Current wildfire risk assessments rely on coarse hazard maps and opaque machine learning models that optimize regional accuracy while sacrificing interpretability at the decision scale. WildfireGenome addresses these gaps through three components: (1) fusion of seven federal wildfire indicators into a sign-aligned, PCA-based composite risk label at H3 Level-8 resolution; (2) Random Forest classification of local wildfire risk; and (3) SHAP and ICE/PDP analyses to expose county-specific nonlinear driver relationships. Across seven ecologically diverse U.S. counties, models achieve accuracies of 0.755-0.878 and Quadratic Weighted Kappa up to 0.951, with principal components explaining 87-94% of indicator variance. Transfer tests show reliable performance between ecologically similar regions but collapse across dissimilar contexts. Explanations consistently highlight needleleaf forest cover and elevation as dominant drivers, with risk rising sharply at 30-40% needleleaf coverage. WildfireGenome advances wildfire risk assessment from regional prediction to interpretable, decision-scale analytics that guide vegetation management, zoning, and infrastructure planning.

While this could be done using the synthesis formulation, we demonstrate that this leads to slower performances.

While this could be done using the synthesis formulation, we demonstrate that this leads to slower performances.

The preliminary spacecraft trajectory design phase can be posed as a parameterized global search problem for optimal spacecraft trajectories. At each stage of the preliminary design, the mission objectives, requirements, and constraints may change, resulting in variations of the global search problem parameters. Parameters may also change to represent increased modeling fidelity. The aim at any stage of the preliminary design is to solve for a large set of high quality spacecraft trajectories with diverse, or similarly qualitatively different, features. High quality is naturally defined by the value of a solution's objective value relative to the best known. Examples of qualitatively different features may include trajectories that have a different number of revolutions around a central body, a different number or sequence of gravity assist flybys, solutions that avoid radiation belts or other hazards, or solutions that depart the original or target orbital planes. The benefit of having different qualitative solutions is that it allows mission designers to trade different priorities in their design and reflects the fact that not all relevant objectives and constraints can be incorporated into the optimal spacecraft trajectory problem so early or readily in the design phase (i.e., without prior knowledge of what is relevant and when designing at a quick cadence). In the simplest of cases, a mission designer's past experience may be sufficient to guide them in finding a high quality set of solutions.

Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors such as points of presence and other environmental knowledge have been introduced to facilitate cellular traffic forecasting. In this study, we focus on smart roads and explore road traffic measures to model the processes underlying cellular traffic generation with the goal to improve prediction performance. Comprehensive experiments demonstrate that by employing road flow and speed, in addition to cellular network metrics, cellular load prediction errors can be reduced by as much as 56.5 %. The code and more detailed results are available on https://github.com/nvassileva/DataMatters.