We propose a novel scheme for simultaneous retiming and reshaping of two wavelength-division-multiplexing channels using two independent phase clock pump and polarization multiplexing in fiber-optic parametric amplification, with the benefits of suppressing the interchannel cross-phase-modulation effect induced by the clock pumps and four-wave-mixing crosstalk. Operation of two 10-Gb/s channels is demonstrated experimentally. In both single- and dual-channel operation, eye diagrams after regeneration are significantly cleared with pulsewidth narrowed, and the nonlinear interferences are effectively reduced. An average 2-dB power penalty improvement at a bit-error rate of 10^-9 is obtained in both single- and dual-channel operation.

Amid the global energy transition, geothermal energy, as a clean, stable, and renewable energy source, serves as a core direction for energy structure optimization. The development of medium-deep geothermal reservoirs is dominated by thermo–hydro–mechanical (THM) multi-physics coupling effects, yet the quantitative regulation laws of their operational parameters remain unclear. In this study, a numerical model for geothermal extraction considering THM multi-physics coupling was established. Using the single-factor variable method, simulations were conducted within the set parameter ranges of injection–production pressure difference, well spacing, and injection temperature. The spatiotemporal evolution characteristics of the temperature field, the dynamic temperature–pressure responses at the midpoint of injection–production wells and production wells, and efficiency indicators, such as instantaneous heat extraction power and cumulative heat extraction, were analyzed and quantified. The results show that a larger pressure difference accelerates the expansion of the cold zone in the reservoir, which improves short-term heat extraction efficiency but increases the risk of long-term thermal depletion; a smaller well spacing leads to higher initial heat production power but results in lower long-term cumulative heat extraction due to rapid heat consumption; within the normal temperature range of 16–24 °C, the injection temperature has a negligible impact on heat extraction efficiency. This study clarifies the regulatory laws of operational parameters and provides theoretical support for well pattern design and injection–production process optimization in medium-deep geothermal development.

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases.