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Z-scheme systems of ASi_(2)N_4(A=Mo or W)for photocatalytic water splitting and nanogenerators

Tungsten 2022年第1期4卷 52-59页

作者： Jian Zeng Liang Xu Xin Luo Tong Chen Shuai-Hao Tang Xin Huang Ling-Ling Wang Energy Materials Computing Center School of Energy and Mechanical EngineeringJiangxi University of Science and TechnologyNanchang 330013China Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Applied Physics School of ScienceEast China Jiaotong UniversityNanchang 330013China

Z-scheme heterojunction catalysts have received great attention due to their efficient ability to separate electrons and ***,using the first-principles calculations,we designed a series of promising two-dimensional(2D)/2D Z-scheme systems with interlayer inequivalent,including MoSi_(2)N_(4)/MoSi_(2)N_(4),WSi_(2)N_(4)/WSi_(2)N_(4) and MoSi_(2)N_(4)/WSi_(2)N_(4).Molecular dynamics simulation and phonon dispersion show that they have sufficient environmental *** inequivalent structure between the layers caused the directional formation of built-in potentials,driving the transfer of net charge between layers,which greatly enhanced their catalytic *** smaller band gap and enhanced light absorption performance further revealed their perfect catalytic ***,all they met the redox potential requirements of water splitting in a range of pH 0-7,demonstrate they are very remarkable photocatalysts for H_(2)*** interestingly,they also have good sliding ferroelectricity,and the opposite built-in potential can be obtained by sliding between layers,which is very promising for future *** works may provide new insights into energy conversion devices.

关键词： Tungsten MoSi_(2)N_4 WSi_(2)N_4 Z-scheme systems Water splitting Nanogenerators First-principles calculations

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Rsta: A Reversible Signal Transmission Architecture for Breaking the Limitation of Unidirectional Transmission

SSRN

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SSRN 2025年

作者： Liu, Xin Cui, Shuang Zhang, Xun Zhou, Fuxing Sheng, Kaikai Wang, Bin Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian116622 China

Molecular circuits are essential tools for biosensing and molecular information processing. However, the long-distance and reversible transmission of orthogonal signals remains a significant challenge in molecular circuits, preventing molecular signals from achieving reversible transmission similar to that of electrical signals in wires. To overcome the limitations of unidirectional signal transmission and facilitate the integration of antagonistic circuits. We propose a reversible signal transmission architecture (RSTA) with wire-like characteristics to achieve reversible signal transmission based on fundamental molecular devices known as reversible signal fan-out devices (RSFDs). The polarity of the input signal dictates the transmission direction, establishing the initial condition for the output signals. Cascaded RSFDs within RSTA offer two key advantages. First, the temporal response to signals. Second, the reversible transformation of opposite circuit types without structural modification. We experimentally validated the principles and functionality of RSTA using temporal logic device and integrated molecular circuits. This work advances the field of reversible molecular signal transmission, enabling the rational design of complex molecular circuits and molecular information processing systems. We envision applications of this technology in nanomachines, molecular computing, and biosensing. © 2025, The Authors. All rights reserved.

关键词： Temporal logic

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GAN you see me? enhanced data reconstruction attacks against split inference 23

GAN you see me? enhanced data reconstruction attacks against...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Ziang Li Mengda Yang Yaxin Liu Juan Wang Hongxin Hu Wenzhe Yi Xiaoyang Xu Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Department of Computer Science and Engineering University at Buffalo SUNY

Split Inference (SI) is an emerging deep learning paradigm that addresses computational constraints on edge devices and preserves data privacy through collaborative edge-cloud approaches. However, SI is vulnerable to Data Reconstruction Attacks (DRA), which aim to reconstruct users' private prediction instances. Existing attack methods suffer from various limitations. Optimization-based DRAs do not leverage public data effectively, while Learning-based DRAs depend heavily on auxiliary data quantity and distribution similarity. Consequently, these approaches yield unsatisfactory attack results and are sensitive to defense mechanisms. To overcome these challenges, we propose a GAN-based LAtent Space Search attack (GLASS) that harnesses abundant prior knowledge from public data using advanced StyleGAN technologies. Additionally, we introduce GLASS++ to enhance reconstruction stability. Our approach represents the first GAN-based DRA against SI, and extensive evaluation across different split points and adversary setups demonstrates its state-of-the-art performance. Moreover, we thoroughly examine seven defense mechanisms, highlighting our method's capability to reveal private information even in the presence of these defenses.

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Mechanical Properties and Degradation Mechanism of Coral Aggregate Seawater Concrete Exposed to Different Marine Environments

SSRN

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SSRN 2024年

作者： Yu, Peng Qin, Kaizhu Zhang, Jiafeng Ren, Zhaoyong School of Civil Engineering and Architecture State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Key Laboratory of Disaster Prevention and Structural Safety Ministry of Education Guangxi Key Laboratory of Disaster Prevention and Structural Safety Scientific Research Center of Engineering Mechanics Guangxi University Nanning530004 China

The potential application of coral aggregates in island projects has received continued attention. Durability of concrete exposed to marine environment is a critical factor in structural design. In this paper, the mechanical properties and degradation mechanism of Coral Aggregate Seawater Concrete (CASC) exposed to different marine environments were investigated. A laboratory accelerated corrosion experiment was designed to simulate three kinds of immersion conditions, namely Full Immersion zone (FI), Semi-Immersion zone (SI) and Salt Spray zone (SS). The effects of immersion conditions and corrosion times on the mass loss, dynamic elastic modulus, porosity, and compressive strength were studied. The results show that CASCs in all three immersion conditions exhibited degradation in mechanical properties as the corrosion time increased. At the same corrosion time, CASC in the Salt Spray zone (SS-CASC) experienced the most severe corrosion degradation, while CASC in the Full Immersion zone (FI-CASC) experienced the slightest. Moreover, Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) tests were conducted to analyze the degradation mechanism of CASC. XRD and SEM results indicate Friedel's salt, ettringite, gypsum, and magnesium hydroxide are the main corrosion products of CASC exposed to the marine environment. In addition, the presence of hydration effects in the CASC system during the early stage increases the strength, and the expansion of ettringite and Friedel's salt during the later stage cracks the matrix and leads to a decrease in strength. Finally, a mechanism-based model and an empirical formula for the prediction of compressive strength of CASC were proposed. © 2024, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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具有可生物降解、细胞相容、抗菌及生物膜控制的壳聚糖磺基甜菜碱衍生物薄膜生物材料的研究

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engineering 2024年第4期35卷 95-103页

作者： Maoli Yin Yingfeng Wang Xuehong Ren Tung-Shi Huang School of Textile and Garment Anhui Polytechnic UniversityWuhu 241000China State Key Laboratory of New Textile Materials and Advanced Processing Technologies&Key Laboratory of Textile Fiber and Products Ministry of Education&Hubei International Scientific and Technological Cooperation Base of Intelligent Textile Materials and ApplicationSchool of Textile Science and EngineeringWuhan Textile UniversityWuhan 430200China Department of Poultry Science Auburn UniversityAuburnAL 36849USA

The purpose of this research was to develop a chitosan sulfobetaine(CS-SNCC)film via the solutioncasting method as a biodegradable antibacterial material for biomedical *** and monochloro-triazine sulfobetaine were used as the raw materials for CS-SNCC preparation,and Fourier-transform infrared(FTIR),ultraviolet–visible(UV-Vis),energy-dispersive X-ray(EDX),and Xray photoelectron spectroscopy(XPS)spectra were used to characterize and analyze the structure of the synthesized ***,the swelling property,thermal stability,biodegradability,cytocompatibility,and antibacterial properties of the CS-SNCC film were comprehensively investigated and compared with those of the chitosan *** results for the film’s enzymatic biodegradation behavior show that the CS-SNCC film undergoes a weight loss of 45.54%after 21 days of *** addition,the CS-SNCC film effectively resists bacterial adhesion,prevents the formation of bacteria biofilms,and exhibits high antibacterial activity,with inactivation rates of 93.43%for Escherichia coli and 91.00%for Staphylococcus ***,the CS-SNCC film shows good cellular activity and cytocompatibility according to the cytotoxicity ***,the prepared biodegradable,cytocompatible,antibacterial,and biofilm-controlling CS-SNCC film has potential for biomedical applications.

关键词： Chitosan Sulfobetaine Antibacterial Biofilm-controlling Film

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Multi-Needle Particle Implantation Computer Assisted Surgery Based on Virtual Reality

Multi-Needle Particle Implantation Computer Assisted Surgery...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Wenjun Tan Jinsong Wang Peifang Huang Guangqiang Yang Qinghua Zhou Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China

Multi-needle radioactive seed implantation is a scientific and effective minimally invasive technique for tumor treatment. Because of the difficulties in planning, low accuracy of puncture operation and non-standard system, it can not be widely used. In this work, a virtual reality based multi-needle particle implantation surgery planning system is developed to help doctors conduct surgical training and planning, and three-dimensional surgical guide model is constructed to assist doctors in positioning in real surgery. The results show that this work can effectively solve the problems of “unclear insertion” and “inaccurate insertion” in clinical multi-needle particle implantation.

关键词： Training Solid modeling Three-dimensional displays Computational modeling Surgery Data visualization Virtual reality

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LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yang, Qize Mo, Qijie Yan, Junkai Wei, Xihan Meng, Jingke Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Tongyi Lab Alibaba Group China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China China

Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset is available at https://***/iSEE-laboratory/LLMDet. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

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International Journal of Mining Science and Technology 2022年第2期32卷 309-322页

作者： Jiayao Chen Hongwei Huang Anthony G.Cohn Dongming Zhang Mingliang Zhou Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Department of Geotechnical EngineeringTongji UniversityShanghai 200092China School of Computing University of LeedsLS29JT LeedsUnited Kingdom Department of Computer Science and Technology Tongji UniversityShanghai 211985China School of Civil Engineering Shandong UniversityJinan 250061China Luzhong Institute of Safety Environmental Protection Engineering and MaterialsQingdao University of Science and TechnologyZibo 255000China School of Mechanical and Electrical Engineering Qingdao University of Science and TechnologyQingdao 260061China

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image *** data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace *** is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:***,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance *** results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace ***,discussions on feature importance,generalization ability and classification error are conducted for the proposed *** experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity ***,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification *** proposed method provides a new alternative approach for the identification of 3D rock trace.

关键词： Tunnel face Rock discontinuity trace Machine learning Gradient boosting tree Generalization ability

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UAV-Assisted Joint Mobile Edge computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Influence of Different Cement Matrix on the Static and Dynamic Mechanical Properties of Ultra-High Performance Concrete (Uhpc)

SSRN

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SSRN 2024年

作者： Yu, Peng Zhong, Li Chen, Zheng Yun, Weijing Li, Wei School of Civil Engineering and Architecture State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Key Laboratory of Disaster Prevention and Structural Safety Ministry of Education Guangxi Key Laboratory of Disaster Prevention and Structural Safety Scientific Research Center of Engineering Mechanics Guangxi University Nanning530004 China

The development of environmentally friendly and sustainable Ultra-High Performance Concrete (UHPC) with reduced carbon footprint has garnered significant attention in response to the challenges posed by global warming and the intricate international landscape. Portland cement (PC) is mainly utilized in the production of UHPC. Nevertheless, it is plagued with drawbacks such as long setting time, low early strength, high shrinkage, and poor toughness, particularly, its significant negative impact on the environment. In contrast, Sulphoaluminate cement (SAC) and aluminate cement (AC) have the advantages of lower CO2 emission, fast early strength development and excellent durability properties compared to PC. While SAC-UHPC and AC-UHPC perform better in terms of dynamic performance, there are fewer studies in this area. Therefore, this paper investigates the carbon footprint, workability, static and dynamic mechanical properties, porosity, physical phase composition and microstructure of UHPC prepared from PC, SAC, AC. The results show that aluminate cement-based UHPC (AC-UHPC) and Sulphoaluminate cement-based UHPC (SAC-UHPC) can reduce CO2 emissions by 204.92 kg and 199.09 kg per unit volume respectively, compared to portland cement-based UHPC (PC-UHPC);the workability of AC-UHPC is better than that of SAC-UHPC and PC-UHPC. SAC-UHPC and AC-UHPC have excellent early static mechanical properties, the compressive strength of SAC-UHPC reaches 76.3 MPa at the age of 3 days, while PC-UHPC is 54.8 MPa, showing a difference of 21.5 MPa, which is approximately 39% higher. SAC-UHPC is weaker than PC-UHPC and AC-UHPC in the terms of dynamic compression resistance, but it shows good deformation capacity and impact toughness under different strain rates, respectively. The hydration products of PC-UHPC are dominated by Ca(OH)2 and C-S-H, while SAC-UHPC are dominated by ettringite (AFt), and AC-UHPC are dominated by CAH10, C2AH8, and C3AH6. Hence, employing SAC and AC in the producti

关键词： Microstructure

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