Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.

Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era

The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems(CPSs)seamlessly integrate physical processes with advanced digital technologies.However,as industries become increasingly interconnected and reliant on smart digital technologies,the intersection of physical and cyber domains introduces novel security considerations,endangering the entire industrial ecosystem.The transition towards a more cooperative setting,including humans and machines in Industry 5.0,together with the growing intricacy and interconnection of CPSs,presents distinct and diverse security and privacy challenges.In this regard,this study provides a comprehensive review of security and privacy concerns pertaining to CPSs in the context of Industry 5.0.The review commences by providing an outline of the role of CPSs in Industry 5.0 and then proceeds to conduct a thorough review of the different security risks associated with CPSs in the context of Industry 5.0.Afterward,the study also presents the privacy implications inherent in these systems,particularly in light of the massive data collection and processing required.In addition,the paper delineates potential avenues for future research and provides countermeasures to surmount these challenges.Overall,the study underscores the imperative of adopting comprehensive security and privacy strategies within the context of Industry 5.0.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

美国教师评估与退出机制的构建及其启示:基于对IMPACT教师评估系统的剖析

为探索我国教师评估与退出机制的建立与运行,采用文本分析方法对美国华盛顿哥伦比亚特区IMPACT评估系统操作指南进行分析,总结IMPACT评估系统的运行体系与特点,为我国教师评估与退出机制提供借鉴。IMPACT评估系统通过覆盖全体的评估对象、专业多元的评估责任主体、丰富具体的评估内容、清晰透明的评估流程以及奖罚分明的评估结果对华盛顿特区公立学校系统教师进行评估,整个系统呈现出评估内容专业化、评估过程公正化、评估结果差异化和评估系统动态化等特点。综观IMPACT评估系统的设计与运行,我国在构建教师评估与退出机制时,应注意在体系建设上强调政府提供制度支持;在评估内容与标准上关注教师能力;在评估责任主体上要兼顾多方利益群体;在系统设计上要因地制宜;最后在系统实施过程中更要注重宽严适度。

废冰箱回收利用环境效益评估——基于IMPACT WORLD+方法

废电器是产生量大且增速快的一类固体废物.我国构建的废电器回收体系是实施碳达峰?碳中和的重要举措,但废电器回收利用体系的环境效益尚不清楚.利用IMPACTWORLD+方法,从不同环境影响类型出发评估废电器回收处理的环境效益,并通过损害类型指标量化评价.结果表明,废冰箱回收利用的损害类型指标“人体健康”和“生态环境质量”的结果分别为-10592.501EUR2015和-8211.357EUR2015,合计为-18803.857EUR2015,即环境收益相当于18803.857EUR2015.据此估算,中国2021年废电器回收处理体系中仅废冰箱回收利用的环境收益可达12.82亿EUR2015.研究结果表明废电器回收处理的环境收益十分可观,有望为废电器回收行业的可持续发展提供新的助力.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

The impact of demographic dynamics on food consumption and its environmental outcomes:Evidence from China

With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.

Impact resistance performance and optimization of the sand-EPE composite cushion in rock sheds

Rock sheds are widely used to prevent rockfall disasters along roads in mountainous areas.To improve the capacity of rock sheds for resisting rockfall impact,a sand and expandable polyethylene(EPE)composite cushion was proposed.A series of model experiments of rockfall impact on rock sheds were conducted,and the buried depth of the EPE foam board in the sand layer was considered.The impact load and dynamic response of the rock shed were investigated.The results show that the maximum impact load and dynamic response of the rock shed roof are all significantly less than those of the sand cushion.Moreover,as the distance between the EPE foam board and rock shed roof decreases,the maximum rockfall impact force and impact pressure gradually decrease,and the maximum displacement,acceleration and strain of the rock shed first decrease and then change little.In addition,the vibration acceleration and vertical displacement of the rock shed roof decrease from the centre to the edge and decrease faster along the longitudinal direction than that along the transverse direction.In conclusion,the buffering effect of the sand-EPE composite cushion is better than that of the pure sand cushion,and the EPE foam board at a depth of 1/3 the thickness of the sand layer is appropriate.

Correction:Impact of Transition Metal Layer Vacancy on the Structure and Performance of P2 Type Layered Sodium Cathode Material

Following publication of the original article[1],the authors reported that the author Hun-Gi Jung should be affiliated as 3,4 and 5 instead of 4 and 5.The author’s name“A.-Yeon Kim”needed to be updated to“A-Yeon Kim”,removing the period.The correct author’s name and affiliation have been provided in this Correction.The original article[1]has been corrected.

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical environment.Internet-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds.Since the complexity level of the CPS increases,an adversary attack becomes possible in several ways.Assuring security is a vital aspect of the CPS environment.Due to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve it.This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS environment.The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment.The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format.Besides,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature subset.Along with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS environment.The ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS method.The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset.The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

Reinforcing effects of polypropylene on energy absorption and fracturing of cement-based tailings backfill under impact loading

Polypropylene(PP)fiber-reinforced cement-based tailings backfill(FRCTB)is a green compound material with superior crack resistance and has good prospects for application in underground mining.However,FRCTB exhibits susceptibility to dynamic events,such as impact ground pressure and blast vibrations.This paper investigates the energy and crack distribution behavior of FRCTB under dynamic impact,considering the height/diameter(H/D)effect.Split Hopkinson pressure bar,industrial computed tomography scan,and scanning electron microscopy(SEM)experiments were carried out on six types of FRCTB.Laboratory outcomes confirmed fiber aggregation at the bottom of specimens.When H/D was less than 0.8,the proportion of PP fibers distributed along theθangle direction of80°-90°increased.For the total energy,all samples presented similar energy absorption,reflectance,and transmittance.However,a rise in H/D may cause a rise in the energy absorption rate of FRCTB during the peak phase.A positive correlation existed between the average strain rate and absorbed energy per unit volume.The increase in H/D resulted in a decreased crack volume fraction of FRCTB.When the H/D was greater than or equal to 0.7,the maximum crack volume fraction of FRCTB was observed close to the incidence plane.Radial cracks were present only in the FRCTB with an H/D ratio of 0.5.Samples with H/D ratios of 0.5 and 0.6 showed similar distributions of weakly and heavily damaged areas.PP fibers can limit the emergence and expansion of cracks by influencing their path.SEM observations revealed considerable differences in the bonding strengths between fibers and the FRCTB.Fibers that adhered particularly well to the substrate were attracted together with the hydration products adhering to surfaces.These results show that FRCTB is promising as a sustainable and green backfill for determining the design properties of mining with backfill.

Impacts of Future Changes in Heavy Precipitation and Extreme Drought on the Economy over South China and Indochina

Heavy precipitation and extreme drought have caused severe economic losses over South China and Indochina(INCSC)in recent decades.Given the areas with large gross domestic product(GDP)in the INCSC region are distributed along the coastline and greatly affected by global warming,understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation(RX5day)and the maximum consecutive dry days(CDD)is critical for adaptation planning in this region.Based on the latest data released by phase 6 of the Coupled Model Intercomparison Project(CMIP6),future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under the fossil-fueled development Shared Socioeconomic Pathway(SSP5-8.5)are investigated.Results indicate that RX5day will intensify robustly throughout the INCSC region,while CDD will lengthen in most regions under global warming.The changes in climate consistently dominate the effect on GDP over the INCSC region,rather than the change of GDP.If only considering the effect of climate change on GDP,the changes in precipitation extremes bring a larger impact on the economy in the future to the provinces of Hunan,Jiangxi,Fujian,Guangdong,and Hainan in South China,as well as the Malay Peninsula and southern Cambodia in Indochina.Thus,timely regional adaptation strategies are urgent for these regions.Moreover,from the sub-regional average viewpoint,over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the economic impacts from heavy precipitation over the INCSC region.

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental monitoring.The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction.This study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within CPS.The primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of anurans.It has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient noises.Initially,the data is augmented and preprocessed.Next,the mel spectrogram features are extracted through two-way feature extraction.First,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature extraction.Finally,the classification is performed using the CNN-LSTM process.This methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification accuracy.The study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural regions.The dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

Enhanced structural damage behavior of liquid-filled tank by reactive material projectile impact

A series of ballistic experiments were performed to investigate the damage behavior of high velocity reactive material projectiles(RMPs) impacting liquid-filled tanks,and the corresponding hydrodynamic ram(HRAM) was studied in detail.PTFE/Al/W RMPs with steel-like and aluminum-like densities were prepared by a pressing/sintering process.The projectiles impacted a liquid-filled steel tank with front aluminum panel at approximately 1250 m/s.The corresponding cavity evolution characteristics and HRAM pressure were recorded by high-speed camera and pressure acquisition system,and further compared to those of steel and aluminum projectiles.Significantly different from the conical cavity formed by the inert metal projectile,the cavity formed by the RMP appeared as an ellipsoid with a conical front.The RMPs were demonstrated to enhance the radial growth velocity of cavity,the global HRAM pressure amplitude and the front panel damage,indicating the enhanced HRAM and structural damage behavior.Furthermore,combining the impact-induced fragmentation and deflagration characteristics,the cavity evolution of RMPs under the combined effect of kinetic energy impact and chemical energy release was analyzed.The mechanism of enhanced HRAM pressure induced by the RMPs was further revealed based on the theoretical model of the initial impact wave and the impulse analysis.Finally,the linear correlation between the deformation-thickness ratio and the non-dimensional impulse for the front panel was obtained and analyzed.It was determined that the enhanced near-field impulse induced by the RMPs was the dominant reason for the enhanced structural damage behavior.

Velocity equation for grenades while impacting on dry sand media

This paper deals with the collision of sphere shape grenades with sand media.The central issue of the article is the establishing of an empirical velocity equation of the grenade while impacting on sand that is used to solve motion equations of the mechanical mechanism inside the impact grenade fuze.The paper focuses on impact velocities that are lower than 5 m s^(-1).An experiment was conducted to study the velocity of the grenade while impacting on dry sand.A high-speed camera video was used to capture the grenade positions.The grenade velocity in the impact process was generated from these video data.Some types of fitting curves are used to regress the velocity equation of the grenade while interacting with the sand media and the best-fitting model is chosen.The result shows the regression curve has a high correlation with the experiment data for grenade velocities below 5 m s^(-1).The received regression equation is useful for analyzing the working ability of the inertial mechanism inside the impact grenade or analyzing and choosing the appropriate parameters of each part in the inertial mechanism to meet the required characteristics of the mechanism.

Instrumented oscillographic study on impact toughness of an axle steel DZ2 with different tempering temperatures

Compared with the conventional Charpy impact test method,the oscillographic impact test can help in the behavioral analysis of materials during the fracture process.In this study,the trade-off relationship between the strength and toughness of a DZ2 axle steel at various tempering temperatures and the cause of the improvement in impact toughness was evaluated.The tempering process dramatically influenced carbide precipitation behavior,which resulted in different aspect ratios of carbides.Impact toughness improved along with the rise in tempering temperature mainly due to the increase in energy required in impact crack propagation.The characteristics of the impact crack propagation process were studied through a comprehensive analysis of stress distribution,oscilloscopic impact statistics,fracture morphology,and carbide morphology.The poor impact toughness of low-tempering-temperature specimens was attributed to the increased number of stress concentration points caused by carbide morphology in the small plastic zone during the propagation process,which resulted in a mixed distribution of brittle and ductile fractures on the fracture surface.