The relation between conflict and governance has been dominated by type of government rather than by system of government.With increasing conflict in countries with ethnic and religious diversity,the debate has gradua...The relation between conflict and governance has been dominated by type of government rather than by system of government.With increasing conflict in countries with ethnic and religious diversity,the debate has gradually shifted to understand better the link between conflict and system of government.There is a growing evidence that suggests federal system performs better than unitary system in managing diversity and reducing conflict.Decentralization is even seen to be more effective than federal system not only in managing diversity and reducing conflict but also in delivering public goods.This article provides an account of evolution of system of government in South Sudan.It finds a clear association of centralized unitary system with violent conflict and a relative peace during period of decentralized government or federal system.A decentralized federal system may be appropriate for South Sudan in managing diversity and mitigating conflict.Despite the popular demand by their citizens for a federal system to manage diversity and reduce violent conflict,the ruling elites in the post-independent South Sudan adopted instead an autocratic centralized unitary system that contributed among other factors to the persistent violent conflicts,erosion of social cohesion,and rising mistrust between state and citizens and between and among the communities of South Sudan.展开更多
There is a gradual increase in the proportion of renewable energy sources.Green hydrogen has the potential to become one of the major energy carriers in the future.The Russian Federation,in partnership with countries ...There is a gradual increase in the proportion of renewable energy sources.Green hydrogen has the potential to become one of the major energy carriers in the future.The Russian Federation,in partnership with countries in the Asia-Pacific region and especially China,has the potential to play a significant role in green hydrogen market.This study assessed the potential of developing green hydrogen energy based on wind power in the Far Eastern Federal District(FEFD)of the Russian Federation.Empirical wind speed data were collected from 20 meteorological stations in 4 regions(Sakhalinskaya Oblast’,Primorskiy Krai,Khabarovskiy Krai,and Amurskaya Oblast’)of the FEFD.The Weibull distribution was used to predict the potential of green hydrogen production.Five different methods(Empirical Method of Justus(EMJ),Empirical Method of Lysen(EML),Maximum Likelihood Method(MLE),Power Density Method(PDM),and Median and Quartiles Method(MQM))were used to determine the parameters(scape factor and scale factor)of the Weibull distribution.We calculated the total electricity generation potential based on the technical specifications of the three wind turbines:Senvion 6150 onshore,H165-4.0 MW,and Vestas V150-4.2 MW.The results showed that Vladivostok,Pogibi,Ilyinskiy,Yuzhno-Kuril’sk,Severo-Kuril’sk,Kholmsk,and Okha stations had the higher potential of green hydrogen production,of which Vladivostok exhibited the highest potential of green hydrogen production using the wind turbine of H165-4.0 MW,up to 2.56×10^(5) kg/a.In terms of economic analysis,the levelized cost of hydrogen(LCOH)values of lower than 4.00 USD/kg were obtained at Yuzhno-Kuril’sk,Ilyinskiy,Pogibi,and Vladivostok stations using the wind turbine of H165-4.0 MW,with the values of 3.54,3.50,3.24,and 2.55 USD/kg,respectively.This study concluded that the FEFD possesses significant potential in the production of green hydrogen and,with appropriate investment,has the potential to become a significant hub for green hydrogen trading in the Asia-Pacific region.展开更多
1|Introduction The Federal Republic of Somalia,often perceived as linguistically homogeneous,is home to a rich tapestry of dialects and minority languages that reflect its diverse cultural heritage.While Somali is the...1|Introduction The Federal Republic of Somalia,often perceived as linguistically homogeneous,is home to a rich tapestry of dialects and minority languages that reflect its diverse cultural heritage.While Somali is the official medium of communication,it is divided into two major dialects:Maxaa Tiri(spoken by approximately 60%of the population)and Maay(spoken by approximately 20%of the population)[1].Minority languages such as Bravanese(also known as Chimwiini or Chimbalazi),Mushunguli,Benadiri Somali,and Kibajuni are spoken by smaller communities,particularly in the southern and coastal regions[1].展开更多
Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi...Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estima...A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.展开更多
In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realizatio...In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realization of different work actions and activities. The methodology is based on the combination of cabinet and field work, performed in three stages. In the first, a documental review was carried out within the topic of Environment Impact (EI), the EIA and the study area, with a subsequent analysis of the environment indicators at an international, national and regional scale. In the second, the systematization of information was performed for the sixteen study cases at a local scale and the organization and analysis of a data base with the allotted information. And in the last stage, a field work was realized with participative observations in three verification sites and interview applications to the principal actors of the EIA process. These results allowed: to determine the main limitations within the EIA process (methodological, technical and operational), to propose an indicators scheme, and to formulate recommendations focused on the improvement of this Environment Public Policy instrument.展开更多
In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended ...In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.展开更多
In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening i...In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening in monetary policy and putting the independence of the Federal Reserve into question. However, this is only the latest development of a longer process: since the financial crisis, the Federal Reserve has been gradually losing its independence, in a quiet and perhaps permanent way. There are several reasons for this trend: the Federal Reserve’s performance during the financial crisis undermined its credibility, the consolidation of political factors arranged against its independence, and the consequences of the financial crisis weakened the economic foundation for its independence. Trump’s rise to power has only strengthened these factors, bringing an additional loss of independence, which will have a profound impact on the economy, society, and politics.展开更多
This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two yea...This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.展开更多
In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach...In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dys...Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dyspareunia and its effect on sexual life among gynaecology clinic attendees in Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Materials and Methods: A cross-sectional study was conducted on consenting participants between 12th May 2016 and 25th July 2016. Anonymous self-administered questionnaires were used collection information on dyspareunia and its effect on sexual life at the Gynaecology clinic. The data was analyzed using Epiinfo version 7.1.5. Results: One hundred and four (104) women participated in this study. Most of the women studied were Igbos (95.19%), and were mainly between the age ranges of 21 - 30 years (66.35%). Most of them were married (89.42%), and were also mainly of the Pentecostal denomination (40.78%). The mean age at coitarche was 20.6 ± 3.95 years. Prevalence of dyspareunia was 36% and only 16% sought medical help. The various responses to dyspareunia were avoidance of sex 11%, reduced frequency of intercourse 8%, less desire for sex 19%, while majority of women with dyspareunia tolerated it (62%). Conclusion: The prevalence of dyspareunia is high in our society afflicting young women in their reproductive years with associated enormous stress on their sexual life.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous...Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.展开更多
Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA colla...Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.展开更多
In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the ...In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.展开更多
文摘The relation between conflict and governance has been dominated by type of government rather than by system of government.With increasing conflict in countries with ethnic and religious diversity,the debate has gradually shifted to understand better the link between conflict and system of government.There is a growing evidence that suggests federal system performs better than unitary system in managing diversity and reducing conflict.Decentralization is even seen to be more effective than federal system not only in managing diversity and reducing conflict but also in delivering public goods.This article provides an account of evolution of system of government in South Sudan.It finds a clear association of centralized unitary system with violent conflict and a relative peace during period of decentralized government or federal system.A decentralized federal system may be appropriate for South Sudan in managing diversity and mitigating conflict.Despite the popular demand by their citizens for a federal system to manage diversity and reduce violent conflict,the ruling elites in the post-independent South Sudan adopted instead an autocratic centralized unitary system that contributed among other factors to the persistent violent conflicts,erosion of social cohesion,and rising mistrust between state and citizens and between and among the communities of South Sudan.
文摘There is a gradual increase in the proportion of renewable energy sources.Green hydrogen has the potential to become one of the major energy carriers in the future.The Russian Federation,in partnership with countries in the Asia-Pacific region and especially China,has the potential to play a significant role in green hydrogen market.This study assessed the potential of developing green hydrogen energy based on wind power in the Far Eastern Federal District(FEFD)of the Russian Federation.Empirical wind speed data were collected from 20 meteorological stations in 4 regions(Sakhalinskaya Oblast’,Primorskiy Krai,Khabarovskiy Krai,and Amurskaya Oblast’)of the FEFD.The Weibull distribution was used to predict the potential of green hydrogen production.Five different methods(Empirical Method of Justus(EMJ),Empirical Method of Lysen(EML),Maximum Likelihood Method(MLE),Power Density Method(PDM),and Median and Quartiles Method(MQM))were used to determine the parameters(scape factor and scale factor)of the Weibull distribution.We calculated the total electricity generation potential based on the technical specifications of the three wind turbines:Senvion 6150 onshore,H165-4.0 MW,and Vestas V150-4.2 MW.The results showed that Vladivostok,Pogibi,Ilyinskiy,Yuzhno-Kuril’sk,Severo-Kuril’sk,Kholmsk,and Okha stations had the higher potential of green hydrogen production,of which Vladivostok exhibited the highest potential of green hydrogen production using the wind turbine of H165-4.0 MW,up to 2.56×10^(5) kg/a.In terms of economic analysis,the levelized cost of hydrogen(LCOH)values of lower than 4.00 USD/kg were obtained at Yuzhno-Kuril’sk,Ilyinskiy,Pogibi,and Vladivostok stations using the wind turbine of H165-4.0 MW,with the values of 3.54,3.50,3.24,and 2.55 USD/kg,respectively.This study concluded that the FEFD possesses significant potential in the production of green hydrogen and,with appropriate investment,has the potential to become a significant hub for green hydrogen trading in the Asia-Pacific region.
文摘1|Introduction The Federal Republic of Somalia,often perceived as linguistically homogeneous,is home to a rich tapestry of dialects and minority languages that reflect its diverse cultural heritage.While Somali is the official medium of communication,it is divided into two major dialects:Maxaa Tiri(spoken by approximately 60%of the population)and Maay(spoken by approximately 20%of the population)[1].Minority languages such as Bravanese(also known as Chimwiini or Chimbalazi),Mushunguli,Benadiri Somali,and Kibajuni are spoken by smaller communities,particularly in the southern and coastal regions[1].
基金supported by National Natural Science Foundation of China(62466045)Inner Mongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
文摘A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.
文摘In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realization of different work actions and activities. The methodology is based on the combination of cabinet and field work, performed in three stages. In the first, a documental review was carried out within the topic of Environment Impact (EI), the EIA and the study area, with a subsequent analysis of the environment indicators at an international, national and regional scale. In the second, the systematization of information was performed for the sixteen study cases at a local scale and the organization and analysis of a data base with the allotted information. And in the last stage, a field work was realized with participative observations in three verification sites and interview applications to the principal actors of the EIA process. These results allowed: to determine the main limitations within the EIA process (methodological, technical and operational), to propose an indicators scheme, and to formulate recommendations focused on the improvement of this Environment Public Policy instrument.
基金Supported by the National Nature Science Foundation of China(No.61771006)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)+1 种基金the Outstanding Young Cultivation Foundation of Henan university(No.0000A40366) the Basic and Advanced Technology Foundation of Henan Province(No.152300410195)
文摘In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.
文摘In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening in monetary policy and putting the independence of the Federal Reserve into question. However, this is only the latest development of a longer process: since the financial crisis, the Federal Reserve has been gradually losing its independence, in a quiet and perhaps permanent way. There are several reasons for this trend: the Federal Reserve’s performance during the financial crisis undermined its credibility, the consolidation of political factors arranged against its independence, and the consequences of the financial crisis weakened the economic foundation for its independence. Trump’s rise to power has only strengthened these factors, bringing an additional loss of independence, which will have a profound impact on the economy, society, and politics.
文摘This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.
基金supported by Systematic Major Project of Shuohuang Railway Development Co.,Ltd.,National Energy Group(Grant Number:SHTL-23-31)Beijing Natural Science Foundation(U22B2027).
文摘In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
文摘Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dyspareunia and its effect on sexual life among gynaecology clinic attendees in Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Materials and Methods: A cross-sectional study was conducted on consenting participants between 12th May 2016 and 25th July 2016. Anonymous self-administered questionnaires were used collection information on dyspareunia and its effect on sexual life at the Gynaecology clinic. The data was analyzed using Epiinfo version 7.1.5. Results: One hundred and four (104) women participated in this study. Most of the women studied were Igbos (95.19%), and were mainly between the age ranges of 21 - 30 years (66.35%). Most of them were married (89.42%), and were also mainly of the Pentecostal denomination (40.78%). The mean age at coitarche was 20.6 ± 3.95 years. Prevalence of dyspareunia was 36% and only 16% sought medical help. The various responses to dyspareunia were avoidance of sex 11%, reduced frequency of intercourse 8%, less desire for sex 19%, while majority of women with dyspareunia tolerated it (62%). Conclusion: The prevalence of dyspareunia is high in our society afflicting young women in their reproductive years with associated enormous stress on their sexual life.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
基金supported by the Research and application of Power Business Data Security and Trusted Collaborative Sharing Technology Based on Blockchain and Multi-Party Security Computing(J2022057).
文摘Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.
基金supported in part by an award from the VHA Office of Rural Health,Veterans Rural Health Resource CenterDIowa City(VRHRC-IC),Iowa City VA Health Care System,Iowa City,IA(Award#7345)。
文摘Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.
文摘In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.
