Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Mechanical behaviors of coal measures and ground control technologies for China's deep coal mines-A review

This paper reviews the major achievements in terms of mechanical behaviors of coal measures,mining stress distribution characteristics and ground control in China’s deep underground coal mining.The three main aspects of this review are coal measure mechanics,mining disturbance mechanics,and rock support mechanics.Previous studies related to these three topics are reviewed,including the geo-mechanical properties of coal measures,distribution and evolution characteristics of mining-induced stresses,evolution characteristics of mining-induced structures,and principles and technologies of ground control in both deep roadways and longwall faces.A discussion is made to explain the structural and mechanical properties of coal measures in China’s deep coal mining practices,the types and dis-tribution characteristics of in situ stresses in underground coal mines,and the distribution of mining-induced stress that forms under different geological and engineering conditions.The theory of pre-tensioned rock bolting has been proved to be suitable for ground control of deep underground coal roadways.The use of combined ground control technology(e.g.ground support,rock mass modification,and destressing)has been demonstrated to be an effective measure for rock control of deep roadways.The developed hydraulic shields for 1000 m deep ultra-long working face can effectively improve the stability of surrounding rocks and mining efficiency in the longwall face.The ground control challenges in deep underground coal mines in China are discussed,and further research is recommended in terms of theory and technology for ground control in deep roadways and longwall faces.

The Effect of Procrastination on Physical Exercise among College Students—The Chain Effect of Exercise Commitment and Action Control

Background:Exercise procrastination is prevalent among college students,causing decline in physical fitness.It is imperative to investigate the mechanism affecting college students’physical activity behaviors.This study was aimed at investigating the effect of procrastination on college students’physical exercise behavior,and the chain mediation effects of exercise commitment and action control(AC),to provide a theoretical basis for interventions targeting physical exercise behavior among college students.Methods:A questionnaire survey was conducted using convenience sampling.The General Procrastination Scale,Exercise Commitment Scale,Action Control Scale,and Physical Activity Rating Scale-3 questionnaires were used.Participants were 581 college students(age 19.27±0.94 years;243 males and 338 females).Statistical methods of regression analysis and structural equation modeling(SEM)were applied.Results:Procrastination,exercise commitment,and action control were found to be significant predictors of physical exercise behavior.Among these predictors,exercise commitment and action control showed full mediation effects in the relationship between procrastination and physical exercise behavior,and explained 25.48%and 30.77%of the total variance,respectively.The chain mediation effect of exercise commitment-action control was significant,accounting for 22.60%of the total variance,and the total indirect effect was 79.33%.Conclusion:Therefore,higher procrastination was associated with less participation in physical exercise behavior among college students.Improvements in exercise commitment and volitional decision-making ability for physical exercise behavior promoted physical exercise behavior,and increased exercise commitment promoted volitional decision-making ability among the students.The chain reaction effect of exercise commitment and action control also buffered the negative effects of procrastination on physical exercise behavior,thereby increasing physical exercise behavior among college students.

In-situ pressure-preserved coring for deep exploration:Insight into the rotation behavior of the valve cover of a pressure controller

In-situ pressure-preserved coring(IPP-Coring)is considered to be the most reliable and efficient method for the identification of the scale of oil and gas resources.During IPP-Coring,because the rotation behavior of the pressure controller valve cover in different medium environments is unclear,interference between the valve cover and inner pipe may occur and negatively affect the IPP-Coring success rate.To address this issue,we conducted a series of indoor experiments employing a high-speed camera to gain greater insights into the valve cover rotation behavior in different medium environments,e.g.,air,water,and simulated drilling fluids.The results indicated that the variation in the valve cover rotation angle in the air and fluid environments can be described by a one-phase exponential decay function with a constant time parameter and by biphasic dose response function,respectively.The rotation behavior in the fluid environments exhibited distinct elastic and gravitational acceleration zones.In the fluid environments,the density clearly impacted the valve cover closing time and rotation behavior,whereas the effect of viscosity was very slight.This can be attributed to the negligible influence of the fluid viscosity on the drag coefficient found in this study;meanwhile,the density can increase the buoyancy and the time period during which the valve cover experienced a high drag coefficient.Considering these results,control schemes for the valve cover rotation behavior during IPP-Coring were proposed for different layers and geological conditions in which the different drilling fluids should be used,e.g.,the use of a high-density valve cover in high-pore pressure layers.

Randomized control trial of a culturally adapted behavioral activation therapy for Muslim patients with depression in Pakistan

BACKGROUND Behavioral activation therapy(BA)is as effective as cognitive behavior therapy(CBT)in treating depression and can be delivered by practitioners with much less psychological training,making it particularly suitable for low resource settings.BA that is culturally adapted for Muslims(BA-M)is a culturally adapted form of BA that has been found acceptable and feasible for Muslims with depression in the United Kingdom and Turkey;however,this is the first time that its efficacy has been determined through a definitive randomized controlled trial.AIM To compare the effectiveness of BA-M with CBT for Muslim patients with depression in Pakistan.METHODS One hundred and eight patients were randomized 1:1 to treatment arms in a parallel-group randomized controlled trial in hospital or community sites in Lahore,Pakistan.Recruitment followed self-referral or referrals from clinicians,consultants or relevant professionals at each site.Four measures were recorded by blinded assessors:The patient health questionnaire-9(PHQ-9);the BA for depression scale short form(BADS-SF);symptom checklist-revised and the World Health Organization Quality-of-Life Brief Scale.All measures were recorded at baseline and post treatment;PHQ-9 and BADS-SF were also recorded at each session and at three month follow up.The primary analysis was to regress the PHQ-9 score after therapy upon the PHQ-9 score before therapy(baseline)and the type of therapy given,that is,analysis of covariance.In addition,analysis using PHQ-9 scores collected at each therapy session was employed in a 2-level regression model.RESULTS Patients in the BA-M arm experienced greater improvement in PHQ-9 score of 1.95 units compared to the CBT arm after adjusting for baseline values(P=0.006)The key reason behind this improvement was that patients were retained in therapy longer under BA-M,in which patients were retained for an average 0.75 sessions more than CBT patients(P=0.013).Patients also showed significant differences on physical(P<0.001),psychological(P=0.004)and social(P=0.047)domains of Quality of Life(QoL)at post treatment level,indicating an increased QoL in the BA-M group as compared to the treatment as usual group.Some baseline differences were noted in both groups for BA scores and two domains of QoL scale:Physical and environment,which might have influenced the results,though the BA-M group showed more improvement at completion of therapy.CONCLUSION Results proved the efficacy of BA-M in reducing symptoms for depressed patients in Pakistan,indicating BA-M is a promising treatment modality for depression in future,particularly in low resource settings.

Deformation behaviors of 21-6-9 stainless steel tube numerical control bending under different friction conditions

For contact dominated numerical control(NC) bending process of tube, the effect of friction on bending deformation behaviors should be focused on to achieve precision bending forming. A three dimensional(3D) elastic-plastic finite element(FE) model of NC bending process was established under ABAQUS/Explicit platform, and its reliability was validated by the experiment. Then, numerical study on bending deformation behaviors under different frictions between tube and various dies was explored from multiple aspects such as wrinkling, wall thickness change and cross section deformation. The results show that the large friction of wiper die-tube reduces the wrinkling wave ratio η and cross section deformation degree ΔD and increases the wall thinning degree Δt. The large friction of mandrel-tube causes large η, Δt and ΔD, and the onset of wrinkling near clamp die. The large friction of pressure die-tube reduces Δt and ΔD, and the friction on this interface has little effect on η. The large friction of bending die-tube reduces η and ΔD, and the friction on this interface has little effect on Δt. The reasonable friction coefficients on wiper die-tube, mandrel-tube, pressure die-tube and bending die-tube of 21-6-9(0Cr21Ni6Mn9N) stainless steel tube in NC bending are 0.05-0.15, 0.05-0.15, 0.25-0.35 and 0.25-0.35, respectively. The results can provide a guideline for applying the friction conditions to establish the robust bending environment for stable and precise bending deformation of tube bending.

Positive correlations between the health locus of control and self-management behaviors in hemodialysis patients in Xiamen

Objective:To investigate the association between the health locus of control and selfmanagement behaviors in patients receiving hemodialysis.Methods:Patients receiving hemodialysis in Xiamen,Fujian province,from December 2014 to March 2015 answered the Chinese version of the Multidimensional Health Locus of Control scale and the self-management behavior questionnaire.Results:The participants in this study indicated that they believed others exerted more control over their health than themselves or chance.In addition,the majority of participants had a medium to low level of self-management behaviors.Positive correlations were also observed between a participant's health locus of control and their level of selfmanagement behaviors.Internal health locus of control and external(others)health locus of control were significant predictors of self-management behaviors controlling for the effect of payment method.Conclusion:The results of this study provided evidence that there is a strong relationship between the health locus of control and self-management behaviors in hemodialysis patients.This study provides important information for medical professionals as they design strategies to educate hemodialysis patients on their health locus of control and selfmanagement behaviors.

The Relationship of Effortful Control to Academic Achievement via Children’s Learning-Related Behaviors

Effortful control (EC) is a temperamental self-regulatory capacity, defined as the efficiency of executive attention [1], which is related to individual differences in self-regulation. Although effortful control covers some dispositional self-regulatory abilities important to cope with social demands of successful adaptation to school, such as attention regulation, individual differences in EC have recently been associated with school functioning through academic achievement including the efficient use of learning-related behaviors, which have been found to be a necessary precursor of learning and they refer to a set of children’s behaviors that involve organizational skills and appropriate habits of study. Therefore, the aim of this study is to review the literature on EC’s relationship to academic achievement via learning-related behaviors, which reflect the use of metacognitive control processes in kindergarten and elementary school students. The findings indicate that EC affects academic achievement through the facilitation of the efficient use of metacognitive control processes.

Effects of anti-pull ties on the bearing behaviors of shallow tunnel-type anchorages in soft rock

Tunnel-type anchorages(TTAs)installed in human gathering areas are characterized by a shallow burial depth,and in many instances,they utilize soft rock as the bearing stratum.However,the stability control measures and the principle of shallow TTAs in soft rock have not been fully studied.Hence,a structure suitable for improving the stability of shallow TTAs in soft rock strata,named the anti-pull tie(APT),was added to the floor of the back face.Physical tests and numerical models were established to study the influence of the APT on the load transfer of TTAs,the mechanical response of the surrounding rock,the stress distribution of the interface,and the failure model.The mechanical characteristics of APTs were also studied.The results show that the ultimate bearing capacity of TTAs with an APT is increased by approximately 11.8%,as compared to the TTAs without an APT.Also,the bearing capacity of TTAs increases approximately linearly with increasing height,width,length,and quantity of APTs,and decreases approximately linearly with increasing distance from the back face and slope angle of the tie slope.The normal squeezing between the tie slope and the surrounding rock increases the shear resistance of the interface and expands the range of the surrounding rock participating in bearing sharing.Both tension and compression zones exist in the APT during loading.The tension zone extends from the tie toe to the tie bottom along the tie slope.The range of the tie body tension zone constantly expands to the deep part of the APT with an increasing load.The peak tensile stress value is located at the tie toe.The distribution of compressive stress in the tie body is the largest at the tie top,followed by the tie slope,and then the tie bottom.

Backstepping Sliding Mode Control Based on Extended State Observer for Hydraulic Servo System

Hydraulic servo system plays an important role in industrial fields due to the advantages of high response,small size-to-power ratio and large driving force.However,inherent nonlinear behaviors and modeling uncertainties are the main obstacles for hydraulic servo system to achieve high tracking perfor-mance.To deal with these difficulties,this paper presents a backstepping sliding mode controller to improve the dynamic tracking performance and anti-interfer-ence ability.For this purpose,the nonlinear dynamic model is firstly established,where the nonlinear behaviors and modeling uncertainties are lumped as one term.Then,the extended state observer is introduced to estimate the lumped distur-bance.The system stability is proved by using the Lyapunov stability theorem.Finally,comparative simulation and experimental are conducted on a hydraulic servo system platform to verify the efficiency of the proposed control scheme.

Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Based on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed.Based on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered comprehensively.The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks.The simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.

Leaching behavior of Pb and Zn in air pollution control residues and their modeling prediction

Increasing attention has been paid to air pollution control （APC） residues in China recently due to the rising proportion of waste incineration and the hazardous characteristics of the residues, among which heavy metal leaching toxicity plays an important role. Leaching behavior and potential risk of Pb and Zn in the APC residues from a Shanghai municipal solid waste （MSW） incinerator was studied, based on the leaching tests under different conditions and theoretical calculation using a geochemical thermodynamic equilibrium model MINTEQA2. Results showed that, extractant species and liquid to solid （L/S） ratio predominantly controlled the leaching toxicity of Pb and Zn, while ionic strength, vibration method and leaching time had less effect on the metals release. Leachate/final pH determined the metal leaching behavior, which changed the speciation of heavy metals in the extraction system. The equilibrium aqueous speciation, precipitation-dissolution of Pb and Zn was investigated according to the model computation, which was well in agreement with the experimental results.

Visual Object Tracking and Servoing Control of a Nano-Scale Quadrotor:System,Algorithms,and Experiments

There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.

Behavior Measurement Model Based on Prediction and Control of Trusted Network

In order to construct the trusted network and realize the trust of network behavior,a new multi-dimensional behavior measurement model based on prediction and control is presented.By using behavior predictive equation,individual similarity function,group similarity function,direct trust assessment function,and generalized predictive control,this model can guarantee the trust of an end user and users in its network.Compared with traditional measurement model,the model considers different characteristics of various networks.The trusted measurement policies established according to different network environments have better adaptability.By constructing trusted group,the threats to trusted group will be reduced greatly.Utilizing trusted group to restrict individuals in network can ensure the fault tolerance of trustworthiness of trusted individuals and group.The simulation shows that this scheme can support behavior measurement more efficiently than traditional ones and the model resists viruses and Trojans more efficiently than older ones.

Acute histopathological responses and long-term behavioral outcomes in mice with graded controlled cortical impact injury

While animal models of controlled cortical impact often display short-term motor dysfunction after injury, histological examinations do not show severe cortical damage. Thus, this model requires further improvement. Mice were subjected to injury at three severities using a Pin-Point^(TM)-controlled cortical impact device to establish secondary brain injury mouse models. Twenty-four hours after injury, hematoxylin-eosin staining, Fluoro-Jade B histofluorescence, and immunohistochemistry were performed for brain slices. Compared to the uninjured side, we observed differences of histopathological findings, neuronal degeneration, and glial cell number in the CA2 and CA3 regions of the hippocampus on the injured side. The Morris water maze task and beam-walking test verified long-term(14–28 days) spatial learning/memory and motor balance. To conclude, the histopathological responses were positively correlated with the degree of damage,as were the long-term behavioral manifestations after controlled cortical impact. All animal procedures were approved by the Institutional Animal Care and Use Committee at Shanghai Jiao Tong University School of Medicine.

Constraints Adjustment and Objectives Coordination of Satisfying Optimal Control Applied to Heavy Oil Fractionators

In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.

Group-Consensus of Hierarchical Containment Control for Linear Multi-Agent Systems

The existing containment control has been widely developed for several years, but ignores the case for large-scale cooperation. The strong coupling of large-scale networks will increase the costs of system detection and maintenance. Therefore, this paper is concerned with an extensional containment control issue, hierarchical containment control. It aims to enable a multitude of followers achieving a novel cooperation in the convex hull shaped by multiple leaders. Firstly, by constructing the three-layer topology, large-scale networks are decoupled. Then,under the condition of directed spanning group-tree, a class of dynamic hierarchical containment control protocol is designed such that the novel group-consensus behavior in the convex hull can be realized. Moreover, the definitions of coupling strength coefficients and the group-consensus parameter in the proposed dynamic hierarchical control protocol enhance the adjustability of systems. Compared with the existing containment control strategy, the proposed hierarchical containment control strategy improves dynamic control performance. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed hierarchical control protocol.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.Recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional Archimedes Optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid applications.The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter.In addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

Reinforcement Learning Behavioral Control for Nonlinear Autonomous System

Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error learning.Specifically,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission priority.Compared with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks.In the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control policy.Compared with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption.All error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.