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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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How Transparency Shapes the Quality of Human-Robot Interaction: An Examination of Trust, Perception, and Workload

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International Journal of Social Robotics 2025年 1-28页

作者： Cai, Min Jin, Qixuan Zhou, Jia Luo, Xinggang Experimental Center of Data Science and Intelligent Decision-Making Hangzhou Dianzi University Hangzhou310018 China Research Institute for Neuroergonomics and Management Innovation School of Management Science and Real Estate Chongqing University Chongqing400044 China

As Artificial Intelligence (AI) is increasingly employed in robots and makes interaction with robots more intricate, humans’ need for transparency becomes predominant;however, current research demonstrates debates regarding the form, degree, and implementation of transparency. The industrial and military robotics sectors highlighted the positive role of transparency in improving user experiences. In contrast, medical and service robotics usually discussed the negative impact of transparency in diminishing users’ evaluations of robots and escalating their cognitive workloads. The proposed meta-analysis, drawing from 644 publications and distilling 25 studies, sought to comprehensively comprehend the role of transparency in robotics, specifically, its impact on user trust, perception, and workload. These findings suggest that excessive transparency may undermine the trust in humanoid service robots. Subtle variations in the experimental design and nature of online platforms could influence the outcomes. The focus lies on methodological recommendations, improving existing transparency models to enable customization of transparency levels based on the practical application contexts of robots. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Anthropomorphic robots

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data Augmentation Using Large Language Model for Fake Review Identification 23rd

Data Augmentation Using Large Language Model for Fake Review...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Li, Qingxu Chen, Jindong Zhang, Wen Beijing Information Science and Technology University Beijing100192 China Intelligent Decision Making and Big Data Application Beijing International Science and Technology Cooperation Base Beijing China Beijing University of Technology Beijing China

ISBN: (纸本)9789819601776

With the development of e-commerce, fake review identification is crucial for both platform users and merchants. Most datasets of fake reviews are imbalance, and the number of fake reviews is far less than that of real reviews. Due to the excellent performance of Large Language Modeling (LLM) in text generation, this paper proposes a fake review recognition method based on data augmentation by LLM. Firstly, several typical LLMs are selected to generate fake reviews, and the quality of the generated text is evaluated by the computational novelty and diversity. Subsequently, based on the data generated by different LLMs, and the performances of the different classification methods are compared. Finally, all the reviews generated by different LLMs are added to the original dataset, and the classification performances are compared with the original dataset. The experimental results of fake review identification show that the augmented dataset has about 10% improvement in classification accuracy compared to the original dataset only. It can be concluded that data augmentation through LLM is indeed significant for fake review identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data accuracy

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Evolution of Cumulative Reciprocity in Structured Populations 23rd

Evolution of Cumulative Reciprocity in Structured Population...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Luo, Shuangling Tian, Zhenjia Li, Juan Xia, Haoxiang School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China

ISBN: (纸本)9789819601776

Direct and network reciprocity are key mechanisms that promote cooperation in social dilemma games. Direct reciprocity focuses on exploring successful strategies in repeated interaction, while network reciprocity often simplifying players’ strategies to pure strategies or a limited set of classic strategies. Consequently, understanding the evolutionary dynamics of large-scale and diversity strategies in structured populations remains a challenge. Recent study reveals the cumulative reciprocity strategy’s extraordinary evolutionary advantages in well-mixed populations, but its performance in structured populations remains unclear. This study addresses this research gap by investigating the impact of the cumulative reciprocity strategy on the evolution of large-scale reactive strategy populations across different networks including lattice, small-world, and random networks. Through ecological evolutionary simulations, the results reveal that introducing cumulative reciprocity leads to noticeable changes in the evolutionary dynamics of populations employing reactive strategies. Cumulative reciprocity replaces the traditional generous tit-for-tat strategy as an evolutionarily dominant strategy, thereby fostering stable cooperation within the population. This conclusion holds true across various network densities, population size, mutation mechanisms, and network structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abiotic

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SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

arXiv

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arXiv 2025年

作者： Xiao, Wei Wang, Huiying Zhou, Qifeng Wang, Qing Department of Automation Xiamen University Xiamen China School of Engineering Westlake University Hangzhou China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen University Xiamen China Department of Cognitive Service Foundations IBM T.J. Watson Research Center New York United States

Sequential recommendation is a key area in the field of recommendation systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance

arXiv

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arXiv 2025年

作者： Jin, Jiaqi Wang, Siwei Dong, Zhibin Yang, Xihong Liu, Xinwang Zhu, En He, Kunlun School of Computer National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Sciences Beijing China Medical Big Data Research Center Chinese PLA General Hospital Beijing China

Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors like privacy concerns and device malfunctions. A key challenge is effectively utilizing available instances to recover missing views. Existing methods frequently overlook the heterogeneity among views during recovery, leading to significant distribution discrepancies between recovered and true data. Additionally, many approaches focus on cross-view correlations, neglecting insights from intra-view reliable structure and cross-view clustering structure. To address these issues, we propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency. Extensive experiments on benchmarks demonstrate the superiority of BURG in the incomplete multi-view scenario. Copyright © 2025, The Authors. All rights reserved.

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Overlapping community detection via Layer-Jaccard similarity incorporated nonnegative matrix factorization

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Neural Networks 2025年

作者： Zhijian Zhuo Bilian Chen Department of Automation Xiamen University 361005 China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen 361005 China

As information modernization progresses, the connections between entities become more elaborate, forming more intricate networks. Consequently, the emphasis on community detection has transitioned from discerning disjoint communities towards the identification of overlapping communities. A variety of algorithms based on the sparse adjacency matrix, which are sensitive to edge connections, are suitable for detecting edge-sparse areas between overlapping communities but lack the ability to detect edge-dense areas within the overlapping communities. Additionally, most algorithms do not take into account multihop information. To mitigate the aforementioned limitations, we propose an innovative approach termed Layer-Jaccard similarity incorporated nonnegative matrix factorization (LJSINMF), which utilizes both the adjacency matrix and the Layer-Jaccard similarity matrix. Our method initially employs a newly proposed Onion-shell method to decompose the network into layers. Subsequently, the layer of each node is used to construct a Layer-Jaccard similarity matrix, which facilitates the identification of edge-dense areas within the overlapping communities and serves as a general approach for enhancing other nonnegative matrix factorization-based algorithms. Ultimately, we integrate the adjacency matrix and the Layer-Jaccard similarity matrix into the nonnegative matrix factorization framework to determine the node-community membership matrix. Moreover, integrating the Layer-Jaccard similarity matrix into other algorithms is a promising approach to enhance their performance. Comprehensive experiments have been conducted on real-world networks and the results substantiate that the LJSINMF algorithm outperforms most state-of-the-art baseline methods in terms of three evaluation metrics.

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Dual-structure community preserving network embedding

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Neural Networks 2025年 189卷

作者： Bai, Xu Chen, Bilian Department of Automation School of Aerospace Engineering Xiamen University 361005 China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Xiamen 361005 China

Network embedding, an effective method for learning low-dimensional representations of nodes, plays a crucial role in various network learning scenarios. However, existing network embedding learning methods fail to learn node embeddings from the perspective of the implicit network, leading to embeddings that only incorporate neighborhood details from neighboring nodes across various levels. In this paper, we propose a network embedding model based on the non-negative matrix factorization (NMF) framework, called Dual-structure Community Preserving Network Embedding (DCPNE), where the dual-structure means that it not only considers the explicit structure of the network, but also constructs the implicit structure of the network. The main benefit is that DCPNE empowers the embedding of nodes to comprehend the dual structure of the network and broadens the embedding information of a node from its neighborhood to its community. Furthermore, we develop an updating rule for optimizing DCPNE, and analyze the time complexity of our model. The results of the experiment on eight real network datasets show that the proposed model performs better than the mainstream algorithms. © 2025 Elsevier Ltd

关键词： Community Dual-structure Network embedding Non-negative matrix factorization (NMF) Optimization

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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