The rational construction of lightweight composites with multiple heterogeneous interfaces represents an effective strategy for achieving efficient electromagnetic wave(EMW)absorption.However,the impact of multiple he...The rational construction of lightweight composites with multiple heterogeneous interfaces represents an effective strategy for achieving efficient electromagnetic wave(EMW)absorption.However,the impact of multiple heterogeneous interfaces on electromagnetic performance still needs further exploration.Herein,reduced graphene oxide(rGO)@Ni-FeCo layered hydroxide(LDH)derivatives with multiple heterostructures were synthesized by a series of processes including electrostatic self-assembly,freeze-drying and thermal annealing.The conductive network in rGO and the cavities inside LDH facilitate electron migration and effectively prolong the propagation path of EMW,thereby enhancing conductivity loss.The abundant heterogeneous interfaces between carbon components and metal nanoparticles induce interfacial polarization.In addition,the catalytic activity differences of different metal particles generate different dielectric electromagnetic interfaces,which further promote interfacial polarization.The natural and exchange resonance formed by magnetic particles under a magnetic field provides magnetic losses.Therefore,the successful construction of multiple heterogeneous interfaces effectively enhances the conductivity loss and polarization loss.With a thickness of only 1.4 mm,the composite achieves a minimum reflection loss of-51.8 dB and an effective absorption bandwidth of 4.5 GHz.This work provides an effective strategy for achieving thin thickness and efficient EMW absorption through precise structural design and multi-component construction of absorbers.展开更多
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 detecti...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.展开更多
An analysis and control approach is presented for the active queue management(AQM) problem in network control system supporting multiple links and heterogeneous sources transmission control protocol(TCP).Using additiv...An analysis and control approach is presented for the active queue management(AQM) problem in network control system supporting multiple links and heterogeneous sources transmission control protocol(TCP).Using additive increase multiplicative decrease(AIMD) model,some studies are carried out on multiple links and heterogeneous sources TCP network control system,and some conditions are derived to ensure the stabilization of the given feedback control system by exploiting a general LyapunovKrasovskii functional and some techniques for time-delay systems.And the controller gain is designed further.A simulation is to be provided to verify the algorithm in the paper.展开更多
The transition metal chalcogenides represented by MoS_(2)are the ideal choice for non-precious metal-based hydrogen evolution catalysts.However,whether in acidic or alkaline environments,the catalytic activity of pure...The transition metal chalcogenides represented by MoS_(2)are the ideal choice for non-precious metal-based hydrogen evolution catalysts.However,whether in acidic or alkaline environments,the catalytic activity of pure MoS_(2)is still difficult to compete with Pt.Recent studies have shown that the electronic structure of materials can be adjusted by constructing lattice-matched heterojunctions,thus optimizing the adsorption free energy of intermediates in the catalytic hydrogen production process of materials,so as to effectively improve the electrocatalytic hydrogen production activity of catalysts.However,it is still a great challenge to prepare heterojunctions with lattice-matched structures as efficient electrocatalytic hydrogen production catalysts.Herein,we developed a one-step hydrothermal method to construct Ni-MoS_(2)@NiS_(2)@Ni_(3)S_(2)(Ni-MoS_(2)on behalf of Ni doping MoS_(2))electrocatalyst with multiple heterogeneous interfaces which possesses rich catalytic reaction sites.The Ni-MoS_(2)@NiS_(2)@Ni_(3)S_(2)electrocatalyst produced an extremely low overpotential of 69.4 mV with 10 mA·cm^(−2)current density for hydrogen evolution reaction(HER)in 1.0 M KOH.This work provides valuable enlightenment for exploring the mechanism of HER enhancement to optimize the surface electronic structure of MoS_(2),and provides an effective idea for constructing rare metal catalysts in HER and other fields.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52103334,52071053,U1704253,52272288,52401035)the Fundamental Research Funds for the Central Universities(No.DUT24GF102).
文摘The rational construction of lightweight composites with multiple heterogeneous interfaces represents an effective strategy for achieving efficient electromagnetic wave(EMW)absorption.However,the impact of multiple heterogeneous interfaces on electromagnetic performance still needs further exploration.Herein,reduced graphene oxide(rGO)@Ni-FeCo layered hydroxide(LDH)derivatives with multiple heterostructures were synthesized by a series of processes including electrostatic self-assembly,freeze-drying and thermal annealing.The conductive network in rGO and the cavities inside LDH facilitate electron migration and effectively prolong the propagation path of EMW,thereby enhancing conductivity loss.The abundant heterogeneous interfaces between carbon components and metal nanoparticles induce interfacial polarization.In addition,the catalytic activity differences of different metal particles generate different dielectric electromagnetic interfaces,which further promote interfacial polarization.The natural and exchange resonance formed by magnetic particles under a magnetic field provides magnetic losses.Therefore,the successful construction of multiple heterogeneous interfaces effectively enhances the conductivity loss and polarization loss.With a thickness of only 1.4 mm,the composite achieves a minimum reflection loss of-51.8 dB and an effective absorption bandwidth of 4.5 GHz.This work provides an effective strategy for achieving thin thickness and efficient EMW absorption through precise structural design and multi-component construction of absorbers.
文摘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.
基金Fundamental Research Funds for the Central Universities,China(No.3132014092)
文摘An analysis and control approach is presented for the active queue management(AQM) problem in network control system supporting multiple links and heterogeneous sources transmission control protocol(TCP).Using additive increase multiplicative decrease(AIMD) model,some studies are carried out on multiple links and heterogeneous sources TCP network control system,and some conditions are derived to ensure the stabilization of the given feedback control system by exploiting a general LyapunovKrasovskii functional and some techniques for time-delay systems.And the controller gain is designed further.A simulation is to be provided to verify the algorithm in the paper.
基金the National Natural Science Foundation of China(No.51902101)Natural Science Foundation of Jiangsu Province(No.BK20201381)+1 种基金Science Foundation of Nanjing University of Posts and Telecommunications(Nos.NY219144 and NY221046)the National College Student Innovation and Entrepreneurship Training Program(No.202210293171K).
文摘The transition metal chalcogenides represented by MoS_(2)are the ideal choice for non-precious metal-based hydrogen evolution catalysts.However,whether in acidic or alkaline environments,the catalytic activity of pure MoS_(2)is still difficult to compete with Pt.Recent studies have shown that the electronic structure of materials can be adjusted by constructing lattice-matched heterojunctions,thus optimizing the adsorption free energy of intermediates in the catalytic hydrogen production process of materials,so as to effectively improve the electrocatalytic hydrogen production activity of catalysts.However,it is still a great challenge to prepare heterojunctions with lattice-matched structures as efficient electrocatalytic hydrogen production catalysts.Herein,we developed a one-step hydrothermal method to construct Ni-MoS_(2)@NiS_(2)@Ni_(3)S_(2)(Ni-MoS_(2)on behalf of Ni doping MoS_(2))electrocatalyst with multiple heterogeneous interfaces which possesses rich catalytic reaction sites.The Ni-MoS_(2)@NiS_(2)@Ni_(3)S_(2)electrocatalyst produced an extremely low overpotential of 69.4 mV with 10 mA·cm^(−2)current density for hydrogen evolution reaction(HER)in 1.0 M KOH.This work provides valuable enlightenment for exploring the mechanism of HER enhancement to optimize the surface electronic structure of MoS_(2),and provides an effective idea for constructing rare metal catalysts in HER and other fields.
