Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

Data Driven Vibration Control:A

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

Cross-Domain Bilateral Access Control on Blockchain-Cloud Based Data Trading System

Data trading enables data owners and data requesters to sell and purchase data.With the emergence of blockchain technology,research on blockchain-based data trading systems is receiving a lot of attention.Particularly,to reduce the on-chain storage cost,a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform.Moreover,the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace.In the data marketplace,it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains.In this paper,we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems.We consider a system model that consists of domain authorities,data senders,data receivers,a blockchain layer,and a cloud provider.The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques.In the proposed protocol,the outsourced data of the sender is encrypted under the target receiver’s identity,and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme.Therefore,data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other.To demonstrate efficiency,we evaluate the performance of the proposed protocol and compare it with existing studies.

Analysis of the Impact of Big Data Technology on Environmental Pollution Control Audit

As China strives towards the second centenary goal,increasing attention is being paid to environmental pollution and other related issues.Concurrently,with the rapid development of big data technology,many big data solutions have been applied to environmental pollution control audits,exerting a significant impact.This paper presents the current situation of environmental pollution audits,summarizing the application of big data from the perspectives of both domestic and international research.In terms of data collection and data analysis for environmental pollution audits,cloud platform technology,and visualization technology are selected based on multiple data sources.The impact in the field of environmental pollution control audits is further analyzed.It is found that the environmental pollution audit cloud platform is not yet perfect,the technical skills of audit personnel are insufficient,and some technologies are not mature.Relevant suggestions are put forward to provide a reference for the future development of big data technology and its integration with environmental pollution control audits.

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.

Fast and scalable routing protocols for data center networks

Data center networks may comprise tens or hundreds of thousands of nodes,and,naturally,suffer from frequent software and hardware failures as well as link congestions.Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays.In such dynamic networks,links frequently fail or get congested,making the recalculation of the shortest paths a computationally intensive problem.Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed.Surprisingly,the design of fast shortest-path algorithms for data centers was largely neglected,though they are universal components of routing protocols.Moreover,parallelization techniques were mostly deployed for random network topologies,and not for regular topologies that are often found in data centers.The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware.We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks.Software-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network resources.This study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.Within this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node selection.We advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm convergence.By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster reliability.The simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in practical application,and can meet the high requirements of modern sea,land,and air operations to aviation airborne communication networks.

Congestion Control Using In-Network Telemetry for Lossless Datacenters

In the Ethernet lossless Data Center Networks (DCNs) deployedwith Priority-based Flow Control (PFC), the head-of-line blocking problemis still difficult to prevent due to PFC triggering under burst trafficscenarios even with the existing congestion control solutions. To addressthe head-of-line blocking problem of PFC, we propose a new congestioncontrol mechanism. The key point of Congestion Control Using In-NetworkTelemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry(INT) technology to obtain comprehensive congestion information, which isthen fed back to the sender to adjust the sending rate timely and accurately.It is possible to control congestion in time, converge to the target rate quickly,and maintain a near-zero queue length at the switch when using ICC. Weconducted Network Simulator-3 (NS-3) simulation experiments to test theICC’s performance. When compared to Congestion Control for Large-ScaleRDMA Deployments (DCQCN), TIMELY: RTT-based Congestion Controlfor the Datacenter (TIMELY), and Re-architecting Congestion Managementin Lossless Ethernet (PCN), ICC effectively reduces PFC pause messages andFlow Completion Time (FCT) by 47%, 56%, 34%, and 15.3×, 14.8×, and11.2×, respectively.

Blockchain-Enabled Privacy Protection and Access Control Scheme Towards Sensitive Digital Assets Management

With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.

Efficacy and safety of mild-warm moxibustion in treating diarrhea-predominant irritable bowel syndrome(spleen deficiency and dampness excess syndrome):a study protocol for a randomized controlled trial

1Background:Irritable bowel syndrome(IBS)is a disorder of bowel function,and diarrhea-predominant irritable bowel syndrome(IBS-D)is the most common.The current treatment for IBS-D is focused on improving patients’gastrointestinal-related symptoms,but there are limitations such as unstable effects and adverse drug reactions.Acupuncture and moxibustion exerts advantages in treating IBS-D.They include several forms,of which moxibustion is one of the most commonly used.And moxibustion is a common way used in treating IBS-D,but there is a lack of relevant evidence-based medical research data.This protocol aims to compare the efficacy of moxibustion(mild-warm moxibustion)in treating IBS-D(spleen deficiency and dampness excess syndrome)with the first-line treatment.Methods:In this prospective,parallel,randomized controlled trial(RCT)protocol,patients will be randomly allocated for 4-week treatment or control therapies and then 4-week follow-up in both groups.We will use Irritable Bowel Syndrome-Symptom Severity Scale(IBS-SSS)score,Irritable Bowel Syndrome-Quality of Life(IBS-QOL)score,serum brain-gut peptide levels,and traditional Chinese medicine(TCM)syndrome scale score to produce more evidence on IBS-D treatment with moxibustion.Finally,we will use SPSS 22.0 software to statistically analyze the data.Discussion:Mild-warm moxibustion is a complementary alternative therapy that fits with the pathogenesis of IBS-D.We hope to see more clinical evidence for mild-warm moxibustion against IBS-D that this RCT supported.

The Construction and Implementation Path of the Audit System for Quality Control in the Big Data Era

In the era of big data,the construction and implementation of a quality control audit system are particularly crucial.This article delves into the impact of big data technology on quality control auditing,establishes a quality control auditing system in the big data era,and elucidates the pathway to realizing this system.Through the application of big data technology to quality control audits,there is an enhancement in audit efficiency,the attainment of more accurate risk assessment,and the provision of robust support for the sustainable development of enterprises.

Research on Influencing Factors and Countermeasures of Risk Control of State-Owned Enterprises under the Background of Big Data

This study explores the risk control and response strategies of state-owned enterprises in the context of big data.Global economic uncertainty poses new challenges to state-owned enterprises,necessitating innovative risk management approaches.This article proposes response strategies from four key aspects:establishing a proactive risk management culture,building a foundation in technology and data,conducting big data-driven risk analysis,and implementing predictive analysis and real-time monitoring.State-owned enterprises can foster a proactive risk management culture by cultivating employee risk awareness,demonstrating leadership,and establishing transparency and open communication.Additionally,data integration and analysis,leveraging the latest technology,are crucial factors that can help companies better identify risks and opportunities.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Air pollution poses a critical threat to public health and environmental sustainability globally, and Nigeria is no exception. Despite significant economic growth and urban development, Nigeria faces substantial air quality challenges, particularly in urban centers. While outdoor air pollution has received considerable attention, the issue of indoor air quality remains underexplored yet equally critical. This study aims to develop a reliable, cost-effective, and user-friendly solution for continuous monitoring and reporting of indoor air quality, accessible from anywhere via a web interface. Addressing the urgent need for effective indoor air quality monitoring in urban hospitals, the research focuses on designing and implementing a smart indoor air quality monitoring system using Arduino technology. Employing an Arduino Uno, ESP8266 Wi-Fi module, and MQ135 gas sensor, the system collects real-time air quality data, transmits it to the ThingSpeak cloud platform, and visualizes it through a user-friendly web interface. This project offers a cost-effective, portable, and reliable solution for monitoring indoor air quality, aiming to mitigate health risks and promote a healthier living environment.

基于ControlVAE的约束嵌入稠密时变阵列构建

在对探地雷达(Ground Penetrating Radar,GPR)数据进行三维建模与可视化分析过程中,其所依赖的三维时变阵列通常是由一维数据道或二维阵列间接生成的.由于采集到的数据往往比较稀疏,且存在不规则偏移,需要进行稠密处理,以获得高密度三维时变阵列.本文提出了一种基于可控变分自编码(Controllable Variational AutoEncoder,ControlVAE)的约束嵌入稠密时变阵列构建方法 .使用基于ControlVAE的时变数据重构网络,通过隐空间采样插值和深浅层特征信息的融合,生成接近真实分布的伪道数据来增加阵列密度.构建了基于尺度不变特征变换的数据配准模块,提取道间梯度特征与结构对称性特征,可以在时空域内完成数据配准.同时将浅层特征作为约束信息嵌入数据重构网络,以消除数据道偏移对阵列生成的影响.实验结果表明,本文方法仅使用单测线二维B-scan数据集即可重建稠密时变阵列,可以有效降低重构模型累积误差,提高面对复杂真实数据时的鲁棒性.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

Functional and psychosocial effects of pulmonary Daoyin on patients with COPD in China:study protocol of a multicenter randomized controlled trial

BACKGROUND： Chronic obstructive pulmonary disease （COPD） is a major public health problem worldwide. Pulmonary rehabilitation （PR） is an established intervention for the management of patients with COPD. Exercise training is an important part of PR, and its effectiveness in patients with COPD is well established. However, alternative methods of PR training such as Daoyin have not been appropriately studied. Hence, alternative forms of exercise training that require less exercise equipment and no specific training place should be evaluated. This paper describes the study protocol of a clinical trial that aims to determine if pulmonary Daoyin training will improve the exercise capacity and psychosocial function of patients with COPD in China. METHODS AND DESIGN： A multicenter, randomized, controlled trial will be conducted. A total of 464 patients meeting the inclusion criteria will be enrolled into this study with 232 patients in each of the trial group and the control group. Based on patient education, patients in the trial group will receive pulmonary Daoyin and continue with their usual therapy for three months. In the control group, patients will continue with their usual therapy. The primary outcome measures are exercise capacity assessed by the six-minute walking distance test and lung function. Secondary outcomes include dyspnea and quality of life. Measurements will be taken at baseline （month 0） and after the study period （month 3）. DISCUSSION： It is hypothesized that pulmonary Daoyin will have beneficial effects in improving exercise capacity and psychosocial function of patients with stable COPD, and will provide an alternative form of exercise training that is accessible for the large number of people with COPD. TRIAL REGISTRATION： This trial has been registered in ClinicalTrials.gov. The identifier is NCT01482000.

Stability of Iterative Learning Control with Data Dropouts via Asynchronous Dynamical System

In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control （ILC） system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities （LMIS） depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.

Quality control of marine big data——a case study of real-time observation station data in Qingdao

Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.

Chinese herbal medicine Xinfeng Capsule in treatment of rheumatoid arthritis:study protocol of a multicenter randomized controlled trial

BACKGROUND： Rheumatoid arthritis （RA）, as a common systemic inflammatory autoimmune disease, affects approximately 1 in 100 individuals. Effective treatment for RA is not yet available because current research does not have a clear understanding of the etiology and pathogenesis of RA. Xinfeng Capsule, a patent Chinese herbal medicine, has been used in the treatment of RA in recent years. Despite its reported clinical efficacy, there are no large-sample, multicenter, randomized trials that support the use of Xinfeng Capsule for RA. Therefore, we designed a randomized, double-blind, multicenter, placebo-controlled trial to assess the efficacy and safety of Xinfeng Capsule in the treatment of RA. METHODS AND DESIGN： This is a 12-week, randomized, placebo-controlled, double-blind, multicenter trial on the treatment of RA. The participants will be randomly assigned to the experimental group and the control group at a ratio of 1：1. Participants in the experimental group will receive Xinfeng Capsule and a pharmaceutical placebo （imitation leflunomide）. The control group will receive leflunomide and an herbal placebo （imitation Xinfeng Capsule）. The American College of Rheumatology （ACR） Criteria for RA will be used to measure the efficacy of the Xinfeng Capsule. The primary outcome measure will be the percentage of study participants who achieve an ACR 20% response rate （ACR20）, which will be measured every 4 weeks after randomization. Secondary outcomes will include the ACR50 and ACR70 responses, the side effects of the medications, the Disease Activity Score 28, RA biomarkers, quality of life, and X-rays of the hands and wrists. The first four of the secondary outcomes will be measured every 4 weeks and the others will be measured at baseline and after 12 weeks of treatment. DISCUSSION： The result of this trial will help to evaluate whether Xinfeng Capsule is effective and safe in the treatment of RA. TRIAL REGISTRATION： This trial has been registered in ClinicalTrials.gov. The identifier is N CT01774877.