Veterans utilizing a federally qualified health center: a clinical snapshot

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.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

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.

Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain

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.

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

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.

Development of an Indicator Scheme for the Environment Impact Assessment in the Federal District, Mexico

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.

Federal extended Kalman filter based on reconstructed observation in incomplete observations

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.

Assessment of the Demarcation Method for Federal Riverine and Accreted Lands: Case Study of the Rio De Janeiro State Section of the South Paraíba River

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.

Changes and Trends in the Independence of the US Federal Reserve Following the Financial Crisis

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.

Prevalence of Dyspareunia and Its Effect on Sexual Life among Gynaecological Clinic Attendees in Alex Ekwueme Federal University Teaching Hospital Abakaliki, Nigeria

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.

Federalism and Euro-Regions in Europe： What Have We Learned From Twenty-Five Years of Cross-Border Cooperation in the Euro-region of Galicia-North Portugal？

The present paper is focused on the analysis of the European building processes from the historical perspective of federalism （from ethnic federalism to current federalism） applied to the current framework of Europeanization and cross-border cooperation in Europe. With the objective of reviewing some of its processes and impacts, an analysis structure has been set, being the main purpose to extract conclusions on the long Europeanization process undertaken by the EU institutions. One of these recent processes reached the consolidation of Euroregions as cross-border cooperation institutions within the framework of multilevel governance. For the main purpose of the paper, the following questions are raised： How has contributed the perspective of federalism to the building of cross-border institutions, namely Euroregions？ After three decades of implementation of the formal cooperation in Europe through institutions as the Euroregions, can it be confirmed that the Eurnregions are consolidated as an institutional benchmark within the cross-border cooperation in Europe （CBC-E）？ In order to answer these questions, a review of the historic perspective of ethnic federalism applied to the classical models of formal cooperation is undertaken. From this historical revision, the development of the Euroregion within the EU will be analyzed. Finally, the present paper is focused on the case study of the cross-border space that are the Autonomous Region of Galician and the Regiao Norte de Portugal, as well as its most important cooperative institution, the Euro-region Galicia-North Portugal.

Director of Informenergo of Federal Russia solute to the counterparts all over the world!

DEAR SIRS,Informenergo-Scientific and Technological Information Centerin Power Engineering and Electrification of Federal Russia is a stateorganization which deals with:-inquire and information servicing of enterprises,organizations,specialists with different scientific and technological data in thefield of power engineering (allotment of copies of original docu-ments,data bases,access to data bases,information about

群星璀灿 共铸辉煌——Federal Mogul(辉门)在中国

FEDERAL MOGUL 在汽车零部件行业,每当人们提起Champion(冠军)、Ferodo(菲罗多)、Goetze(戈茨)、Payen(培英)、AE等品牌时,无不为其悠久的历史、精良的品质和享誉全球的知名度而倾倒,这些国际品牌在如林的竞争对手中独树一帜、久负盛誉.它们犹如一颗颗璀灿的明星,汇集成了一个耀眼的星座,它就是当今世界汽车零部件工业的巨子、集二十余个世界著名品牌于一身的跨国集团--Federal-Mogul(辉门)公司.

The Origin Review and A Case Study of Plain Language in Federal Documents

Plain Language has made a great difference nowadays. As it turns out, Plain Language works effectively to express clearly, concisely and systematically. However, it is necessary for contemporary practitioners to review the origin and development of Plain Language Movement and to examine whether it has thoroughly implemented Plain Language policies in every federal document. Examining a contemporary federal document against the Guidelines for Document Designers reveals existing problems for further development.

MPHM:Model poisoning attacks on federal learning using historical information momentum

Federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.Federated learning allows individual participants to jointly train a global model without sharing local data,which significantly enhances data privacy.However,federated learning is vulnerable to poisoning attacks by malicious participants.Since federated learning does not have access to the participants’training process,i.e.,attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants.Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible.In contrast,we propose a novel model poisoning attack based on the momentum of history information(MPHM),that is,the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training,which will make the new malicious updates more effective and stealthy.Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information.Experiments show that in the classical defense case,our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.

Determinants of Satisfaction at Work and Its Reflections on Performance

This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

Has Fiscal Federalism Worked for Macroeconomic Purposes?The Chinese Experience 1994-2003

China＇s central government undertook major tax regime reform in 1994 that was characterized by fiscal federalism. In hindsight, this reform might be viewed as being more emphatic towards the revenue side than the expenditure side. The reform has resulted in certain success both for revenue shifting and inflation fighting purposes. However, the reform and its subsequent follow-ups have not addressed some fundamental issues pertaining to China＇s government finance system, such as the overhauling of the function of government finance and redrawing lines between the central and regional governments with regard to their fiscal responsibilities and duties. Moreover, fiscal federalism might have actually increased fiscal burden on the economy, especially on domestic sectors of the economy. However, coupled with enhanced policy support for China＇ s external development, fiscal federalism might have helped to further accelerate resource shifts toward the external sector, thus resulting in an unprecedented rapid expansion in China＇ s exports since the mid1990s.