We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangian (NCL), an augmented Lagrangian method in which the subproblems are solved using an interior-point method. NCL has two key advantages for large-scale SCOPF. First, NCL handles difficult problems such as infeasible ones or models with complementarity constraints. Second, the augmented Lagrangian term naturally regularizes the Newton linear systems within the interior-point method, enabling solution of the Newton systems with a pivoting-free factorization that can be efficiently parallelized on GPUs. We assess the performance of our implementation, called MadNCL, on large-scale corrective AC-SCOPFs, with complementarity constraints modeling the corrective actions. Numerical results show that MadNCL can solve AC-SCOPF with 500 buses and 256 contingencies fully on the GPU in less than 3 minutes, whereas Knitro takes more than 3 hours to find an equivalent solution.
Land degradation and native vegetation loss in Brazil have persisted despite a comprehensive land use regulatory framework. This study examines how Brazilian federal land use policies changed between 2000 and 2025 and how these changes relate to institutional government change. Drawing on historical institutionalism, qualitative policy analysis, and process tracing, the research analyzes 239 laws and decrees across eight environmental and agricultural policies over eight administrations. The findings show that policy goals persisted across governments, while rules, instruments, budgets, and institutional capacities shifted. Two critical junctures, Dilma Rousseff’s impeachment in 2015 and the start of Bolsonaro’s presidency in 2019, coincided with environmental policy replacement, institutional dismantling, and weakened enforcement. However, prior layering and conversion of environmental policies created resilience, enabling institutional recovery under Lula’s third administration beginning in 2023. Agricultural policies exhibited greater stability, reflecting entrenched economic and political coalitions. Linking policy change processes to vegetation loss data shows that periods of weakened implementation were associated with increased vegetation loss, whereas restored institutional coordination was associated with reduced deforestation. Targeted strategies, especially the Action Plans for the Prevention and Control of Deforestation, were highly effective when supported by institutional capacity. This study calls for greater integration between agricultural and environmental policies, using cross-compliant financial and technical instruments to combat land use change driving vegetation loss, matched with continued whole-territory monitoring and targeted actions across biomes. The findings underscore the critical role of stable interministerial coordination and long-term institutional foundations in sustaining conservation policy amid political volatility.
Electroweak radiative corrections form a crucial ingredient in modern precision calculations for particle processes at high-energy colliders such as the Large Hadron Collider. The salient features of electroweak corrections as well as currently used techniques and concepts for their calculation are reviewed. Recent progress in this enterprise is illustrated in a discussion of electroweak multi-gauge-boson production processes: massive di-boson production, vector-boson scattering, and massive tri-boson production.
Landslides pose significant threats to life, property and sustainable development in mountainous regions worldwide, with their occurrence increasingly influenced by climate change. This study addresses the critical need for accurate landslide susceptibility models in the Western Province of Rwanda, where traditional methods have shown limitations. It employed and compared three deep learning models: convolutional neural network (CNN), deep neural network (DNN), and multi-layer perceptron (MLP), to assess the landslide risks, incorporating climate change considerations. The study utilised 16 conditioning factors, carefully selected to avoid multicollinearity, with the digital surface model (DSM) showing the highest variance inflation factor (VIF) of 3.9730. The CNN model demonstrated superior performance, achieving the highest overall accuracy (93.7
