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The Dedicated Bus Lane (DBL) is often adopted to ensure transit priority. This is because transit priority can effectively mitigate congestion at the signalized intersection. However, the DBL would cause heavy sacrifices from general vehicles when the frequency of buses is low. To address this issue, many studies were proposed to reduce general vehicles’ sacrifice by converting DBLs into Bus-Priority Lanes (BPLs). Such BPLs can be intermittently open to general vehicles. However, these studies cannot ensure absolute transit priority when general vehicles access BPLs. With the advance of Connected Automated Vehicle (CAV) technology, this paper proposes a Trajectory-Based Control (TBC) method for connected automated traffic to approach signalized intersections considering absolute transit priority. A TBC controller is designed to control general vehicles’ trajectories to access BPLs without interference with buses. The TBC controller can balance the multiple cost factors and ensure absolute bus priority. The proposed TBC controller is evaluated against the noncontrol baseline and the state-of-the-art TBC. Sensitivity analysis is conducted under four different congestion levels. The results demonstrate that the proposed TBC method outperforms and has benefits in improving throughputs and fuel efficiency and reducing delays.

Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models.

The waterproof curtain plays an important role in the dewatering of a deep foundation pit. Recognition of the depth of the waterproof curtain inserted into the confined aquifer at different depths may help control ground subsidence due to dewatering, but subsidence analysis of the interaction between dewatering and the waterproof curtain requires further study. In this study, we mainly analyze the relationship between ground subsidence and dewatering based on the shield shaft pit of the Qianhai-Nanshan deep tunnel project in Shenzhen. Our numerical simulation results show that the ground subsidence around the foundation pit decreases with an increase in the depth of the waterproof curtain inserted into the confined aquifer, and when the waterproof curtain completely penetrates the confined aquifer, the ground subsidence caused by pit dewatering is minimal. Our numerical simulation results are consistent with the actual on-site dewatering monitoring data. Our results suggest that the diaphragm wall is an effective measure to control the ground subsidence in deep foundations, helping to reduce excessive dewatering.

Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor μ, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation.

Background: Scientific, accurate, and rapid detection of the composition and content of tea polyphenols is an important basis for their rational use and giving full play to their physiological effect. The spectrophotometric assays for total polyphenols have poor selectivity. Therefore, there is a need to develop a simple and reliable method for the determination of the total polyphenolic level in tea products. Objective: The aim of this research was to develop a flow injection chemiluminescence (FI-CL) method based on the Ag(III)-luminol system for the total polyphenol content analysis of tea and substitute tea. Method: Through Box-Behnken experimental design, we selected the optimum determination condition. The Ag(III) concentration was 5x10-5mol/L, and the luminol concentration was 3x10-7mol/L (including 0.15mol/L NaOH). The peristaltic pump is 25 r/min, and the photomultiplier voltage is 600 v. Sample extracts were diluted 100 000 times for the FI-CL assay. Results: Under optimal conditions, CL intensities were proportional to total polyphenol content (in terms of gallic acid concentrations) in the range of 0.1-100 lg/L. The LOD and LOQ were 0.03 lg/L and 0.1 lg/L. The recovery values were in the range of 86.3-111.0% with a RSD of 1.04~2.62%. The polyphenolic content of 12 teas and 6 substitute teas was determined, and the results of the developed method and Folin-Ciocalteu method were highly correlated (r=0.9493 for tea and r=0.8533 for substitute tea). Conclusions: The proposed method is better than the Folin-Ciocalteu method in terms of selectivity, sensitivity, and accuracy. It is suitable for the determination of polyphenol content not only in tea, but also in substitute tea. Highlights: We developed a new flow-injection analysis method for polyphenolic content determination based on the Ag(III)-luminol chemiluminescence system. It is simple, rapid, sensitive, and accurate. It is suitable for the determination of polyphenols content not only in tea, but also in substitute tea. [ABSTRACT FROM AUTHOR]