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ACS ES&T Air 2025年第2期2卷 249-263页

作者： Yicong He Kelsey R. Bilsback Manish Shrivastava Rahul A. Zaveri John E. Shilling John H. Seinfeld Bin Zhao Shuxiao Wang Christopher D. Cappa Jeffrey R. Pierce Shantanu H. Jathar Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States；State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States；PSE Healthy Energy Oakland California 94612 United States Pacific Northwest National Laboratory Richland Washington 99354 United States Divison of Chemistry and Chemical Engineering California Institute of Technology Pasadena California 91125 United States State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Atmospheric Science Colorado State University Fort Collins Colorado 80521 United States Department of Civil and Environmental Engineering University of California Davis Davis California 95616 United States Department of Mechanical Engineering Colorado State University Fort Collins Colorado 80523 United States

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, aqueous chemistry, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of 4 in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). In addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol number size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model.

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Optimal design and techno-economic analysis of a desert off-grid photovoltaic-to-hydrogen system integrated with pipeline networks

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Energy Conversion and Management 2025年 336卷

作者： Xu, Jiaxin Lin, Jin Li, Jiarong Lin, Ruixiao Pan, Guangsheng Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China Tsinghua-Sichuan Energy Internet Research Institute Chengdu China Harvard John A Paulson School of Engineering and Applied Sciences Harvard University Cambridge United States School of Electrical Engineering Southeast University Nanjing China

Addressing the challenge of harnessing remote solar resources for sustainable hydrogen production, this study presents a scalable off-grid photovoltaic-to-hydrogen (OgPVtH) system. We propose a bottom-up design methodology for the OgPVtH system, capturing the interconnections between land, water, and energy at micro and macro levels. At the micro level, we propose and consider the dynamic water-power-hydrogen characteristics of electrolyzers in a precise capacity allocation optimization model. At the macro level, we consider pipeline dynamic operating pressure constraints and establish a robust method for generating water-hydrogen pipeline network topologies. We conduct a real case study in the Hobq Desert. It reveals a levelized cost of hydrogen (LCOH) of 2.59 $/kg for the proposed OgPVtH system, indicating a 3.0 % lower cost compared to coal-based hydrogen production with carbon capture and storage (CCS) and 25 %-50 % lower cost compared to bioenergy to hydrogen, which demonstrates the system's economic competitiveness. The precise modeling of water-power-hydrogen characteristics and topologies can reduce LCOH by 0.35 $/kg and 0.01 $/kg, respectively. Sensitivity analysis highlights the trade-offs between investment in transmission and storage due to operational volatility, with an optimal hydrogen flow rate volatility of 80 % minimizing the total LCOH. © 2025 Elsevier Ltd

关键词： Hydro energy

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Selective Regulation of ray tissue for achieving ultrastable Zero-Poisson's-ratio material out of wood

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Journal of Advanced Research 2025年

作者： Yu, Xia Liu, Bo Zhou, Xinyi Wu, Tong Ren, Bohua Fang, Tao Cong, Chaonan Wu, Guofang Yao, Lihong Wei, Xiaoding Lu, Yun Research Institute of Wood Industry Chinese Academy of Forestry Beijing 100091 China College of Material Science and Art Design Inner Mongolia Agricultural University Hohhot 010018 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science College of Engineering Peking University Beijing 100871 China

Introduction: Materials exhibiting a Poisson's ratio of zero have attracted considerable interest due to their unique properties and potential applications in various fields, including aerospace, athletic footwear, and sporting equipment. However, the high costs associated with their structural fabrication and the dependence on synthetic chemical materials for most zero Poisson's ratio materials complicate the preparation processes of current elastic materials, resulting in negative environmental impacts. Objectives: This study presents a sustainable treatment strategy that utilizes the inherent cellular structure of wood to achieve a zero Poisson's ratio, thereby enhancing its elasticity. Methods: By strategically selecting tree species with varying tissue compositions and employing simple chemical and heat treatments, we developed a commercially viable elastic wood material with a zero Poisson's ratio that meets diverse stress rebound requirements. Results: The unique internal structure of the wood not only provides high fatigue resistance—capable of withstanding 5000 cycles of compression at a strain of 40 %—but also ensures excellent resilience and processability. At a deformation level of 60 %, the elastic modulus reaches 90.9 MPa. Additionally, the material retains its elasticity even at extremely low temperatures of −196 °C and demonstrates the ability to endure elevated temperatures following carbonization at 1200 °C. Conclusion: This study demonstrates that wood-based materials with a zero Poisson's ratio exhibit remarkable stability after cyclic compression, presenting a viable pathway for developing superelastic materials suitable for both high- and low-temperature applications. © 2024

关键词： Ray tissue Superelastic Ultrastability Zero Poisson's ratio

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An Accurate Dynamic Time Domain Model for LLC Resonant Converter by Considering Non-ideal Components, Various Modulation Strategies, and Dynamic Process

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Zhang, Shuo Li, Ziang Xu, Mochen Luo, Ting Sun, Peng Luo, Quanming Liu, Jinjun Wei, Yuqi Xi'an Jiaotong University School of Electrical Engineering Xi'an710049 China Shanghai Institute of Space Power-Sources State Key Laboratory of Space Power-Sources Shanghai200245 China Chongqing University School of Electrical Engineering Chongqing400044 China North China Electric Power University School of State Key Laboratory of Alternate Electrical Power System Renewable Energy Sources Beijing100096 China

In order to improve the performance of LLC resonant converters in wide voltage range applications, many novel modulation strategies other than traditional pulse-frequency modulation (PFM) have been proposed, such as pulse-width modulation (PWM) and phase-shift modulation (PSM). However, the existing modeling method cannot be extended to other modulation methods easily. In most cases, the existing models are developed based on the assumption of ideal components, which neglect parasitic components and dead time effect. Practically, these non-ideal factors have a significant influence on the steady state operation and the soft switching performance. Besides, due to the complexity and nonlinearity of the operation process, it is difficult to conduct the small signal analysis. In this article, an accurate dynamic time domain model is developed based on stages iteration, considering common parasitic components, the dead time effect, and various modulation methods. It can reflect the implementation of zero voltage switching (ZVS) and the high frequency oscillation under the light load. Based on the improved dynamic model, a convenient and accurate small signal analysis method is proposed. simulation and experimental results are presented to verify the accuracy of the model. Compared to the traditional model, the relative error of the voltage gain can be reduced from 14.2% to 5.0%. © 2013 IEEE.

关键词： Signal modulation

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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

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Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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A fast calculation method for three-impulse contingency return trajectory during the near-moon phase based on differential algebra

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Chinese Journal of Aeronautics 2025年第04期 290-308页

作者： Lin LU Haiyang LI Tianshan DONG Wanmeng ZHOU College of Aerospace Science and Technology National University of Defense Technology Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Beijing Institute of Spacecraft System Engineering China Astronaut Research and Training Center

Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions,a fast calculation method for three-impulse contingency return trajectories is ***,a three-impulse contingency return trajectory scheme is presented by combining the Lambert transfer and maneuver at the special ***,a calculation model of three-impulse contingency return trajectories is ***,fast calculation methods are proposed by adopting the high-order Taylor expansion of differential algebra in the twobody trajectory dynamics model and perturbed trajectory dynamics ***,the performance of the proposed methods is verified by numerical *** results indicate that the fast calculation method of two-body trajectory has higher calculation efficiency compared to the semi-analytical calculation method under a certain accuracy *** to its high efficiency,the characteristics of the three-impulse contingency return trajectories under different contingency scenarios are further analyzed *** findings can be used for the design of contingency return trajectories in future manned lunar landing missions.

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Urban Land Surface Temperature Retrieval From Landsat-9 Satellite Data Using Nonlinear Split-Window Algorithm

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Junwei Tang, Bo-Hui He, Zhi-Wei Fan, Dong Zhu, Xinming Li, Menghua Huang, Liang Kunming University of Science and Technology Faculty of Land Resource Engineering Yunnan Key Laboratory of Quantitative Remote Sensing Yunnan International Joint Laboratory for Integrated Sky-Ground Intelligent Monitoring of Mountain Hazards Kunming650093 China Chinese Academy of Sciences State Key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources Research Beijing100101 China

Land surface temperature (LST) is a key factor in monitoring and improving thermal environments. However, conventional LST retrieval algorithms have not sufficiently accounted for the cavity and adjacency effects caused by the 3-D structures in urban settings. In this study, we propose an urban multiple scattering radiative transfer model (UMS-RTM). Based on this model, we develop an urban nonlinear split-window (UNSW) algorithm to retrieve urban LST (ULST) from Landsat-9 satellite data. The UMS-RTM optimizes the thermal radiation transfer process by correcting the cavity and adjacency effects. Analysis shows that land surface emissivity (LSE) and sky view factor (SVF) are the primary factors influencing these effects. The cavity effect increases the effective LSE by 0.01–0.08, while the adjacency effect raises the ground-leaving brightness temperature (BT) by 0.82–3.51 K. The UNSW algorithm’s coefficients were calibrated across various LST, water vapor content (WVC), and SVF groupings to eliminate atmospheric effects and correct for cavity and adjacency effects. Sensitivity analyses of instrument noise, WVC, effective LSE, and SVF uncertainties demonstrated the reliability of the UNSW algorithm. Validation using simulated data showed that the ULST retrieved by the UNSW algorithm had a root-mean-square error (RMSE) of 0.35 K. When applied to Landsat-9 satellite data, the UNSW algorithm revealed that conventional algorithms and LST products overestimate ULST by 0–2 K, with overestimations exceeding 1 K in areas with low SVF. The UNSW algorithm provides more accurate ULST retrieval and finer spatial distribution details. © 1980-2012 IEEE.

关键词： Mean square error

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Collaborative Perception Datasets for Autonomous Driving: A Review

arXiv

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arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-Datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

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Intra-day dispatch method via deep reinforcement learning based on pre-training and expert knowledge

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International Journal of Electrical Power and Energy systems 2025年 169卷

作者： Chen, Yanbo Du, Qintao Dong, Huayu Huang, Tao Ma, Jiahao Xu, Zitao Wang, Zhihao The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources and School of Electrical & Electronic Engineering North China Electric Power University Beijing102206 China The Key Laboratory of Control of Power Transmission and Conversion Ministry of Education and Shanghai Non-Carbon Energy Conversion and Utilization Institute Shanghai Jiao Tong University Shanghai200240 China

Traditional economic dispatch algorithms rely on the accuracy of all parameters and also lack the adaptability to the high uncertainties brought by the dynamic changes happening in the current power systems. Its computing efficiency also needs to be improved with the increased operational complexities. In recent years, due to high self-learning and self-optimization ability, reinforcement learning has emerged in the field of economic dispatch, which can solve model-free dynamic programming problems that cannot be effectively solved by traditional optimization methods. In this paper, we construct a reinforcement agent for intra-day dispatch to optimize generator output, using a twin delayed deep deterministic policy gradient algorithm based on pre-training and expert knowledge (PEK-TD3). Aiming at solving the problems of long exploration time and poor convergence of conventional deep reinforcement learning, we propose an initial policy network training method based on pre-training with supervised learning, which significantly speeds up the training process of deep reinforcement learning and greatly reduces the model development cycle. At the same time, expert knowledge is embedded in the deep reinforcement learning to guide the training of the agent. With the guidance of expert knowledge, on the one hand, the agent quickly learns to limit the search direction to the feasible region of the power system operation so as to improve the convergence. On the other hand, in order to obtain higher rewards, agent learns to prioritize the renewable energy utilization which significantly reduces the curtailment rate of renewable energy. Finally, the modify IEEE 118-node system is used to verify the performance of the proposed method. © 2025 The Author(s)

关键词： Self-supervised learning

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Computer software maintenance

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