Access to high-resolution underwater images is crucial for the conservation and development of marine resources. Light scattering and light absorption are two fundamental issues in improving the quality of underwater images. Many of the captured images have severe degradation, which harms the systems and activities that rely on these images. To address this problem, we introduce an auxiliary network to enhance the contrast of underwater images. This network consists of three critical components. In the first step, a decoder network is used to recover gradient maps and enhance the brightness of the images. Then, we take the help of a brightness adjustment network to control the brightness of the hidden image, and finally, we use an adaptive contrast module to adjust the contrast. To improve the performance, we use a normalizer module to solve the problem of not paying attention to the increase in image contrast when increasing the brightness. Evaluation of the proposed method with public dataset images shows that our method can increase the resolution of underwater images. In addition, the proposed model can increase the resolution of images in complex images, low-light, and dark conditions.

Accurately forecasting the energy consumption of electric buses is important for fleet operation, charging planning, and service reliability. This study proposes an interpretable and uncertainty-aware machine learning framework for mission-level electric bus energy prediction under heterogeneous operating conditions. Using the public ZTBus dataset as a case study, the framework combines a global XGBoost model with cluster-specific models to examine variability across urban, mixed, and highway operating regimes. SHAP-based explainability is used to provide global feature attribution and mission-level local explanations, linking predictions to operationally meaningful factors such as speed, stop frequency, elevation, route distance, and environmental conditions. To support uncertainty-aware planning, bootstrap ensembles are used to characterize prediction dispersion, while conformal prediction intervals provide calibrated operational coverage. On temporally unseen data, the global XGBoost model achieves MAE ≈ 3.2 kWh and R² ≈ 0.90, while cluster-specific modeling reduces RMSE by up to approximately 28% in high-variability urban conditions. The framework shows moderate robustness under temporal distribution shift and recovers near-baseline performance after incremental retraining, while maintaining inference latency below 150 ms per prediction in the evaluated setting. Overall, the results suggest that combining heterogeneity-aware learning, explainability, and calibrated uncertainty estimation can support mission-level electric bus energy prediction within the evaluated ZTBus case study. Broader generalization should be confirmed through external validation across additional fleets, cities, vehicle types, and climatic conditions.

Buildings account for approximately 30–40% of global final energy consumption and nearly one-third of greenhouse gas emissions, making intelligent energy management a critical pathway toward decarbonization. Model predictive control (MPC) has emerged as a powerful framework for building energy management due to its ability to optimize energy cost, thermal comfort, and operational constraints under uncertainty. However, the effectiveness of MPC is highly dependent on the quality and temporal consistency of load and temperature forecasts, while access to sufficient real building data is often limited by privacy and availability constraints. This paper proposes an integrated building energy management framework that combines multi-scale synthetic data generation, a modern state-space sequence forecaster, and MPC. A multi-scale hierarchical generative adversarial network (MS-HGAN) is employed to generate privacy-preserving synthetic trajectories that augment limited real training data for forecasting, rather than acting as a control surrogate. Forecasting is performed using a Mamba-based state-space model with a Random Forest (RF-Tree) output regressor, and the resulting predictions are embedded into an MPC architecture with probabilistic constraints and a safety filter. The proposed approach is evaluated on a large commercial multi-zone shopping mall (retail center) operating in a temperate climate region (Tehran-like, Köppen Csa). To support augmentation under privacy constraints, MS-HGAN is trained on auxiliary building energy time-series datasets and is used to enrich the training distribution of the forecaster via a controlled real-only, synthetic-only, and mixed real–synthetic study. Comprehensive evaluations are conducted using both short-term (30-day) and long-term (one-year) closed-loop simulations. Results show that mixed real–synthetic training improves forecasting robustness and translates into superior MPC performance in terms of energy savings, reduced comfort violations, and peak-load shaving, compared with deterministic MPC and strong learning-based baselines, including LSTM/GRU, ARX, S4, and diffusion-based predictors. The findings provide direct evidence that privacy-motivated synthetic augmentation can play a complementary and measurable role in improving predictive control performance for commercial buildings through the forecasting-to-MPC pathway.

Access to high-resolution underwater images is vital in preserving and developing marine resources. Underwater light scattering and light absorption are two Underwater lights scattering, and light absorption are two fundamental issues in improving the quality of underwater images. Many of the captured images have severe degradation that damages the systems and activities that rely on these images. Most of the obtained images have severe degradation that negatively affects the systems and actions based on these images. To address this issue, we have introduced an auxiliary network to enhance the contrast of underwater images. This network comprises three critical components. A decoder network is used to recover the gradient maps and increase the brightness of the images. We use a brightness adjustment network to control the brightness of the hidden image, and finally, we use an adaptive contrast module to adjust the contrast. We use the normalization module to solve the problem of not paying attention to the increase in image contrast when increasing the brightness. The evaluation and comparison of our method using constructed images or images available in public datasets shows that our model effectively increased the resolution of underwater images. In addition, our model can enhance the resolution of complex images in low-light conditions in the deep sea, scenes with dim backgrounds, and images captured in dark environments.