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One-step synthesis of seamlessly contacted non-precious metal cocatalyst modified CdS hollow nanoflowers spheres for photocatalytic hydrogen production

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Journal of Materials Science & Technology 2024年第28期195卷 146-154页

作者： Haibo Zhang Chunfeng Shao Zhongliao Wang Jinfeng Zhang Kai Dai Laboratory of Green and Precise Synthetic Chemistry and Applications Ministry of EducationAnhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental RemediationAnhui Province Key Laboratory of Intelligent Computing and ApplicationsAnhui Province Industrial Generic Technology Research Center for Alumics MaterialsHuaibei Normal UniversityHuaibei 235000China

This study ingeniously synthesized a novel CdS/NiS hollow nanoflower sphere(HNS)using a one-step method to enhance photocatalytic hydrogen production *** to conventional preparation methods,this approach features seamlessly interfaced contact that facilitates efficient electron transfer across the *** internal hollow structure allows for multiple light reflections,maximizing light absorption,while the exterior shell and inner surfaces simultaneously offer active sites for *** modification with non-noble metal NiS enables the extraction of electrons from CdS to the NiS surface,achieving rapid charge ***,adsorption-free energy calculations reveal that the NiS surface is more conducive to photocatalytic hydrogen generation,providing additional reaction active *** results demonstrate a hydrogen production rate of 2.18 mmol g^(-1)h^(-1)for CdS/NiS HNS,which is 9.48 times greater than that of pristine *** work presents a novel approach for synthesizing seamlessly interfaced contacts between photocatalysts and cocatalysts,offering new insight into efficient one-step synthesis for enhanced photocatalytic performance.

关键词： Seamlessly contacted Hollow nanoflower sphere Photocatalytic hydrogen production Cocatalyst

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DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis 38

DisenGCD: A Meta Multigraph-assisted Disentangled Graph Lear...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Shangshang Chen, Mingyang Wang, Ziwen Yu, Xiaoshan Zhang, Panpan Ma, Haiping Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Anhui Hefei230601 China Anhui Province Key Laboratory of Intelligent Computing and Applications China Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province China

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module;multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned;the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD. © 2024 Neural information processing systems foundation. All rights reserved.

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Power and Bandwidth Allocation for Low-Altitude Target Localization in Airborne Phased Array Radar 4

Power and Bandwidth Allocation for Low-Altitude Target Local...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Cui, Mingxu Zhang, Weiwei Zhang, Min Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei China

ISBN: (纸本)9798350354560

To effectively localize low-altitude targets under clutter interference, this article proposes a power and bandwidth optimization strategy within bandwidth budget constraints. Firstly, using prior information of the target, it computes the necessary range resolution to suppress clutter, thereby determining the minimum bandwidth required for target localization. Secondly, derive joint the time of arrival (TOA) and the direction of arrival (DOA) Cramér-Rao lower bound (CRLB) as the metric of localization. Then, the problem for minimizing total power by allocating bandwidth and power is established, which is solved by the particle swarm optimization (PSO) algorithm and penalty function. At last, numerical simulations validate the effectiveness of the proposed optimization model in distinguishing low-altitude targets from clutter and minimizing total power, thereby enhancing the performance of radar localization systems. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Power Allocation and Node Selection Algorithms for Radar-Communication Integration 4

Power Allocation and Node Selection Algorithms for Radar-Com...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Wang, Zhenlong Zhang, Weiwei Zhang, Min Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei China

ISBN: (纸本)9798350354560

In this paper, we propose an optimization scheme for joint radar node selection and power allocation to reduce the total power consumption of a distributed multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system. The proposed scheme minimizes the total power of the system while satisfying the number of nodes, target localization accuracy and communication rate, where the target localization accuracy and communication rate are evaluated using Cramér-Rao lower bound (CRLB) and communication data rate (CDR), respectively. The generated constrained optimization problem can be divided into two parts. Firstly, for the node selection problem, it can be solved by the greedy algorithm. Then, for power distribution issue, the question is non-convex, which renders it challenging to identify an optimal solution. Depending on the properties of the problem, it can be transformed into two convex optimization subproblems and solved using semi-definite programming (SDP) and Karush-Kuhn-Tuckers (KKT) conditions. Finally, the results of the simulations demonstrate that the proposed algorithm is capable of significantly enhancing the energy-saving performance of the system. © 2024 IEEE.

关键词： Constrained optimization

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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

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Single-layer crystalline triazine-based organic framework photocatalysts with different linking groups for H_(2)O_(2)production

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Chinese Journal of Structural Chemistry 2023年第12期42卷 33-42页

作者： Jing Wang Zhongliao Wang Jinfeng Zhang Kai Dai Laboratory of Green and Precise Synthetic Chemistry and Applications Ministry of EducationAnhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental RemediationAnhui Province Key Laboratory of Intelligent Computing and ApplicationsAnhui Province Industrial Generic Technology Research Center for Alurnics MaterialsHuaibei Normal UniversityHuaibei235000PR China

Harnessing solar energy for photocatalytic hydrogen peroxide(H_(2)O_(2))synthesis represents a pinnacle of environmentally-sensitive and sustainable *** single-layer crystalline triazine-based organic frameworks(CTFs)are known for their prodigious photocatalytic potential in H_(2)O_(2)generation,ramifications of the connecting group within the triazine ring(TR)on underlying photocatalytic mechanism warrant deeper *** this study,we simulate three distinct CTFs characterized by different TR linkers:CTF-1(benzene group(BG)),CTF-2(horizontally-oriented naphthyl group(NGH)),and CTF-DCN(vertically-oriented naphthyl group(NGV)).These diverse TR linkers profoundly modulate the absorption band edge of CTFs,subsequently dictating the orientation and constitution of the frontier *** modulation plays a decisive role in determining the requisite energy for photoexcitation in CTFs,orchestrating the generation and distribution of photo-induced electrons and ***,the NGV linkage imparts CTF-DCN with unparalleled light ab-sorption,superior charge separation efficiency,and the lowest energy barrier for associated *** this investigation,we illuminate the pivotal influence of TR linkers in sculpting the photocatalytic dynamics of CTFs,providing fresh perspectives for architecting CTFs with amplified photocatalytic prowess in H_(2)O_(2)synthesis.

关键词： Single-layer CTFs H_(2)O_(2)photoreduction DFT calculation Structural design

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Remote Sensing Few-shot Object Detection Using Adaptive Feature Modification and Gaussian Dynamic Dilated Convolution 16

Remote Sensing Few-shot Object Detection Using Adaptive Feat...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhiwei, Zhang Jiangtao, Wang Penglei, Chen Cheng, He School of Physics and Electronic Information Huaibei Normal University Anhui Province Huaibei City China Anhui Province Key Laboratory of Intelligent Computing and Applications Anhui Province Huaibei City China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Object detection in remote sensing images relies on a large amount of annotated data for training, but adequately annotating novel classes is difficult. Few-shot object detection(FSOD) address this problem by fine-tuning on finitely annotated novel classes, this results in its within-class variation being limited, making it difficult to adequately fit the within-class distribution. In order to alleviate this problem, this paper proposed a few-shot object detection method in remote sensing based on adaptive feature modification and guided by Gaussian dynamic dilated convolution. Firstly, in view of the difficulty of novel classes recognition, an adaptive feature modification algorithm is proposed. By using the characteristics of the two-branch network architecture of meta-learning itself, the support features are integrated into the query feature extraction network as convolutional bias to adaptively modify the query features. Secondly, in order to simulate intra-class variation of noise (feature perturbation caused by noise in the foreground), the backbone network weights were reparameterized to make them fit Gaussian distribution variables during fine-tuning, and the feature perturbation was achieved with parameter variation. Finally, in order to simulate intra-class scale variation (different foreground sizes), the convolution is changed into a dynamic dilation convolution, and different dilation rates are used to replace feature scale variation. Extensive experiments on two widely used remote sensing datasets (NWPU.V2, dior) demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Gaussian distribution

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Amplified emission and lasing enabled by high-Q quasi-bound states in asymmetric dielectric metasurfaces via eigenfield perturbation

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Optics Express 2025年第10期33卷 21551-21565页

作者： Xu, Ke Fei, Yuhang Sun, Yingying Feng, Jian Deng, Xuesong Fang, Ming Huang, Zhixiang The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China

Symmetry-protected bound states in the continuum represent a distinct class of nonradiative modes in continuous media. By introducing asymmetric parameters into a symmetric metasurface, the inherent symmetry can be broken, transforming BICs—originally decoupled from external radiation channels—into high-Q quasi-BICs with controlled radiation capabilities. However, manufacturing constraints pose a significant challenge in fabricating nanostructures with ultra-small asymmetric parameters, thereby limiting the achievable Q-factor. In this work, we propose an asymmetric perforated metasurface structure that supports BICs. When the perforations are shifted along the x or y axis to a specific position, the linewidth vanishes, and simulations confirm the emergence of a BIC in the system, with a quality factor reaching up to 109. Furthermore, we demonstrate that selective eigenmode perturbation can effectively transform BICs into practical quasi-BICs. Notably, for the same structure, perturbing the weak-field region yields higher Q-factors, offering a flexible approach to optimizing performance. This approach enables precise Q-factor control and enhanced radiation properties. Furthermore, we design a laser cavity structure based on these quasi-BICs, achieving amplified emission and lasing with excellent agreement between simulations and theoretical predictions. These findings expand the BIC library and offer new methods for Q-factor manipulation, paving the way for high-performance optoelectronic devices such as nanolasers, biosensors, and nonlinear harmonic generators. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Q factor measurement

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FRLI-Net: Feature Reconstruction and Learning Interaction Network for Tiny Object Detection in Remote Sensing Images

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IEEE Signal Processing Letters 2025年 32卷 2159-2163页

作者： Chen, Penglei Wang, Jiangtao Zhang, Zhiwei He, Cheng Huaibei Normal University School of Physics and Electronic Information Huaibei235000 China Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei235000 China

In the field of computer vision, detecting tiny objects in remote sensing images has long been a challenging task. This difficulty primarily stems from the poor matching of tiny objects with specific feature scales, compounded by susceptibility to interference from complex surroundings. To this end, we propose a Feature Reconstruction and Learning Interaction Network (FRLI-Net) for overcoming the aforementioned difficulties in tiny object detection. Specifically, to mitigate information loss of tiny objects in the feature space, we introduce a Feature Reconstruction Supervision Branch (FRSB) used only during training, which utilizes supervised loss to guide the attention of tiny objects within the reconstructed feature regions. Then, to tackle false detections caused by blurred boundaries of tiny objects, we design a Saliency Learning Interaction Module (SLIM) that employs learnable weights and proposed sub-functions to facilitate feature interaction, thereby reducing interference from complex backgrounds in the foreground. Extensive experiments conducted on the remote sensing image datasets AI-TOD-v2 and TinyPerson demonstrate that our FRLI-Net significantly enhances the detection of tiny objects, outperforming other state-of-the-art detection methods. © 2025 IEEE.

关键词： Image reconstruction

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DMFVAE:miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

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Frontiers of Computer Science 2024年第6期18卷 259-270页

作者： Pijing WEI Qianqian WANG Zhen GAO Ruifen CAO Chunhou ZHENG Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China

MicroRNAs(miRNAs)are closely related to numerous complex human diseases,therefore,exploring miRNA-disease associations(MDAs)can help people gain a better understanding of complex disease *** increasing number of computational methods have been developed to predict ***,the sparsity of the MDAs may hinder the performance of many *** addition,many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor *** this study,we propose a deep matrix factorization model with variational autoencoder(DMFVAE)to predict potential *** first decomposes the original association matrix and the enhanced association matrix,in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method,to obtain sparse vectors and dense vectors,***,the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors,and meanwhile,node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense ***,sample features are acquired by combining the latent vectors and network structure embedding vectors,and the final prediction is implemented by convolutional neural network with channel *** evaluate the performance of DMFVAE,we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs ***,case studies on lung neoplasms,colon neoplasms,and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

关键词： miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure

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