Resilience-driven cooperative reconfiguration strategy for unmanned weapon system-of-systems

As the unmanned weap system-of systems(UWSoS)becomes complex,the inevitable uncertain interference gradu-ally increases,which leads to a strong emphasis on the resilience of UWSoS.Hence,this paper presents a resilience-driven cooperative reconfiguration strategy to enhance the resilience of UWSoS.First,a unified resilience-driven coopera-tive reconfiguration strategy framework is designed to guide the UWSoS resilience enhancement.Subsequently,a cooperative reconfiguration strategy algorithm is proposed to identify the optimal cooperative reconfiguration sequence,combining the cooperative pair resilience contribution index(CPRCI)and coop-erative pair importance index(CPII).At last,the effectiveness and superiority of the proposed algorithm are demonstrated through various attack scenario simulations that include differ-ent attack modes and intensities.The analysis results can pro-vide a reference for decision-makers to manage UWSoS.

Attribution of Biases of Interhemispheric Temperature Contrast in CMIP6 Models

One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical continents.Models that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.

Altered synaptic currents,mitophagy,mitochondrial dynamics in Alzheimer's disease models and therapeutic potential of Dengzhan Shengmai capsules intervention

Emerging research suggests a potential association of progression of Alzheimer's disease(AD)with alterations in synaptic currents and mitochondrial dynamics.However,the specific associations between these pathological changes remain unclear.In this study,we utilized Aβ42-induced AD rats and primary neural cells as in vivo and in vitro models.The investigations included behavioural tests,brain magnetic resonance imaging(MRI),liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis,Nissl staining,thioflavin-S staining,enzyme-linked immunosorbent assay,Golgi-Cox staining,transmission electron microscopy(TEM),immunofluorescence staining,proteomics,adenosine triphosphate(ATP)detection,mitochondrial membrane potential(MMP)and reactive oxygen species(ROS)assessment,mitochondrial morphology analysis,electrophysiological studies,Western blotting,and molecular docking.The results revealed changes in synaptic currents,mitophagy,and mitochondrial dynamics in the AD models.Remarkably,intervention with Dengzhan Shengmai(DZSM)capsules emerged as a pivotal element in this investigation.Aβ42-induced synaptic dysfunction was significantly mitigated by DZSM intervention,which notably amplified the frequency and amplitude of synaptic transmission.The cognitive impairment observed in AD rats was ameliorated and accompanied by robust protection against structural damage in key brain regions,including the hippocampal CA3,primary cingular cortex,prelimbic system,and dysgranular insular cortex.DZSM intervention led to increased IDE levels,augmented long-term potential(LTP)amplitude,and enhanced dendritic spine density and length.Moreover,DZSM intervention led to favourable changes in mitochondrial parameters,including ROS expression,MMP and ATP contents,and mitochondrial morphology.In conclusion,our findings delved into the realm of altered synaptic currents,mitophagy,and mitochondrial dynamics in AD,concurrently highlighting the therapeutic potential of DZSM intervention.

Unveiling the early stage evolution of local atomic structures in the crystallization process of a metallic glass

The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders,and this leads to an observation of a novel symmetry convergence phenomenon,which can be understood as an atomic structure manifestation of the ergodicity.Furthermore,in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass is not the total amount of icosahedral clusters,but the degree of global connectivity among them.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

Understanding the Resilience of Urban Rail Transit:Concepts,Reviews,and Trends

As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.

Evolving patterns of agricultural production space in China:A network-based approach

The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Discontinuous and continuous transitions of collective behaviors in living systems

Living systems are full of astonishing diversity and complexity of life.Despite differences in the length scales and cognitive abilities of these systems,collective motion of large groups of individuals can emerge.It is of great importance to seek for the fundamental principles of collective motion,such as phase transitions and their natures.Via an eigen microstate approach,we have found a discontinuous transition of density and a continuous transition of velocity in the Vicsek models of collective motion,which are identified by the finite-size scaling form of order-parameter.At strong noise,living systems behave like gas.With the decrease of noise,the interactions between the particles of a living system become stronger and make them come closer.The living system experiences then a discontinuous gas-liquid like transition of density.The even stronger interactions at smaller noise make the velocity directions of the particles become ordered and there is a continuous phase transition of collective motion in addition.

Inference of General Mass Action-Based State Equations for Oscillatory Biochemical Reaction Systems Using <i>k</i>-Step Genetic Programming

Systems biology requires the development of algorithms that use omics data to infer interaction networks among biomolecules working within an organism. One major type of evolutionary algorithm, genetic programming (GP), is useful for its high heuristic ability as a search method for obtaining suitable solutions expressed as tree structures. However, because GP determines the values of parameters such as coefficients by random values, it is difficult to apply in the inference of state equations that describe oscillatory biochemical reaction systems with high nonlinearity. Accordingly, in this study, we propose a new GP procedure called “k-step GP” intended for inferring the state equations of oscillatory biochemical reaction systems. The k-step GP procedure consists of two algorithms: 1) Parameter optimization using the modified Powell method—after genetic operations such as crossover and mutation, the values of parameters such as coefficients are optimized by applying the modified Powell method with secondary convergence. 2) GP using divided learning data—to improve the inference efficiency, imposes perturbations through the addition of learning data at various intervals and adaptations to these changes result in state equations with higher fitness. We are confident that k-step GP is an algorithm that is particularly well suited to inferring state equations for oscillatory biochemical reaction systems and contributes to solving inverse problems in systems biology.

Role of Stratospheric Processes in Climate Change: Advances and Challenges

In this review,instead of summarizing all the advances and progress achieved in stratospheric research,the main advances and new developments in stratosphere-troposphere coupling and stratospheric chemistry-climate interactions are summarized,and some outstanding issues and grand challenges are discussed.A consensus has been reached that the stratospheric state is an important source of improving the predictability of the troposphere on sub-seasonal to seasonal(S2S)time scales and beyond.However,applying stratospheric signals in operational S2S forecast models remains a challenge because of model deficiencies and the complexities of the underlying mechanisms of stratosphere-troposphere coupling.Stratospheric chemistry,which controls the magnitude and distribution of many important climate-forcing agents,plays a critical role in global climate change.Convincing evidence has been found that stratospheric ozone depletion and recovery have caused significant tropospheric climate changes,and more recent studies have revealed that stratospheric ozone variations can even exert an impact on SSTs and sea ice.The climatic impacts of stratospheric aerosols and water vapor are also important.Although their quantitative contributions to radiative forcing have been reasonably well quantified,there still exist large uncertainties in their long-term impacts on climate.The advances and new levels of understanding presented in this review suggest that whole-atmosphere interactions need to be considered in future for a better and more thorough understanding of stratosphere-troposphere coupling and its role in climate change.

Study on conversion of microstates in breast cell ensemble at the gene level based on the eigen-microstate method

Breast cancer is a malignant disease that seriously threatens women's health.Studying the mechanism of cancer occurrence and development is an urgent problem to be solved.In this paper,the eigen-microstate method was used to study conversion of normal breast cells into breast cancer cells and the reason.The main conclusions are as follows:the microstates of normal breast cell and breast cancer cell are different.There is a state conversion when a normal breast cell transforms into a breast cancer cell.The main reason for this state conversion is the combined effect of tumor suppressor genes and oncogenes.By analyzing the function of key genes,it was found that these genes do play an important role in the development of breast cancer.The findings contribute to understanding the mechanism by which breast cancer occurs and progresses,and key genes can serve as potential biomarkers or target genes for breast cancer treatment.

Ground-state phase diagram,symmetries,excitation spectra and finite-frequency scaling of the two-mode quantum Rabi model

We investigate the two-mode quantum Rabi model(QRM)describing the interaction between a two-level atom and a two-mode cavity field.The quantum phase transitions are found when the ratioηof transition frequency of atom to frequency of cavity field approaches infinity.We apply the Schrieffer–Wolff(SW)transformation to derive the low-energy effective Hamiltonian of the two-mode QRM,thus yielding the critical point and rich phase diagram of quantum phase transitions.The phase diagram consists of four regions:a normal phase,an electric superradiant phase,a magnetic superradiant phase and an electromagnetic superradiant phase.The quantum phase transition between the normal phase and the electric(magnetic)superradiant phase is of second order and associates with the breaking of the discrete Z_(2) symmetry.On the other hand,the phase transition between the electric superradiant phase and the magnetic superradiant phase is of first order and relates to the breaking of the continuous U(1)symmetry.Several important physical quantities,for example the excitation energy and average photon number in the four phases,are derived.We find that the excitation spectra exhibit the Nambu–Goldstone mode.We calculate analytically the higher-order correction and finite-frequency exponents of relevant quantities.To confirm the validity of the low-energy effective Hamiltonians analytically derived by us,the finite-frequency scaling relation of the averaged photon numbers is calculated by numerically diagonalizing the two-mode quantum Rabi Hamiltonian.

Research on Pulsar Time Steered Atomic Time Algorithm Based on DPLL

In today’s society,there is a wide demand for high-precision and high-stability time service in the fields of electric power,communication,transportation and finance.At present,the time standard in various countries is mainly based on atomic clocks,but the frequency drift of atomic clocks will affect the long-term stability performance.Compared with atomic clocks,millisecond pulsars have better long-term stability and can complement with the excellent short-term stability of atomic clocks.In order to improve the long-term stability of the atomic timescale,and then improve the timing accuracy,this paper proposes an algorithm for steering the atomic clock ensemble(ACE)by ensemble pulsar time(EPT)based on digital phase locked loop(DPLL).First,the ACE and EPT are generated by the ALGOS algorithm,then the ACE is steered by EPT based on DPLL to calibrate the long-term frequency drift of the atomic clock,so that the generated steered atomic time follows both the short-term stability characteristics of ACE and the long-term stability characteristics of EPT,and finally,the steered atomic time is used to calibrate the local cesium clock.The experimental results show that the long-term stability of atomic time after steering is improved by 2 orders of magnitude compared with that before steering,and the daily drift of a local cesium clock after calibration is less than 9.47 ns in 3 yr,3 orders of magnitude higher than that before calibration on accuracy.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

Node2vec Representation for Clustering Journals and as A Possible Measure of Diversity

Purpose: To investigate the effectiveness of using node2 vec on journal citation networks to represent journals as vectors for tasks such as clustering, science mapping, and journal diversity measure.Design/methodology/approach: Node2 vec is used in a journal citation network to generate journal vector representations. Findings: 1. Journals are clustered based on the node2 vec trained vectors to form a science map. 2. The norm of the vector can be seen as an indicator of the diversity of journals. 3. Using node2 vec trained journal vectors to determine the Rao-Stirling diversity measure leads to a better measure of diversity than that of direct citation vectors.Research limitations: All analyses use citation data and only focus on the journal level.Practical implications: Node2 vec trained journal vectors embed rich information about journals, can be used to form a science map and may generate better values of journal diversity measures.Originality/value: The effectiveness of node2 vec in scientometric analysis is tested. Possible indicators for journal diversity measure are presented.

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

Renormalization Group Theory of Eigen Microstates

We propose a renormalization group(RG)theory of eigen microstates,which are introduced in the statistical ensemble composed of microstates obtained from experiments or computer simulations.A microstate in the ensemble can be considered as a linear superposition of eigen microstates with probability amplitudes equal to their eigenvalues.Under the renormalization of a factor b,the largest eigenvalueσ1 has two trivial fixed points at low and high temperature limits and a critical fixed point with the RG relationσb1=bβ/νσ1,whereβandνare the critical exponents of order parameter and correlation length,respectively.With the Ising model in different dimensions,it has been demonstrated that the RG theory of eigen microstates is able to identify the critical point and to predict critical exponents and the universality class.Our theory can be used in research of critical phenomena both in equilibrium and non-equilibrium systems without considering the Hamiltonian,which is the foundation of Wilson’s RG theory and is absent for most complex systems.

Strong Anisotropy of 3D Diffusion in Living Cells

Starting from the never-ending agitated dance of pollen grains firstly discovered by Robert Brownin 1828,Brownian motion was known to represent the randomly diffusive move ment of small particles in a simple solvent.

Infrastructure of Scientometrics: The Big and Network Picture

A network is a set of nodes connected via edges,with possibly directions and weights on the edges.Sometimes,in a multi-layer network,the nodes can also be heterogeneous.In this perspective,based on previous studies,we argue that networks can be regarded as the infrastructure of scientometrics in the sense that networks can be used to represent scientometric data.Then the task of answering various scientometric questions related to this data becomes an algorithmic problem in the corresponding network.