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The effects of 17β-trenbolone and bisphenol A on sexual behavior and social dominance via the hypothalamic-pituitary-gonadal axis in male mice

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Journal of Environmental Sciences 2025年第5期151卷 54-67页

作者： Xiang Zuo Minghe Sun Huijuan Bai Shuhui Zhang Jialu Luan Qian Yu Zhenhua Fu Qili Zhao Mingzhu Sun Xin Zhao Xizeng Feng College of Life Science State Key Laboratory of Medicinal Chemical BiologyThe Key Laboratory of Bioactive MaterialsMinistry of EducationNankai UniversityTianjin 300071China Institute of Robotics&Automatic Information System College of Artificial IntelligenceNankai UniversityTianjin 300071China

17β-Trenbolone(17-TB)is well documented as an environmental endocrine disruptor in aquatic biological studies,but its effects on mammals remain poorly ***,17-TB acts as a hormone with properties similar to testosterone,and the consequences of juvenile exposure on adult social behavior remain *** A(BPA)acts as an estrogen-like hormone,compared to ***-week-old male Balb/c mice were exposed orally to 17-TB(100μg/(kg·day))and BPA(4 mg/(kg·day))for 28 *** of social interactions and a three-chamber test showed that 17-TB increased virility in male mice,intensified both male and female sexual behavior,and attracted and accepted female *** also increased social dominance through tube tests inmalemice and markedly activated the c-Fos^(+)immune response in themedial prefrontal cortex(mPFC)and basal amygdala(BLA).ELISA data showed that 17-TB and BPA exposure significantly affected serum gonadotropin-releasing hormone(GnRH),growth hormone(GH),estradiol(E2),and luteinizing hormone(LH)levels,as well as testicular lesions and androgen receptor(ARβ)and estrogen receptor(ERα)*** transcriptomic analysis further confirmed that could disrupt steroid synthesis and linoleic acid-related biometabolic *** findings suggest the influence of 17-TB and BPA exposure on sexual behavior and fertility in male mice,possibly through modulation of the hypothalamic-pituitary-gonadal *** study provides insights relevant to human reproductive health and neuro-social behavioral research,and the potential risk of environmental disturbances should not be overlooked.

关键词： 17β-Trenbolone(17-TB) Bisphenol A(BPA) hypothalamic-pituitary-gonadal axis(HPG) Sex hormones Social dominance Sexual behavior

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UAGE: A Supervised Contrastive Method for Unconstrained Adaptive Gaze Estimation 17th

UAGE: A Supervised Contrastive Method for Unconstrained Ada...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Lan, Enfan Hu, Zhengxi Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Tianjin China TBI Center Nankai University Tianjin China

ISBN: (纸本)9789819609628

Gaze estimation, which involves perceiving human gaze directions, is the foundation of gaze analysis. It provides crucial clues for understanding human attention and intention. However, most existing methods are designed for constrained environments, which leads to a significant performance drop in unconstrained practical applications. In this work, we propose a supervised contrastive method for Unconstrained Adaptive Gaze Estimation (UAGE), which consists of an unconstrained gaze estimation method and a Gaze-guided Contrastive Domain Adaptation (GCDA) framework. Our method leverages the entire human body states and the uncertainty of gaze behaviors to robustly estimate gazes in unconstrained environments, rather than solely relying on head states. Additionally, we employ the GCDA framework to adapt the model to new domains, thereby improving its generalization ability. Experiment results show that our UAGE method has achieved state-of-the-art within-domain performance on the unconstrained GAFA dataset and has reduced the angular error by 14% compared to the baseline in cross-domain gaze estimation, with GAFA as the source domain and Gaze360 as the target domain. The code is available at https://***/youthhfor/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain adaptation Gaze estimation Unconstrained environment

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FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments

arXiv

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arXiv 2025年

作者： Zhang, Shiyong Zhang, Xuebo Dong, Qianli Wang, Ziyu Xi, Haobo Yuan, Jing Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China

In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed for further improving the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume. © 2025, CC BY.

关键词： Micro air vehicle (MAV)

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STVO: Spatial-Temporal Constrained Velocity Obstacle for Safe Navigation among Pedestrians

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IEEE Transactions on Vehicular Technology 2025年

作者： Song, Yichen Wang, Runhua Bi, Qingchen Pan, Zhangchao Gao, Haiming Zhang, Xuebo Nankai University College of Artificial Intelligence Institute of Robotics and Automatic Information System The Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

This paper presents a novel motion planning method that safely navigates the unmanned ground vehicle (UGV) among pedestrian-rich environments by constructing the spatial-temporal constrained velocity obstacle (STVO) region. Compared with existing velocity obstacle (VO) type-based methods, the novelty of this work is twofold: 1) An innovative temporal constraint is introduced into the velocity space, also playing the role as an adaptive collision time constraint to dynamically adjust the velocity obstacle region according to real-time perceived pedestrian density;2) The spatial constraint is proposed to foresee and evade potential entrapments for the UGV, by carefully considering the position relationship between the UGV and the VO region. Additionally, a velocity resampling mechanism is further designed to prevent the UGV from being trapped. Above techniques augments the navigation flexibility and safety of the UGV, thereby significantly improves the success rate of practical navigation in dynamic and challenging pedestrian environments. Extensive simulations and real-world experiments have been conducted, especially the experiment in pedestrian-rich school cafeteria demonstrating satisfactory practicality of our approach. (Supplementary video link: https://***/xt4KosuPMYs). © 1967-2012 IEEE.

关键词： Pedestrian safety

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Optimization-free Smooth Control Barrier Function for Polygonal Collision Avoidance

arXiv

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arXiv 2025年

作者： Wu, Shizhen Fang, Yongchun Sun, Ning Lu, Biao Liang, Xiao Zhao, Yiming Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Polygonal collision avoidance (PCA) is short for the problem of collision avoidance between two polygons (i.e., polytopes in planar) that own their dynamic equations. This problem suffers the inherent difficulty in dealing with non-smooth boundaries and recently optimization-defined metrics, such as signed distance field (SDF) and its variants, have been proposed as control barrier functions (CBFs) to tackle PCA problems. In contrast, we propose an optimization-free smooth CBF method in this paper, which is computationally efficient and proved to be nonconservative. It is achieved by three main steps: a lower bound of SDF is expressed as a nested Boolean logic composition first, then its smooth approximation is established by applying the latest log-sum-exp method, after which a specified CBF-based safety filter is proposed to address this class of problems. To illustrate its wide applications, the optimization-free smooth CBF method is extended to solve distributed collision avoidance of two underactuated nonholonomic vehicles and drive an underactuated container crane to avoid a moving obstacle respectively, for which numerical simulations are also performed. © 2025, CC BY.

关键词： Magnetic levitation vehicles

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Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method

arXiv

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arXiv 2025年

作者： Dong, Jinyang Wu, Shizhen Liu, Rui Liang, Xiao Lu, Biao Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300071 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear disturbance observer (DOB) based on the robust integral of the sign of the error (RISE) is used to estimate system uncertainties, ensuring that the estimation error converges to zero exponentially. This error bound is integrated into the safety-critical controller to reduce conservativeness while ensuring safety. To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem. To ensure that the feasible space does not vanish under disturbances, a DOB-VCBF-based method is introduced, ensuring system safety while maintaining the feasibility of the resulting QP. Subsequently, several groups of simulation and experimental results are provided to validate the effectiveness of the proposed controller. © 2025, CC BY.

关键词： Quadratic programming

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Simulating Automotive Radar with Lidar and Camera Inputs

arXiv

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arXiv 2025年

作者： Song, Peili Song, Dezhen Yang, Yifan Lan, Enfan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China TBI center Nankai University Tianjin300350 China Abu Dhabi United Arab Emirates

Low-cost millimeter automotive radar has received more and more attention due to its ability to handle adverse weather and lighting conditions in autonomous driving. However, the lack of quality datasets hinders research and development. We report a new method that is able to simulate 4D millimeter wave radar signals including pitch, yaw, range, and Doppler velocity along with radar signal strength (RSS) using camera image, light detection and ranging (lidar) point cloud, and ego-velocity. The method is based on two new neural networks: 1) DIS-Net, which estimates the spatial distribution and number of radar signals, and 2) RSS-Net, which predicts the RSS of the signal based on appearance and geometric information. We have implemented and tested our method using open datasets from 3 different models of commercial automotive radar. The experimental results show that our method can successfully generate high-fidelity radar signals. Moreover, we have trained a popular object detection neural network with data augmented by our synthesized radar. The network outperforms the counterpart trained only on raw radar data, a promising result to facilitate future radar-based research and development. © 2025, CC BY-NC-ND.

关键词： Automotive radar

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Deep reinforcement learning-based trajectory planning for double pendulum cranes: Design and experiments

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Mechanical systems and Signal Processing 2025年 234卷

作者： Ding, Weili Zhang, Heng Hua, Changchun Lu, Biao Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Since cranes usually possess double pendulum dynamics, the mass of the payload often changes and there are frequently lifting/lowering operations simultaneously. Moreover, most crane systems are driven by motors, whose velocities and accelerations are often limited. To solve the above problems, this paper proposes a deep reinforcement learning (DRL) reference trajectory generation method based on virtual–physical joint training. Firstly, a DRL module based on deep deterministic policy gradient (DDPG), along with a double pendulum crane dynamic model and an adaptive controller, are established within the virtual environment for training the reference trajectory. When the reward reaches the threshold, the training is switched to the physical environment to further optimize the reference trajectory, realizing the swing suppression of the hook and the payload. In addition, to satisfy the performance of the drive motors, velocity and acceleration thresholds are set to constrain the performance of the drive motors. Finally, in view of the fact that the operation process of the double pendulum crane is divided into payload transportation and payload loading/unloading, an event-triggering mechanism is designed to switch different control policies in accordance with different operation processes, thus reducing the consumption of computing resources. Through experiments on the actual double pendulum crane and comparison with the existing reference trajectories and input shapers, the superiority of this method is demonstrated. © 2025 Elsevier Ltd

关键词： Model reference adaptive control

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Composite iterative learning controller design for gradually varying references with applications in an AFM system

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Journal of Central South University 2014年第1期21卷 180-189页

作者：方勇纯张玉东董晓坤 Institute of Robotics and Automatic Information System Nankai University

Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-dependent trajectories. Specifically, by decoupling the current reference into the desired trajectory of the last trial and a disturbance signal with small magnitude, the learning and feedback parts were designed respectively to ensure fine tracking performance. After some theoretical analysis, the judging condition on whether the composite iterative learning control approach achieves better control results than pure feedback contro! was obtained for varying references. The convergence property of the closed-loop system was rigorously studied and the saturation problem was also addressed in the controller. The designed composite iterative learning control strategy is successfully employed in an atomic force microscope system, with both simulation and experimental results clearly demonstrating its superior performance.

关键词： iterative learning control saturation feedback control feedforward control atomic force microscope

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