From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized t...From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.展开更多
The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key fron...The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key front for safeguarding national security,a priority area in managing overall diplomacy,and a crucial link in promoting the building of a community with a shared future for mankind.”展开更多
Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community wi...Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community with a shared future for mankind.China adheres to fostering an amicable,secure,and prosperous neighborhood and works with neighboring countries to create a better future.By seeking an amicable,secure,and prosperous neighborhood,following the principles of amity,sincerity,mutual benefit,and inclusiveness,and sharing weal and woe with its neighbors,China remains committed to deepening exchanges and cooperation with neighboring countries in various fields.Facing a complex and unstable international situation,China and neighboring countries jointly advocate the Asian values of peace,cooperation,openness,and inclusiveness and are committed to promoting indivisible security,common development,and shared prosperity in Asia.China has cooperated with neighboring countries to build a high-quality Belt and Road Initiative and promote global economic recovery.In the face of a critical phase where regional dynamics and global transformations are deeply intertwined,China has put forward the Asian security model,proposed the vision of com mon,comprehensive,cooperative,and sustainable security in Asia,stuck to seeking common ground while shelving differences,and advocated equal-footed consultation.China is taking the initiative to shape the regional security pattern with a positive attitude to safeguard peace and development in Asia.展开更多
Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee c...Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee convened the central conference on work related to neighboring countries,first of its kind since the founding of the People’s Republic of China,stressing“let awareness of a community with a shared future take root in the neighboring countries”.展开更多
Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor c...Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.展开更多
Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system pa...This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system parameters.When the number of closest neighbors is at least half of the population,it is shown that convergence to flocking occurs regardless of the initial state provided that the Lipschitz constant of nonlinear dynamics is smaller than the coupling strength.In contrast,when this number of closest neighbors is less than half of the population,we need to impose some restrictive conditions on the initial state to ensure the emergence of flocking based on the disturbed graphs approach.Our results are applicable to both continuous and discrete time cases.Finally,the validity of our theoretical analysis is tested by numerical simulations.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,t...The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,the intrinsic complexity of this multi-step reaction severely constrains both the selectivity and efficiency of NO3RR.Copper-based electrocatalysts have been extensively investigated for NO_(3)RR but often suffer from nitrite(NO_(2)^(−))accumulation,which stems from insufficient NO_(3)^(−)adsorption strength.This limitation often leads to rapid catalyst deactivation,hindered hydrogenation pathways,and reduced overall efficiency.Herein,we report a one-step green chemical reduction method to synthesize PtCuSnCo quarternary alloy nanoparticles with homogeneously distributed elements.Under practical NO3−concentrations,the optimized catalyst exhibited an impressive Faradaic efficiency approaching 100%and an outstanding selectivity of 95.6±2.9%.Mechanistic insights uncovered that SnCo sites robustly facilitated NO_(3)^(−)adsorption,complemented by the proficiency of PtCu sites in NO3−reduction.The synergistic spatial neighborhood effect between SnCo and PtCu sites efficiently stabilizes NO_(3)^(−)deoxygenation and suppresses NO_(2)^(−)accumulation.This tandem architecture achieves a finely tuned balance between adsorption strength and deoxygenation kinetics,enabling highly selective and efficient NO3RR.Our findings emphasize the indispensable role of engineered multi-metallic catalysts in overcoming persistent challenges of NO3RR,paving the way for advanced NH3 synthesis and environmental remediation.展开更多
As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a commun...As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a community with a shared future with neighboring countries.Shortly after the 2025 Central Conference on Work Related to Neighboring Countries,Chinese President Xi Jinping paid state visits to Vietnam,Malaysia,and Cambodia.展开更多
The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and ...The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.展开更多
Against the backdrop of the international political and economic configuration featuring that“the East is rising and the West is declining”,relations between China and its neighboring countries enjoy steady developm...Against the backdrop of the international political and economic configuration featuring that“the East is rising and the West is declining”,relations between China and its neighboring countries enjoy steady development in general.However,the return of the Cold War mentality,rampant protectionism and prominent security governance issues have seriously threatened peace and stability in China’s neighboring region.展开更多
In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communicati...In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.展开更多
Neighboring optimal guidance,a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory,is widely used in the aerospace field due to its hi...Neighboring optimal guidance,a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory,is widely used in the aerospace field due to its high computational efficiency and low resource usage.For more advanced scenarios,the existing methods still have a problem that the guidance accuracy and optimality will seriously degrade when the actual state largely deviates from the nominal trajectory.This is mainly caused by the approximate description of the first-order conditions in terms of total flight time and nonlinear constraints.To address this problem,a higher-order neighboring optimal guidance method is proposed.First,a novel total flight time updating strategy,together with a normalized time scale,is presented that transforms the optimal problem with free total flight time into a more tractable optimal problem with fixed total flight time.Then,using the vector partial derivative method,a higher-order approximation is adopted,instead of the first-order approximation,to accurately describe the nonlinear dynamical and terminal constraints,thus obtaining a polynomially constrained quadratic optimal problem.Finally,to numerically solve the polynomially constrained quadratic optimal problem,a Newton-type iterative algorithm based on the orthogonal decomposition is designed.Through the iterative solution within each guidance period,the corrections to control quantities and total flight time are generated.The proposed method is applied to a launch vehicle orbital injection problem,and simulation results show that it achieves high accuracy of orbital injection and optimality of performance index.展开更多
In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish...In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artifi cial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples’inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classifi cation function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.展开更多
In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work invo...In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.展开更多
In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared...In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average...In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.展开更多
文摘From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.
文摘The Central Conference on Work Related to Neighboring Countries held on April 8–9,2025,highlighted the importance of China's neighborhood as“a vital foundation for achieving development and prosperity,a key front for safeguarding national security,a priority area in managing overall diplomacy,and a crucial link in promoting the building of a community with a shared future for mankind.”
文摘Neighborhood is an important strategic support for China to take into account both the domestic and international situations and coordinate development and security.It is also a crucial link in building a community with a shared future for mankind.China adheres to fostering an amicable,secure,and prosperous neighborhood and works with neighboring countries to create a better future.By seeking an amicable,secure,and prosperous neighborhood,following the principles of amity,sincerity,mutual benefit,and inclusiveness,and sharing weal and woe with its neighbors,China remains committed to deepening exchanges and cooperation with neighboring countries in various fields.Facing a complex and unstable international situation,China and neighboring countries jointly advocate the Asian values of peace,cooperation,openness,and inclusiveness and are committed to promoting indivisible security,common development,and shared prosperity in Asia.China has cooperated with neighboring countries to build a high-quality Belt and Road Initiative and promote global economic recovery.In the face of a critical phase where regional dynamics and global transformations are deeply intertwined,China has put forward the Asian security model,proposed the vision of com mon,comprehensive,cooperative,and sustainable security in Asia,stuck to seeking common ground while shelving differences,and advocated equal-footed consultation.China is taking the initiative to shape the regional security pattern with a positive attitude to safeguard peace and development in Asia.
文摘Since the 18th National Congress of the Communist Party of China(CPC)in 2012,neighborhood diplomacy has been at the top of China’s diplomatic agenda with growing importance.In October 2013,the CPC Central Committee convened the central conference on work related to neighboring countries,first of its kind since the founding of the People’s Republic of China,stressing“let awareness of a community with a shared future take root in the neighboring countries”.
文摘Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
基金supported by the National Key R&D Program of China(2023YFA1009200)the National Natural Science Foundation of China(12171069)the Fundamental Research Funds for the Central Universities.
文摘This paper studies the emergent dynamics of a flock with nonlinear inherent dynamics under closest neighbors model.We establish sufficient frameworks for convergence to flocking in terms of initial state and system parameters.When the number of closest neighbors is at least half of the population,it is shown that convergence to flocking occurs regardless of the initial state provided that the Lipschitz constant of nonlinear dynamics is smaller than the coupling strength.In contrast,when this number of closest neighbors is less than half of the population,we need to impose some restrictive conditions on the initial state to ensure the emergence of flocking based on the disturbed graphs approach.Our results are applicable to both continuous and discrete time cases.Finally,the validity of our theoretical analysis is tested by numerical simulations.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
文摘The electrochemical reduction of nitrate(NO_(3)−)to ammonia(NH_(3))(NO3RR)represents an environmentally sustainable strategy for NH_(3)production while concurrently addressing water pollution challenges.Nevertheless,the intrinsic complexity of this multi-step reaction severely constrains both the selectivity and efficiency of NO3RR.Copper-based electrocatalysts have been extensively investigated for NO_(3)RR but often suffer from nitrite(NO_(2)^(−))accumulation,which stems from insufficient NO_(3)^(−)adsorption strength.This limitation often leads to rapid catalyst deactivation,hindered hydrogenation pathways,and reduced overall efficiency.Herein,we report a one-step green chemical reduction method to synthesize PtCuSnCo quarternary alloy nanoparticles with homogeneously distributed elements.Under practical NO3−concentrations,the optimized catalyst exhibited an impressive Faradaic efficiency approaching 100%and an outstanding selectivity of 95.6±2.9%.Mechanistic insights uncovered that SnCo sites robustly facilitated NO_(3)^(−)adsorption,complemented by the proficiency of PtCu sites in NO3−reduction.The synergistic spatial neighborhood effect between SnCo and PtCu sites efficiently stabilizes NO_(3)^(−)deoxygenation and suppresses NO_(2)^(−)accumulation.This tandem architecture achieves a finely tuned balance between adsorption strength and deoxygenation kinetics,enabling highly selective and efficient NO3RR.Our findings emphasize the indispensable role of engineered multi-metallic catalysts in overcoming persistent challenges of NO3RR,paving the way for advanced NH3 synthesis and environmental remediation.
文摘As an important part of China's neighborhood,Southeast Asia has always been a high priority in China's neighborhood diplomacy,playing a benchmark and example-setting role in China's drive to build a community with a shared future with neighboring countries.Shortly after the 2025 Central Conference on Work Related to Neighboring Countries,Chinese President Xi Jinping paid state visits to Vietnam,Malaysia,and Cambodia.
基金The National Key Research and Development Program of China under contract No.2017YFC0602202.
文摘The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.
文摘Against the backdrop of the international political and economic configuration featuring that“the East is rising and the West is declining”,relations between China and its neighboring countries enjoy steady development in general.However,the return of the Cold War mentality,rampant protectionism and prominent security governance issues have seriously threatened peace and stability in China’s neighboring region.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2024ZCJH01in part by the National Natural Science Foundation of China(NSFC)under Grant No.62271081in part by the National Key Research and Development Program of China under Grant No.2020YFA0711302.
文摘In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.
基金This study was co-supported by the National Natural Science Foundation of China(No.62103014).
文摘Neighboring optimal guidance,a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory,is widely used in the aerospace field due to its high computational efficiency and low resource usage.For more advanced scenarios,the existing methods still have a problem that the guidance accuracy and optimality will seriously degrade when the actual state largely deviates from the nominal trajectory.This is mainly caused by the approximate description of the first-order conditions in terms of total flight time and nonlinear constraints.To address this problem,a higher-order neighboring optimal guidance method is proposed.First,a novel total flight time updating strategy,together with a normalized time scale,is presented that transforms the optimal problem with free total flight time into a more tractable optimal problem with fixed total flight time.Then,using the vector partial derivative method,a higher-order approximation is adopted,instead of the first-order approximation,to accurately describe the nonlinear dynamical and terminal constraints,thus obtaining a polynomially constrained quadratic optimal problem.Finally,to numerically solve the polynomially constrained quadratic optimal problem,a Newton-type iterative algorithm based on the orthogonal decomposition is designed.Through the iterative solution within each guidance period,the corrections to control quantities and total flight time are generated.The proposed method is applied to a launch vehicle orbital injection problem,and simulation results show that it achieves high accuracy of orbital injection and optimality of performance index.
基金supported by the National Key R&D Program of China(No.2018YFA0702504)the National Natural Science Foundation of China(No.42174152 and No.41974140)the Strategic Cooperation Technology Projects of CNPC and CUPB(No.ZLZX2020-03).
文摘In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artifi cial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples’inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classifi cation function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope.
基金supported in part by the National Natural Science Foundations of CHINA(Grant No.61771392,No.61771390,No.61871322 and No.61501373)Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(Grant No.201955053002 and No.20185553035)。
文摘In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.
基金This work was supported by Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-M202300502,KJQN201800539).
文摘In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.
