Caulfield Hospital

Current peer-to-peer (P2P) energy trading systems face important challenges in decentralised trading environments, particularly in managing participant trustworthiness, preventing dishonest behaviour, and mitigating transaction defaults. These limitations reduce transaction reliability and weaken trust among participants in community-scale energy trading markets. Although P2P energy trading enables communities to exchange locally generated renewable energy in smart environments, existing platforms often lack effective mechanisms to regulate participant behaviour and support reliable transactions. This paper proposes RepuTrade, a blockchain-based P2P energy trading platform tailored for community-scale microgrids. The proposed framework integrates a reputation-based consensus mechanism and a dynamic collateral management scheme that is directly linked to participant reputations such that trading reliability can be strengthened through behavioural incentives. In addition, a reputation-driven matching algorithm preferentially pairs highly reputable participants to improve market stability and trust. Simulation-based evaluation, involving 200 users across 8 trading rounds, shows that the RepuTrade framework consistently achieves higher trade success rates (92–99% compared to 83–95% in the baseline) and reduces defaults by more than 40% (27–44 vs. 55–72 per run). The results further reveal a strong negative correlation between user reputation and default probability, indicating that higher reputation is associated with a lower likelihood of dishonest behaviour. Overall, under the simulated settings considered in this study, the proposed framework improves transaction reliability and execution efficiency by reducing failed trades and lowering consensus validation latency. These findings contribute to the design of trust-aware decentralised energy trading mechanisms and provide simulation-based insights for developing more reliable and transparent community-scale renewable energy markets.

Panoramic video generation has attracted growing attention due to its applications in virtual reality and immersive media. However, existing methods lack explicit motion control and struggle to generate scenes with large and complex motions. We propose PanFlow a novel approach that exploits the spherical nature of panoramas to decouple the highly dynamic camera rotation from the input optical flow condition, enabling more precise control over large and dynamic motions. We further introduce a spherical noise warping strategy to promote loop consistency in motion across panorama boundaries. To support effective training, we curate a large-scale, motion-rich panoramic video dataset with frame-level pose and flow annotations. We also showcase the effectiveness of our method in various applications, including motion transfer and video editing. Extensive experiments demonstrate that PanFlow significantly outperforms prior methods in motion fidelity, visual quality, and temporal coherence.

The rapid growth of lithium extraction from spodumene ores has resulted in the large-scale accumulation of delithiated β -spodumene (D β S), an emerging industrial by-product with increasing environmental pressure. This review critically synthesizes the formation mechanisms, physicochemical properties, activation strategies, and cementitious reutilization potential of D β S, with emphasis on its intrinsic reactivity and performance relative to conventional supplementary cementitious materials (SCMs) Lithium extraction routes significantly modify the oxide composition, phase assemblage, and amorphous structure of D β S, resulting in high sulfate content, low CaO availability, and heterogeneous aluminosilicate networks that govern its hydration behavior. Quantitative R3 results position D β S as a moderate-reactivity SCM (≈440–450 J/g at 168 h), exceeding fly ash, comparable to calcined clay, and below metakaolin and GGBFS, with a dissolution-controlled, slow but sustained pozzolanic response. Activation strategies, including mechanical and chemical approaches, effectively enhance reaction degree and geopolymerization efficiency. In blended systems, DβS contributes predominantly to medium- and later-age strength development rather than early performance. Key limitations restricting large-scale implementation, including early-age reactivity, sulfate-related effects, and compatibility with conventional cement systems, are identified. Finally, future research directions are proposed to facilitate the integration of D β S into low-carbon cementitious systems and circular construction practices. This review establishes D β S as a viable secondary aluminosilicate resource and provides a mechanistic framework to guide its sustainable application in cement and concrete composites.

A holistic and collaborative approach is urgently needed to decarbonise the building sector and achieve Net Zero Carbon (NZC) targets in alignment with the Paris Agreement. What factors enable or hinder sustainable building (SB) from becoming mainstream practice? In response, this study conducted a systematic literature review and meta-analysis, which included a content analysis and influencing factor dynamics analysis of 49 high-quality articles. 60 barriers and 69 drivers of SB were identified and categorized them into seven groups informed by prior studies and the “Race to Zero Built Environment System Map”: Government related, Social related, Financial related, Technical related, Technology related, Knowledge related, and Environmental related. Based on the mean citation index, the three most significant barrier categories limiting SB include Financial/economy, Social/societal/cultural and Knowledge/training/research. The most significant driver categories enabling SB were identified as Knowledge/training/research, Social/societal/cultural and Technical/professional/market. Temporal analysis presented the evolution of the barrier and drivers of SB over the years. Pareto analysis highlighted the most significant barriers and drivers within each category. Notably, this study used statistical analyses to explore and map associations between SB barriers and drivers, as well as the built environment stakeholders, socio economic statuses, and geographic locations. Furthermore, this study highlighted potential theoretical, practical and policy related intervention points and implications for mitigation measures. This in-depth, holistic and integrated understanding of barriers and drivers of SB serves as an informative tool to the research community and future policy development. This study uniquely integrates Pareto analysis, temporal mapping, with stakeholder and contextual influential factor association analysis to prioritize and relationally map the dynamics of SB barriers and drivers. • 60 barriers and 69 drivers of sustainable building were identified. • Barriers and drivers of sustainable building were grouped into seven categories. • Pareto analysis revealed the most significant barriers and drivers. • Links with stakeholders, socio-economies and geographic locations were revealed.

Prefabricated construction has emerged as a promising solution to the longstanding inefficiencies of the construction sector, offering benefits such as reduced construction time, enhanced safety, and improved environmental outcomes. Existing literature highlights significant advantages of prefabrication, including shortened schedules, cost savings, and sustainability improvements; however, it also acknowledges recurring challenges, notably schedule delays due to upstream and downstream risks. Despite the growing body of research, there remains a lack of a unified framework that systematically quantifies the time-related risks associated with varying levels of prefabrication. To address this gap, this study develops the Time–Risk Impact Comparison of Increasing Prefabrication (TRICIP) framework, offering a probabilistic approach to assess the evolving complexity and time risks in hybrid construction projects. Using case studies, including the Fenner Hall project in Australia, the research integrates Program Evaluation and Review Technique (PERT) analysis with a comprehensive risk factor matrix, quantifying how upstream, lead-time, logistical, and site-specific risks evolve with increasing prefabrication. The results reveal that while prefabrication accelerates project delivery, it substantially heightens complexity, particularly due to increased interdependencies and coordination demands. The study demonstrates that although prefabrication can reduce project timelines by up to 30 %, it concurrently amplifies project complexity by nearly threefold. By emphasizing the importance of early integrated design, robust supply chain collaboration, and digital tools like Building Information Modelling (BIM), the TRICIP framework provides practical guidance for managing prefabrication risks. This research contributes to advancing time-risk assessment methodologies and supports informed decision-making in prefabricated and hybrid construction project delivery. • Developed TRICIP framework to quantify time-risk trade-offs in prefabricated projects. • Integrated PERT analysis with risk trends to assess impacts of prefabrication levels. • Demonstrated prefabrication can cut 30 % project time but triples project complexity. • Identified critical upstream, logistics, and site risks influencing hybrid projects. • Provided strategies using BIM, supply chain integration, and risk-driven scheduling.

Bridging the gap between Artificial Intelligence (AI)-driven generative design and robotic fabrication remains a critical challenge in architectural automation. While Generative Artificial Intelligence (GenAI) tools have advanced conceptual design workflows, their practical deployment in physical construction is hindered by the absence of structured, fabrication-aware datasets to train suitable AI models. This study introduces GDRF (Geometric Data for Robotic Fabrication), an automated pipeline for the generation, evaluation, and encoding of structurally feasible brick wall designs, enabling the creation of machine-learning-compatible data tailored for architectural robotic assembly. We developed a six-stage process that combines parametric modeling, algorithmic design generation, physics-based simulation, data encoding and storage, toolpath generation and assembly simulation, and physical robotic assembly with a robot. Over 33,000 wall configurations were synthetically generated and evaluated for structural stability, of which approximately 52% met the feasibility criteria. Stable and failed designs were identified through displacement-based criteria and encoded using dot-product-based rotational representation, reducing dimensionality while preserving critical geometric features. Comparative analysis revealed that brute-force generation produced more consistent outcomes, while random sampling achieved slightly higher local diversity. This study delivered a data pipeline and the BrickNet dataset, providing a foundation for future research in generative design, structural prediction, and autonomous robotic assembly.

Accurate property valuation is essential for real estate market stability, housing affordability and financial decision-making. However, traditional valuation methods face key limitations. The market approach, reliant on comparable sales data, is prone to subjectivity and data availability constraints. The income approach relies on stable rental income streams, which are often unavailable for newly built dwellings in volatile rental markets. And, the cost approach, based on the Depreciated Replacement Cost (DRC) method, neglects broader market influences by focusing solely on property characteristics. Despite advancements in Automated Valuation Models (AVMs) using Machine Learning (ML), these models remain sensitive to market fluctuations and lack integration with 3D property characteristics. To address these challenges, this study proposes a hybrid Artificial intelligence (AI) and Building Information Modeling (BIM)-driven property valuation model, integrating the DRC method with market-based valuation adjustments using ML, Natural Language Processing (NLP) and BIM 3D models. The framework consists of several key stages, including mass land valuation using ML techniques, automated construction cost estimation through BIM-based Quantity Take-Off (QTO) and NLP-based cost-matching, dynamic depreciation assessment via BIM-integrated maintenance management, entitlement calculation using optimization techniques, and market impact assessment through ML-driven modeling. The methodology was tested on a high-rise residential building in Melbourne, Australia, and the results demonstrated high accuracy, with estimated property values closely aligning with recent market transactions. The estimated values for one-bedroom and two-bedroom units were 100 % within the range of recent market transactions, and the estimate for the three-bedroom units showed only a 0.057 % deviation from the actual market value. The study advances the digital transformation of property valuation, showcasing how AI, ML and BIM enhance automation, accuracy and efficiency. These findings hold significant implications for the real estate sector, offering a scalable and adaptable framework for industry adoption.

ABSTRACT In recent years, there has been a growing interest in the curation of reggaeton as museums working with Latinx Caribbean culture across the United States are making efforts to include the popular music genre in their exhibition programming. This paper draws on interviews with museum and exhibition curators at the Grammy Museum (Los Angeles, USA) and La Casita Cultural Centre (New York, USA) to examine how these spaces have approached the task of including reggaeton in their broader representation of Latinx Caribbean popular culture and music. The article reveals some of the challenges involved in sonically curating contemporary and subversive popular music cultures in museum settings.