To efficiently support the transport of sub-1-Gbps services, an optical service unit (OSU)-based metro-optimized optical transport network (M-OTN) technology framework is proposed and described. The proposed M-OTN/OSU technologies have been implemented by major OTN equipment vendors and verified in a network operator’s lab for the first time to our knowledge. Moreover, a multivendor interoperability test has been conducted in a real-time field trial. The experimental results show that M-OTN/OSU has clear advantages over existing SDH VC/packet-enhanced OTN technologies in several key aspects, e.g., low latency, massive connections, and agile hitless bandwidth adjustment. M-OTN/OSU will be valuable for network operators providing high-quality leased line services and video/VR services in modern OTNs with unprecedented scalability and performance.

Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practical engineering scenarios. To tackle these issues, this study presents a novel slope stability classification model grounded in the Optuna-TPE-CatBoost framework. By leveraging the Tree-structured Parzen Estimator (TPE) within the Optuna framework, the model adaptively optimizes CatBoost hyperparameters, thus enhancing prediction accuracy and robustness. It incorporates six key features—slope height, slope angle, unit weight, cohesion, internal friction angle, and the pore pressure ratio—to establish a comprehensive and intelligent assessment system. Utilizing a dataset of 272 slope cases, the model was trained with k-fold cross-validation and dynamic class imbalance strategies to ensure its generalizability. The optimized model achieved impressive performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.926, an accuracy of 0.901, a recall of 0.874, and an F1-score of 0.881, outperforming benchmark algorithms such as XGBoost, LightGBM, and the unoptimized CatBoost. Validation via engineering case studies confirms that the model accurately evaluates slope stability across diverse scenarios and effectively captures the complex interactions between key parameters. This model offers a reliable and interpretable solution for slope stability assessment under complex failure mechanisms.

The fluid-induced vibration heat transfer enhancement technology has great potential, because it can effectively improve the heat transfer efficiency without consuming additional energy. A reasonab...

Aimed at investigating the differentiation of acoustic emission (AE) signals and fractal precursory characteristics between strong, weak, and no bursting liability coals under uniaxial compression, as well as improving the accuracy of rockburst monitoring and early warning by AE techniques, we experimentally studied the evolution law and differences of AE ring count rate, energy rate, and correlation dimension between different loaded bursting liability coals by the YAW4306 electric mechanical test system and CTA‐1 AE monitor. Our experimental results indicated that the AE count and energy of coal samples with different bursting liabilities showed a similar evolution law of “sharp increase‐calm‐sharp increase” before their main rupture. The active points of AE signals emitted from coal with strong, weak, and no bursting liability appeared at about 85∼90%, 75∼78%, and 51∼55% of the peak stress, respectively. The stronger the bursting liability of coal, the shorter the duration of main rupture and postpeak failure stage, and the greater the AE energy rate in the main rupture. The AE counts of different coals had obvious fractal characteristics, and the AE correlation dimension values of strong and weak bursting liability coal samples presented the phenomenon of “fluctuating rise to a peak value‐sharp drop‐continuous decrease,” which can be used as a precursory information of coal failure.

Achieving rapid and accurate prediction of slope stability is a key scientific issue in slope engineering. However, slope stability is governed by multi-factor coupling effects and exhibits strong nonlinear characteristics, and traditional slope stability prediction methods have clear limitations in predicting the stability of complex slope engineering. To address such problems, a CCO-optimized XGBoost model for slope stability prediction is constructed by using the Cuckoo Catfish Optimizer (CCO) to optimize the hyperparameters of XGBoost, thereby enhancing prediction accuracy and robustness. Based on 832 slope cases covering various slope engineering scenarios, six key feature parameters—slope height (H), slope angle (β), unit weight (γ), cohesion (C), internal friction angle (φ), and pore pressure ratio (r_u)—were selected to construct the dataset. Ten-fold cross-validation was adopted for model training and robustness testing. After optimization, the optimized model achieved an accuracy of 0.898, precision of 0.892, recall of 0.902, F1-score of 0.897, and AUC of 0.944, representing its optimal comprehensive performance. These evaluation metrics are significantly better than those of unoptimized XGBoost, RandomForest, LightGBM, and the representative PSO-XGBoost model. The SHAP analysis method was used to improve the interpretability of model predictions. Prediction analysis was carried out using 12 sets of real engineering cases, and the prediction results were consistent with the actual conditions, further verifying the model’s generalization ability. This model shows favorable performance in slope stability prediction and can provide a reference method for slope stability evaluation, decision optimization and risk prevention and control in slope engineering.

In order to quickly and accurately identify the coal accumulations on the coal grates at the downstream discharge port of the turning machine of the power plant, the YOLOV5s model based on the Pytorch neural network architecture is applied to the self-developed coal grates coal accumulations cleaning device for the research of coal accumulations identification speed and accuracy, and the coal grates coal accumulations data set is created independently and data enhancement is carried out to improve the robustness of the training model. The lower computer takes PLC as the control core, which has high reliability and stability. After the actual test, based on YOLOV5s coal grate cleaning device technology is practicable, both recognition accuracy and cleaning speed requirements, can meet the needs of enterprise production.