Incentive mechanism analysis of information security outsourcing based on principal-agent model

In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote client enterprises to complete outsourcing service actively. The incentive mechanism model of information security outsourcing is designed based on the principal-agent theory. Through analyzing the factors such as enterprise information assets value, invasion probability, information security environment, the agent cost coefficient and agency risk preference degree how to impact on the incentive mechanism, conclusions show that an enterprise information assets value and invasion probability have a positive influence on the fixed fee and the compensation coefficient; while information security environment, the agent cost coefficient and agency risk preference degree have a negative influence on the compensation coefficient. Therefore, the principal enterprises should reasonably design the fixed fee and the compensation coefficient to encourage information security outsourcing agency enterprises to the full extent.

Optimization of Structure of Agricultural Insurance Subsidies:A Multi-task Principal Agent Model

Optimizing the structure of agricultural insurance subsidies is of great significance to increasing the supply of agricultural insurance and strengthening the effects of agricultural insurance policies.This paper optimized the structure of agricultural insurance subsidies.It decomposed insurance activities into three parts:underwriting,claim settlement,and agricultural services.Next,it incorporated adverse selection risks,moral hazards,agricultural production and operation risks,insurance company's behavioral decisions and its risk attitudes into the multi-task principal agent analysis framework.Finally,it discussed how the government designs a subsidy mechanism and adjusts the subsidy structure to increase the insurance supply.

3D DEM simulation of hard rock fracture in deep tunnel excavation induced by changes in principal stress magnitude and orientation

To achieve the loading of the stress path of hard rock,the spherical discrete element model(DEM)and the new flexible membrane technology were utilized to realize the transient loading of three principal stresses with arbitrary magnitudes and orientations.Furthermore,based on the deep tunnel of China Jinping Underground Laboratory II(CJPL-II),the deformation and fracture evolution characteristics of deep hard rock induced by excavation stress path were analyzed,and the mechanisms of transient loading-unloading and stress rotation-induced fractures were revealed from a mesoscopic perspective.The results indicated that the stressestrain curve exhibits different trends and degrees of sudden changes when subjected to transient changes in principal stress,accompanied by sudden changes in strain rate.Stress rotation induces spatially directional deformation,resulting in fractures of different degrees and orientations,and increasing the degree of deformation anisotropy.The correlation between the degree of induced fracture and the unloading magnitude of minimum principal stress,as well as its initial level is significant and positive.The process of mechanical response during transient unloading exhibits clear nonlinearity and directivity.After transient unloading,both the minimum principal stress and minimum principal strain rate decrease sharply and then tend to stabilize.This occurs from the edge to the interior and from the direction of the minimum principal stress to the direction of the maximum principal stress on theε1-ε3 plane.Transient unloading will induce a tensile stress wave.The ability to induce fractures due to changes in principal stress magnitude,orientation and rotation paths gradually increases.The analysis indicates a positive correlation between the abrupt change amplitude of strain rate and the maximum unloading magnitude,which is determined by the magnitude and rotation of principal stress.A high tensile strain rate is more likely to induce fractures under low minimum principal stress.

Local component based principal component analysis model for multimode process monitoring

For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

Leakage Rate Model of Urban Water Supply Networks Using Principal Component Regression Analysis

To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic "leakage rate–leakage factors"(LRLF) model. In this model, we consider the pipe attributes(quality, diameter,age), maintenance cost, valve replacement cost, and annual average pressure. Based on variable selection and principal component analysis results, we extracted three main principle components—the pipe attribute principal component(PAPC), operation management principal component, and water pressure principal component. Of these, we found PAPC to have the most influence. Using principal component regression, we established an LRLF model with no detectable serial correlations. The adjusted R2 and RMSE values of the model were 0.717 and 2.067, respectively.This model represents a potentially useful tool for controlling leakage rate from the macroscopic viewpoint.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Application of synthetic principal component analysis model to mine area farmland heavy metal pollution assessment

Referring to GB5618-1995 about heavy metal pollution,and using statistical analysis SPSS,the major pollutants of mine area farmland heavy metal pollution were identified by variable clustering analysis.Assessment and classification were done to the mine area farmland heavy metal pollution situation by synthetic principal components analysis (PCA).The results show that variable clustering analysis is efficient to identify the principal components of mine area farmland heavy metal pollution.Sort and clustering were done to the synthetic principal components scores of soil sample,which is given by synthetic principal components analysis.Data structure of soil heavy metal contaminations relationships and pollution level of different soil samples are discovered.The results of mine area farmland heavy metal pollution quality assessed and classified with synthetic component scores reflect the influence of both the major and compound heavy metal pol- lutants.Identification and assessment results of mine area farmland heavy metal pollution can provide reference and guide to propose control measures of mine area farmland heavy metal pollution and focus on the key treatment region.

Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

Exploration on the Incentive Mechanism of Regional Ecological Capital Operation from the Perspective of Principal Agent

Ecological capital operation is a major means for innovation of ecological environment protection,and provides ecological security for sustainable economic and social development.In this paper,key factors for construction of incentive mechanism of ecological capital operation are explored from government cognition,enterprise attitude and public awareness.Via model building and parameter setting,incentive mechanism system of single objective is established effectively,to promote effective realization of regional ecological capital operation.

Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length

Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.

Calibration and validation of a sand model considering the effects of wave-induced principal stress axes rotation

Principal stress axes rotation influences the stress-strain behavior of sand under wave loading. A constitutive model for sand, which considers principal stress orientation and is based on generalized plasticity theory, is proposed. The new model, which employs stress invariants and a discrete memory factor during reloading, is original because it quantifies model parameters using experimental data. Four sets of hollow torsion experiments were conducted to calibrate the parameters and predict the capability of the proposed model, which describes the effects of principal stress orientation on the behavior of sand. The results prove the effectiveness of the proposed calibration method.

SCM Implementation Decisions of Principal-Agent under Asymmetric Information

The optimization investment policy decision of SCM-Supply Chain Management-implementation has been analysed under symmetric and asymmetric information conditions. For both conditions, SCM implementation options’ decision optimizing models have been developed. In these models, both clients and vendors try to pursue their own benefits. Based upon the principal-agent theory, the models show to what extent a principal (a client) needs to pay more to an agent (a vendor) in a context of asymmetric information. For the client, it is important to understand the extra costs to be able to adopt effective strategies to stimulate a vendor to perform an optimal implementation of a SCM system. The results of a simulation experiment regarding SCM implementation options illustrate and verify the theoretical findings and confirm the general notion that the less informed party is obliged to pay information rent to the better-informed party.

Multi-agent Based Hierarchy Simulation Models of Carrier-based Aircraft Catapult Launch

With the aid of multi-agent based modeling approach to complex systems, the hierarchy simulation models of carrier-based aircraft catapult launch are developed. Ocean, carrier, aircraft, and atmosphere are treated as aggregation agents, the detailed components like catapult, landing gears, and disturbances are considered as meta-agents, which belong to their aggregation agent. Thus, the model with two layers is formed i.e. the aggregation agent layer and the meta-agent layer. The information communication among all agents is described. The meta-agents within one aggregation agent communicate with each other directly by information sharing, but the meta-agents, which belong to different aggregation agents exchange their information through the aggregation layer first, and then perceive it from the sharing environment, that is the aggregation agent. Thus, not only the hierarchy model is built, but also the environment perceived by each agent is specified. Meanwhile, the problem of balancing the independency of agent and the resource consumption brought by real-time communication within multi-agent system （MAS） is resolved. Each agent involved in carrier-based aircraft catapult launch is depicted, with considering the interaction within disturbed atmospheric environment and multiple motion bodies including carrier, aircraft, and landing gears. The models of reactive agents among them are derived based on tensors, and the perceived messages and inner frameworks of each agent are characterized. Finally, some results of a simulation instance are given. The simulation and modeling of dynamic system based on multi-agent system is of benefit to express physical concepts and logical hierarchy clearly and precisely. The system model can easily draw in kinds of other agents to achieve a precise simulation of more complex system. This modeling technique makes the complex integral dynamic equations of multibodies decompose into parallel operations of single agent, and it is convenient to expand, maintain, and reuse the program codes.

A distributed algorithm for signal coordination of multiple agents with embedded platoon dispersion model

In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.

Agent视域下的人工智能赋能作战系统

针对作战系统的智能化设计问题,提出了基于Agent的人工智能技术概念框架和应用方法。首先,阐述了Agent概念,讨论了在作战系统中研究Agent的重要意义。然后,介绍了基于Agent的人工智能研究框架,列举了Agent在作战系统中的多种应用方式。最后,分析了Agent技术发展趋势及其作战应用可能面临的风险与挑战。

Amelioration of Alzheimer's disease pathology and cognitive deficits by immunomodulatory agents in animal models of Alzheimer's disease

The most common age-related neurodegenerative disease is Alzheimer's disease(AD) characterized by aggregated amyloid-β(Aβ) peptides in extracellular plaques and aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles,together with loss of cholinergic neurons,synaptic alterations,and chronic inflammation within the brain.These lead to progressive impairment of cognitive function.There is evidence of innate immune activation in AD with microgliosis.Classically-activated microglia(M1 state) secrete inflammatory and neurotoxic mediators,and peripheral immune cells are recruited to inflammation sites in the brain.The few drugs approved by the US FDA for the treatment of AD improve symptoms but do not change the course of disease progression and may cause some undesirable effects.Translation of active and passive immunotherapy targeting Aβ in AD animal model trials had limited success in clinical trials.Treatment with immunomodulatory/anti-inflammatory agents early in the disease process,while not preventive,is able to inhibit the inflammatory consequences of both Aβ and tau aggregation.The studies described in this review have identified several agents with immunomodulatory properties that alleviated AD pathology and cognitive impairment in animal models of AD.The majority of the animal studies reviewed had used transgenic models of early-onset AD.More effort needs to be given to creat models of late-onset AD.The effects of a combinational therapy involving two or more of the tested pharmaceutical agents,or one of these agents given in conjunction with one of the cell-based therapies,in an aged animal model of AD would warrant investigation.

Neuroprotection by immunomodulatory agents in animal models of Parkinson's disease

Parkinson’s disease（PD） is an age-related neurodegenerative disease for which the characteristic motor symptoms emerge after an extensive loss of dopamine containing neurons.The cell bodies of these neurons are present in the substantia nigra,with the nerve terminals being in the striatum.Both innate and adaptive immune responses may contribute to dopaminergic neurodegeneration and disease progression is potentially linked to these.Studies in the last twenty years have indicated an important role for neuroinflammation in PD through degeneration of the nigrostriatal dopaminergic pathway.Characteristic of neuroinflammation is the activation of brain glial cells,principally microglia and astrocytes that release various soluble factors.Many of these factors are proinflammatory and neurotoxic and harmful to nigral dopaminergic neurons.Recent studies have identified several different agents with immunomodulatory properties that protected dopaminergic neurons from degeneration and death in animal models of PD.All of the agents were effective in reducing the motor deficit and alleviating dopaminergic neurotoxicity and,when measured,preventing the decrease of dopamine upon being administered therapeutically after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine,6-hydroxydopamine,rotenone-lesioning or delivery of adeno-associated virus-α-synuclein to the ventral midbrain of animals.Some of these agents were shown to exert an anti-inflammatory action,decrease oxidative stress,and reduce lipid peroxidation products.Activation of microglia and astrocytes was also decreased,as well as infiltration of T cells into the substantia nigra.Pretreatment with fingolimod,tanshinoine I,dimethyl fumarate,thalidomide,or cocaine-and amphetamine-regulated transcript peptide as a preventive strategy ameliorated motor deficits and nigral dopaminergic neurotoxicity in brain-lesioned animals.Immunomodulatory agents could be used to treat patients with early clinical signs of the disease or potentially even prior to disease onset in those identified as having pre-disposing risk,including genetic factors.

A Three-Dimensional Model of Transport and Diffusion of Seeding Agents within Stratus

It is essential to learn the temporal and spatial concentration distributions and variations of seeding agents in cloud seeding of precipitation enhancement. A three-dimensional puff trajectory model incorporating a mesoscale nonhydrostatic model has been formulated, and is applied to simulating the transporting and diffusive characteristics of multiple line sources of seeding agents within super-cooled stratus. Several important factors are taken into consideration that affect the diffusion of seeding materials such as effects of topography and vertical wind shear, temporal and spatial variation of seeding parameters and wet deposition. The particles of seeding agents are assumed to be almost inert, they have no interaction with the particles of the cloud or precipitation except that they are washed out by precipitation. The model validity is demonstrated by the analyses and comparisons of model results, and checked by the sensitivity experiments of diffusive coefficients and atmospheric stratification. The advantage of this model includes not only its exact reflection of heterogeneity and unsteadiness of background fields, but also its good simulation of transport and diffusion of multiple line sources. The horizontal diffusion rate and the horizontal transport distance have been proposed that they usually were difficult to obtain in other models. In this simulation the horizontal diffusion rate is 0.82 m s(-1) for average of one hour, and the horizontal average transport distance reaches 65 km after 1 4 which are closely related to the background Fields.

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.