We propose a data-driven interpolation framework for reconstructing real-valued functions on smooth manifolds from scattered pointwise observations. The method combines a Gaussian Nadaraya–Watson kernel interpolant with a Voronoi-adaptive bandwidth determined entirely by the geometry of the sampled data, yielding an explicit closed-form construction that requires neither training, iterative optimization, preprocessing, nor parameter tuning.The proposed interpolant satisfies several theoretical properties. It reproduces the observed data exactly, enforces a vanishing intrinsic gradient at every sample point, and, in the dense-sampling limit, attenuates high-frequency oscillatory components through the geometric regularization induced by the adaptive bandwidth. Furthermore, the construction admits an interpretation in terms of minimizing a discrete total variation–type functional, establishing a natural connection with compressed sensing and sparsity-promoting regularization.Unlike classical kernel interpolation methods employing a fixed global bandwidth, the proposed adaptive strategy automatically adjusts to the local sampling geometry through the Voronoi tessellation while preserving an explicit analytical formulation. Because the interpolant is available in closed form, the overall computational cost is entirely determined by the inference stage: evaluating the interpolant at a query point requires only the computation of Gaussian kernel weights and their weighted combination, resulting in linear complexity with respect to the number of sample points. In contrast to many data-driven interpolation approaches, no additional offline computational stage is required before inference.Finally, we demonstrate the practical performance of the proposed methodology in sparse-angle computed tomography reconstruction, where interpolation of the sinogram prior to filtered back-projection produces accurate reconstructions while substantially reducing the overall computational time compared with standard total variation–based reconstruction methods.
Public transport network design lies at the intersection of strategic theory and algorithmic complexity. While strategic models offer general insights using simplified representations, and algorithmic approaches solve large-scale problems via heuristics, these two perspectives have largely evolved in isolation. This paper bridges the gap by enhancing a state-of-the-art genetic algorithm with a theoretically grounded decision rule: the Divisibility Index introduced by Gómez et al. (2026), a strategic quantity that determines whether a public transport line should be split into two. This index is integrated into a genetic algorithm, potentially splitting the candidate lines in every iteration when convenient.This enhanced method is tested on a stylised city under four distinct demand scenarios, each favouring a different network topology: hub-and-spoke, feeder-trunk, direct-based, and exclusive-based. In all cases, this seemingly minor (yet strategic) tweak leads to significant performance gains: the modified algorithm consistently finds better solutions in a single iteration than the original version does in fourteen. Notably, the resulting networks match expected topologies for each demand pattern. Similar results arise in adapted real-city representations. Those results highlight the potential of combining strategic public transport analysis and complex algorithms − not only to improve solution quality and speed, but also to reveal new strategic insights.
Transmission expansion planning (TEP) plays a critical role in ensuring power system reliability and facilitating the integration of renewable energy resources. However, this process requires planners to constantly deal with significant uncertainty. While multistage stochastic TEP models provide a robust framework for identifying investment plans under uncertainty, the rapid growth in problem size hinders their computational tractability. To address this challenge, this paper develops a hybrid machine learning-optimisation framework for stochastic TEP. The proposed approach uses investment decisions and uncertainty scenarios as input features to train surrogate neural networks, which are then reformulated as mixed-integer linear constraints and embedded within an optimisation model. The surrogate model approximates expected operational costs to inform TEP decisions, reducing the burden arising from large operational problems. Case study applications on IEEE test systems demonstrate that, after training, the proposed approach achieves near-optimal investment costs while reducing total computational time by up to a factor of around 13 compared to a single full-optimisation stochastic formulation. This enables performing extensive multi-scenario analysis and stress testing that would otherwise be computationally prohibitive at scale.
The growing interdependence between integrated electricity and gas systems (IEGS) calls for planning methods that capture their coupled long-term uncertainties and operational interactions. This paper develops a multi-stage stochastic framework for assessing the interplay of integrated electricity and gas systems (IEGS) in expansion planning under uncertainty, leveraging a scalable Column Generation and Sharing (CG-S) solution strategy to tackle computational challenges. The model jointly optimises investments in electricity transmission and generation, as well as natural gas (NG) processing facilities, under uncertainty in NG availability and prices, electricity demand growth, and capital costs. It integrates detailed hourly power system operations and daily gas flows, preserving realistic temporal granularity. Case studies on the Australian East Coast Energy System demonstrate that the CG-S algorithm reduces convergence times by up to 45% while maintaining stable memory use. The proposed planning framework enables the identification of a coherent pathway for strategic, coordinated investments across integrated electricity and gas systems, while uncoordinated deterministic practices risk oversizing early generation investments by up to 55% due to the inability to leverage cross-system synergies.
Las ruinas urbanas, auténticos testimonios de pasados abiertos, llegan a ese estado por distintos acontecimientos, uno de ellos fue el estallido social de Chile, donde el Museo Violeta Parra fue incendiado. El objetivo central es reflexionar sobre las opciones de intervención de las ruinas urbanas, tomando el caso del Museo Violeta Parra. Para analizar este museo, —desde una metodología empírica, descriptiva de carácter mixto—, se revisa el estado del arte, planimetrías, se realizan encuestas y entrevistas, y se estudian referentes nacionales e internacionales. Se abre la discusión sobre las perspectivas de intervención de una ruina urbana, en particular de un museo, que se presentan desde nueve criterios en función de la: reversibilidad, postura conceptual, finalidad, valores, escala, criterios técnicos, temporales, internacionales, legales y administrativos, con la finalidad de mostrar el universo de posibilidades y poder tomar decisiones.
