Papers
Event:
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2604.0171View实习护生压力源及应对方式调查研究目的：调查分析实习护生心理压力现状、应对方式特点及其影响因素，为制定有效的干预措施、减少实习护生压力提供依据。方法：本研究采用便利抽样法，对164名实习护生进行问卷调查，内容包括一般人口学资料、心理压力量表、应对方式量表，使用SPSS 26.0进行信度分析、描述性统计、t检验、方差分析、Spearman相关分析及多元线性回归分析。结果：实习护生心理压力总均分为3.38±0.47，处于中度偏上水平，压力维度得分由高到低依次为角色定位、心理落差、患者态度与评价、护理工作、就业考试、带教、临床考核、知识技能、临床环境；积极应对均分（2.69±0.39）略高于消极应对均分（2.54±0.44）。男性、专科、受他人影响入学、专业不喜欢、父母不支持的护生压力水平更高（P<0.05）。心理压力总分与积极应对、消极应对均呈正相关（P<0.01），与消极应对相关性更强。多元线性回归显示，心理落差、角色定位、就业考试正向预测积极应对；临床考核、知识技能、带教正向预测消极应对。研究表明，实习护生心理压力来源广泛、以中度偏上为主，应对方式尚未形成积极主导模式，受个体、专业、家庭等多因素影响。结论：实习护生心理压力整体处于中度偏上水平，学校和实习医院应给予足够的重视并及时采取积极干预措施帮助其减轻压力，提升护生心理健康水平与实习质量，使其顺利通过实习。
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2602.0003ViewHierarchical Scheduling of Aggregated TCL Flexibility for Transactive Energy in Power SystemsThis paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.
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2511.0030ViewElectionFit: A Computational Laboratory of LLM Agents for Simulating U.S. Presidential ElectionsModeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce ElectionFit, a novel framework that uses Large Language Models (LLMs) to build a ``computational laboratory'' of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g., candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deployed this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicated the real-world outcome, demonstrating the high fidelity of our ``virtual society''. The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. ElectionFit moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.
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2511.0025ViewEstimating Rural Rooftop Solar Potential Using Semantic Segmentation and Multi-Source DataSolar energy is a clean and renewable resource, and the low-rise, unobstructed rural buildings of northern China provide ideal conditions for photovoltaic (PV) installation compared to shaded, high-density urban areas. Yet, progress in assessing rural solar potential is limited by the absence of accurate 3D building data. This study proposes a rapid estimation approach integrating deep learning, parametric modeling, and GPU-accelerated simulation. Convolutional neural net- works (CNNs) extract building footprints from satellite imagery, which are then processed in Grasshopper to generate refined vector outlines. Combined with digital surface model (DSM) data, these outlines produce precise 3D village models. Using Vitality 2.0 for GPU-based solar simulation, the method was applied to 31 villages in Tianjin, generating parametric 3D models and estimating their solar potential. Results show that low building heights and minimal mutual shading make photovoltaic capacity scale with roof area—larger villages have greater generation potential. Moreover, villages with metal roofs exhibit higher conversion efficiency and shorter cost-recovery periods than those with concrete or ceramic-tile roofs, due to better heat dissipation. Overall, the workflow offers a practical and efficient solution for estimating rural solar potential in data-scarce regions to guide renewable energy planning and investment.
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2511.0003ViewAI Empowered Thermal Management Materials DesignThe development of high-performance thermal management materials holds significant importance in fields such as chips, data centers and batteries. Materials informatics, which integrates big data and artificial intelligence, is emerging as the fourth paradigm for materials research. Over the past few years, our team has undertaken preliminary explorations in the development of advanced thermal management materials empowered by big data and artificial intelligence. In this work, we introduce three successful materials informatics applications on thermal management materials design, the construction of machine learning interatomic potentials for thermal property calculations, the discovery and generative design of high-thermal-conductivity materials, and the intelligent design of micro/nano structures for thermal transport. Those successful cases have shown great advantage for thermal management materials design via materials informatics.
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2510.0041ViewGraph neural network for colliding particles with an application to sea ice floe modelingThis paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the rendering of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling.
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2510.0010ViewBioMARS: A Multi-Agent Robotic System for Autonomous Biological ExperimentsLarge language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports conte