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A novel single-phase Ca3Gd2Si3O12:Dy3+, Sm3+ phosphor exhibiting high thermal stability for solid-state lighting applications

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Ceramics International 2025年

作者： Zhang, Yanzhi Deng, Xiaoxu Cui, Ruirui Guo, Xiang Zhang, Jun Deng, Chaoyong Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyan550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China

In order to obtain efficient single-phase co-doped phosphors with excellent luminescence properties under near-ultraviolet excitation, Ca3Gd2Si3O12:xDy3+ (CGSO: xDy3+) and Ca3Gd2Si3O12:0.08Dy3+, ySm3+ (CGSO:0.08Dy3+, ySm3+) phosphors were effectively manufactured using the classical high-temperature solid-phase method. The phase structure, morphology and XPS test results of the phosphor all show that the Ca3Gd2Si3O12(CGSO) material has been successfully synthesized, and Dy3+ and Sm3+ ions have been successfully doped into the matrix lattice. Under 362 nm illumination, the PL spectra of CGSO:0.08Dy3+, ySm3+ phosphor revealed that the typical peak intensity of Dy3+ ions declined as the amount of Sm3+ ions increased, whereas the distinctive peak intensity of Sm3+ ions achieved its maximum at y = 0.07. As Sm3+ doping grows larger, the fluorescence lifetime shortens, signifying the occurrence of transferring energy among Dy3+ and Sm3+ ions, with a maximum efficiency of 81 % being attained. The Ca3Gd2Si3O12:0.08Dy3+, ySm3+ phosphor demonstrates a commendable thermal stability of 90.17 %, with adjustments to the Sm3+ concentration revealing the potential to yield warm white light, as evidenced by both CIE chromaticity coordinates and CCT measurements. In addition, compared with the phosphor containing only Dy3+, the LED assembled with CGSO:0.08Dy3+, ySm3+ phosphor containing Sm3+ ions and 365 nm LED chip emits warmer white light with lower color temperature, which confirms the application value of CGSO:0.08Dy3+, ySm3+ phosphor in the domain of solid-state lighting. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Semiconductor doping

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Experimental and Modeling Study on Void Fraction using Thermal Distribution Sensor in Horizontal Annular Flow

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IEEE Sensors Journal 2025年第10期25卷 17640-17649页

作者： Zhao, Ning Chen, Xiangyi Sun, Mingcong Song, Yajing Fang, Lide Li, Zhibin Hebei University College of Quality and Technical Supervision Hebei Baoding071000 China National and Local Joint Engineering Research Center of Metrology Instrument and System Hebei Baoding071002 China Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University of Arts and Science Dazhou Key Laboratory of Government Data Security Sichuan Dazhou635000 China Chinese Academy of Sciences Xinjiang Technical Institute of Physics & Chemistry Urumqi830011 China

The void fraction is an important parameter to characterize the characteristics of annular flow, which is an important precondition for calculating the flow velocity, average density, pressure gradient and analyzing the flow status. Based on the principle of convective heat transfer, the effects of the void fraction α and the liquid Froude number Frl on the heat transfer coefficient h of annular flow are analyzed. The thermal distribution sensor of constant power type is designed in this paper. Air-water annular flow experiments have been conducted in a stainless-steel tubular test section having an inside diameter 17.0 mm on the high-precision small-diameter two-phase flow experimental device of Hebei University. The 75 sets of temperature T at four angles (0°, 90°, 180°, 270°) of pipe circumference and entrainment E under different flow conditions were collected. Utilizing the S. L. Smith correlation, the void fraction are calculated. By introducing dimensionless heat transfer coefficient h+, the prediction correlation of void fraction α which could acceptably handle the data is proposed. laboratory results indicate that the relative error of 99% the data are within ± 3% error band, the mean absolute percentage error (MAPE) of the correlation is 0.63%, and the root mean square error (RMSE) is 0.76%. © 2001-2012 IEEE.

关键词： Void fraction

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Next-POI Recommendation via Spatial-Temporal knowledge Graph Contrastive Learning and Trajectory Prompt

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IEEE Transactions on knowledge and data engineering 2025年第6期37卷 3570-3582页

作者： Chen, Wei Huang, Haoyu Zhang, Zhiyu Wang, Tianyi Lin, Youfang Chang, Liang Wan, Huaiyu Guilin University of Electronic Technology Guangxi Key Lab of Trusted Software Guangxi Guilin541004 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Chinese University of Hong Kong Department of Computer Science and Engineering Ma Liu Shui Hong Kong Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing100044 China

Next POI (Point-of-Interest) recommendation aims to forecast users' future movements based on their historical check-in trajectories, holding significant value in location-based services. Existing methods address trajectory data sparsity by integrating rich auxiliary information or using spatial-temporal knowledge graphs (STKGs), showing promising results. Yet, they face two main challenges: i) Due to the difficulty of transforming structured trajectory data into trajectory text describing users' spatial-temporal mobility, the powerful reasoning ability of pre-trained language models is rarely explored to enhance recommendation performance. ii) Methods based on STKG can introduce external knowledge inconsistent with user preferences, leading to the knowledge noise generated hampering the accuracy of recommendations. To this end, we propose a novel approach called STKG-PLM that integrates STKG contrastive learning and prompt pre-trained language model (PLM) to enhance the next POI recommendation. Specifically, we design a spatial-temporal trajectory prompt template that transforms structured trajectories into text corpus based on STKG, serving as the input of PLM to understand the movement pattern of users from coarse-grained and fine-grained perspectives. Additionally, we propose an STKG contrastive learning framework to mitigate the introduced knowledge noise. Extensive experiments on three real-world datasets demonstrate that STKG-PLM exhibits notable performance improvements over the state-of-the-art baseline methods. © 1989-2012 IEEE.

关键词： Contrastive Learning

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Early Warning Thresholds for Microalgal Blooms and Environmental Drivers of Substrate Microalgae Recovery in Litopenaeus vannamei Aquaculture

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Water, Air, and Soil Pollution 2025年第6期236卷 1-16页

作者： Ma, Weixing Shi, Yuanyuan Ma, Ni Liu, Gaojian Gao, Yuxin Ma, Jiaru Yi, Zhaohui Yang, Min Li, Zhaoxia College of Environmental Science & Engineering Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province Yancheng Institute of Technology Jiangsu Province Yancheng224051 China School of Marine and Biological Engineering Yancheng Institute of Technology Jiangsu Province Yancheng224051 China Jiangsu YIDA TestingTechnology Co. Ltd. Building A- 15 Big Data Industrial Park Chengnan New District Jiangsu Province Yancheng224051 China Shenzhen518055 China

In response to the global demand for high-quality protein, Litopenaeus vannamei aquaculture has rapidly expanded. However, microalgal blooms often degrade water quality, resulting in reduced yields and significant financial losses. This study investigated the recovery dynamics of substrate microalgae, which act as seed banks for microalgal blooms, and identified early warning thresholds for algae under varying environmental conditions. Sediments from bfIGuccess (BS) and breeding failure (BF) sheds were analyzed to evaluate the effects of temperature (6 °C–21 °C), light intensity (50–2,000 lx), and nutrient types (BG11 and raw water). The findings indicated that environmental factors influencing microalgal recovery followed the priority order: temperature > light > nutrient salts. The highest recovery was observed at temperatures of 11 °C and 16 °C under 2,000 lx illumination. key early warning algal species, including Melosira granulata, Cyclotella, and Chlorella, exhibited thresholds of 0.9–42.9 × 104 cells/g, 0.1–28.8 × 104 cells/g, and 1.0–31.7 × 104 cells/g, respectively. This study uniquely linked substrate algae recovery to aquaculture failures, providing quantitative thresholds and actionable insights for proactive microalgal blooms monitoring and control. These findings form a scientific basis for improving water quality management and ensuring sustainable L. vannamei aquaculture © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquaculture

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Effective Instruction Parsing Plugin for Complex Logical Query Answering on knowledge Graphs 25

Effective Instruction Parsing Plugin for Complex Logical Que...

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Proceedings of the ACM on Web Conference 2025

作者： Xingrui Zhuo Jiapu Wang Gongqing Wu Shirui Pan Xindong Wu The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Beijing University of Technology Beijing China Griffith University Gold Coast Queensland Australia

ISBN: (纸本)9798400712746

knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models' performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP's effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug-and-play method improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods.

关键词： complex query answering

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Dispersive wave control in soliton fiber lasers via online high-order dispersion engineering

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Optics Letters 2025年第8期50卷 2788-2791页

作者： Sun, Suwan Zha, Zimin Bai, Jiamin Shi, Zhiming Chu, Yongyuan Li, Zilong Huang, Qianqian Mou, Chengbo Guo, Hairun Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Engineering and Materials Science Queen Mary University of London Mile end Bethnal Green LondonE1 4NS United Kingdom

Ultrafast fiber lasers generating temporal solitary pulses have found important applications in numerous fields. Circulating within the laser cavity, such solitons would feature dispersive wave generation due to the perturbation of cavity parameters. While Kelly sidebands have been widely recognized as dispersive waves in fiber lasers due to an intrinsic periodic loss perturbation, optical Cherenkov radiation-an alternative dispersive wave regime that is enabled by highorder dispersion-has been less explored. In this Letter, we demonstrate a broad control of dispersive waves in an ultrafast fiber laser via online dispersion engineering, which enables precise tuning of both the Cherenkov radiation and Kelly sidebands and enables a transition in between via manipulation of third- and fourth-order dispersion (TOD and FOD). Furthermore, we identify a retrieval method for tracing cavity high-order dispersion from frequencies of dispersive waves, which has been traditionally challenging. Our findings would not only help gain in-depth understanding of cavity soliton dynamics but may also contribute to advanced design of ultrafast lasers. © 2025 Optica Publishing Group.

关键词： Dispersion (waves)

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Auxiliary Tasks Benefit Skeleton-based Action Recognition

Auxiliary Tasks Benefit Skeleton-based Action Recognition

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuheng Yang Haipeng Chen College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or noisy skeleton data. State-of-the-art methods tend to learn human motion directly from these corrupted skeletons as if they were reliable. Unfortunately, this might lead to unsatisfactory results when key regions of the skeleton are occluded or disturbed. To tackle the problem, we propose a novel framework that integrates auxiliary tasks into a motion modeling network. These auxiliary tasks corrupt partial human skeletons with masking or noise and then force the network to recover the corrupted data, explicitly facilitating robust feature representation learning. We further propose supervising the auxiliary tasks with mutual information losses, mathematically ensuring feature consistency and spatial alignment between the recovered and original skeleton data. Empirically, our approach sets the new state-of-the-art performance on three benchmark datasets.

关键词： Representation learning Noise Speech recognition Benchmark testing Skeleton Mathematical models Reliability Noise measurement Speech processing Mutual information

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

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IEEE Transactions on Transportation Electrification 2025年

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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