What Matters to Boatpeople

Not peer-reviewe

Decentralised adaptive-gain control for eliminating epidemic spreading on networks

This paper considers the classical Susceptible–Infected–Susceptible (SIS) network epidemic model, which describes a disease spreading through n nodes, with the network links governing the possible transmission pathways of the disease between nodes. We consider feedback control to eliminate the disease, focusing especially on scenarios where the disease would otherwise persist in an uncontrolled network. We propose a family of decentralised adaptive-gain control algorithms, in which each node has a control gain that adaptively evolves according to a differential equation, independent of the gains of other nodes. The adaptive gain is applied multiplicatively to either decrease the infection rate or increase the recovery rate. To begin, we assume all nodes are controlled with adaptive gains, and prove that both infection rate control and recovery rate control algorithms eliminate the disease with positive finite limiting gains. Then, we consider the possibility of controlling a subset of the nodes, for both the infection rate control and recovery rate control. We first identify a necessary and sufficient condition for the existence of a subset of nodes, which if controlled would result in the elimination of the disease. For a given network, there may exist several such viable subsets, and we propose an iterative algorithm to identify such a subset. Simulations demonstrate the effectiveness of the proposed controllers.Peer-reviewe

Learning from Positive Deviance in Gender and Fisheries

We present an initial exploration of why and how participation in a case of community-based resource management (CBRM) in a Pacific context could be considered a deviation from gender norms. Using analysis of in-depth interviews with community members in a village in Malaita Province, Solomon Islands, our data suggest four causal factors appear to have contributed to higher participation of women in CBRM compared to surrounding villages in the province: (1) higher than usual participation of women in fishing, (2) inclusive leadership and intrinsic values towards equality by male leaders, (3) experience of women in other non-fisheries leadership roles, and (4) supportive attitudes by the community towards women’s leadership. We suggest that rather than research focusing on gender of individual leaders or attitudes of individuals, as has been the norm, more attention should be given to the enabling context and specific norms around fisheries, work, community governance, and resource management to understand barriers and opportunities for increased inclusion in coastal fisheries management.Open Access funding enabled and organized by CAUL and its Member Institutions The Australian Government funded this work through the Australian Centre for International Agricultural Research (ACIAR) projects FIS/2016/300 and FIS/2020/172.Peer-reviewe

Administration map of Sri Lanka

Map of Sri Lanka showing it's nine provinces, provincial capitals and the districts that make up the provinces

Quantifying the Dietary Overlap of Two Co-Occurring Mammal Species Using DNA Metabarcoding to Assess Potential Competition

Interspecific competition is often assumed in ecosystems where co-occurring species have similar resource requirements. The potential for competition can be investigated by measuring the dietary overlap of putative competitor species. The degree of potential competition between generalist species has often received less research attention than competition between specialist species. We examined dietary overlap between two naturally co-occurring dietary generalist species: the common brushtail possum Trichosurus vulpecula and the bush rat Rattus fuscipes. To gauge the potential for competition, we conducted a diet analysis using DNA extracted from faecal samples to identify the range of food items consumed by both species within a shared ecosystem and quantify their dietary overlap. We used DNA metabarcoding on faecal samples to extract plant, fungal, and invertebrate DNA, identifying diet items and quantifying dietary range and overlap. The species' diets were similar, with a Pianka's overlap index score of 0.84 indicating high dietary similarity. Bush rats had a large dietary range, consisting of many plant and fungal species and some invertebrates, with almost no within-species variation. Possums had a more restricted dietary range, consisting primarily of plants. We suggest that the larger dietary range of the bush rat helps buffer it from the impacts of competition from possums by providing access to more food types. We conclude that, despite the high ostensible overlap in the foods consumed by dietary generalist species, fine-scale partitioning of food resources may be a key mechanism to alleviate competition and permit co-existence.Funding: This work was supported by The Holsworth Wildlife Research Endowment and The Ecological Society of Australia. We acknowledge and thank the Wreck Bay Aboriginal Community, owners of Booderee National Park, for providing access to and support for our work in the park, and to the staff at Booderee National Park and all volunteers for assisting in data collection. We also acknowledge the Biomolecular Resource Facility at the John Curtin School of Medical Research at ANU for processing and sequencing the extracted DNA. Open access publishing facilitated by Australian National University, as part of the Wiley - Australian National University agreement via the Council of Australian University Librarians.Peer-reviewe

Compatible finite element interpolated neural networks

We extend the finite element interpolated neural network (FEINN) framework from partial differential equations (PDEs) with weak solutions in H1 to PDEs with weak solutions in H(curl) or H(div). To this end, we consider interpolation trial spaces that satisfy the de Rham Hilbert subcomplex, providing stable and structure-preserving neural network discretisations for a wide variety of PDEs. This approach, coined compatible FEINNs, has been used to accurately approximate the H(curl) inner product. We numerically observe that the trained network outperforms finite element solutions by several orders of magnitude for smooth analytical solutions. Furthermore, to showcase the versatility of the method, we demonstrate that compatible FEINNs achieve high accuracy in solving surface PDEs such as the Darcy equation on a sphere. Additionally, the framework can integrate adaptive mesh refinements to effectively solve problems with localised features. We use an adaptive training strategy to train the network on a sequence of progressively adapted meshes. Finally, we compare compatible FEINNs with the adjoint neural network method for solving inverse problems. We consider a one-loop algorithm that trains the neural networks for unknowns and missing parameters using a loss function that includes PDE residual and data misfit terms. The algorithm is applied to identify space-varying physical parameters for the H(curl) model problem from partial, noisy, or boundary observations. We find that compatible FEINNs achieve accuracy and robustness comparable to, if not exceeding, the adjoint method in these scenarios.This research was partially funded by the Australian Government through the Australian Research Council (project numbers DP210103092 and DP220103160). This work was also supported by computational resources provided by the Australian Government through NCI under the NCMAS and ANU Merit Allocation Schemes. W. Li gratefully acknowledges the Monash Graduate Scholarship from Monash University, Australia, the NCI computing resources provided by Monash eResearch through Monash NCI scheme for HPC services, and the support from the Laboratory for Turbulence Research in Aerospace and Combustion (LTRAC) at Monash University through the use of their HPC Clusters.Peer-reviewe

Theoretical calculation and machine learning aided design of functional materials for energy conversion

This thesis investigates the integration of machine learning (ML) and theoretical calculations to design and optimize functional materials for photocatalytic applications. By combining experimental techniques with theoretical calculations, that is, finite-difference time-domain (FDTD) simulations, and density functional theory (DFT) calculations, this work aims to accelerate the discovery of efficient, selective, and scalable photocatalytic systems for CO2 reduction and seawater splitting. The central focus is on leveraging ML and advanced simulations into experiments to provide new insights into plasmonic photocatalysts and microenvironmental perturbations in photoreaction. The first study explores the development of Ag-TiO2 core-shell photocatalysts for the selective reduction of CO2 to methane (CH4). A significant contribution of this work is the use of FDTD simulations to model and optimize microenvironmental perturbations, thereby enhancing the catalytic activity of the plasmonic core-shell nanoparticles. Additionally, DFT simulations demonstrate that localized surface plasmon resonance (LSPR)-induced electric field enhancements lower the energy barriers for CO2 activation and methanation. Experimentally, this system achieves 100% selectivity for CH4 with a production rate of 75 umol/g/h. This study emphasizes the advantages of microenvironmental engineering in optimizing photocatalytic activity and selectivity, with FDTD and DFT simulations further elucidating the mechanisms of microenvironmental perturbations. The second study focuses on the design of Co-NC@Cu core-shell photocatalysts for solar-driven hydrogen production from seawater. By dispersing single Co atoms on a nitrogen-doped carbon (NC) shell surrounding a Cu core, this novel catalyst achieves a hydrogen production rate of 9080 umol/g/h and a solar-to-hydrogen (STH) conversion efficiency of 4.78%. A key highlight of this work is the detailed investigation of the local coordination environment of the single Co atoms, as well as the thermodynamic and kinetic effects of electric field perturbations on the catalytic process. DFT calculations reveal that the single Co atoms act as highly active sites for hydrogen evolution, exhibiting low energy barriers for the reaction. Furthermore, the electric field's role in enhancing the reaction thermodynamics and kinetics was elucidated, providing insights for further optimization of catalytic performance. Integrating single atoms, photothermal effects, and localized surface plasmon resonance (LSPR) demonstrates a robust and efficient design for seawater splitting. The third study showcases a comprehensive workflow combining ML and DFT calculations to accelerate the discovery and optimization of single-atom-based (SA) 2D photocatalysts. Using a dataset of Janus-TMD materials as a case study, ML models were trained to identify high-activity catalytic sites and screen potential substrates for photocatalytic CO2 reduction. The ML-driven predictions successfully prioritized optimal single-atom catalysts, with experimental validation confirming the activity and selectivity of two synthesized Janus substrates MoOSe with single-atom Pt. Photocatalytic experiments demonstrated the potential of the ML-guided design in delivering efficient and selective catalysts, underscoring the synergy between computational and experimental approaches. The growing dataset of atomic structures, intermediates, Janus configurations, and adsorption models provides a robust foundation for refining ML models and driving innovations in SA-based 2D materials discovery. In conclusion, this thesis demonstrates the successful integration of ML, FDTD, and DFT techniques with experimental approaches for the design of advanced functional materials, which contribute to the development of sustainable energy solutions through CO2 reduction and hydrogen production

Hybrid economies in practice, Groote Eylandt, Australia

Social enterprises (SEs) are emerging as powerful vehicles for addressing socio-economic challenges in Indigenous communities. On Groote Eylandt, a remote island in northern Australia, Bush Medijina offers a compelling example of how a hybrid economy, one that integrates market, state, and customary economies, can create sustainable development opportunities. Led by Anindilyakwa women, this SE blends traditional knowledge of medicinal plants with modern commercial practices to produce skincare and haircare products. It draws on government support, mining royalties, and cultural practices to deliver social benefits while also providing a platform for women’s leadership and empowerment.Not peer-reviewe

Cycle conditions for “Luce rationality”

We extend and refine conditions for “Luce rationality” (i.e., the existence of a Luce – or logit – model) in the context of stochastic choice. When choice probabilities satisfy positivity, the cyclical independence (CI) condition of Ahumada and Ülkü (2018) and Echenique and Saito (2019) is necessary and sufficient for Luce rationality, even if choice is only observed for a restricted set of menus. We adapt results from the cycles approach (Rodrigues-Neto, 2009) to the common prior problem Harsanyi (1967–1968) to refine the CI condition, by reducing the number of cycle equations that need to be checked. A general algorithm is provided to identify a minimal sufficient set of equations. Three cases are discussed in detail: (i) when choice is only observed from binary menus, (ii) when all menus contain a common default; and (iii) when all menus contain an element from a common binary default set. Investigation of case (i) leads to a refinement of the famous product rule.Peer-reviewe

Alkynyltellurolato ligands including a solvatochromic rhenium(i) complex

Alkynyltellurolato complexes LnM-Te-C = CR (LnM = CpFe(CO)2, CpFe(CO)(PPh3), Re(CO)3(bipy); R = Ph, SiMe3) arise via tellurium insertion into alkynyllithiums followed by metathesis with the corresponding metal halide complex. The rhenium(i) complex displays solvatochromism (hypsochromic shift in polar solvents) for the inter-ligand (TeC CR to bipy) charge transfer which is not, however, observed for the lighter analogue [Re(SeC CSiMe3)(CO)3(bipy)].We gratefully acknowledge the financial support of the Australian Research Council (DP200101222, DP230199215).Peer-reviewe