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Enhanced dynamics of Al^(3+)/H^(+) ions in aqueous aluminum ion batteries:Construction of metastable structures in vanadium pentoxide upon oxygen vacancies

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Journal of Energy Chemistry 2025年第2期101卷 562-569,I0011页

作者： Zhibao Wang Hanqing Gu Tianci Wu Wenming Zhang Zhanyu Li Hebei Key Laboratory of Optic-Electronic Information and Materials National&Local Joint Engineering Laboratory of New Energy Photoelectric DevicesCollege of Physics Science and TechnologyHebei UniversityBaoding 071002HebeiChina

In recent years,aqueous aluminum ion batteries have been widely studied owing to their abundant energy storage and high theo retical *** in-depth study of vanadium oxide materials is necessary to address the precipitation of insoluble products covered cathode surface and the slow reaction ***,a method using a simple one-step hydrothermal preparation and oxalic acid to regulate oxygen vacancies has been reported.A high starting capacity(400 mAh g^(-1))can be achieved by Ov-V2O5,and it is capable of undergoing 200 cycles at 0.4 A g^(-1),with a termination discharge capacity of103 mAh g^(-1).Mechanism analysis demonstrated that metastable structures(AlxV2O5and HxV2O5)were constructed through the insertion of Al^(3+)/H^(+)during discharging,which existed in the lattice intercalation with *** incorporation of oxygen vacancies lowers the reaction energy barrier while improving the ion transport *** addition,the metastable structure allows the electrostatic interaction between Al3+and the main backbone to establish protection and optimize the transport *** parallel,this work exploits ex-situ characterization and DFT to obtain a profound insight into the instrumental effect of oxygen vacancies in the construction of metastable structures during in-situ electrochemical activation,with a view to better understanding the mechanism of the synergistic participation of Al3+and H+in the *** work not only reports a method for cathode materials to modulate oxygen vacancies,but also lays the foundation for a deeper understanding of the metastable structure of vanadium oxides.

关键词： Vanadium pentoxide Oxygen vacancies Electrochemical activation Metastable structure

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Enhancing H^(+)intercalation kinetics and stability in Cu^(2+)pre-intercalatedδ-MnO_(2)for aqueous aluminum batteries

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Journal of Energy Chemistry 2025年第3期102卷 126-133页

作者： Hanqing Gu Mingjun Chen Zhibao Wang Wenming Zhang Zhanyu Li Hebei Key Laboratory of Optic-Electronic Information and Materials National&Local Joint Engineering Laboratory of New Energy Photoelectric DevicesCollege of Physics Science and TechnologyHebei UniversityBaoding 071002HebeiChina

Aqueous aluminum ion batteries(AAIBs)have garnered extensive attention due to their environmental friendliness,high theoretical capacity,and low ***,the sluggish reaction kinetics and severe structural collapse of the cathode material,especially manganese oxide,during the cycling process have hindered its further ***,Cu^(2+)pre-interca la ted layeredδ-MnO_(2)was synthesized via a hydrothermal *** pre-intercalated Cu^(2+)ions not only improve the conductivity of MnO_(2)cathode but also stabilize the structure to enhance stability.X-ray absorption fine structure(XAFS)combined with density functional theory(DFT)calculations confirm the formation of the covalent bond between Cu and O,increasing the electronegativity of O atoms and enhancing the H^(+)adsorption ***,ex-situ measurements not only elucidate the Al^(3+)/H^(+)co-insertion energy storage mechanism but also demonstrate the high reversibility of the Cu-MnO_(2)cathode during *** work provides a promising modification approach for the application of manganese oxides in AAIBs.

关键词： Aqueous aluminum-ion batteries Energy storage mechanism MnO_(2)cathodes Cu^(2+)pre-intercalated

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Can satellite observations detect global ocean heat content change with high resolution by deep learning?

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 52-68页

作者： Su, Hua Teng, Jianchen Zhang, Feiyan Wang, An Huang, Zhanchao Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China Department of Geography Ghent University Ghent9000 Belgium

The development of in situ observations has significantly improved ocean heat content (OHC) estimation. However, high-resolution OHC data remain limited, hindering detailed studies on mesoscale oceanic warming variability. This study used a deep learning method-Densely Deep Neural Network (DDNN) to reconstruct a high-resolution (0.25° × 0.25°) global OHC dataset for the upper 2000m ocean from 1993 to 2023, named the Ocean Projection and Extension Neural Network 0.25° (OPEN0.25°) product. This deep ocean remote sensing approach integrates multi-source remote sensing data, including Sea Surface Temperature (SST), Absolute Dynamic Topography (ADT), and Sea Surface Wind (SSW), alongside spatiotemporal coordinates and in situ observations. The DDNN model was trained using Argo-based gridded data and EN4-profile data, initially undergoing pre-training to assimilate large-scale oceanic features, followed by fine-tuning to enhance its accuracy in capturing mesoscale thermal structures. Our results demonstrate that the DDNN model achieves high accuracy across various depths. Particularly, OPEN0.25° can effectively capture detailed thermal variations in regions with complex dynamics, as well as the heat transfer processes within the ocean interior, outperforming traditional methods in resolution. The research highlights that, influenced by strong El Niño-Southern Oscillation (ENSO) events, OHC in the upper 700m of the Pacific Ocean potentially far exceeding expectations over the past decade. Through this study, OPEN0.25° has demonstrated its critical role in detecting and monitoring long-term changes in global OHC at high resolution. © 2025 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

关键词： Submarine geophysics

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State surveillance and fault diagnosis of distillation columns using residual network-based passive acoustic monitoring

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Chinese Journal of Chemical engineering 2025年第1期77卷 248-258页

作者： Haotian Zheng Zhixi Zhang Guangyan Wang Yatao Wang Jun Liang Weiyi Su Yuqi Hu Xiong Yu Chunli Li Honghai Wang School of Chemical Engineering and Technology National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources UtilizationHebei University of TechnologyTianjin 300130China School of Information Engineering Tianjin University of CommerceTianjin 300134China Coal Chemical R&D Center of Kailuan Group Tangshan 063007China

The operational state of distillation columns significantly impacts product quality and production ***,due to the complex operation and diverse influencing factors,ensuring the safety and efficient operation of the distillation columns becomes *** research combines passive acoustic monitoring with artificial intelligence techniques,proposed a technology based on residual network(ResNet),which involves the transformation of the acoustic signals emitted by three distillation columns under different operating *** acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier ***,this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation *** this approach,the operational states of distillation columns were *** faults,including flooding,entrainment,dry-tray,etc.,were diagnosed with an accuracy of 98.91%.Moreover,an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed *** the transitional state,the acoustic signals achieved an accuracy of 97.85%on the ResNet,which enables early warnings before faults occur,enhancing the safety of chemical production *** approach presents a powerful tool for the monitoring and diagnosis of chemical equipment,particularly distillation columns,ensuring the safety and efficiency.

关键词： Distillation Column Acoustic signal Neural network

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Optimal Planning of a Standalone Microgrid by Constrained Multi-objective Population Extremal Optimization

Optimal Planning of a Standalone Microgrid by Constrained Mu...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Qin, Zhen Zeng, Guo-Qiang Lu, Kang-Di Wang, Rong Wu, Jun-Yi National-Local Joint Engineering Laboratory of Digitalize Electrical Design Technology Wenzhou University Wenzhou325035 China College of Information Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9789819618637

A standalone microgrid plays an important role in supplying power to residential and remote areas by integrating different types of distributed generation. One of the key open issues in both academic and engineering fields is how to provide an optimal planning strategy for distributed generations and storage systems in a standalone microgrid, considering multi-objective performance indices such as reliability and cost. In this paper, a novel constrained multi-objective population extremal optimization (CMOPEO) is proposed for standalone microgrid optimal planning. The fundamental concept of the suggested approach is to frame the problem as a standard constrained multi-objective problem, where power loss probability, fuel emission, and power cost of the system are considered as the objectives, and the maximum amount of extra power and the availability of renewable resources are the constraints. The superiority of the CMOPEO algorithm is demonstrated through simulation results on a standalone microgrid case over multi-objective particle swarm optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Constrained optimization

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Corrigendum to "Intensifying heatwave events affect snow phenology over the Tibetan Plateau" [J. Hydrol. 659 (2025) 133293] (Journal of Hydrology (2025) 659, (S0022169425006316), (10.1016/***.2025.133293))

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Journal of Hydrology 2025年 656卷

作者： Deng, Gang Zhang, Tongchang Liu, Xiuguo He, Yan Chen, He Shen, Qikai Chen, Qihao Tang, Zhiguang School of Geography and Information Engineering China University of Geosciences Wuhan430074 China National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology Hunan University of Science and Technology Xiangtan411201 China

The authors regret that the following minor errors appeared in the original article: (1) The postal code of the second affiliation was missing. The correct and complete affiliation should be: b national-local joint engineering laboratory of geo-spatial information technology, Hunan University of Science and technology, Xiangtan 411201, China. (2) In Section "2.3.4 Structural equation model (SEM)", the beginning of the second paragraph should be revised to "six hypothetical pathways" instead of "four hypothetical pathways". The authors would like to apologise for any inconvenience caused. © 2025 Elsevier B.V.

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Knowledge Graph-Enhanced Recommendation with Box Embeddings 23rd

Knowledge Graph-Enhanced Recommendation with Box Embeddings

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23rd China national Conference on Computational Linguistics, CCL 2024

作者： Liang, Qiuyu Wang, Weihua Lv, Lei Bao, Feilong College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9789819783663

Knowledge graphs are used to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most existing approaches ignore the hierarchical structure of the knowledge graph. In this paper, we propose a box embedding method for knowledge graph-enhanced recommendation system. Specifically, the box embedding represents not only the interaction between the user and the item, but also the head entity, the tail entity and the relation between them in the knowledge graph. Then the interaction between the item and the corresponding entity is calculated by the multi-task attention unit. Experimental results show that our method provides a large improvement over previous models in terms of Area Under Curve (AUC) and accuracy in publicly available recommendation datasets with three different domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning 31

Unifying Dual-Space Embedding for Entity Alignment via Contr...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. In this paper, we propose a novel method named as UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we simultaneously learn graph structure embeddings in both Euclidean and hyperbolic spaces to maximize the consistency between embeddings in the two spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embeddings of similar neighboring entities become too close. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA methods. Our code is available at https://***/wonderCS1213/UniEA. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering 31

C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Se...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Xiaotao Li, Jiang Su, Xiangdong Zhang, Fujun College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://***/JasonGuo1/C3LRSO. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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