from COVID-19 single-use face masks
No full textThe COVID-19 pandemic has resulted in significant pollution due to the rapid increase in the consumption and disposal of face masks. These nonbiodegradable mask wastes pose a considerable threat to the environment, ecology, and public health. Converting mask wastes into valuable construction materials for application in concrete is a solution to address the crisis of plastic pollution related to the pandemic. This chapter reviews in detail the currently available research published on the effect of mask wastes on the physical, mechanical, and durability properties of concrete. It was found that the dimension, dosage, and form of the mask wastes have significant effects on the properties of concrete. Based on the knowledge summarized in this chapter, it is reasonable to suggest that mask wastes, in the form of fibers and strips, could be utilized as a potentially sustainable and valuable building material in concrete.</p
Soil carbon in the world’s tidal marshes
No full textTidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3710 unique locations, landscape-level environmental drivers and a global tidal marsh extent map to produce a global, spatially explicit map of SOC storage in tidal marshes at 30 m resolution. Here we show the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes’ 0–30 and 30–100 cm soil layers are estimated at 83.1 Mg C ha−1 (average predicted error 44.8 Mg C ha−1) and 185.3 Mg C ha−1 (average predicted error 105.7 Mg C ha−1), respectively.</p
The determinants of corporate cost of debt during a financial crisis
No full textPanel data from publicly listed US industrial firms is used to investigate how firm-specific cost of debt (COD) determinants impact COD at different quantiles during a financial crisis. Six COD determinants: firm size, firm age, profitability, leverage, liquidity, and firm value, and advanced estimators: robust and bootstrapped fixed effects, bias-corrected least square dummy variable (LSDVC), and quantile regression, are employed within the context of pecking-order theory. The results show that firm size and leverage negatively impact COD, while liquidity positively impacts it when COD is high (90% quantile). The degree of profitability only confirms the pecking order theory when COD is extremely low (10% quantile) and contrasts with the theory for the 25% and above COD quantiles during the Global Financial Crisis (GFC). These findings confirm that the practicalities of access to finance matter during a financial crisis for corporate financing decisions.</p
Supported decision‐making interventions in mental healthcare: A systematic review of current evidence and implementation barriers
No full textBACKGROUND: There is a growing momentum around the world to foster greater opportunities for the involvement of mental health service users in their care and treatment planning. In-principle support for this aim is widespread across mental healthcare professionals. Yet, progress in mental health services towards this objective has lagged in practice.
OBJECTIVES: We conducted a systematic review of quantitative, qualitative and mixed-method research on interventions to improve opportunities for the involvement of mental healthcare service users in treatment planning, to understand the current research evidence and the barriers to implementation.
METHODS: Seven databases were searched and 5137 articles were screened. Articles were included if they reported on an intervention for adult service users, were published between 2008 and October 2023 and were in English. Evidence in the 140 included articles was synthesised according to the JBI guidance on Mixed Methods Systematic Reviews.
RESULTS: Research in this field remains exploratory in nature, with a wide range of interventions investigated to date but little experimental replication. Overarching barriers to shared and supported decision-making in mental health treatment planning were (1) Organisational (resource limitations, culture barriers, risk management priorities and structure); (2) Process (lack of knowledge, time constraints, health-related concerns, problems completing and using plans); and (3) Relationship barriers (fear and distrust for both service users and clinicians).
CONCLUSIONS: On the basis of the barriers identified, recommendations are made to enable the implementation of new policies and programs, the designing of new tools and for clinicians seeking to practice shared and supported decision-making in the healthcare they offer.
PATIENT OR PUBLIC CONTRIBUTION: This systematic review has been guided at all stages by a researcher with experience of mental health service use, who does not wish to be identified at this point in time. The findings may inform organisations, researchers and practitioners on implementing supported decision-making, for the greater involvement of people with mental ill health in their healthcare.</p
Co-evolutionary dynamics and heterogeneity in corporate social responsibility: A case study on multinational corporation subsidiaries
No full textThis study aims to understand the heterogeneity of corporate social responsibility (CSR) approaches across subsidiaries of the same multinational corporations (MNCs) and determine the co-evolutionary dynamics that enhance the global cohesiveness of MNCs' CSR strategies and performance. The study draws on co-evolutionary theory and adopts an explanatory research design based on embedded multiple case study methodology. Semi-structured interviews with top management and employees responsible for CSR or related areas were conducted, and secondary data collection was done for triangulation and validation. The findings suggest that MNCs experience heterogeneity in their CSR, particularly in the implementation approaches, philanthropic and environmental CSR dimensions, and that MNCs with significant heterogeneity at the strategic level have less cohesive strategic CSR practices. The study also found that MNCs that encourage and facilitate active collaboration between subsidiaries have more cohesive and strategic CSR, and each MNC's CSR is driven by different co-evolutionary dynamics. The study contributes to the expansion of co-evolutionary theory to the study of CSR and offers a dynamic CSR co-evolution model explaining MNC CSR heterogeneity in real-world situations, along with potential levers to achieve more globally cohesive strategic CSR.</p
Enhancement of impact resistance of alkali‐activated slag concrete through biochar supplementation
No full textBiochar is a well‐known sustainable and effective additive used in mortar/concrete to improve its mechanical properties. However, its potential to improve the impact resistance of concrete is still unexplored. This paper investigates biochar's effectiveness in improving the strength and impact performance of alkali‐activated slag concrete (AASC). Five AASC samples with 0%, 2%, 4%, 6%, and 8% rice husk biochar (RB) were employed in an experimental program. The strength and the impact resistance were tested, and the latter was assessed over a drop‐weight test conforming to the ACI Committee 544 guidelines. The crack propagation of the impact‐tested samples was examined using micro‐CT images. The results showed that adding RB up to 6% improved, notably the 28‐day compressive strength of AASC. At 6% RB, the strength enhancement was 44.6%, whereas no additional gain was observed at the 8% RB blend. More importantly, except for the 8% RB sample, the impact resistance was considerably augmented with the RB level increment. The increment in the impact number at the first crack and the failure in the 6% RB sample were as high as 185% and 180%, respectively. The reduction in the solution/binder ratio of the mix with the addition of biochar and the internal curing effect of biochar were deemed to be responsible for these improvements. However, possibly due to biochar's brittle characteristics, the increase in RB dosage from 6% to 8% reduced the impact resistance drastically.</p
Investigation of AI-based Image, Video, and Voice Analysis to Assess Clinical Symptoms
No full textThe rapid advancement of artificial intelligence (AI) is transforming healthcare, offering the potential to enhance diagnostic accuracy, streamline clinical workflows, and personalize treatment plans. However, the comprehensive application and integration of AI technologies in healthcare face challenges, particularly in enhancing non-invasive screening methods. This thesis investigates the application of AI-assisted tools across three key modalities—video, voice, and image—to improve clinical decision-making and patient outcomes through non-invasive methods. Focusing on neurological conditions such as Parkinson's disease, stroke, and Amyotrophic Lateral Sclerosis (ALS), as well as ophthalmology and wound care, the research is guided by three main questions. While the first two research questions leverage video and voice analysis to detect subtle neurological symptoms in Parkinson’s disease, stroke, and ALS—addressing key challenges of non-invasive diagnostics such as subjective clinical assessments, delayed timeliness, and limited patient monitoring—the third question aims to enhance AI non-invasive methods in ophthalmology and wound care by overcoming data scarcity and advancing image translation techniques. The three questions are: 1. How can AI-assisted facial expression analysis enhance the detection and understanding of neurological conditions such as Parkinson's disease, stroke, and ALS? The study demonstrated that AI-assisted facial expression models could detect subtle symptoms of these disorders, achieving 83% accuracy in identifying hypomimia associated with Parkinson's disease. Similar techniques effectively detected facial weaknesses in Post-Stroke and ALS patients, highlighting the value of AI-driven video analysis for non-invasive assessments. This approach offers a groundbreaking non-invasive way to identify subtle symptoms that might otherwise go unnoticed. Additionally, an AI-driven stroke app can assist in screening cases with just a smile in emergency departments, highlighting the potential of video analysis for rapid and non-invasive assessments. 2. In what ways can AI-based voice analysis tools improve the remote assessment of Parkinson's disease severity and support ongoing monitoring? This work integrate large language models (LLMs) into a chatbot for voice assessment, enabling scalable, remote Parkinson’s disease monitoring. This innovation allowed AI-based voice analysis to accurately categorize symptoms with a classification accuracy of 72%, using vocal biomarkers from phoneme tasks. The chatbot-guided assessments made early detection and continuous care more accessible, particularly in underserved regions. 3. How do AI-powered synthetic imaging techniques contribute to the detection and diagnosis of medical conditions like age-related macular degeneration and venous leg ulcers? In imaging, deep learning models such as StyleGAN-2 achieved 85% accuracy in detecting age-related macular degeneration, outperforming human experts. Additionally, AI-generated thermal imaging achieved promising results for chronic wound assessment with an SSIM score of 0.84, although further validation is necessary. In conclusion, this thesis underscores the transformative potential of AI in healthcare, providing non-invasive solutions that improve early detection, facilitate remote monitoring, and enhance diagnostic precision. Future efforts must address demographic biases, ensure ethical data use, and work with regulatory bodies to integrate these tools into clinical practice, advancing towards more accessible and effective healthcare solutions.</p
AI Venn Outcome Context Method (OCM) Framework
No full textThe AI Venn Outcome Context Method (OCM) Framework has been developed for use in educator development workshops to support educators in designing learning for generative artificial intelligence. The resource provides a full explanation of the OCM Framework’s purpose, its relationship to supporting the demonstration of outcomes, how educators and discipline experts can use it, and includes easy to understand analogies and examples. The handout has been designed to be used with RMIT’s AI Venn Outcome Context Method (OCM) Handout, RMIT’s Artificial Intelligence Assurance of Learning Typology, RMIT's Artificial Intelligence Assurance of Learning Typology Extended Guide and Curriculum Mapping GenAI Handout. </p
Four Revisions toward an Embodied Landscape Architecture Practice
No full textThis paper suggests a revision of four landscape architectural concepts as a way to make room for working with the generative force of embodied citizen action in practice. This embodied landscape architecture practice recognises the performative capacity and generative force of citizens’ action as a primary means of enacting environmental stewardship and care. The Four Revisions Toward an Embodied Landscape Architecture Practice discussed in this paper emerge as the characteristics of embodied action challenge established landscape architectural concepts and traditional disciplinary knowledge. More specifically, accommodating citizen action within a practice means moving towards a way of working that is not grounded in geographical space but instead occurs in a relational paradigm whereby citizen’s actions, rather than those of the landscape architect, change landscapes. The four proposed revisions include addressing discursive sites, embodied action, facilitation, and embodying tactical urban opportunities. While these ideas are not new, their use in landscape architecture practice helps foreground embodied action in ways that can be useful catalysts for citizens to enact essential practices of care for landscapes.</p
Upscaling marine forest restoration: challenges, solutions and recommendations from the Green Gravel Action Group
No full textIntroductionTo counteract the rapid loss of marine forests globally and meet international commitments of the UN Decade on Ecosystem Restoration and the Convention on Biological Diversity ‘30 by 30’ targets, there is an urgent need to enhance our capacity for macroalgal restoration. The Green Gravel Action Group (GGAG) is a global network of 67 members that are working on the restoration of a diverse range of macroalgal forests and it aims to facilitate knowledge exchange to fast-track innovation and implementation of outplanting approaches worldwide.MethodsHere, we overview 25 projects conducted by members of the group that are focused on testing and developing techniques for macroalgal restoration. Based on these projects, we summarise the major challenges associated with scaling up the area of marine forests restored.ResultsWe identify several critical challenges that currently impede more widespread rollout of effective large-scale macroalgal restoration worldwide: 1) funding and capacity limitations, 2) difficulties arising from conditions at restoration sites, 3) technical barriers, and 4) challenges at the restoration-policy interface.DiscussionDespite these challenges, there has been substantial progress, with an increasing number of efforts, community engagement and momentum towards scaling up activities in recent years. Drawing on the collective expertise of the GGAG, we outline key recommendations for the scaling up of restoration efforts to match the goals of international commitments. These include the establishment of novel pathways to fund macroalgal restoration activities, building skills and capacity, harnessing emerging innovations in mobile hatchery and seeding technologies, and the development of the scientific and governance frameworks necessary to implement and monitor macroalgal restoration projects at scale.</p