Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Implementation of FPGA Based MPPT Techniques for Grid-Connected PV System

Global energy demand is growing rapidly owing to industrial growth and urbanization.Alternative energy sources are driven by limited reserves and rapid depletion of conventional energy sources(e.g.,fossil fuels).Solar photovol-taic(PV),as a source of electricity,has grown in popularity over the last few dec-ades because of their clean,noise-free,low-maintenance,and abundant availability of solar energy.There are two types of maximum power point track-ing(MPPT)techniques:classical and evolutionary algorithm-based techniques.Precise and less complex perturb and observe(P&O)and incremental conduc-tance(INC)approaches are extensively employed among classical techniques.This study used afield-programmable gate array(FPGA)-based hardware arrange-ment for a grid-connected photovoltaic(PV)system.The PV panels,MPPT con-trollers,and battery management systems are all components of the proposed system.In the developed hardware prototype,various modes of operation of the grid-connected PV system were examined using P&O and incremental con-ductance MPPT approaches.

Approximately Bi-Similar Symbolic Model for Discretetime Interconnected Switched System

Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.

FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Evaluating the Robustness of MDSS Maintenance Forecasts Using Connected Vehicle Data

The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.

Application of Connected Truck Data to Evaluate Spatiotemporal Impact of Rest Area Closures on Ramp Parking

Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.

Study on the Measures of Connectedness Between Herds

Pig breeding is generally conducted among many herds, so EBV comparisons across populationsare necessary. Genetic connectedness is required for reliable between-farm animal EBV comparisons.Five quantitative overall connectedness measures among populations have been proposed so far,coefficient of connectedness(γ*), coefficient of determination (CD) and overall indices ofprecision, connectedness rating, number of direct genetic links between subpopulations due tocommon sires and dams (GLt), and average genetic covariance (AGC) are reviewed and theirproperties are discussed in this paper. It is recommended to use AGC at present for measuringgenetic connectedness between herds.

Elucidation of the ‘Honeycrisp’ pedigree through haplotype analysis with a multi-family integrated SNP linkage map and a large apple (Malus×domestica) pedigree-connected SNP data set

The apple(Malus×domestica)cultivar Honeycrisp has become important economically and as a breeding parent.An earlier study with SSR markers indicated the original recorded pedigree of‘Honeycrisp’was incorrect and‘Keepsake’was identified as one putative parent,the other being unknown.The objective of this study was to verify‘Keepsake’as a parent and identify and genetically describe the unknown parent and its grandparents.A multi-family based dense and high-quality integrated SNP map was created using the apple 8 K Illumina Infinium SNP array.This map was used alongside a large pedigree-connected data set from the RosBREED project to build extended SNP haplotypes and to identify pedigree relationships.‘Keepsake’was verified as one parent of‘Honeycrisp’and‘Duchess of Oldenburg’and‘Golden Delicious’were identified as grandparents through the unknown parent.Following this finding,siblings of‘Honeycrisp’were identified using the SNP data.Breeding records from several of these siblings suggested that the previously unreported parent is a University of Minnesota selection,MN1627.This selection is no longer available,but now is genetically described through imputed SNP haplotypes.We also present the mosaic grandparental composition of‘Honeycrisp’for each of its 17 chromosome pairs.This new pedigree and genetic information will be useful in future pedigree-based genetic studies to connect‘Honeycrisp’with other cultivars used widely in apple breeding programs.The created SNP linkage map will benefit future research using the data from the Illumina apple 8 and 20 K and Affymetrix 480 K SNP arrays.

Effect of Genetic Connectedness on the Selection Results of Breeding Pigs

Two pig populations were simulated with Monte Carlo method; each consisted of 5 boars and 50 sows per generation. Genetic connectedness between herds was established by randomly selecting 1 or 2 boars from one population to mate sows of the other population. Breeding pigs were selected within populations according to animal model BLUP. The benefits of genetic connectedness between herds were examined. The results showed that, the average coefficients of inbreeding decreased, while the cumulative selection responses of populations increased, and the higher response occurred randomly in the two populations at generation 5 with the increase of the genetic connectedness between herds. Selection response was affected by genetic connectedness and trait heritability, the lower heritability and higher connectedness, the better selection results. When the number of exchanged litters between populations per generation was 6 litters, the selection results reached a reflection point; if the number of exchanged litters between populations increased further from this point, neither the increase of the cumulative selection responses nor the decrease of coefficients of inbreeding was significant.

Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

Connectedness of Cone-efficient Solution Set for Cone-quasiconvex Multiobjective Programming in Hausdorff Topological Vector Spaces

This paper deals with the connectedness of the cone-efficient solution set for vector optimization in locally convex Hausdorff topological vector spaces. The connectedness of the cone-efficient solution set is proved for multiobjective programming defined by a continuous one-to-one cone-quasiconvex mapping on a compact convex set of alternatives. During the proof, the generalized saddle theorem plays a key role.

Ionothermal Synthesis,Structure and Luminescent Properties of a New 2-D Bismuth(Ⅲ) Coordination Polymer with(6,5)-Connected Topological Sheet

A new 2-D Bi（3＋） coordination polymer {Bi4（μ3-O）2（μ4-O）2（C7H3NO4）2}n（1,C7H3NO4 = pyridine-2,6-dicarboxylate） containing（6,5）-connected topological sheet with the Schl？fli symbol of（4~4·6~3·8~4·12~4）（4~5·6~4·8） has been synthesized by an ionothermal method using the ionic liquid 1-ethyl-3-methylimidazolium bromide（[Emim]Br） as solvent,and characterized by C,H and N elemental analysis,energy-dispersive X-ray spectroscopy（EDS）,infrared spectrum（IR spectrum） and single-crystal X-ray diffraction.The results indicate that 1 belongs to the monoclinic system,space group P21/n with a = 13.766（7）,b = 4.142（2）,c = 16.439（8） ？,β = 99.099（9）°,Z = 2,V = 925.6（8） ？~3,Dc = 4.414 g·cm^（-3）,F（000） = 1064,μ = 37.985 mm-1,the final R = 0.0326,wR = 0.0727 and S = 1.022.The molecular structural units [Bi4（μ3-O）2（μ4-O）2（C7H3NO4）2] are connected to each other via bridging O atoms,extending into an interesting 2-D layered structure.These resulting layers are further assembled by weak C-H…O hydrogen bonds into a 3-D supramolecular architecture.Moreover,its thermal stability and the luminescent properties have been also studied.

An 8-Connected Chiral Lanthanide Metal-organic Framework Constructed from Naturally Camphoric Acid:Crystal Structure,Vibrational Circular Dichroism Spectroscopy and Second-order Nonlinear Optical Effect

A chiral lanthanide metal-organic framework based on enantiopure camphoric acid （D-H2cam）, [Nd3（D-cam）8（H2O）4Cl]n （1）, has been synthesized and characterized by single-crystal X-ray structural analysis, elemental analysis, IR, thermal gravimetric, and X-ray powder diffraction. Crystal data for the title compound are as follows： orthorhombic system, space group P212121 with a = 13.8287（7）, b = 14.0715（7）, c = 25.7403（12） A^°, V = 5008.8（4） A^°3, Mr = 1333.08, Z = 4, F（000） = 2644, Dc = 1.768 g/cm^3, μ（MoKα） = 3.189 mm^-1, the final R = 0.0351 and wR = 0.0814 （I 〉 2σ（I））. Compound 1 displays an 8-connected bcu topology 3D framework and hydrogen-bonding interactions stabilize the solid-state structure. The vibrational circular dichroism （VCD） spectrum and second-order nonlinear optical effect of compound 1 have been studied in the solid state.

Liquidity connectedness in cryptocurrency market

We examine the dynamics of liquidity connectedness in the cryptocurrency market.We use the connectedness models of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012)and Baruník and Křehlík(J Financ Econom 16(2):271–296,2018)on a sample of six major cryptocurrencies,namely,Bitcoin(BTC),Litecoin(LTC),Ethereum(ETH),Ripple(XRP),Monero(XMR),and Dash.Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies,whereas BTC and LTC play a significant role in connectedness magnitude.A distinct liquidity cluster is observed for BTC,LTC,and XRP,and ETH,XMR,and Dash also form another distinct liquidity cluster.The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium-and long-run time horizons.In the short run,BTC,LTC,and XRP are the leading contributor to liquidity shocks,whereas,in the long run,ETH assumes this role.Compared with the medium term,a tight liquidity clustering is found in the short and long terms.The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time,pointing to the possible effect of rising demand and higher acceptability for this unique asset.Furthermore,more pronounced liquidity connectedness patterns are observed over the short and long run,reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

Dynamic connectedness between stock markets in the presence of the COVID‑19 pandemic:does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.

A Novel 3D Pillar-layered Co(Ⅱ)-MOF with(4,8)-Connected Topology:Synthesis,Structure and Magnetic Properties

By using ＂pillaring＂ strategy, a new Co（Ⅱ）-MOF, [Co_2（abtc）（bimb）_2]·2H_2O（1, H_4abtc = 3,3？,5,5？-azobenzenetetracarboxylic acid, bimb = 4,4？-bis（imidazole-1-ylmethyl）biphenyl）, has been solvothermally synthesized and structurally characterized. The structural determination revealed that 1 features a 3D pillar-layered framework with（4,8）-connected {4^4.6^2}{4^8.6^（20）} topology based on dinuclear Co（Ⅱ）-SBUs. The magnetic investigation shows that the dominant antiferromagnetic coupling is observed in compound 1.

The Classical Hall Effect in Multiply-Connected Plane Regions Part I: Topologies with Stream Function

Typical Hall plates for practical magnetic field sensing purposes are plane, simply-connected regions with peripheral contacts. Their output voltage is the sum of even and odd functions of the applied magnetic field. They are commonly called offset and Hall voltage. Contemporary smart Hall sensor circuits extract the Hall voltage via spinning current Hall probe schemes, thereby cancelling out the offset very efficiently. The magnetic field response of such Hall plates can be computed via the electric potential or via the stream function. Conversely, Hall plates with holes show new phenomena: 1) the stream function exists only for a limited class of multiply-connected domains, and 2) a sub-class of 1) behaves like a Hall/Anti-Hall bar configuration, i.e., no Hall voltage appears between any two points on the hole boundary if current contacts are on their outer boundary. The paper studies the requirements under which these effects occur. Canonical cases of simply and doubly connected domains are computed analytically. The focus is on 2D multiply-connected Hall plates where all boundaries are insulating and where all current contacts are point sized.

Time–frequency co-movement and risk connectedness among cryptocurrencies:new evidence from the higher-order moments before and during the COVID-19 pandemic

Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management.However,most existing research focuses on the lower-order moment nexus(i.e.the return and volatility interactions).For the first time,this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains.We combine the realized moment measures and wavelet coherence,and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach(Chatziantoniou et al.in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach.Technical report,University of Pretoria,Department of Economics,2021)using intraday high-frequency data.The empirical results demonstrate that the comovement of realized volatility between BTC and other cryp-tocurrencies is stronger than that of the realized skewness,realized kurtosis,and signed jump variation.The comovements among cryptocurrencies are both time-dependent and frequency-dependent.Besides the volatility spillovers,the risk spillovers of high-order moments and jumps are also significant,although their magnitudes vary with moments,making them moment-dependent as well and are lower than volatility connectedness.Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term(1–7 days).Furthermore,the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic.Several practical implications are drawn for crypto investors,portfolio managers,regulators,and policymakers in optimizing their investment and risk management tactics.