This paper proposes a physics-informed hybrid digital CO2 emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO2 emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO2 emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO2 emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO2 emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions.

During a tunnel fire, the foremost priority is the safe evacuation of passengers. Extreme temperatures and toxic combustion products can quickly lead to mass casualties, so evacuation support systems require fast forecasts of how hazardous conditions will evolve in space and time. This study investigates whether sparse sensor measurements can be used to reconstruct future tunnel-wide fire conditions on two-dimensional sections that are directly relevant to structural assessment and human exposure. To this end, we develop 2D ST-FAM, a data-driven forecasting model that maps time-resolved measurements from 75 tunnel sensors to future temperature, soot, and carbon monoxide (CO) fields derived from 108 computational fluid dynamics (CFD) fire simulations. The study is organized around three questions: whether the model can accurately reconstruct future tunnel fields from sparse measurements, whether this performance is maintained on both the vertical center plane and the horizontal breathing plane, and which physical quantities remain most challenging to predict. Results show high structural agreement with the CFD reference fields over the full 1800 s prediction horizon, with average structural similarity index (SSIM) values of 0.964 for temperature, 0.984 for CO, and 0.937 for soot. These findings indicate that sparse-sensor forecasting is feasible for tunnel-scale temperature and toxic-gas field prediction, while soot prediction remains comparatively more difficult because of its sharper spatial structures.

Building materials’ fire properties directly affect the fire risk of buildings. Ignition, the initiating event of any building fire, occurs when a heat source ignites surrounding combustible materials. Although several parameters—such as the Thermal Response Parameter (TRP), thermal inertia, ignition temperature, ignition time, critical heat flux (CHF), and heat of combustion—have been used to characterize ignition behavior, a unified metric capable of representing overall ignitability under diverse and often unknown and unintended heat source (UHS) patterns is generally lacking. To address this gap, we propose a new method to evaluate material ignitability by generalizing UHS patterns and linking them to known or readily obtainable material properties, including ignition temperature and thermal inertia. The UHS patterns are represented using lognormal distributions for both exposure duration and incident heat flux (IHF), reflecting conditions that may occur in real buildings. Monte Carlo simulations are employed to generate a large number of heat exposure events from these UHS patterns, enabling statistical determination of material ignitability. The method applies to both thermally thick and thermally thin materials, with a simple expression provided to determine the critical thickness separating these behaviors. Sensitivity analysis demonstrates that the ignitability metric is robust with respect to variations in the lognormal distribution parameters. The proposed ignitability metric provides a general measure of a material’s susceptibility to ignition under typical building fire scenarios and enables relative comparison of fire risk for buildings differing only in the materials adopted.

Wildfires are increasingly complex and geographically dynamic phenomena that require timely and context-sensitive decision support across the management cycle. Artificial intelligence (AI) has been widely applied to wildfire detection, prediction, and remote sensing; however, a systemic understanding of how AI methods are structurally integrated into decision-support architectures remains limited. The present configurational mapping review, reported in alignment with PRISMA-ScR guidance, examines AI applications in rural wildfire management between 2020 and 2024. Using a configurational framework, explicit scope–algorithm–vector relations are mapped, identifying how specific AI paradigms are operationalised through technological infrastructures to support decision-relevant functions. A total of 27 articles were included, from which 168 scope–algorithm–vector triplets were extracted and analysed descriptively. The results reveal a concentration of applications in detection and evolution prediction tasks, predominantly supported by machine learning methods and remote sensing platforms. Explicitly linked configurations to action-oriented or prescriptive decision functions are less frequently documented. The findings contribute to a structured mapping of AI deployment patterns in wildfire management and provide a conceptual basis for future research addressing integrative and action-oriented system design.

The background modification of ecosystems affected by fire can cause black or dark colours in animals to become adaptive, providing better protection against visually oriented predators. We surveyed fire-prone Mediterranean woodlands to describe the behaviour, position and background characteristics of the black cicada Tibicina quadrisignata Hagen, 1855 found in recently burnt and unburnt trees. A human detectability test, using cicada pictures in natural backgrounds taken during the fieldwork, was used to assess detection risk. Most cicadas found were solitary males uttering courtship song. Many cicadas flew when approached, with 82% of flight initiation distances being less than 3 m and half of the flights being less than 30 m. Cicadas favoured sunny locations in early morning, and shady sites as the temperature increased. Fire altered fine-scale microhabitat use by cicadas, since cicadas were found in 71% thicker stems and at 14% lower height on the tree, in burnt trees, in relation to unburnt trees. Generalised Linear Mixed Models (GLMMs) revealed a negative fire effect on cicada detection by human test participants. The probability of detection fell from 0.62 in unburnt backgrounds to 0.48 in burnt backgrounds, while the time needed for detection did not change between burnt and unburnt sites. Overall, these results show that T. quadrisignata cicadas adjust their substrate use after fire and are less detectable on burnt backgrounds. Real predation risk, however, also depends on thermoregulation-associated exposure, courtship song activity and predator densities.

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants.

This study explores the viability of condensate petroleum, an ultra-light hydrocarbon derived from natural gas production, as an alternative diesel engine fuel. The researchers tested six different fuel blends, increasing the condensate volume by 10% increments, in a compression ignition engine under three distinct load conditions (25%, 50%, and 75%) to evaluate both combustion characteristics and emission performance. The results demonstrate that condensate blends significantly enhance key combustion parameters. The heat release rate, in-cylinder pressure, and in-cylinder temperature all increased, with the highest heat release rate improvement of 35.6% observed at a 75% load using a 60% condensate petroleum blend. However, increasing the condensate ratio also extended ignition delay times and raised the ringing intensity, which peaked with a 34.7% increase at a 25% load. Brake thermal efficiency improved at lower and medium loads—achieving a maximum 11.2% increase with the 50% condensate petroleum blend at 50% load—but decreased when the engine reached 75% load. In terms of environmental impact, the condensate blends proved largely beneficial. Carbon monoxide emissions dropped by 57.9% (at 75% load, 60% condensate petroleum), smoke opacity decreased by 72.6% (at 25% load, 40% condensate petroleum), and hydrocarbons fell by 34.4% (at 50% load, 60% condensate petroleum). The primary drawback was that nitrogen oxide emissions worsened, increasing by 20.4% at 75% load with the 50% condensate petroleum blend. Overall, the study concludes that the effects of condensate petroleum are highly acceptable, making it a promising alternative fuel and additive for diesel engines.

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes.

Wildland fires, including surface and crown fires, present significant challenges for ecosystems and forest management. Accurate fire modelling is crucial for risk assessment and mitigation strategies. The Fire Dynamics Simulator (FDS) v6.8.0, developed by the National Institute of Standards and Technology (NIST), is a physics-based model that simulates fire behaviour by incorporating advanced physics and chemistry. However, its reliability requires thorough validation. This study validates FDS 6.8.0’s performance in modelling both surface fires and single tree burning. Two separate simulation sets were conducted. For surface fires, pine needle fuel beds were used at a laboratory scale to examine fire behaviour on slopes of 0°, 10°, and 20°. The results were validated against experimental data. A burning Douglas fir tree was simulated, and the results were compared with experimental measurements. The surface fire simulations at 0° and 10° slopes showed strong agreement with experimental data. In single-tree burning, both experimental and simulated results exhibited similar trends, with a rapid increase to a peak mass-loss rate (MLR) followed by a gradual decline. Validating FDS 6.8.0 forms an essential first step toward supporting the investigation of complex wildland fire behaviour, such as surface-to-crown fire transition, canyon fire, and dynamic escalation, using the same FDS version.

In Canada and elsewhere, there is an ascendancy of a whole-of-society approach that centres shared responsibility for wildfire management. This article engages the debates on the rise of shared responsibility for wildfire management to argue that this context demands a renewed research focus on understanding how the public allocates responsibility for wildfire management. We illustrate this argument through a case study of public engagement with wildfire risk and shared responsibility in Whistler, British Columbia, western Canada. Our case study draws on evidence from a quantitative survey administered to 1311 participants in the spring and summer of 2024. The study reveals a near-universal concern about wildfires among the participants and a high level of risk perception. This is consistent with community climate and wildfire reports and plans. This level of concern is driving a high level of mitigation activity completion among participants, even though the level of preparedness is mixed. Our study found a marked pattern of responsibility allocation across the phases of wildfire management. Participants put the municipal government at the forefront of mitigation, preparedness, and response. The provincial government was ranked as most responsible for recovery. Homeowner responsibility declined as one moves from mitigation and preparedness through to response and recovery. Private actors, such as insurance, have greater responsibility in the recovery phase. Multivariate General Linear Models (GLMs) show that how respondents allocate responsibility for various aspects of wildfire management is influenced by home ownership, prior wildfire experience, perceived preparedness, and commitment to bearing the costs of FireSmart assessment. We conclude that a sustained research commitment is needed to further elucidate the dynamics of public expectations and attitudes in the context of shared responsibility for wildfire management.