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Exploring the frontiers of plant health:Harnessing NIR fluorescence and surface-enhanced Raman scattering modalities for innovative detection

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Chinese Chemical Letters 2025年第3期36卷 134-143页

作者： Shu Tian Wenxin Huang Junrui Hu Huiling Wang Zhipeng Zhang Liying Xu Junrong Li Yao Sun State Key Laboratory of Green Pesticide College of ChemistryCentral China Normal UniversityWuhan 430079China Department of Pharmacy Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430000China Xianning Medical College College of PharmacyHubei University of Science&TechnologyXianning 437100China Zhongnan Hospital of Wuhan University Wuhan 430062China

Plants play a crucial role in maintaining ecological balance and ***,plant health is easily affected by environmental ***,the rapid and precise monitoring of plant health is crucial for global food security and ecological ***,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread ***,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in *** fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale *** SERS enables highly sensitive and specific detection of trace chemical substances within plant ***,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status *** article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.

关键词： NIR fluorescence SERS Plant biomarker detection Plant imaging Phytohormone

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Microneedle-delivered biomimetic nanodecoys target type Ⅳ collagen and scavenge multiple cytokines to alleviate psoriatic inflammation

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Nano Research 2025年第3期18卷 401-416页

作者： Fei Qu Jinying Qin Min Li Yuting Xia Yihui Wang Hongmei Liu Juan Tao Yijing Liu Jintao Zhu Hubei Key Laboratory of Bioinorganic Chemistry and Materia Medica Hubei Engineering Research Center for Biomaterials and Medical Protective MaterialsSchool of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan 430074China Department of Dermatology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Department of Ultrasound Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China

The immunomodulatory efficacy of current psoriasis biological therapies is hindered by their limited ability to scavenge multiple cytokines,inefficient delivery to specific inflamed skin regions,and potential side *** analyzing samples from both patients and mice,we identify a significant increase in type Ⅳ collagen within the extracellular matrix(ECM)of psoriatic ***,we report the microneedle(MN)delivery of type Ⅳ collagen targeting peptide-modified dual-cell membrane biomimetic nanodecoys(CRHM@lip)with multiple cytokines scavenging ability for treating *** CRHM@lip can scavenge both tumor necrosis factor-α(TNF-α)and interleukin(IL)-*** MN delivery,the nanodecoys target ECM and exhibit skin retention for over 120 *** treatment by CRHM@lip-integrated MNs reduces skin thickness in mice by 57.9%and shows decreased levels of TNF-α,IL-17,IL-23,and interferon(IFN)-γ in skin sections compared to the psoriasis ***,the CRHM@lip treatment reduces the CD4^(+)T cells,M1 macrophages,and dendritic cells in the spleen,and suppresses various inflammatory mediators in serum,significantly demonstrating immunological microenvironmental *** to systemic administration routes,MN delivery improves treatment *** noticeable adverse effects on hepatic and renal functions are observed in mice after *** approach enhances the effectiveness of biological therapies and has the potential for translation.

关键词： nanodecoy microneedle type IV collagen-targeting cytokines scavenging psoriasis

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Highly efficient catalytic transfer hydrogenation for the conversion of nitrobenzene to aniline over PdO/TiO_(2):The key role of in situ switching from PdO to Pd

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Journal of Environmental sciences 2025年第2期148卷 515-528页

作者： Anqi Lu Xiaokang Xiang Ming Lei Shuangshuang Huang Bingbing Liang Siyu Zhao Lihua Zhu Heqing Tang College of Resources and Environmental Science South-Central Minzu UniversityWuhan 430074China School of Chemistry and Chemical Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Physics and Technology Center for Electron MicroscopyMOE Key Laboratory of Artificial Micro-and Nano-Structuresand Institute for Advance StudiesWuhan UniversityWuhan 430072China

The reduction of nitrobenzene to aniline is very important for both pollution control and chemical ***,difficulties still remain in developing a catalytic system having high efficiency and selectivity for the production of ***,it was found that PdO nanoparticles highly dispersed on TiO_(2)support(PdO/TiO_(2))functioned as a highly efficient catalyst for the reduction of nitrobenzene in the presence of *** favorable conditions,95%of the added nitrobenzene(1 mmol/L)was reduced within 1 min with an ultra-low apparent activation energy of 10.8 kJ/mol by using 0.5%PdO/TiO_(2)as catalysts and 2 mmol/L of NaBH4 as reductants,and the selectivity to aniline even reached up to 98%.The active hydrogen specieswere perceived as dominant species during the hydrogenation of nitrobenzene by the results of isotope labeling experiments and ESR spectroscopic.A mechanismwas proposed as follows:PdO activates the nitro groups and leads to in-situ generation of Pd,and the generated Pd acts as the reduction sites to produce active hydrogen *** this catalytic system,nitrobenzene prefers to be adsorbed on the PdO nanoparticles of the PdO/TiO_(2)***,the addition of NaBH_(4) results in in-situ generation of a Pd/PdO/TiO_(2)composite from the PdO/TiO_(2)composite,and the Pd nanoclusters would activate NaBH_(4) to generate active hydrogen species to attack the adsorbed nitro *** work will open up a new approach for the catalytic transfer hydrogenation of nitrobenzene to aniline in green chemistry.

关键词： Catalytic transfer hydrogenation Reduction Pd Nitrobenzene Aniline

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VECSNet: A Nondestructive Automatic VCSEL Chip Detection Network With Pixelwise Segmentation

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IEEE Transactions on Semiconductor Manufacturing 2025年第2期38卷 270-280页

作者： Guo, Shuai Li, Dengao Zhao, Jumin Qiu, Shuang Tang, Bao Luo, Biao Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan030024 China Taiyuan University of Technology College of Electronic Information Engineering Taiyuan030024 China Taiyuan University of Technology College of Artificial Intelligence Taiyuan030024 China Accelink Technologies Company Ltd. Department of Laser Wuhan430074 China Accelink Technologies Company Ltd. Department of Chip Wuhan430074 China

Dark line defects (DLDs) are critical factors that significantly limit the performance of vertical-cavity surface-emitting lasers (VCSELs). Recently, convolutional neural network (CNN)-based methods have shown strong feature extraction capabilities, achieving exceptional performance across various fields. However, these methods still face limitations on the segmentation samples with weak texture, varying shapes and blurred boundary information. To overcome these limitations, a novel segmentation method named VECSNet is proposed in this work. Electroluminescence imaging technology is employed to capture the emission characteristics of VCSELs and develop the corresponding dataset. To improve the extraction of emission features, a parallel dual-encoding structure is designed to capture both spatial and semantic information. Additionally, a feature fusion attention (FFA) block is introduced to effectively fuse features extracted from different branches. Faced with blurred boundary information, a boundary detector is proposed to guide each fusion connection in acquiring boundary feature information and enrich feature representation. Furthermore, to improve segmentation precision for areas with varying shapes, auxiliary logits are introduced to enhance discriminative ability of the network from multiple levels. Experimental results on the VCSEL emission segmentation dataset demonstrate that the proposed method achieves a high Dice score (92.5%) with fewer parameters (6.4M), outperforming other state-of-the-art segmentation approaches. © 1988-2012 IEEE.

关键词： Convolutional neural networks

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Unaligned Federated Knowledge Graph Embedding 23rd

Unaligned Federated Knowledge Graph Embedding

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23rd International Semantic Web Conference, ISWC 2024

作者： Chen, Deyu Zhu, Hong Gu, Jinguang Chen, Rusi Xie, Meiyi School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China State Grid Hubei Electric Power Research Institute Wuhan China

ISBN: (纸本)9783031778438

Recently, knowledge graph embedding (KGE) methods under the federated learning paradigm have received much attention. Its privacy-preserving decentralized training method effectively utilizes the knowledge graphs held by different clients. Existing federated KGE frameworks collaboratively train the global model by aggregating aligned entity embeddings among clients. However, in real-world scenarios, the lack of aligned entities and the high heterogeneity among knowledge graphs constrain their potential. To address these issues, we propose a federated KGE framework that does not depend on any aligned set but uses structure information. The framework introduces a set of basis edges to model the general structure information. Then, we use two separate modules on clients to encode structure and feature representations, respectively. Finally, clients only upload structure parameters for aggregation on the server. The framework uses a new unaligned federated KGE paradigm to tackle the heterogeneity of multi-source knowledge graphs. Experimental results on benchmark datasets show that UniFE achieves superior results even compared to federated KGE frameworks using the aligned set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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CRT and PUF-based Self/Mutual-healing Key Distribution Protocol with Collusion Resistance and Revocation Capability

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4607-4622页

作者： Othman, Wajdy Hong, Zhong Fuyou, Miao Xue, Kaiping Hawbani, Ammar Amin, Ruhul Zhao, Liang Li, Tao Nankai University Haihe Lab of ITAI College of Cyber Science Tianjin China Anhui University School of Computer Science and Technology China University of Science and Technology of China School of Computer Science and Technology School of Information Science and Technology Anhui Hefei China Shenyang Aerospace University School of Computer Science Shenyang110136 China Department of Computer Science & Engineering IIIT-NR China Nankai University Haihe Lab of ITAI College of Cyber Science China

Self-healing group key distribution (SGKD) protocols guarantee the security of group communications by allowing authorized users to independently recover missed previous session keys from the current broadcast without retransmission. However, existing SGKD protocols have flaws: (1) collusion resistance and revocable nodes are both upper-bounded by the degree of polynomials used, (2) the disclosure of personal secrets enables the recovery of group key, (3) temporary revocation of a group member is not possible, and (4) a revoked node may obtain the session key when initiating mutual healing, moreover, a malicious node may cause the recovery of false group keys. To address these limitations, we propose an SGKD protocol using the Chinese remainder theorem (CRT) and Physical Unclonable Function (PUF). Our proposed SGKD protocol generates a PUF-based dynamic secret by stimulating nodes' PUF using a polynomial-based encrypted challenge. This secret is then employed to retrieve a CRT-based encrypted group key. By combining PUF and CRT, we can generate dynamic secrets on the fly and reduce computation time significantly. Utilizing such a technique, our protocol achieves superior security goals, including resistance to any coalition of group nodes even if nodes' personal secrets were disclosed. Furthermore, the proposed protocol provides an unlimited number of revocable nodes. Additionally, a revoked node can rejoin its group in later sessions without affecting backward secrecy. Moreover, the protocol provides a backward secrecy guaranteed mutual-healing feature free from desynchronization. Our performance and security analyses (i.e., theorem-based formal analysis, NS3-based experiment, and formal verification using the AVIPSA tool) show that our proposed protocol achieves stronger security goals and better efficiency in terms of computation, communication, and storage costs compared to existing SGKD schemes. © 2002-2012 IEEE.

关键词： Authentication Protocol

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Enhanced spray cooling on patterned perforated micromesh surface

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science China(Technological sciences) 2025年第4期68卷 24-33页

作者： Yongyan HU Xiuliang LIU Ronggui YANG School of Energy and Power Engineering Huazhong University of Science and TechnologyWuhan430074China College of Engineering Peking UniversityBeijing100871China

The increasing power density in applications such as aerospace,nuclear power,laser weapons,and phased array radar has led to a growing demand for efficient thermal management *** cooling,known for its high heat flux dissipation and excellent temperature uniformity,has garnered great ***/nano-structured surfaces have been adopted to enhance spray cooling,especially in the phase-change heat transfer ***,manipulating bubble dynamics for non-interfering liquid and vapor flow paths remains a challenge under high heat flux *** work employs a costeffective laser drilling technique to fabricate patterned multilayer perforated copper micromesh surfaces to enhance spray *** creating separated liquid-vapor flow paths,bubble escape resistance is reduced,and the formation of vapor blankets within the perforated micromesh stack is *** the diameter and decreasing the separation between the perforations can strengthen the liquid and vapor transport,while reducing the liquid film boiling *** analysis of flow resistance along bubble escape paths,caused by the anisotropic permeability of the multilayer micromesh in different directions,is performed to optimize the design of perforated microporous structures.A critical heat flux(CHF)of 1213 W/cm^(2),with a heat transfer coefficient(HTC)of 352.6 kW/(m^(2)K),is achieved on the patterned perforated micromesh surface,which is an improvement of 115%and 142%,respectively,compared to the plain surface.

关键词： spray cooling high heat flux bubble departure perforated surface copper micromesh

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Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based on Internet of Things

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IEEE Internet of Things Journal 2025年第10期12卷 13746-13756页

作者： Lyu, Zhihan Wan, Zhibo Bian, Zengxu Liu, Yuqi Zhao, Wei Xidian University State Key Laboratory of Integrated Services Networks School of Computer Science and Technology Shaanxi Xi'an China Qingdao University College of Computer Science and Technology China

Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors such as weather conditions, load changes, holidays, seasons and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, Particle Swarm Optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into an digital twins system including transformer condition monitoring, oil temperature prediction and intelligent maintenance based on internet of things(IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This paper presents a hybrid framework that achieves a accurate transformer oil temperature prediction. © 2014 IEEE.

关键词： Oil filled transformers

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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science China(Information sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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