Finite-time fault-tolerant control of teleoperating cyber physical system against faults

This paper studies a finite-time adaptive fractionalorder fault-tolerant control(FTC)scheme for the slave position tracking of the teleoperating cyber physical system(TCPS)with external disturbances and actuator faults.Based on the fractional Lyapunov stability theory and the finite-time stability theory,a fractional-order nonsingular fast terminal sliding mode(FONFTSM)control law is proposed to promote the tracking and fault tolerance performance of the considered system.Meanwhile,the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances.Furthermore,the finite-time stability of the closed-loop system is proved.Finally,comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

A Survey of Cyber Attacks on Cyber Physical Systems:Recent Advances and Challenges

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over Internet.It plays a key role in modern industry since it connects physical and cyber worlds.In order to meet ever-changing industrial requirements,its structures and functions are constantly improved.Meanwhile,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and practitioners.This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems.First,as typical system models are employed to study these systems,time-driven and event-driven systems are reviewed.Then,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are discussed.In particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders.Namely,both attack and defense strategies are discussed based on different system models.Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Semantic Social Service Organization Mechanism in Cyber Physical System

Cyber physical system(CPS)provides more powerful service by cyber and physical features through the wireless communication.As a kind of social organized network system,a fundamental question of CPS is to achieve service self-organization with its nodes autonomously working in both physical and cyber environments.To solve the problem,the social nature of nodes in CPS is firstly addressed,and then a formal social semantic descriptions is presented for physical environment,node service and task in order to make the nodes communicate automatically and physical environment sensibly.Further,the Horn clause is introduced to represent the reasoning rules of service organizing.Based on the match function,which is defined for measurement between semantics,the semantic aware measurement is presented to evaluate whether environment around a node can satisfy the task requirement or not.Moreover,the service capacity evaluation method for nodes is addressed to find out the competent service from both cyber and physical features of nodes.According to aforementioned two measurements,the task semantic decomposition algorithm and the organizing matrix are defined and the service self-organizing mechanism for CPS is proposed.Finally,examinations are given to further verify the efficiency and feasibility of the proposed mechanism.

Digital Twins and Cyber Physical Systems toward Smart Manufacturing and Industry 4.0:Correlation and Comparison

State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.

A quantitative analysis model of grid cyber physical systems

Conventional power systems are being developed into grid cyber physical systems(GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analysis method for a GCPS. Based on this, we discuss the relationship between cyberspace and physical space, especially the computational similarity within the GCPS both in undirected and directed bipartite networks. We then propose a model for evaluating the fusion of the three most important factors: information, communication, and security. We then present the concept of the fusion evaluation cubic for the GCPS quantitative analysis model. Through these models, we can determine whether a more realistic state of the GCPS can be found by enhancing the fusion between cyberspace and physical space. Finally, we conclude that the degree of fusion between the two spaces is very important, not only considering the performance of the whole business process, but also considering security.

Paraconsistent Annotated Logic in Analysis of Physical Systems:Introducing the Paraquantum Factor of Quantization h_(ψ)

We present in this paper an alternative of modeling physical systems through a non-Classical logic namely the Paraconsistent Logic (PL) whose main feature is the revocation of the principle of non-contradiction. The Paraconsistent Annotated Logic with annotation of two values (PAL2v) is a type of PL and has in its theoretical structure the main feature of dealing with contradictions offering flexibility in drawing conclusions. Several works about applications of PAL2v have shown that such logic is able to provide us with an adequate treatment to uncertainties. Based on the foundations of the PAL2v we presented the ParaQuantum logic (PQL) with the goal of performing analysis of signals from information sources which model physical systems. The formalization of the concepts of the logics PQL, that it is represented in a Lattice, requires the considering of Paraquantum logical states ψ which are propagated through variations of the evidence Degrees μ and λ which come out from measurements performed in Observable Variables in the physical world. When we analyze the lattice of the PQL, we obtain equations which quantify values of physical quantities from where we obtain the effects of propagation of the Paraquantum logical states ψ. In this paper, we introduce the Paraquantum Factor of quantization hψ whose value is associated with a special logical state on the lattice which is identified with the Planck constant h. We conclude through these studies that the Paraquantum Logical Model based on the ParaQuantum logics PQL can link the several fields of the physical sciences by means of quantization of values. It is an innovative approach of formulating natural phenomena.

Analysis of Physical Systems with Paraconsistent Annotated Logic: Introducing the Paraquantum Gamma Factor γ_(ψ)

In this paper we use a non-classical logic called ParaQuantum Logic (PQL) which is based on the foundations of the Paraconsistent Annotated logic with annotation of two values (PAL2v). The formalizations of the PQL concepts, which is represented by a lattice with four vertices, leads us to consider Paraquantum logical states ψ which are propagated by means of variations of the evidence Degrees extracted from measurements performed on the Observable Variables of the physical world. In this work we introduce the Paraquantum Gamma Factor γPψ which is an expansion factor on the PQL lattice that act in the physical world and is correlated with the Paraquantum Factor of quantization hψ whose value is associated with a special logical state on the lattice which is identified with the Planck constant h. Our studies show that the behavior of the Paraquantum Gamma Factor γPψ, at the time of reading the evidence Degrees through measurements of the Observable Variables in the physical world, is identical to that one of the Lorentz Factor γ used in the relativity theory. In the final part of this paper we present results about studies of expansion and contraction of the Paraquantum Logical Model which correlate the factors γPψ, and γ. By applying these correlation factors, the lattice of the PQL suitable for the universe understudy can be contracted or expanded, allowing the quantization model to cover the several study fields of physics.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Event-triggered finite-time command-filtered tracking control for nonlinear time-delay cyber physical systems against cyber attacks

This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems(CPSs)subject to cyber attacks.Under the attack circumstance,the output and state information of CPSs is unavailable for the feedback design,and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking task.To solve this,a new coordinate conversion is proposed by considering the attack gains and the reference signal simultaneously.By employing the transformed variables,a modified fractional-order command-filtered signal is incorporated to overcome the complexity explosion issue,and the Nussbaum function is used to tackle the varying attack gains.By systematically constructing the Lyapunov-Krasovskii functional,an adaptive event-triggered mechanism is presented in detail,with which the communication resources are greatly saved,and the finite-time tracking of CPSs under cyber attacks is guaranteed.Finally,an example demonstrates the effectiveness.

Alexa,let's train now!——A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging

Background:There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging.Consequently,the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions.In this context,digital and home-based physical training interventions might be a promising alternative to center-based intervention programs.Thus,this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance.Methods:In this pre-registered systematic review(PROSPERO;ID:CRD42022320031),5 electronic databases(PubMed,Web of Science,Psyclnfo,SPORTDiscus,and Cochrane Library)were searched by 2 independent researchers(FH and PT)to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults.The systematic literature search yielded 8258 records(extra17 records from other sources),of which 27 controlled trials were considered relevant.Two reviewers(FH and PT)independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale).Results:Of the 27 reviewed studies,15 reported positive effects on cognitive and motor-cognitive outcomes(i.e.,performance improvements in measures of executive functions,working memory,and choice stepping reaction test),and a considerable heterogeneity concerning study-related,population-related,and intervention-related characteristics was noticed.A more detailed analysis suggests that,in particular,interventions using online classes and technology-based exercise devices(i.e.,step-based exergames)can improve cognitive performance in healthy older adults.Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence(≤85%)and monitoring of the level of regular physical activity in the control group.Conclusion:The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall,though there is limited evidence that specific types of digital and home-based physical training interventions(e.g.,online classes and step-based exergames)can be an effective strategy for improving cognitive performance in older adults.However,due to the limited number of available studies,future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Association between physical activity and risk of gastroesophageal reflux disease:A systematic review and meta-analysis

Background:Lifestyle plays an important role in preventing and managing gastroesophageal reflux disease(GERD).In response to the conflicting results in previous studies,we performed a systematic review and meta-analysis to investigate this association.Methods:Relevant studies published until January 2023 were retrieved from 6 databases,and the prevalence of symptomatic gastroesophageal reflux(GER)or GERD was determined from the original studies.A random effects model was employed to meta-analyze the association by computing the pooled relative risk(RR)with 95%confidence intervals(95%CIs).Furthermore,subgroup and dose-response analyses were performed to explore subgroup differences and the association between cumulative physical activity(PA)time and GERD.Results:This meta-analysis included 33 studies comprising 242,850 participants.A significant negative association was observed between PA and the prevalence of symptomatic GER(RR=0.74,95%CI:0.66-0.83;p<0.01)or GERD(RR=0.80,95%CI:0.76-0.84;p<0.01),suggesting that engaging in PA might confer a protective benefit against GERD.Subgroup analyses consistently indicated the presence of this association across nearly all subgroups,particularly among the older individuals(RR_(<40 years):RR_(≥40 years)=0.85:0.69,p<0.01)and smokers(RR_(smoker):RR_(non-smoker)=0.67:0.82,p=0.03).Furthermore,a dose-response analysis revealed that individuals who engaged in 150 min of PA per week had a 72.09%lower risk of developing GERD.Conclusion:Maintaining high levels of PA decreased the risk of GERD,particularly among older adults and smokers.Meeting the recommended PA level of 150 min per week may significantly decrease the prevalence of GERD.

The effectiveness of physical activity interventions on blood pressure in children and adolescents:A systematic review and network meta-analysis

Background:High blood pressure(BP)is a major contributor to mortality and cardiovascular diseases.Despite the known benefits of exercise for reducing BP,it is crucial to identify the most effective physical activity(PA)intervention.This systematic review and network meta-analysis(NMA)aimed to evaluate the available evidence on the effectiveness of various PA interventions for reducing BP and to determine their hierarchy based on their impact on BP.Methods:A search of PubMed,SPORTDiscus,PsycINFO,Web of Science,CINAHL,Cochrane,and Eric databases was conducted up to December 2022 for this systematic review and NMA.Randomized controlled trials and quasi-experimental studies targeting healthy children and adolescents aged 6-12 years old were included in this study.Only studies that compared controlled and intervention groups using PA or exercise as the major influence were included.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.Three independent investigators performed the literature screening,data extraction,and risk of bias assessment.We used Bayesian arm-based NMA to synthesize the data.The primary outcomes were systolic BP and diastolic BP.We calculated the mean differences(MDs)in systolic BP and diastolic BP before and after treatment.Mean treatment differences were estimated using NMA and random-effect models.Results:We synthesized 27 studies involving 15,220 children and adolescents.PA combined with nutrition and behavior change was the most effective intervention for reducing both systolic BP and diastolic BP(MD=-8.64,95%credible interval(95%CI):-11.44 to-5.84;MD=-6.75,95%CI:-10.44 to-3.11),followed by interventions with multiple components(MD=-1.39,95%CI:-1.94 to-0.84;MD=-2.54,95%CI:-4.89 to-0.29).Conclusion:Our findings suggest that PA interventions incorporating nutrition and behavior change,followed by interventions with multiple components,are most effective for reducing both systolic BP and diastolic BP in children and adolescents.

2024 Adult Compendium of Physical Activities:A third update of the energy costs of human activities

Background:The Compendium of Physical Activities was published in 1993 to improve the comparability of energy expenditure values assigned to self-reported physical activity(PA)across studies.The original version was updated in 2000,and again in 2011,and has been widely used to support PA research,practice,and public health guidelines.Methods:This 2024 update was tailored for adults 19-59 years of age by removing data from those≥60 years.Using a systematic review and supplementary searches,we identified new activities and their associated measured metabolic equivalent(MET)values(using indirect calorimetry)published since 2011.We replaced estimated METs with measured values when possible.Results:We screened 32,173 abstracts and 1507 full-text papers and extracted 2356 PA energy expenditure values from 701 papers.We added303 new PAs and adjusted 176 existing MET values and descriptions to reflect the addition of new data and removal of METs for older adults.We added a Major Heading(Video Games).The 2024 Adult Compendium includes 1114 PAs(912 with measured and 202 with estimated values)across 22 Major Headings.Conclusion:This comprehensive update and refinement led to the creation of The 2024 Adult Compendium,which has utility across research,public health,education,and healthcare domains,as well as in the development of consumer health technologies.The new website with the complete lists of PAs and supporting resources is available at https://pacompendium.com.

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

Use of new technologies for the promotion of physical activity in patients with mental illness:A systematic review

BACKGROUND Physical exercise is an underutilized tool for the management of mental disorders.New technologies have made a breakthrough in health care,and one of its possible applications(apps)could be that of customizing exercise programs for special populations,such as patients with mental disorders.However,the app of the so-called e-health to mental health care is still limited.AIM To know the efficacy of apps to promote physical activity in patients with mental disorders.METHODS We conducted a systematic review of the PubMed and Embase databases with the aim of exploring the use of new technologies for the enhancement of physical exercise in patients with a psychiatric illness.Following the selection process,10 articles were included in the review.RESULTS The most commonly used devices in this type of intervention are wearable devices and web platforms.Good results in terms of effectiveness and acceptability were obtained in most of the studies.CONCLUSION Our findings suggest that the use of new technologies in mental health represents a feasible strategy with great potential in clinical practice.