Background: The efficacy of lapatinib is limited by the development of acquired resistance. The aim of this study was to investigate the role of estrogen receptor (ER) signaling compensatory activation in acquired resistance to lapatinib in breast cancer cells BT474 and the related mechanism.Methods: Acquired resistant cell model resistant (r)BT474 was generated with an increasing concentration of lapatinib. Real‐time polymerase chain reaction and Western blotting were used to determine the changes of human epidermal growth factor receptor (HER)2 and ER pathways in breast cancer cell BT474 after treatment with lapatinib and the distinction between BT474 and rBT474. Methyl thiazolyl tetrazolium and colony formation assays were employed to detect the proliferation of rBT474 and BT474 cells treated with lapatinib and/or an ER inhibitor, fulvestrant, respectively.Results: Lapatinib could inhibit phosphorylation of HER2 and induce expression of forkhead‐box protein O3a and progesterone receptor. Acquired resistant cell model rBT474 could grow in the presence of 5 μM lapatinib, with an apoptosis rate of only 5%. Significant inhibition of phosphatidylinositol‐4,5‐bisphosphate 3‐kinase (PI3K)/protein kinase B (AKT) pathway and the activation of the mitogen‐activated protein kinases (MAPK) and ER pathways were detected in rBT474, compared with BT474. Furthermore, the expressions of Src phosphorylation and caveolin‐1 were also upregulated. The viability of rBT474 was markedly suppressed by the lapatinib/fulvestrant combination in vitro, confirmed by the BT474 xenograft model.Conclusion: ER signaling compensatory activation may partly contribute to lapatinib acquired resistance in HER2‐overexpressing/ERα‐positive breast cancer cells, which might be related to PI3K/AKT inhibition and MAPK pathway activation.

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.

With the development of automatic driving technology, the correct driving of autonomous vehicles on the road is a research hotspot at present. Affected by the complex road traffic conditions and driving environment, the current autonomous vehicle's ability to accurately identify road targets through on-board cameras still needs to be improved. Aiming at the problems of near-target error detection and remote target missing detection in road target detection of autonomous vehicles, an improved road target detection algorithm YOLOv8-RTDAV based on YOLOv8n was proposed. To address the issue of false alarms caused by close-range overlapping road targets, we propose a new C2f module, C2f-EFB, which combines an efficient channel attention (ECA) mechanism. To address the issue of missed detections in long-distance road target recognition, we have added a P2 small target detection layer for improvement. To improve the adaptability of the model and better capture image details and feature information, we replaced the SPPF module of the original model with the SPPELAN module and implemented upsampling using the point sampling module DySample. Improve the loss function CIOU of the original model to EIOU to further enhance the detection accuracy of the model for road targets. Experiments have shown that in the KITTI and TT100K datasets, the improved road object detection algorithm has improved accuracy to varying degrees compared to YOLOv8n. Improved the recognition accuracy of close-range overlapping targets on the road and increased the recognition rate of small targets at long distances.

In the complex and stochastic traffic flow, ensuring safe driving requires improvements in perception and decision-making. This paper proposed a decision-control method that leveraged the scene perception and understanding capabilities of semantic segmentation networks and the stable convergence strategies of Deep Reinforcement Learning (DRL) algorithms to achieve more accurate and effective autonomous driving decision-control. Perception features obtained from cameras and sensors equipped with a semantic segmentation model were used as input for the intelligent agent. DRL algorithms were employed to update decisions based on reward feedback. Experimental results on the CARLA simulation platform demonstrated that the semantic segmentation network effectively identified obstacles, vehicles, and drivable areas, providing high-quality perception data input for the intelligent agent’s decision-making model. Compared to the original algorithms, the proposed Double Deep Q-Network-Semantic Segmentation (DDQN-SS) and Proximal Policy Optimization-Semantic Segmentation (PPO-SS) increased the reward value by approximately 25% and enhanced driving stability by 14.2% and 28.5%, respectively, enabling more stable and precise decision-control during driving. The method proposed in this paper has better improved the decision-control performance of PPO and DDQN in complex scenarios.

In response to energy consumption and traffic efficiency reduction caused by intersection congestion, a global speed planning that considered both ecological speed and green wave speed was conducted under the vehicle–road–cloud integration framework. After establishing an instantaneous energy consumption model for pure electric vehicles, a radial basis neural network model was used to estimate the queue length of traffic flow, and an isolated-intersection-based eco-approach and departure (I-EAD) plan was proposed based on a valid traffic signal light model. A two-stage optimization multi-intersections-based eco-approach and departure (M-EAD) strategy with multiple objectives and constraints was proposed to solve the optimal green light window and the optimal speed trajectory. The results of the SUMO/Matlab/Simulink/Python joint simulation platform show that the M-EAD strategy reduces the average travel energy consumption by 16.65% and 8.31%, and the average travel time by 26.33% and 12.53%, respectively, compared to the intelligent driver model (IDM) and I-EAD strategy. The simulation results of the typical traffic scenarios and random traffic scenarios indicate that the speed optimization strategies in this study have good optimization effects on energy conservation and traffic efficiency.

Since fare discounts have been regarded as an effective economical measure to increase passenger flow, it is helpful for local governments and transit operators to understand its impact on ridership. Taking Xiamen, China, as an example, this study uses transaction data to analyze the changes of weekday daily metro ridership after the opening of Xiamen Metro Line 1. At the initial stage of operation of Xiamen Metro Line 1, there are three preferential schemes: discount per trip, money reduced per trip, and discount after reaching the accumulated fare. Therefore, the algorithm of the iterated cumulative sums of squares is introduced to identify structural change points of the time series of daily ridership, which varies according to the type of ticket. The effects of different fare discounts on total ridership and ridership varied by ticket types are analyzed by the regression discontinuity method. The results show that the dates of structural change points are well-matched with the start and end dates of preferential schemes. Each preferential scheme has its own benefited groups. During the fare discount period, the number of passengers gradually increased. But after the cancellation of the favorable preferential scheme, the number of passengers decreased sharply. By understanding the impact of fare discounts on ridership in Xiamen, China, several metro ticketing policy recommendations are put forward, including raising the focus on E-Tickets, formulating more attractive preferential measures to promote the mode conversion of private cars and vehicles to metro, paying attention to high-frequency passengers, and seeking common subsidies from the financial industry to achieve a win-win situation. In addition, the analytical framework proposed in this study can be used to evaluate the effectiveness of other transportation policies in the future.