State Of System Research Articles

Dynamic Framing and Power Allocation for Real-Time Wireless Networks with Variable-Length Coding: A Tandem Queue Approach

Ensuring high reliability and low latency poses challenges for numerous applications that require rigid performance guarantees, such as industrial automation and autonomous vehicles. Our research primarily concentrates on addressing the real-time requirements of ultra-reliable low-latency communication (URLLC). Specifically, we tackle the challenge of hard delay constraints in real-time transmission systems, overcoming this obstacle through a finite blocklength coding scheme. In the physical layer, we encode randomly arriving packets using a variable-length coding scheme and transmit the encoded symbols by truncated channel inversion over parallel channels. In the network layer, we model the encoding and transmission processes as tandem queues. These queues backlog the data bits waiting to be encoded and the encoded symbols to be transmitted, respectively. This way, we represent the system as a two-dimensional Markov chain. By focusing on instances when the symbol queue is empty, we simplify the Markov chain into a one-dimensional Markov chain, with the packet queue being the system state. This approach allows us to analytically express power consumption and formulate a power minimization problem under hard delay constraints. Finally, we propose a heuristic algorithm to solve the problem and provide an extensive evaluation of the trade-offs between the hard delay constraint and power consumption.

Diffusion Behaviors of CaCl2-NaCl Molten Salt under an Electric Field: A Deep Potential Molecular Dynamics Simulation.

CaCl2-NaCl molten salt is one of the widely used electrolytes, and the effect of the electric field on it needs to be considered in the environment of manufacturing metals and alloys as well as in battery applications. To be closer to the state of the CaCl2-NaCl molten salt system in practical applications, this study uses the training-based deep potential, combined with the ionic structure, to analyze the electric field effects on the drift velocity, ionic mobility, ion diffusion, and viscosity of the CaCl2-NaCl system by molecular dynamics simulations. It is shown that the electric field has a stronger effect on the ion diffusion behavior parallel to the direction of the electric field in the CaCl2-NaCl molten salt system. Due to the looser coordinate structure of Na+, the interaction force between Na-Cl is small compared with that between Ca-Cl. Na+ is easily driven and more sensitive to the electric field, whose drift velocity, ionic mobility, and diffusion coefficient are larger than those of the other two ions, with an order of Na+ > Cl- > Ca2+. All ionic drift velocities increase linearly as electric field intensity increases and the ionic mobilities tend to be constant. The diffusion coefficient parallel to the direction of the electric field exponentially increases and the viscosity tends to exponentially decrease with the increase in the electric field intensity. This work is important for accurately predicting the properties and performance of molten salts and their improvement for applications such as solar thermal energy conversion.

The Impact of Education on Chinese Economic Growth

Chinese traditional culture has consistently exerted a profound influence on everyday life in China and its educational system. Despite significant advancements in technology, the Chinese continue to employ traditional cognitive approaches in acquiring advanced knowledge. This traditional mindset has notable implications for the Chinese economy. This paper will explore the historical development and current state of the Chinese educational system. Through an analysis of the Chinese educational landscape, this paper will discuss its present impact on the Chinese economy. Additionally, this paper will examine its future implications, focusing particularly on the training of creative individuals and the related industries such as artificial intelligence (AI). A comparative analysis between China and the United States will be conducted to shed light on these areas.

Polaron spin states in chiral systems

Polaron spin states in chiral systems

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

Adaptive fault-tolerant control for spacecraft: A dynamic Stackelberg game approach with advantage actor-critic reinforcement learning

This study introduces an innovative approach that harnesses the Stackelberg differential game framework in combination with advantage actor-critic (A2C) reinforcement learning (RL) to attain adaptive and dynamic control, in the presence of complex faults within spacecraft systems. By reconfiguring the problem of fault tolerance into a strategic interaction involving two pivotal players, specifically, the leader (fault-tolerant control, FTC) and the follower (fault detection observer, FDO), our approach facilitates the dynamic adaptation of their control strategies in response to the system's state, effectively providing a solution for addressing faults within spacecraft systems. This approach overcomes the challenges of cross-optimal indexes commonly found in traditional FTC, resulting in improved efficiency and stability of RL. It harbors substantial potential for applications requiring robust and adaptive FTC strategies. Simulation validation, both the leader and follower can successfully control the spacecraft's velocity before and after a fault occurs, ultimately achieving optimal fault estimation (FE) and FTC objectives.

Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability

This study addresses the problem of attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability. Unlike traditional Markov/semi-Markov models that rely on transition probabilities, a novel switching rule is introduced that focuses on sojourn probability information associated with the target mode and sojourn time, which are easier to obtain than the well-known transition probabilities. Considering the vulnerability of stochastic switching systems to cyber-attacks, where the system state becomes unobservable and difficult to manage, a compensation-based output feedback controller framework is proposed. Using the Lyapunov method and stochastic analysis, sufficient conditions are provided to ensure system stability. Finally, the effectiveness and applicability of the developed approach are demonstrated using an F-404 aircraft engine system model.

Static and dynamic stabilities of modified gradient elastic Kirchhoff–Love plates

The static and dynamic stabilities of modified gradient elastic Kirchhoff–Love plates (MGEKLPs), which incorporate two length-scale parameters related to strain gradient and rotation gradient effects, are comprehensively analyzed under various load forms and boundary conditions (BCs). The study of static stability employs static balance method and an improved energy method by introducing higher-order deformation gradients and corresponding energy terms. Utilizing the variational method, a sixth-order fundamental buckling differential equation for MGEKLPs under both transverse and in-plane loads is derived, serving as the foundation for the static balance method. The static stability analysis of MGEKLPs examines the combined effects of strain and rotation gradients on size-dependent critical buckling loads. Building on generalized strain energy with higher-order deformation energy, the energy method of classical elastic thin plate model is enhanced and applied to the static stability analysis of MGEKLPs. This approach enables the investigation of static stability without being constrained by the need to solve complex differential equations, making it applicable to various BCs and load scenarios. While static stability provides a description of stable state of an elastic system, dynamic stability offers a more scientific and rigorous analysis. The dynamic stability of simplified gradient elastic Kirchhoff–Love plates (SGEKLPs) with curved edges and different BCs is further investigated by combining the generalized strain energy with Lyapunov’s second stability method, presenting the dynamic stability criterion in the form of norms. A strict description of the dynamic stability of a SGEKLP over the entire time domain is provided for different supporting conditions, including case where all edges are supported and case with free edges. The analysis of size-dependent static and dynamic stabilities offers theoretical guidance for designing elastic thin plates with microstructures.

Detection of Cyber-Attacks in a Discrete Event System Based on Deep Learning

This paper addresses the problem of cyber-attack detection in a discrete event system by proposing a novel model. The model utilizes graph convolutional networks to extract spatial features from event sequences. Subsequently, it employs gated recurrent units to re-extract spatio-temporal features from these spatial features. The obtained spatio-temporal features are then fed into an attention model. This approach enables the model to learn the importance of different event sequences, ensuring that it is sufficiently general for identifying cyber-attacks, obviating the need to specify attack types. Compared with traditional methods that rely on synchronous product computations to synthesize diagnosers, our deep learning-based model circumvents state explosion problems. Our method facilitates real-time and efficient cyber-attack detection, eliminating the necessity to specifically identify system states or distinguish attack types, thereby significantly simplifying the diagnostic process. Additionally, we set an adjustable probability threshold to determine whether an event sequence has been compromised, allowing for customization to meet diverse requirements. Experimental results demonstrate that the proposed method performs well in cyber-attack detection, achieving over 99.9% accuracy at a 1% threshold and a weighted F1-score of 0.8126, validating its superior performance.

Cooperative spin crossover leading to bistable and multi-inert system states in an iron(III) complex

Cooperativity among spin centres has long been the royal road in spin crossover (SCO) research to impose magnetic bistability in terms of thermal hysteresis. In this work we access magnetic multi-inert states of the iron(III) compound {FeL2[B(Ph)4]} ≡ FeB at low temperature, in addition to thermal bistability. The packing of the low-spin and high-spin forms of crystalline FeB differs only marginally what ultimately leads to structural conservatism. This indicates that the SCO-immanent breathing of the complex cation is almost fully compensated by the anion matrix. The unique cooling rate dependence of the residual low-temperature magnetisation in FeB unveils continuous switching between the trapped high-spin (ON) and the relaxed low-spin state (OFF). The macroscopic ratio of the spin states (ON:OFF) can be adjusted as a simple function of the cooling rate. That is, cooperative spin crossover can be the source of bistable and multi-inert system states in the very same material.

Predefined time quasi-sliding mode control with fast convergence based on a switchable exponent for nonlinear systems

This paper proposed a new predefined time nonsingular sliding mode control (SMC) method for nonlinear systems. Firstly, based on the definition of predefined time stability (PTS), a new sufficient condition is designed to ensure that the system states converge to the origin within a predefined time. The design of a simple variable exponent not only guarantees PTS, but also enables adaptive adjustment when the system states are far away from and near the equilibrium point. And compared with traditional methods, the proposed Lemma 2 enhances the control effect and achieves faster convergence whether the system states are far from or near to the equilibrium point. Secondly, based on the proposed stability condition, a new nonsingular SMC method is designed to ensure that the tracking error converges to an arbitrarily small region within a predefined time. Finally, the proposed method is verified to have good performance through simulation and physical experiments.

Free-will arbitrary time adaptive chaos control for permanent magnet synchronous motor

Permanent magnet synchronous motor (PMSM) is a multivariable and highly coupled nonlinear system, which is prone to chaotic behavior during actual operation. In order to suppress the chaotic behavior of the PMSM, this work designs a free-will arbitrary time adaptive controller based on the inverse step derivation, adaptive control theorem and Lyapunov stability theory. Firstly, the corresponding coordination transformations are designed according to the PMSM. Secondly, the bounded problem of the controlled system is analyzed by using the Lyapunov stability theory, and the free-will arbitrary time adaptive controller is designed by backstepping derivation. Finally, numerical simulation verifies that the designed controller quickly stabilizes the system state variables to the output signals and proves that the controller is free-will arbitrary time stable for all closed-loop signals.

Real-time integrated water availability – Salt intrusion modelling and management during droughts

Climate change and population increase are major challenges when guaranteeing a sustainable water supply in densely populated river basins worldwide, where different water users such as industry, drinking water production and agriculture typically come into conflict. During dry periods, real time monitoring and short term forecasting of the water availability are crucial to prepare and take appropriate action. Integrated water quantity and water quality models of complex surface water systems are indispensable to achieve this. However, existing model techniques often focus on a single aspect of the water system, are computationally too demanding and are hard to integrate with other sub models. This paper proposes a framework for developing efficient, integrated water quantity/quality models in support of real-time management and forecasting of complex surface water systems. Its three main components are: a low-flow prediction for the supplying river(s) during dry periods, a conceptual water quantity-quality model with semi-automated retrieval of necessary boundary conditions and input data, and classification models to predict the water quality state of the considered system during forecasts or scenario analyses. The novelty of the methodology lies in the use of parsimonious, computationally efficient sub-models that still allow to consider numerous operational rules, competing water users and water supply and quality constraints. The framework was tested in the context of water availability and salt intrusion management during droughts in the Campine Canals in the Scheldt-Meuse-Rhine delta, considering boundary conditions of shipping traffic, pumping at locks, hydropower stations, level regulation, and water abstractions for drinking water supply, industry, agriculture and nature reserves. Despite the complexity of the considered system, the model is able to describe the state of the water system in terms of water levels, discharges and salt intrusion impacts on water production with an acceptable accuracy. The research shows that the Campine Canal network is susceptible to water scarcity due to the increased risk of low flows in summer as a consequence of climate change. Local decision makers should adopt both effective adaptation strategies as well as real-time forecasts in acute drought situations to increase the region’s defence and resiliency against water shortages.

Positive steady states in a two-species chemotaxis-competition system with signal-dependent diffusion and sensitivity

– In this paper, we consider the following stationary two-species chemotaxis-competition system with signal-dependent diffusion and sensitivity (0.1)−d1Δu=λu−u2−buv,x∈Ω,−div[d(w)∇v+χ(w)v∇w]=μv−v2−cuv,x∈Ω,−d3Δw=−κw+τu,x∈Ω,u=v=w=0,x∈∂Ωin a bounded smooth domain Ω⊂RN(N≥1), where λ,μ,b,c,d1,d3,κ,τ are positive constants, and (d(w),χ(w))∈[C1[0,∞)]2 with d(w),χ(w)>0 for all w≥0. Since there does not exist an immediate change variable that transforms (0.1) into a semilinear system when (0.1) is considered with d(w),α(w) being arbitrary functions in w, this makes the analysis of system (0.1) much more difficult. By the W2,p-estimate and the global bifurcation method, we obtain a bounded connected set of positive steady states joining the semitrivial solution of the form (u,0,w) with u,w>0 and the semitrivial solution of the form (0,v,0) with v>0, which provides the sufficient condition for the existence of positive steady states. Combining the eigenvalue theory, homogenization technique and various elliptic estimates, we also derive some sufficient conditions for the nonexistence of positive steady states.

FateNet: an integration of dynamical systems and deep learning for cell fate prediction.

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. github.com/ThomasMBury/fatenet.

Accurate computation of quantum excited states with neural networks.

We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can be calculated, including off-diagonal expectations, such as the transition dipole moment. The method works particularly well with neural network ansätze, and by combining this method with the FermiNet and Psiformer ansätze, we can accurately recover excitation energies and oscillator strengths on a range of molecules. We achieve accurate vertical excitation energies on benzene-scale molecules, including challenging double excitations. Beyond the examples presented in this work, we expect that this technique will be of interest for atomic, nuclear, and condensed matter physics.

Forecasting of functional readiness and construction of variable tactics of elite single combatants on the basis of assessment of psychophysiological state

The goal is to substantiate and experimentally verify the effectiveness of using the results of the tapping test to assess readiness for a match. Methodology: analysis and synthesis of data from modern scientific and scientific and methodical literature as well as Internet on the problem being studied, dynamic tapping testing, forecasting. Scientific novelty. The article provides new data on the generalization of the results of the informativeness and effectiveness of the assessment of the current psychophysiological state for predicting changes in the functional preparedness of elite single combatants and the possibility of forming strategies and tactics for future matches taking into account the individual dynamics of changes in the studied parameters of the tapping test. Conclusions. In the course of the conducted research and generalization of the obtained individual results, it was established that the state of "strength - weakness" is the limit of physical and mental capacity, i.e., the time during which excitement in the brain cells can be maintained during long-term actions of the enemy. It was also found that the most informative for monitoring the psychophysiological state and functional abilities of martial artists are the indicators of the maximum frequency of movements in 30 sec. When choosing the time of the examination, it is necessary to take into account that the dynamics of the pace of hand movements are affected by the peculiarities of not only the nervous system, but also the muscular system, therefore, in the presence of weak muscle development or during particularly long examinations, the presence of fatigue may affect the results. It is very important that tapping metrics allows you to determine: the level of the total amount of work performed, or the most favorable time for the match, an indicator of the level of activity of attacking and defensive actions for the entire period of the fight; a favorable time for carrying out a series of actions; stability of the battle; the speed of single action, and also simplifies the process of selection and selection of the national team for participation in competitions. The use of the method of controlling the psychophysiological state by conducting a tapping test to assess functional abilities optimizes the process of forming a strategy and tactics of fighting a match depending on the state and stability of the central nervous system of the wrestler.

Event-triggered fixed-time distributed observers for general linear systems

This paper delves into the implementation of a distributed fixed-time state estimation approach for linear systems, utilizing an event-triggered strategy. Particularly, the output data from the linear systems are strategically distributed across various observers. A novel fixed-time distributed observer is proposed to reconstruct full states of the linear system cooperatively with the observer error converges to zero within fixed time. Based on the event-triggered strategy, the communication times among observers are reduced by intermittent communication. Simultaneously, Zeno-behavior is ruled out completely. Finally, the proposed results are verified by a simulation example.

A New Method Based on Locally Optimal Step Length in Accelerated Gradient Descent for Quantum State Tomography.

Quantum state tomography (QST) is one of the key steps in determining the state of the quantum system, which is essential for understanding and controlling it. With statistical data from measurements and Positive Operator-Valued Measures (POVMs), the goal of QST is to find a density operator that best fits the measurement data. Several optimization-based methods have been proposed for QST, and one of the most successful approaches is based on Accelerated Gradient Descent (AGD) with fixed step length. While AGD with fixed step size is easy to implement, it is computationally inefficient when the computational time required to calculate the gradient is high. In this paper, we propose a new optimal method for step-length adaptation, which results in a much faster version of AGD for QST. Numerical results confirm that the proposed method is much more time-efficient than other similar methods due to the optimized step size.

The structure of functional training of athletes as a ensuring factor competitive activities in sports games

Assessment of the level of structural components of the functional fitness of athletes' bodies is one of the priority factors for improving the training system in handball, because it allows you to create the necessary conditions for the rational management of the athletes' physical condition and the course of adaptive changes, which ensure that the level of preparedness corresponds to the given sports result in the competitive period. The goal was to study the peculiarities of the formation of the structure of the functional readiness of the body of young men aged 16-17 years who specialize in handball, within the preparatory period of the macrocycle. The analysis of integral indicators characterizing the level of the physical condition of the body of handball players revealed positive dynamics of the level of general physical capacity, the functional state of the cardiorespiratory system, the general level of functional preparedness and adaptive capabilities of the cardiovascular system. All indicators corresponded to the "above average" functional class. Experimental data confirmed the effectiveness of planning specialized training programs that are purposefully related to the volume and functional orientation of the training session in the structural components of the preparatory period of the macrocycle. The use of a computer program made it possible to determine the general level of functional fitness and the state of the cardiovascular and respiratory systems of athletes, which is important for monitoring their physical readiness for the competitive game season.