Intrusion Detection System for PS-Poll DoS Attack in 802.11 Networks Using Real Time Discrete Event System

Wi-Fi devices have limited battery life because of which conserving battery life is imperative. The 802.11 Wi-Fi standard provides power management feature that allows stations(STAs) to enter into sleep state to preserve energy without any frame losses. After the STA wakes up, it sends a null data or PS-Poll frame to retrieve frame(s) buffered by the access point(AP), if any during its sleep period. An attacker can launch a power save denial of service(PS-DoS) attack on the sleeping STA(s) by transmitting a spoofed null data or PS-Poll frame(s) to retrieve the buffered frame(s) of the sleeping STA(s) from the AP causing frame losses for the targeted STA(s). Current approaches to prevent or detect the PS-DoS attack require encryption,change in protocol or installation of proprietary hardware. These solutions suffer from expensive setup, maintenance, scalability and deployment issues. The PS-DoS attack does not differ in semantics or statistics under normal and attack circumstances.So signature and anomaly based intrusion detection system(IDS) are unfit to detect the PS-DoS attack. In this paper we propose a timed IDS based on real time discrete event system(RTDES) for detecting PS-DoS attack. The proposed DES based IDS overcomes the drawbacks of existing systems and detects the PS-DoS attack with high accuracy and detection rate. The correctness of the RTDES based IDS is proved by experimenting all possible attack scenarios.

Modelling Problems of Hybrid Event Systems

This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and information interconnection as its tie. In this paper, an event, instead of a process, is regarded as an analyzed element. The hybrid event systems are described in mathematical models with the following tools: chains of time, generalized space and interconnecting information.

Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

The Relation between the Stabilization Problem for Discrete Event Systems Modeled with Timed Petri Nets via Lyapunov Methods and Max-Plus Algebra

A discrete event system is a dynamical system whose state evolves in time by the occurrence of events at possibly irregular time intervals. Timed Petri nets are a graphical and mathematical modeling tool applicable to discrete event systems in order to represent its states evolution where the timing at which the state changes is taken into consideration. One of the most important performance issues to be considered in a discrete event system is its stability. Lyapunov theory provides the required tools needed to aboard the stability and stabilization problems for discrete event systems modeled with timed Petri nets whose mathematical model is given in terms of difference equations. By proving stability one guarantees a bound on the discrete event systems state dynamics. When the system is unstable, a sufficient condition to stabilize the system is given. It is shown that it is possible to restrict the discrete event systems state space in such a way that boundedness is achieved. However, the restriction is not numerically precisely known. This inconvenience is overcome by considering a specific recurrence equation, in the max-plus algebra, which is assigned to the timed Petri net graphical model.

Liveness Problem of Petri Nets Supervisory Control Theory for Discrete Event Systems

A quite great progress of the supervisory control theory for discrete event systems (DES)has been made in the past nearly twenty years, and now, automata, formal language and Petri nets become the main research tools. This paper focus on the Petri nets based supervisory control theory of DES. Firstly, we review the research results in this field, and claim that there generally exists a problem in Petri nets based supervisory control theory of DES, that is, the deadlock caused by the controller introduced to enforce the given specification occurs in the closed-loop systems, especially the deadlock occurs in the closed-loop system in which the original plant is live. Finally, a possible research direction is presented for the solution of this problem.

Reliability Assessment of Distribution SystemBased on Discrete-event System

Discrete-event system simulation technology is used to analyze distribution system reliability in this paper. A simulation model, including entity state models, system state models, state transition models, reliability criterion model, is established. ‘Next happen event’ is taken as impulse principle of simulator clock to determine the sequence of random event occurrence dynamically. The results show this method is feasible.

Detectability for Nondeterministic Discrete Event Systems and Its Applications in Medical Diagnosis

In this paper,we extend our previous results on detectability to nondeterministic discrete event systems.Many practical systems are nondeterministic,especially those in biomedicine.Disease models of patients are usually nondeterministic because hardly anything is deterministic in biological systems.The goal is to determine or estimate the current and subsequent states of a system based on a sequence of observations when the initial state of the system is unknown.We say that a system is detectable if one can determine its state after observing some outputs.The observation includes partial event observation and/or partial state observation.We define four types of detectabilities:strong detectability,(weak) detectability,strong periodic detectability,and (weak) periodic detectability.We derive necessary and sufficient conditions for these detectabilities.These conditions can be checked by constructing an observer,which models the estimation of states under different observation.Furthermore,we apply the results to medical diagnosis by considering a realistic example of diagnosing whether a patient suffers from one of the following five similar diseases:(1) rheumatoid arthritis,(2) rheumatic arthritis,(3) systemic lupus eruthematosus,(4) bony ankylosis,or (5) spondylitis ankylopoietica.

Supervisory Control of Fuzzy Discrete Event Systems Based on Agent

FDES(fuzzy discrete event systems) can effectively represent a kind of complicated systems involving deterministic uncertainties and vagueness as well as human subjective observation and judgement from the view of discrete events, here the information system is divided into some independent intelligent entitative Agents. The concept of information processing state based on Agents was proposed. The processing state of Agent can be judged by some assistant observation parameters about the Agent and its environment around, and the transition among these states can be represented by FDES based on rules. In order to ensure the harmony of the Agents for information processing, its upstream and downstream buffers are considered in the modeling of the Agent system, and the supervisory controller based on FDES is constructed. The processing state of Agent can be adjusted by the supervisory controller to improve the stability of the system and the efficiency of resource utilization during the process according to the control policies. The result of its application was provided to illustrate the validity of the supervisory adjustment.

Decentralized supervisory control of continuous timed discrete event systems

In this paper,we presented the decentralized supervisory control problem of discrete event system with continuous-time variable.By presenting the definition of coobservability for the timed specification,a necessary and sufficient condition for the existence of decentralized supervisors is obtained.Finally,a numerical example is given.

Grassland-type ecosystem stability in China differs under the influence of drought and wet events

Ecological stability is a core issue in ecological research and holds significant implications forhumanity. The increased frequency and intensity of drought and wet climate events resulting from climatechange pose a major threat to global ecological stability. Variations in stability among different ecosystemshave been confirmed, but it remains unclear whether there are differences in stability within the sameterrestrial vegetation ecosystem under the influence of climate events in different directions and intensities.China's grassland ecosystem includes most grassland types and is a good choice for studying this issue.This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify thedirections and intensities of different types of climate events, and based on Normalized DifferenceVegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measurestability). Based on a traditional regression model, standardized methods were integrated to analyze theimpacts of the intensity and duration of drought and wet events on vegetation stability. The resultsshowed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had thelowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at anintermediate level. Regarding the impact of the duration and intensity of climate events on vegetationecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainlyaffected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppewas mainly affected by intensity in wet environments, and duration had no significant impact. Ourconclusions can provide decision support for the future grassland ecosystem governance.

Studies of an event-building algorithm of the readout system for the twin TPCs in HFRS

The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis

BACKGROUND The incidence of chronic kidney disease among patients with diabetes mellitus(DM)remains a global concern.Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus.However,no previous meta-analysis has assessed the effects of body mass index(BMI)on adverse kidney events in patients with DM.AIM To determine the impact of BMI on adverse kidney events in patients with DM.METHODS A systematic literature search was performed on the PubMed,ISI Web of Science,Scopus,Ovid,Google Scholar,EMBASE,and BMJ databases.We included trials with the following characteristics:(1)Type of study:Prospective,retrospective,randomized,and non-randomized in design;(2)participants:Restricted to patients with DM aged≥18 years;(3)intervention:No intervention;and(4)kidney adverse events:Onset of diabetic kidney disease[estimated glomerular filtration rate(eGFR)of<60 mL/min/1.73 m2 and/or microalbuminuria value of≥30 mg/g Cr],serum creatinine increase of more than double the baseline or end-stage renal disease(eGFR<15 mL/min/1.73 m2 or dialysis),or death.RESULTS Overall,11 studies involving 801 patients with DM were included.High BMI(≥25 kg/m2)was significantly associated with higher blood pressure(BP)[systolic BP by 0.20,95%confidence interval(CI):0.15–0.25,P<0.00001;diastolic BP by 0.21 mmHg,95%CI:0.04–0.37,P=0.010],serum albumin,triglycerides[standard mean difference(SMD)=0.35,95%CI:0.29–0.41,P<0.00001],low-density lipoprotein(SMD=0.12,95%CI:0.04–0.20,P=0.030),and lower high-density lipoprotein(SMD=–0.36,95%CI:–0.51 to–0.21,P<0.00001)in patients with DM compared with those with low BMIs(<25 kg/m2).Our analysis showed that high BMI was associated with a higher risk ratio of adverse kidney events than low BMI(RR:1.22,95%CI:1.01–1.43,P=0.036).CONCLUSION The present analysis suggested that high BMI was a risk factor for adverse kidney events in patients with DM.

Anthropogenic Influence on Decadal Changes in Concurrent Hot and Dry Events over China around the Mid-1990s

The frequency and duration of observed concurrent hot and dry events(HDEs) over China during the growing season(April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China(SEC), northern China(NC), and northeastern China(NEC). The frequency of HDEs averaged over China in the present day(PD,1994–2011) is double that in the early period(EP, 1964–81);the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model(MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes,suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas(GHG) concentrations dominates the simulated decadal changes,increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol(AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed.

Investigating adverse events in long-term care facilities:a systematized review

Objective:This systematized review aimed to synthesize the results of empirical studies focused on the types and factors of adverse events(AEs)that contribute to them in long-term care(LTC)settings.Methods:The search was conducted in Pro Quest,Scopus,and Pub Med in January 2021 and resulted in 1057 records.The content analysis method was used in the data analysis.Results:In all,35 studies were identified as relevant for the review.The analysis revealed 133 different types of AEs and 60 factors that contributed to them.Conclusions:In LTC,various AEs occur,most of which are preventable,while many factors that influence their occurrence could be significantly modifiable.Through an effective analysis of AEs in LTC,it is possible to minimize their occurrence and,at the same time,minimize their negative impact on all par ties concerned.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Computing large deviation prefactors of stochastic dynamical systems based on machine learning

We present a large deviation theory that characterizes the exponential estimate for rare events in stochastic dynamical systems in the limit of weak noise.We aim to consider a next-to-leading-order approximation for more accurate calculation of the mean exit time by computing large deviation prefactors with the aid of machine learning.More specifically,we design a neural network framework to compute quasipotential,most probable paths and prefactors based on the orthogonal decomposition of a vector field.We corroborate the higher effectiveness and accuracy of our algorithm with two toy models.Numerical experiments demonstrate its powerful functionality in exploring the internal mechanism of rare events triggered by weak random fluctuations.

Power equipment vibration visualization using intelligent sensing method based on event-sensing principle

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.

Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China

Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.