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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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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and artificial intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Joint Power and Duty-Cycle Design Using Alternating Optimization Algorithm under Energy Harvesting Architectures

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China Communications 2020年第12期17卷 139-155页

作者： Tong Wang Xiang Yang Feng Deng Lin Gao Yufei Jiang Zhihua Yang School of Electronics and Information Engineering Harbin Institute of TechnologyShenzhen 518055China shenzhen institute of artificial intelligence and robotics for society Shenzhen 518129China

In the emerging sixth generation(6G)communication network,energy harvesting(EH)is a promising technology to achieve the unlimited energy supply and hence makes the wireless communication systems self-sustainable in terms of ***,in practice,the efficiency of energy harvesting is often low due to the limited device *** this paper,we formulate three types of different EH architectures,i.e.,the harvest-use architecture,the harvest-store-use architecture,and the harvest-use-store architecture from the perspective of energy storage *** propose resource allocation schemes to jointly design the sensor power and duty-cycle via an alternating optimization algorithm under the above EH architectures,in both simultaneous and non-simultaneous harvesting and utilization models,aiming at achieving a higher throughput and energy ***-ideal circuit power is also *** results show that our proposed schemes under EH architectures outperform the existing classic continuous transmission schemes.

关键词： resource allocation alternating optimization energy harvesting self-sustainable

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Vision‐audio fusion SLAM in dynamic environments

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CAAI Transactions on intelligence Technology 2023年第4期8卷 1364-1373页

作者： Tianwei Zhang Huayan Zhang Xiaofei Li shenzhen institute of artificial intelligence and robotics for society ShenzhenChina Department of robotics Ritsumeikan UniversityShigaJapan Westlake University and Westlake institute for Advanced Study HangzhouChina

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment *** adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving ***,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile *** the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is ***,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more *** address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent *** heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

关键词： dynamic environment intelligent robots sensor fusion

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Electric Load Clustering in Smart Grid:Methodologies,Applications,and Future Trends

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 237-252页

作者： Caomingzhe Si Shenglan Xu Can Wan Dawei Chen Wenkang Cui Junhua Zhao The Chinese University of Hong Kong(shenzhen) ShenzhenChina the College of Electrical Engineering Zhejiang University.Hangzhou.China shenzhen institute of artificial intelligence and robotics for society ShenzhenChina

With the increasingly widespread of advanced metering infrastructure,electric load clustering is becoming more essential for its great potential in analytics of consumers’energy consumption patterns and preference through data ***,a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data,observe the characteristics of load clusters,and classify the components of the total *** can give rise to the development of related techniques and research in the smart grid,such as demand-side *** paper summarizes the basic concepts and the general process in electric load *** similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and ***,eight indices widely used to evaluate the validity of electric load clustering are ***,vital applications are discussed thoroughly along with future trends including the tariff design,anomaly detection,load forecasting,data security and big data,etc.

关键词： Electric load clustering similarity measurement clustering technique cluster validity indicator smart grid

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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

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Science China(Information Sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG institute of artificial intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University shenzhen institute of artificial intelligence and robotics for society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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Magnetic Microrobots with Folate Targeting for Drug Delivery

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Cyborg and Bionic Systems 2023年第1期4卷 309-317页

作者： Min Ye Yan Zhou Hongyu Zhao Xiaopu Wang shenzhen institute of artificial intelligence and robotics for society(AIRS) The Chinese University of Hong KongShenzhenGuangdong 518129China

Untethered microrobots can be used for cargo delivery(e.g.,drug molecules,stem cells,and genes)targeting designated ***,it is not enough to just reach the lesion site,as some drugs can only play the best therapeutic effect within the *** this end,folic acid(FA)was introduced into microrobots in this work as a key to mediate endocytosis of drugs into *** microrobots here were fabricated with biodegradable gelatin methacryloyl(GelMA)and modified with magnetic metal-organic framework(MOF).The porous structure of MOF and the hydrogel network of polymerized GelMA were used for the loading of enough FA and anticancer drug doxorubicin(DOX)*** the magnetic property of magnetic MOF,these microrobots can gather around the lesion site with the navigation of magnetic *** combination effects of FA targeting and magnetic navigation substantially improve the anticancer efficiency of these *** result shows that the cancer cells inhibition rate of microrobots with FA can be up to 93%,while that of the ones without FA was only 78%.The introduction of FA is a useful method to improve the drug transportation ability of microrobots,providing a meaningful reference for further research.

关键词： drugs enough meaningful

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基于图U-nets架构的图生成网络PM2.5概率预测系统（英文）

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Journal of Central South University 2025年第01期 304-318页

作者：李燕飞杨睿段铸刘辉 School of Mechatronic Engineering Hunan Agricultural University institute of artificial intelligence and robotics (IAIR) Key Laboratory of Traffic Safety on Track of Ministry of Education School of Traffic and Transportation Engineering Central South University

城市空气污染给人体身心健康、经济发展、环境保护等各方面带来了极大困扰，预测空气污染的变化趋势可为治理和防治工作提供科学依据。本文提出一种考虑多站点PM2.5信号时空特征信息的区间预测方法，利用K最近邻（KNN）算法对采集、传... 详细信息

城市空气污染给人体身心健康、经济发展、环境保护等各方面带来了极大困扰，预测空气污染的变化趋势可为治理和防治工作提供科学依据。本文提出一种考虑多站点PM2.5信号时空特征信息的区间预测方法，利用K最近邻（KNN）算法对采集、传输和存储过程中丢失的信号进行插值，保证数据的连续性。利用图生成网络（GGN）处理结构复杂的时间序列气象数据，在GGN模型中引入图U-Nets框架，增强其对图生成过程的可控性，有利于提高模型的效率和鲁棒性。此外，结合稀疏贝叶斯回归，改进传统核密度估计（KDE）区间预测的维数灾难缺陷。在稀疏策略的支持下，稀疏贝叶斯回归核密度估计（SBR-KDE）在处理高维大规模数据时非常高效。采用北京34个空气质量监测点的春、夏、秋、冬季PM2.5数据验证了所提模型的准确性、泛化能力以及区间预测的优越性。

关键词： PM2.5区间预测图生成网络图U-Nets 稀疏贝叶斯回归核密度估计时空特征

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research institute Northeast Petroleum University the artificial intelligence Energy Research institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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A novel spiking neural network of receptive field encoding with groups of neurons decision

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Frontiers of Information Technology & Electronic Engineering 2018年第1期19卷 139-150页

作者： Yong-qiang MA Zi-ru WANG Si-yu YU Ba-dong CHEN Nan-ning ZHENG Peng-ju REN institute of artificial intelligence and robotics Xi'an Jiaotong University

Human information processing depends mainly on billions of neurons which constitute a complex neural network,and the information is transmitted in the form of neural *** this paper,we propose a spiking neural network(SNN),named MD-SNN,with three key features:(1) using receptive field to encode spike trains from images;(2) randomly selecting partial spikes as inputs for each neuron to approach the absolute refractory period of the neuron;(3) using groups of neurons to make *** test MD-SNN on the MNIST data set of handwritten digits,and results demonstrate that:(1) Different sizes of receptive fields influence classification results significantly.(2) Considering the neuronal refractory period in the SNN model,increasing the number of neurons in the learning layer could greatly reduce the training time,effectively reduce the probability of over-fitting,and improve the accuracy by 8.77%.(3) Compared with other SNN methods,MD-SNN achieves a better classification;compared with the convolution neural network,MD-SNN maintains flip and rotation invariance(the accuracy can remain at 90.44% on the test set),and it is more suitable for small sample learning(the accuracy can reach 80.15%for 1000 training samples,which is 7.8 times that of CNN).

关键词： Tempotron Receptive field Difference of Gaussian(DoG) Flip invariance Rotation invariance

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