The Philippines is one of the most climate-vulnerable countries in the world, with Central Luzon often experiencing extreme rainfall, floods, and droughts that are expected to worsen with ongoing warming. Using regional evidence from CORDEX–SEA (CMIP5) and CMIP6 multi-model ensembles, which show stronger rainfall extremes across Southeast Asia, this study produces basin-scale projections for the Pampanga River Basin and the Pasig-Marikina-Laguna-Lake Basin. High-resolution (5 km) climate projections were created with the Weather Research and Forecasting (WRF) model, dynamically downscaled from MRI-AGCM 3.2H (60 km) and 3.2S (20 km), and bias-corrected using the quantile-mapping method. The study analyzed projected changes in seasonal rainfall, heavy-rainfall indices (≥50 mm/day; 95th–99th percentiles), drought indicators (CDD, CWD), and design rainfall (from annual maxima) under RCP2.6 and RCP8.5 scenarios. The results show a clear intensification of heavy rainfall in both basins, especially during the southwest monsoon and under RCP8.5, with annual-maximum rainfall distributions indicating a consistent rise in extreme values and higher design rainfall for all return periods. Spatial patterns vary by basin: Pampanga has the largest increases in the lower western catchment, while the Pasig-Marikina-Laguna-Lake Basin shows a more uniform increase across the basin. At the same time, more consecutive dry days (CDD) and fewer consecutive wet days (CWD) suggest increased hydroclimatic variability, with wetter wet seasons and drier dry periods. These basin-specific, bias-corrected projections offer physically consistent data for flood and drought hazard assessment, agricultural and economic modeling, and climate-resilient water infrastructure design.
Autonomous obstacle removal from the ground is an important earthwork task, but this is difficult to automate because an excavator must adapt its excavation trajectories over repeated cycles as soil-obstacle conditions change. Learning such state-dependent behavior requires a training environment that reproduces accumulated soil-obstacle interactions, including contact states, terrain deformation, and obstacle visibility. Accordingly, particle-based simulation is suitable for the relevant policy learning. However, particle simulation is computationally expensive, and repeated excavation cycles further increase the learning cost. We observe that the burial condition of an obstacle governs both task difficulty and simulation cost: deeper burial makes obstacle removal harder while also requiring more particles for accurate simulation. This observation motivates a burial-conditioned curriculum learning strategy. We propose a time-efficient sim-to-real policy learning framework in which the policy observes terrain and obstacle information from RGB-D measurements and then outputs a parameterized excavation trajectory; in this process, the simulator reproduces in a real-world excavator the same observation-action interface it uses under controllable burial conditions. The curriculum begins with shallow burial conditions and progressively increases burial depth while adjusting particle count, thus simultaneously controlling task difficulty and simulation cost. Experiments show that the proposed framework successfully learns an effective obstacle-removal policy, whereas baseline methods fail even after a full week of training. The proposed curriculum achieves effective performance within three days and achieves successful transfer to a real 12-ton excavator operating on open ground with various steel obstacles, thus demonstrating robust obstacle removal.
High-rate contact stabilization (HiCS) processes for efficiently recovering organic matter from municipal wastewater are gaining attention as a means of producing renewable energy. In this study, a pilot-scale continuous HiCS process was applied to the effluent from a primary clarifier at a municipal wastewater treatment plant-a setting rarely reported in the literature, despite its importance for future full-scale implementation. The results confirmed that the continuous HiCS process recovers more organic matter than the conventional activated sludge (CAS) process while reducing organic matter oxidation. Biomethane potential testing further demonstrated that sludge recovered from the HiCS process generated methane at higher yields and faster rates compared to sludge from the CAS process. Moreover, the energy recovered per unit of removed organic matter in the HiCS process under practical operating conditions was higher than that achieved with the CAS process.
Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical calibration is challenging due to this task's highly nonlinear particle dynamics and the black-box nature of conventional simulators. Although simulation-based inference (SBI) can estimate posterior distributions over simulation parameters solely from forward simulations, applying SBI directly to high-fidelity (HF) particle simulators is often computationally prohibitive. Low-fidelity (LF) simulators with coarser particles can reduce this cost, but changes in particle size and particle count shift the parameter values needed to reproduce the same observation, producing biased LF posteriors. We propose Bridged SBI, which leverages a biased but informative LF posterior to guide HF inference. This method first uses inexpensive LF simulations to identify a coarse high-density parameter region, and then it learns a local residual bridge to transport LF posterior samples toward HF-consistent regions by correcting the LF–HF discrepancy. We analyze how sequential multi-fidelity SBI (Naive-MF) can suffer from LF-induced posterior miscoverage when it directly relies on the LF posterior without discrepancy correction. We then show that Bridged SBI is designed to alleviate this issue by explicitly modeling the LF–HF discrepancy through residual correction. Experiments on both sim-to-sim particle-parameter calibration and real-to-sim calibration with real soil observation show that Bridged SBI produces more accurate and reliable HF posteriors than HF-only SBI or the Naive-MF baseline, especially under limited HF simulation costs.
