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.

Controllable Condensation of Aromatics and Its Mechanisms in Carbonization

In order to obtain liquefied products with higher yields of aromatic molecules to produce mesophase pitch,a good understanding of the relevant reaction mechanisms is required.Reactive molecular dynamics simulations were used to study the thermal reactions of pyrene,1-methylpyrene,7,8,9,10-tetrahydrobenzopyrene,and mixtures of pyrene with 1-octene,cyclohexene,or styrene.The reactant conversion rates,reaction rates,and product distributions were calculated and compared,and the mechanisms were analyzed and discussed.The results demonstrated that methyl and naphthenic structures in aromatics might improve the conversion rates of reactants in hydrogen transfer processes,but their steric hindrances prohibited the generation of high polymers.The naphthenic structures could generate more free radicals and presented a more obvious inhibition effect on the condensation of polymers compared with the methyl side chains.It was discovered that when different olefins were mixed with pyrene,1-octene primarily underwent pyrolysis reactions,whereas cyclohexene mainly underwent hydrogen transfer reactions with pyrene and styrene,mostly producing superconjugated biradicals through condensation reactions with pyrene.In the mixture systems,the olefins scattered aromatic molecules,hindering the formation of pyrene trimers and higher polymers.According to the reactive molecular dynamics simulations,styrene may enhance the yield of dimer and enable the controlled polycondensation of pyrene.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

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.

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.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Geometry and formation mechanism of tension gashes and their implication on the hydrocarbon accumulation in the deep-seated strata of sedimentary basin:A case from Shunnan area of Tarim Basin

With the theoretical and technological developments related to cratonic strike-slip faults,the Shuntuoguole Low Uplift in the Tarim Basin has attracted considerable attention recently.Affected by multi-stage tectonic movements,the strike-slip faults have controlled the distribution of hydrocarbon resources owing to the special fault characteristics and fault-related structures.In contrast,the kinematics and formation mechanism of strike-slip faults in buried sedimentary basins are difficult to investigate,limiting the discussion of these faults and hydrocarbon accumulation.In this study,we identified the characteristics of massive sigmoidal tension gashes(STGs)that formed in the Shunnan area of the Tarim Basin.High-resolution three-dimensional seismic data and attribute analyses were used to investigate their geometric and kinematic characteristics.Then,the stress state of each point of the STGs was calculated using seismic curvature attributes.Finally,the formation mechanism of the STGs and their roles in controlling hydrocarbon migration and accumulation were discussed.The results suggest that:(1)the STGs developed in the Shunnan area have a wide distribution,with a tensile fault arranged in an enéchelon pattern,showing an S-shaped bending.These STGs formed in multiple stages,and differential rotation occurred along the direction of strike-slip stress during formation.(2)Near the principal displacement zone of the strike-slip faults,the stress value of the STGs was higher,gradually decreasing at both ends.The shallow layer deformation was greater than the deep layer deformation.(3)STGs are critical for connecting source rocks,migrating oil and gas,sealing horizontally,and developing efficient reservoirs.This study not only provides seismic evidence for the formation and evolution of super large STGs,but also provides certain guidance for oil and gas exploration in this area.

Fecal microbiota transplantation for treatment of non-alcoholic fatty liver disease:Mechanism,clinical evidence,and prospect

The population of non-alcoholic fatty liver disease(NAFLD)patients along with relevant advanced liver disease is projected to continue growing,because currently no medications are approved for treatment.Fecal microbiota transplantation(FMT)is believed a novel and promising therapeutic approach based on the concept of the gut-liver axis in liver disease.There has been an increase in the number of pre-clinical and clinical studies evaluating FMT in NAFLD treatment,however,existing findings diverge on its effects.Herein,we briefly summarized the mechanism of FMT for NAFLD treatment,reviewed randomized controlled trials for evaluating its efficacy in NAFLD,and proposed the prospect of future trials on FMT.

Combustion mechanism and control approaches of underground coal fires:a review

With the large-scale mining of coal resources,the huge economic losses and environmental problems caused by underground coal fires have become increasingly prominent,and the research on the status quo and response strategies of underground coal fires is of great significance to accelerate the green prevention and control of coal fires,energy conservation and emission reduction.In this paper,we summarized and sorted out the research status of underground coal fires,focused on the theoretical and technical issues such as underground coal fire combustion mechanism,multiphysics coupling effect of coal fire combustion,fire prevention and extinguishing technology for underground coal fires,and beneficial utilization technology,and described the latest research progress of the prevention and control for underground coal fire hazards.Finally,the key research problems in the field of underground coal fire hazards prevention and control were proposed in the direction of the basic theory,technology research,comprehensive management and utilization,with a view to providing ideas and solutions for the management of underground coal fires.

Ground response and failure mechanism of gob-side entry by roof cutting with hard main roof

This study is the result of long-term efforts of the authors’team to assess ground response of gob-side entry by roof cutting(GSERC)with hard main roof,aiming at scientific control for GSERC deformation.A comprehensive field measurement program was conducted to determine entry deformation,roof fracture zone,and anchor bolt(cable)loading.The results indicate that GSERC deformation presents asymmetric characteristics.The maximum convergence near roof cutting side is 458 mm during the primary use process and 1120 mm during the secondary reuse process.The entry deformation is closely associated with the primary development stage,primary use stage,and secondary reuse stage.The key block movement of roof cutting structure,a complex stress environment,and a mismatch in the supporting design scheme are the failure mechanism of GSERC.A controlling ideology for mining states,including regional and stage divisions,was proposed.Both dynamic and permanent support schemes have been implemented in the field.Engineering practice results indicate that the new support scheme can efficiently ensure long-term entry safety and could be a reliable approach for other engineering practices.

Research on the mechanism of rockburst induced by mined coal-rock linkage of sharply inclined coal seams

In recent years,the mining depth of steeply inclined coal seams in the Urumqi mining area has gradually increased.Local deformation of mining coal-rock results in frequent rockbursts.This has become a critical issue that affects the safe mining of deep,steeply inclined coal seams.In this work,we adopt a perspective centered on localized deformation in coal-rock mining and systematically combine theoretical analyses and extensive data mining of voluminous microseismic data.We describe a mechanical model for the urgently inclined mining of both the sandwiched rock pillar and the roof,explaining the mechanical response behavior of key disaster-prone zones within the deep working face,affected by the dynamics of deep mining.By exploring the spatial correlation inherent in extensive microseismic data,we delineate the“time-space”response relationship that governs the dynamic failure of coal-rock during the progression of the sharply inclined working face.The results disclose that(1)the distinctive coal-rock occurrence structure characterized by a“sandwiched rock pillar-B6 roof”constitutes the origin of rockburst in the southern mining area of the Wudong Coal Mine,with both elements presenting different degrees of deformation localization with increasing mining depth.(2)As mining depth increases,the bending deformation and energy accumulation within the rock pillar and roof show nonlinear acceleration.The localized deformation of deep,steeply inclined coal-rock engenders the spatial superposition of squeezing and prying effects in both the strike and dip directions,increasing the energy distribution disparity and stress asymmetry of the“sandwiched rock pillar-B3+6 coal seam-B6 roof”configuration.This makes worse the propensity for frequent dynamic disasters in the working face.(3)The developed high-energy distortion zone“inner-outer”control technology effectively reduces high stress concentration and energy distortion in the surrounding rock.After implementation,the average apparent resistivity in the rock pillar and B6 roof substantially increased by 430%and 300%,respectively,thus guaranteeing the safe and efficient development of steeply inclined coal seams.

Regarding the Regulatory Sandbox Route and Mechanism for Governance of Artificial Intelligence

The main challenge in AI governance today is striking a balance between controlling AI dangers and fostering AI innovation.Regulators in a number of nations have progressively extended the regulatory sandbox,which was first implemented in the banking sector,to AI governance in an effort to reduce the conflict between regulation and innovation.The AI regulatory sandbox is a new and feasible route for AI governance in China that not only helps to manage the risks of technology application but also prevents inhibiting AI innovation.It keeps inventors'trial-and-error tolerance space inside the regulatory purview while offering a controlled setting for the development and testing of novel AI that hasn't yet been put on the market.By providing full-cycle governance of AI with the principles of agility and inclusive prudence,the regulatory sandbox offers an alternative to the conventional top-down hard regulation,expost regulation,and tight regulation.However,the current system also has inherent limitations and practical obstacles that need to be overcome by a more rational and effective approach.To achieve its positive impact on AI governance,the AI regulatory sandbox system should build and improve the access and exit mechanism,the coordination mechanism between the sandbox and personal information protection,and the mechanisms of exemption,disclosure,and communication.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

Design and Research of Form Controlled Planar Folding Mechanism based on 4D Printing Technology

The use of non-smart materials in structural components and kinematic pairs allows for flexible assembly in practical applications and is promising for aerospace applications.However,this approach can result in a complex structure and excessive kinematic pairs,which limits its potential applications due to the difficulty in controlling and actuating the mechanism.While smart materials have been integrated into certain mechanisms,such integration is generally considered a unique design for specific cases and lacks universality.Therefore,organically combining universal mechanism design with smart materials and 4D printing technology,innovating mechanism types,and systematically exploring the interplay between structural design and morphing control remains an open research area.In this work,a novel form-controlled planar folding mechanism is proposed,which seamlessly integrates the control and actuation system with the structural components and kinematic pairs based on the combination of universal mechanism design with smart materials and 4D printing technology,while achieving self-controlled dimensional ratio adjustment under a predetermined thermal excitation.The design characteristics of the mechanism are analyzed,and the required structural design parameters for the preprogrammed design are derived using a kinematic model.Using smart materials and 4D printing technology,folding programs based on material properties and control programs based on manufacturing parameters are encoded into the form-controlled rod to achieve the preprogrammed design of the mechanism.Finally,two sets of prototype mechanisms are printed to validate the feasibility of the design,the effectiveness of the morphing control programs,and the accuracy of the theoretical analysis.This mechanism not only promotes innovation in mechanism design methods but also shows exceptional promise in satellite calibration devices and spacecraft walking systems.

Polarization characteristics and controllability mechanism of passive scattering elements

Polarization feature is one of the important features of radar targets,which has been used in many fields.In this paper,the grid models of some typical foreign moving targets are constructed on the simulation platform,such as glider,cruiser,fixed wing aircraft,and rotorcraft.The electromagnetic scattering characteristics of the moving platforms under the incidence of circular polarization waves are calculated.The typical polarization characteristics which the orthogonal and in-phase components have in the echoes are analyzed and proved.Based on the polarization scattering matrix(PSM)theory,from the point of view of the physical reproduction,the technical status quo that the existing technical approaches are difficult to realize the passive simulation of polarization characteristic of the target is summarized.To solve this problem,combined with the vector synthesis law,the realization mechanism of controllable polarization characteristic of target echoes is proposed,the analytical expressions of polarization control matrix and polarization ratio are deduced,and the controllability of polarization ratio feature in the case of circular polarization is verified by simulation calculation.