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Forecasting tephra fall building impacts and losses at Awu volcano, Sangihe Island (Indonesia)
(Elsevier, 2026) Tennant E; Jenkins S; Widiwijayanti C; Basuki A; Purnamasari H
Moving beyond volcanic hazard assessment to forecast how hazards affect communities provides valuable information for risk mitigation. Awu, a remote volcano on Sangihe Besar Island, Indonesia, is currently experiencing unrest (December 2025). With the entire population living within 45 km of the volcano, there is a need for actionable risk information. We integrated probabilistic tephra fall hazard analysis with building exposure and vulnerability information to forecast losses from a likely VEI 4 eruption. To address limited exposure data, we extracted building roof types from satellite imagery and applied Bayesian inference to combine existing vulnerability models into a hybrid framework suited to local conditions. Results indicate a 95% probability of exceeding ∼$11 million USD (∼179 billion IDR) in building damage and a 75% probability of exceeding $25 million USD (∼407 billion IDR). Losses are concentrated in the northern districts, with Tahuna, the capital most affected. Losses at Awu were not well forecast by column height and wind conditions, parameters currently used to trigger insurance payouts from volcanic catastrophe risk bonds. Existing probabilistic volcanic loss calculations often couple a single hazard footprint representing the Nth percentile intensity with exposure and vulnerability data. Here we calculated loss for each individual hazard footprint computing 100 million values to capture uncertainty in both hazard and vulnerability. Losses calculated using the median hazard footprint were 5% higher than the median of the loss population. In addition to supporting decision making during the current unrest, our work presents key advancements in volcanic risk assessment that may be applied to other locations worldwide.
Global urban exposure near volcanoes is increasing: a spatio-temporal analysis from 1975 to 2030
(Springer, 2026) Meredith ES; Teng RXN; Jenkins SF; Tennant E; Lallemant D; Hayes J; Biass S
Urban populations are increasingly moving into hazardous areas, which leads to widespread and frequent impacts from hazard events. Although these exposure trends have been quantified for other hazards, global exposure analyses for volcanic hazards remain limited. Here, we quantify global and regional changes in city exposure to volcanic hazards through time. With GHS-UCDB and GHS-POP datasets, we use spatio-temporal metrics to track urban expansion within 100 km of volcanoes active in the Holocene from 1975 to 2020, with projections to 2030. The number of cities within 100 km of volcanoes is projected to more than double, with populations increasing by 154%. The proportion of people within 100 km of volcanoes who live in cities increases from 44% (~186 million) in 1975 to over 50% by 2030 (~473 million). Exposed city populations concentrate within 20–30 km from volcanoes, and average urban population density generally decreases closer to volcanoes. Exposure growth is highest in Southeast Asia and East Africa. Most cities expand and densify, with many growing faster toward nearby volcanoes. Case studies show how urban expansion intersects with volcanic impact pathways. These trends indicate that urban expansion is amplifying volcanic risk and highlight the need to integrate hazard data into urban planning.
Differences in emotion regulation components underlie the sexual orientation disparity in depressive symptoms: a prospective, population-based study of young adults
(Springer, 2026) Seager van Dyk I; Rutherford CG; Pachankis JE; Bränström R; Hatzenbuehler ML
Purpose Hatzenbuehler’s psychological mediation framework proposes that difficulties in emotion regulation (ER), which are driven in part by excess exposure to stigma-related experiences, contribute to sexual orientation-related mental health disparities. However, existing research on the framework has largely focused on a small number of ER variables in non-probability samples. Methods To address these limitations, we examined whether a large complement of ER components mediates the prospective association between sexual minority status and depressive symptoms, using longitudinal data from a population-based sample of 1,208 Swedish young adults (aged 18–35). Data were collected in 2020 (ER, depressive symptoms) and 2021 (depressive symptoms). Participants completed 12 measures of ER, spanning a diverse array of ER constructs (e.g., emotional awareness, cognitive reappraisal, access to ER strategies). Results Sexual minorities exhibited significantly more ER difficulties on nine out of the 12 ER components, and higher depressive symptoms, compared to heterosexuals. Eight of the 12 ER components independently mediated the association between sexual minority status and increases in depressive symptoms one year later, and two components (brooding rumination, difficulty identifying positive emotions) mediated this relationship when all 12 ER components were entered into the model simultaneously. Conclusion These findings provide evidence from a population-based, longitudinal study that a wide range of ER factors underlie sexual orientation-related disparities in depressive symptoms during a developmental period of heightened risk.
Promoting psychosocial safety climate across school teams: A schoolwide leadership responsibility
(Taylor and Francis Group, 2026-08-27) Cataloni S; Forsyth D; Brougham D; Thorn K
This qualitative case study uses document analysis, interviews, and focus groups to explore how the principal and line managers promote psychosocial safety climate (PSC) across teams in an Aotearoa/New Zealand school. While principals’ support for teachers’ psychological health has been examined, how they support line managers in developing team and individual-level PSC remains unclear. Findings indicate that PSC is not always directly transferred from the principal to teachers but operationalized by line managers, positioning PSC as a multilevel, collectively enacted process. A multilevel model shows how leadership helps operationalize and transfer PSC across school, team, and individual levels.
A*-guided deep reinforcement learning for single- and dual-agent navigation : cross-algorithm evaluation and transformer-based policies : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Computer Science at Massey University, Auckland, New Zealand
(Massey University, 2026) Liu, Yahong
Integrating classical path planning with deep reinforcement learning (DRL) offers a promising approach for navigation in obstacle-dense environments, where purely learned policies often suffer from sparse rewards, inefficient exploration, unstable con vergence, and collision-prone behaviour. This thesis investigates the integration of the A-star (A*) path-planning algorithm with deep and multi-agent reinforcement learning (MARL), with a particular focus on reproducibility, cross-algorithm evaluation, policy architecture design, and the transition from single-agent to dual-agent navigation. The study builds on previous A*-guided DRL research by reproducing and extending its core ideas within the Vectorized Multi-Agent Simulator (VMAS) and Benchmarking Multi-Agent Reinforcement Learning (BenchMARL) frameworks. The thesis makes four main contributions. First, it establishes a reproducible experimental pipeline for evaluating A*-guided reinforcement learning in navigation tasks. Second, it examines how A*-generated waypoint guidance affects different reinforcement learning algorithms, including Multi-Agent Proximal Policy Optimization (MAPPO), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and the Q value Mixing Network (QMIX). Third, it evaluates the use of structured waypoint information through a Task-Adaptive Waypoint-Aware Transformer configuration. Fourth, it investigates how A*-guided learning and waypoint-aware policies behave when extended from controlled single-agent navigation to dual-agent navigation. In the proposed framework, A* is used to generate waypoint paths that support reinforcement learning through reward shaping and, in some configurations, through explicit waypoint observation features. For the multilayer perceptron (MLP) baseline and Standard Transformer configurations, A*-derived guidance is used during training and removed during evaluation. For the Task-Adaptive Waypoint-Aware Transformer configurations, A*-based reward shaping is disabled during evaluation, but A*-derived waypoint observation features may remain part of the policy input. The baseline re production shows positive descriptive differences in final-window evaluation reward and the collision-penalty proxy in controlled single-agent settings, particularly in compact obstacle-dense environments. However, the results also show that A* guidance does not necessarily improve sample efficiency or convergence speed and introduces additional computational overhead. Extending the framework to MAPPO, MADDPG, and QMIX reveals that the effects of A* guidance are algorithm-dependent, with different learning paradigms responding differently to planning-derived reward signals. The architectural evaluation shows that a Standard Transformer Policy Baseline does not automatically outperform the Fully Connected MLP Policy Baseline. This indicates that simply replacing an MLP with a Transformer is insufficient for improving navigation performance. In contrast, the Task-Adaptive Waypoint-Aware Trans former shows positive descriptive trends in selected final performance metrics, including higher average-reward and final-window evaluation-reward values in some configurations. However, the five-seed statistical analysis does not establish statistically significant improvements after Holm-Bonferroni correction. The dual-agent experiments reveal an important limitation of directly transferring single-agent A*-guided strategies to multi-agent navigation. While A* guidance reduces collisions in single-agent settings, independently generated A* paths can create inter agent path conflicts in dual-agent scenarios. This produces a qualitative shift in the role of A* guidance: its collision-reduction benefits do not automatically transfer to multi-agent settings, although it may still support generalization and task completion in some cases. The results also show that scaling waypoint-aware Transformer policies to dual-agent navigation increases training difficulty and produces algorithm-dependent effects. MADDPG benefits most clearly from the waypoint-aware architecture, QMIX shows mixed effects, and MAPPO exhibits instability under the tested configuration. Overall, this thesis demonstrates that classical planning signals can provide useful structure for reinforcement learning-based navigation, but their benefits depend strongly on the learning algorithm, policy architecture, evaluation setting, and number of agents. The findings clarify both the potential and the limitations of A*-guided DRL and MARL. They suggest that waypoint-aware Transformer policies are a promising direction for structured navigation learning, while also highlighting the need for larger scale statistical validation, improved multi-agent coordination mechanisms, and more robust approaches for resolving conflicts between independently planned paths.