Abstract This study examines Indigenous women–led wildfire disaster adaptations through traditional environmental knowledge and community-led strategies. Centering Indigenous traditional story sharing, intergenerational learning, and land stewardship, Indigenous women serve as key leaders in wildfire prevention and forest management. The research highlights the role of Indigenous fire governance in sustainable land management and the critical intersection between traditional knowledge and Western scientific tools in Chang Mai, Thailand. Using decolonial methodologies, the study highlights the necessity of Indigenous women leadership in climate-resilience efforts. Findings reveal that Indigenous women’s practices contribute significantly to biodiversity conservation, climate adaptation, and community-led disaster response. The study calls for stronger academic engagement with Indigenous knowledge and policy reforms that center Indigenous women’s expertise into global forest fire governance. The research provides a foundation for future transdisciplinary collaborations, advocating for Indigenous women’s leadership in sustainable wildfire management.
Traditional road safety planning is largely reactive, while existing Collision Prediction Models (CPMs) often function as disconnected post-processing tools. However, a growing body of research has highlighted the need for proactive safety assessment at the transportation planning stage. Building on this research, the present study demonstrates a large-scale, automated framework that integrates CPMs with a calibrated, tour-based Regional Transportation Model (RTM), enabling travel demand and network-wide collision outcomes to be evaluated within a unified modelling workflow. Using the Calgary metropolitan region as a case study, Negative Binomial models were developed to estimate Fatal/Injury (FI) and Property-Damage-Only (PDO) collisions at mid-block segments and intersections. To ensure seamless operational integration, all explanatory variables used by the CPMs were derived exclusively from the RTM’s multimodal outputs, thereby eliminating the need for additional external data collection. Validation against independent historical data from 2017 to 2019 demonstrated robust predictive accuracy at both the aggregate network level and the individual facility level. The framework was then applied to a 2028 planning horizon to evaluate twelve demand-side policy scenarios involving changes in fuel prices, parking costs, and transit fares. The results revealed a consistent, monotonic dose–response relationship across all interventions. Increases in fuel prices produced the largest network-wide reductions in collision, while lower transit fare and higher parking costs generated more targeted safety benefits. Intersections were particularly sensitive, especially with respect to FI collisions under the transit fare scenarios. By generating severity-stratified safety estimates alongside conventional demand outputs, without requiring additional data inputs or manual post-processing, the framework enables planners to evaluate mobility and safety trade-offs for proactive, data-informed long-range transportation planning.
Abstract There is growing demand for river-network models that can estimate sediment transport at fine temporal resolution. Although coupling physically based sediment modules with hydrological and hydrodynamic models is a logical path forward, these approaches require intensive calibration and substantial computing power, limiting their practical use. This study develops a physics-informed neural network (PINN) that embeds sediment transport and advection-dispersion equations directly into a neural-network architecture, boosting predictive accuracy while reducing the need for large observational datasets. We introduce a novel interconnected PINN scheme in which PINNs for separate river reaches are coupled through loss functions at their boundaries, creating a cohesive model for the river network. The resulting framework predicts suspended-sediment concentrations reliably throughout the catchment during flood events. We evaluated three configurations of the PINN model and compared their performance against a purely data-driven machine learning model and an optimized physically based model. Trained on a subset of flood events and tested on unseen events, the interconnected PINN framework (root mean squared error = 5.8 kt) outperformed both the data-driven (33.8 kt) and physical (27.8 kt) models in estimating event loads. This novel PINN framework is transferable and can be applied to other catchment and river-network modeling, supporting simulations of both hydrology and flow-transported constituents. It is among the first to leverage comprehensive real-world data, using observations from the Manawatū River in New Zealand for both model training and validation. Requiring only standard river-network descriptors and hydrological time series, the approach can be transferred readily to other catchments with similar datasets.
Hospitalized patients are particularly susceptible to infections brought on by methicillin-resistant Staphylococcus aureus (MRSA), and these infections usually have a high morbidity and mortality rate. Several strategies can help prevent MRSA from spreading, but the efficacy of each strategy remains unclear, as most past research evaluated multiple interventions together rather than examining each one individually. To assess the effects of hand hygiene and screening, we created a novel compartmental MRSA spread framework for healthcare facilities in Edmonton, Alberta. The model accounts for MRSA patients (hospital-acquired (HA) MRSA or community-acquired (CA) MRSA colonization or infections) and isolated patients. To examine the effects of interventions, we first estimated the posterior mean of the parameters in our model, as well as a sub-model of it, using a Bayesian framework with Markov chain Monte Carlo (MCMC) techniques, utilizing data from hospitals in Edmonton. Next, we incorporated different intervention rates into the model to ascertain their effect on colonized and infected patients. One intervention is practicing hand hygiene among healthcare workers in hospitals to reduce transmission rates between susceptible individuals and MRSA patients. The other intervention is to screen patients for MRSA colonization to place them in isolation rooms swiftly. The two interventions were first examined alone, and then simultaneously. We compared the overall number of HA-MRSA colonization and infections as the intervention rates varied, and the findings show that combined interventions are more effective than individual interventions at limiting MRSA transmission in hospitals.
Coal biogasification is a biotechnological process in which microorganisms convert coal into methane. The dynamic characteristics of this system represent a critical gap in current understanding. This study aims to elucidate the temporal evolution patterns of the gas-solid-liquid (including microbial components) triphase system throughout the entire process of coal biogasification, using a temporal sampling approach. The research further seeks to uncover the underlying coordinated response mechanisms and construct a descriptive model of coal biodegradation. Results indicate that coal biogasification is a temporally dynamic, triphase evolutionary process driven by microbial community succession. Based on the flux dynamics of the triphase products and the sequential metabolic transitions of microbial populations, the process can be categorized into three consecutive stages characterized by progressively decreasing intensity, with each stage following an "acidification-methanogenesis" degradation pathway. Microbial activity leads to the accumulation and subsequent consumption of intermediate metabolites, thereby inducing multi-stage methane generation. Concurrently, significant transformations occur in the physicochemical structure of coal: pore connectivity is enhanced through repeated structural rearrangements, while the branching degree of aliphatic chains, oxygen-containing functional groups, and aromatic structures undergo continuous degradation. The organic matter exhibits a dynamic pattern of alternating dissolution and consumption. Drawing upon similarities with the established model of crude oil, this study proposes a generalized model for coal biodegradation, offering a crucial theoretical framework for understanding the formation mechanisms of biogenic coalbed methane.
