基于FP-tree的新能源汽车产业国际竞争力影响因素关联挖掘算法

为充分挖掘新能源汽车产业国际竞争力影响因素、探究价值增长点,提出了基于FP-tree的影响因素关联挖掘算法。通过构建国际竞争力各指标影响因素的关联规则,分析任意事务数据集中的关联数据,利用最小支持度参数minsup按照从上到下的方式搜索,确定最长的频繁项目集,采用FP-tree关联频繁项目集,设定分支关联性挖掘标准,实现了新能源汽车产业国际竞争力影响因素的挖掘。测试结果表明,设计算法的最小支持度和数据关联挖掘时间较短,置信度分析具有较高的稳定性。

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Countermeasure against blinding attack for single-photon detectors in quantum key distribution

Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting single-photon detectors.Here,we propose a concise,robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks.Our strategy uses a dual approach:detecting the bias current of the avalanche photodiode(APD)to defend against con-tinuous-wave blinding attacks,and monitoring the avalanche amplitude to protect against pulsed blinding attacks.By integrat-ing these two branches,the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts,significantly enhancing the resilience of QKD systems against various bright-light blinding attacks.This method forti-fies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.

Parkland trees on smallholder farms ameliorate soil physical-chemical properties in the semi-arid area of Tigray,Ethiopia

Proposed agroforestry options should begin with the species that farmers are most familiar with,which would be the native multipurpose trees that have evolved under smallholder farms and socioeconomic conditions.The African birch(Anogeissus leiocarpa(DC.)Guill.&Perr.)and pink jacaranda(Stereospermum kunthianum Cham.)trees are the dominant species in the agroforestry parkland system in the drylands of Tigray,Ethiopia.Smallholder farmers highly value these trees for their multifunctional uses including timber,firewood,charcoal,medicine,etc.These trees also could improve soil fertility.However,the amount of soil physical and chemical properties enhanced by the two species must be determined to maintain the sustainable conservation of the species in the parklands and to scale up to similar agroecological systems.Hence,we selected twelve isolated trees,six from each species that had similar dendrometric characteristics and were growing in similar environmental conditions.We divided the canopy cover of each tree into three radial distances:mid-canopy,canopy edge,and canopy gap(control).At each distance,we took soil samples from three different depths.We collected 216 soil samples(half disturbed and the other half undisturbed)from each canopy position and soil depth.Bulk density(BD),soil moisture content(SMC),soil organic carbon(SOC),total nitrogen(TN),available phosphorus(AP),available potassium(AK),p H,electrical conductivity(EC),and cation exchange capacity(CEC)were analysed.Results revealed that soil physical and chemical properties significantly improved except for soil texture and EC under both species,CEC under A.leiocarpus,and soil p H under S.kunthianum,all the studied soils were improved under both species canopy as compared with canopy gap.SMC,TN,AP,and AK under canopy of these trees were respectively 24.1%,11.1%,55.0%,and 9.3% higher than those soils under control.The two parkland agroforestry species significantly enhanced soil fertility near the canopy of topsoil through improving soil physical and chemical properties.These two species were recommended in the drylands with similar agro-ecological systems.

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.

Aging Mongolian pine plantations face high risks of drought-induced growth decline:evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and security.For practical security of an LLO CVQKD system,there are two main attack modes referred to as reference pulse attack and polarization attack presently.However,there is currently no general defense strategy against such attacks,and the security of the system needs further investigation.Here,we employ a deep learning framework called generative adversarial networks(GANs)to detect both attacks.We first analyze the data in different cases,derive a feature vector as input to a GAN model,and then show the training and testing process of the GAN model for attack classification.The proposed model has two parts,a discriminator and a generator,both of which employ a convolutional neural network(CNN)to improve accuracy.Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance.It only establishes a detection model by monitoring features of the pulse without adding additional devices.

Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

ATSSC:An Attack Tolerant System in Serverless Computing

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

RPL-Based IoT Networks under Decreased Rank Attack:Performance Analysis in Static and Mobile Environments

The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

K-core attack, equilibrium K-core,and kinetically constrained spin system

Kinetically constrained spin systems are toy models of supercooled liquids and amorphous solids. In this perspective,we revisit the prototypical Fredrickson–Andersen(FA) kinetically constrained model from the viewpoint of K-core combinatorial optimization. Each kinetic cluster of the FA system, containing all the mutually visitable microscopic occupation configurations, is exactly the solution space of a specific instance of the K-core attack problem. The whole set of different jammed occupation patterns of the FA system is the configuration space of an equilibrium K-core problem. Based on recent theoretical results achieved on the K-core attack and equilibrium K-core problems, we discuss the thermodynamic spin glass phase transitions and the maximum occupation density of the fully unfrozen FA kinetic cluster, and the minimum occupation density and extreme vulnerability of the partially frozen(jammed) kinetic clusters. The equivalence between K-core attack and the fully unfrozen FA kinetic cluster also implies a new way of sampling K-core attack solutions.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

Rising utilization of stable isotopes in tree rings for climate change and forest ecology

Analyses of stable isotopes(C,O,H)in tree rings are increasingly important cross-disciplinary programs.The rapid development in this field documented in an increasing number of publications requires a comprehensive review.This study includes a bibliometric analysis-based review to better understand research trends in tree ring stable isotope research.Overall,1475 publications were selected from the Web of Science Core Collection for 1974-2023.The findings are that:(1)numbers of annual publications and citations increased since 1974.From 1974 to 1980,there were around two relevant publications per year.However,from 2020 to 2022,this rose sharply to 109 publications per year.Likewise,average article citations were less than four per year before 1990,but were around four per article per year after 2000;(2)the major subjects using tree ring stable isotopes include forestry,geosciences,and environmental sciences,contributing to 42.5%of the total during 1974-2023;(3)the top three most productive institutions are the Chinese Academy of Sciences(423),the Swiss Federal Institute for Forest,Snow and Landscape Research(227),and the University of Arizona(204).These achievements result from strong collaborations;(4)review papers,for example,(Dawson et al.,Annu Rev Ecol Syst 33:507-559,2002)and(McCarroll and Loader,Quat Sci Rev 23:771-801,2004),are among the most cited,with more than 1000 citations;(5)tree ring stable isotope studies mainly focus on climatology and ecology,with atmospheric CO_(2) one of the most popular topics.Since 2010,precipitation and drought have received increasing attention.Based on this analysis,the research stages,key findings,debated issues,limitations and direc-tions for future research are summarized.This study serves as an important attempt to understand the progress on the use of stable isotopes in tree rings,providing scientific guid-ance for young researchers in this field.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Comprehensive analyses of the proteome and ubiquitome revealed mechanism of high temperature accelerating petal abscission in tree peony

Tree peony(Paeonia suffruticosa Andrews)is a well-known ornamental plant with high economic value,but the short fluorescence is a key obstacle to its ornamental value and industry development.High temperature accelerates flower senescence and abscission,but the associated mechanisms are poorly understood.In this study,the tandem mass tag(TMT)proteome and label-free quantitative ubiquitome from tree peony cut flowers treated with 20℃for 0 h(RT0),20℃or 28℃for 60 h(RT60 or HT60)were examined based on morphological observation,respectively.Totally,6970 proteins and 1545 lysine ubiquitinated(Kub)sites in 844 proteins were identified.Hydrophilic residues(such as glutamate and aspartate)neighboring the Kub sites were in preference,and 36.01%of the Kub sites were located on the protein surface.The differentially expressed proteins(DEPs)and Kub-DEPs in HT60 vs RT60 were mainly enriched in ribosomal protein,protein biosynthesis,secondary metabolites biosynthesis,flavonoid metabolism,carbohydrate catabolism,and auxin biosynthesis and signaling revealed by GO and KEGG analysis,accompanying the increase of endogenous abscisic acid(ABA)accumulation and decrease of endogenous indoleacetic acid(IAA)level.Additionally,the expression patterns of six enzymes(SAMS,ACO,YUC,CHS,ANS and PFK)putatively with Kub modifications were analyzed by proteome and real-time quantitative RT-PCR.The cell-free degradation assays showed PsSAMS and PsACO proteins could be degraded via the 26 S proteasome system in tree peony flowers.Finally,a working model was proposed for the acceleration of flower senescence and abscission by high temperature.In summary,all results contributed to understanding the mechanism of flower senescence induced by high temperature and prolonging fluorescence in tree peony.