State Of System Research Articles

ON THE GAUSSIAN CURVATURE FUNCTIONAL IN THE CLASS OF SURFACES OF POSITIVE GAUSSIAN CURVATURE

The paper establishes the form of the Gaussian curvature functional defined on the class of infinitely differentiable horizontal surfaces of positive Gaussian curvature. With respect to admissible surfaces, it is assumed that they admit a global semi-geodetic parametrization. The paper proves that the first variation of the functional on the class of variations of admissible surfaces admitting connections between the coefficients of the first quadratic form and their geodesic lines similar to the axisymmetric case is determined by the Gaussian curvature of the varied surface. The considerations of the type are closely connected with the problems of the study of the equilibrium forms having one of the main curvatures sufficiently small. In this case, the classical Laplace formula fails. Thus, there appear a necessity to take into account more subtle processes leading to the adequate description of the equilibrium state of the two-phased system. In particular, it is quite natural to introduce into the study an intermediate layer consisting of the molecules of the two different phases, one of the Maxwell’s ideas. The calculations of the work spended by the pressure forces for the formation of intermediate layer leads us to the necessity to introduce Gauss functional into consideration. Linear combination of mean curvature and gaussian curvature functionals gives possibility to construct variational solution of generalized Laplace equation.

Design of shortcuts to adiabaticity for Bose–Einstein condensate dynamics in soliton Josephson junctions

This article primarily establishes a two-soliton system and employs the Lewis–Riesenfeld invariant inverse control method to achieve shortcuts to adiabaticity (STA) technology. We study an atomic soliton Josephson junctions (SJJs) device and subsequently compare and analyze it with atomic bosonic Josephson junctions. Moreover, we use higher-order expressions of the auxiliary equations to optimize the results and weaken the detrimental effect of the sloshing amplitude. We find that in the adiabatic shortcut evolution of two systems with time-containing tunnelling rates, the SJJs system is more robust over a rather short time evolution. In comparison with linear ramping, the STA technique is easier to achieve with the precise modulation of the quantum state in the SJJs system.

Potential energy landscape formalism for quantum molecular liquids

The potential energy landscape (PEL) formalism is a powerful tool within statistical mechanics to study the thermodynamic properties of classical low-temperature liquids and glasses. Recently, the PEL formalism has been extended to liquids/glasses that obey quantum mechanics, but applications have been limited to atomistic model liquids. In this work, we extend the PEL formalism to liquid/glassy water using path-integral molecular dynamics (PIMD) simulations, where nuclear quantum effects (NQE) are included. Our PIMD simulations, based on the q-TIP4P/F water model, show that the PEL of quantum water is both Gaussian and anharmonic. Importantly, the ring-polymers associated to the O/H atoms in the PIMD simulations, collapse at the local minima of the PEL (inherent structures, IS) for both liquid and glassy states. This allows us to calculate, analytically, the IS vibrational density of states (IS-VDOS) of the ring-polymer system using the IS-VDOS of classical water (obtained from classical MD simulations). The role of NQE on the structural properties of liquid/glassy water at various pressures are discussed in detail. Overall, our results demonstrate that the PEL formalism can effectively describe the behavior of molecular liquids at low temperatures and in the glass states, regardless of whether the liquid/glass obeys classical or quantum mechanics.

State estimation of magnetorheological suspension of all-terrain vehicle based on a novel adaptive Sage–Husa Kalman filtering

Abstract For the magnetorheological (MR) suspension control system of all-terrain vehicles (ATVs), state estimation is an effective method to obtain system feedback signals that cannot be directly measured by sensors. However, when confronted with modeling errors and sudden changes in sensor noise during complex road driving, conventional estimation methods with fixed parameters encounter challenges in accurately estimating the states of ATV suspension system. To address this issue, this paper introduces a novel adaptive Sage-Husa Kalman filter (ASHKF) algorithm to estimate the sprung and unsprung velocity of ATV suspension system. The algorithm uses exponential weighting function and gradient detection function to adaptively adjust the attenuation coefficient according to the driving conditions of the ATV, thereby realizing real-time correction of the covariance matrix of the prediction error. Ultimately, through simulation and real-vehicle testing, it is demonstrated that the designed ASHKF is able to effectively improve the state estimation accuracy of the speed signal of the suspension system under off-road driving conditions with low-frequency noise and outlying disturbances, and the accuracy is improved by 62.70% compared with that of the conventional Sage-Husa Kalman filter (SHKF).

Assessment of cognitive function in patients with metabolic dysfunction-associated steatotic liver disease

Background. Since the relationship between the initial stage of steatotic liver disease and dementia and cognitive function (CF) remains undetermined, there is a need to study such a combination. The aim is to evaluate CF in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Materials and methods. Thirty-nine outpatients with chronic forms of coronary heart disease were examined: 26 made up the main group with concomitant MASLD and 13 patients were included in the control group with an intact liver. In all of them, CF was evaluated using the Mini-Mental State Examination (MMSE). Results. The CF according to the MMSE was lower in patients with MASLD than among those with an intact liver (25.27 vs 28.22; p < 0.01). Mild cognitive disorders were diagnosed in 7.69 %, moderate — in 65.38 %, and severe — in 26.93 % of patients with MASLD. In total, the frequency of deterioration of CF (MMSE ≤ 27) among patients with an intact liver was 33.33 %, while it was significantly higher (92.31 %) in patients with concomitant MASLD (p < 0.05). Conclusions. CF can be an important criterion of the state of health and its changes, determines the course of the disease, allows assessing the activity of the endogenous intoxication syndrome and the state of the cardiovascular system. Impairment of cognitive functions occurs already at the stage of liver steatosis, which should be taken into account in the clinic.

Surrogate modeling of pantograph-catenary system interactions

The smooth interaction between the pantograph and the catenary is crucial for the operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary system (PCS) constructed based on physical principles are important tools for analyzing their interactions; however, these models rely on accurate system parameters (such as stiffness, damping, and mass). Under actual operating conditions, the system parameters of the PCS exhibit time-varying characteristics and are difficult to measure, making it challenging for dynamic models to accurately represent the system’s behavior. Data-driven intelligent algorithms, with powerful feature extraction and nonlinear fitting capabilities, provide new approaches for solving system response prediction and state identification problems of the PCS. However, excessive reliance on large amounts of data for training may lead to poor generalization ability and pose challenges to model robustness, such as sensitivity to input noise or outliers. To address these issues, this paper proposes a surrogate model for the interaction of the PCS by integrating physical information with deep neural networks. The model introduces a novel neural operator that combines Transformers and convolutions (Convs), capable of capturing complex mapping relationships among various parameters within the dynamic model of the PCS in the frequency domain. A residual network incorporating physical information is designed to simulate the intricate correlations among system parameters. Additionally, a dynamic weighting balance algorithm is proposed to adjust the losses of different physical equations dynamically, ensuring the balance of physical information during training. The proposed model effectively performs response prediction and state identification of the PCS. It demonstrates excellent performance on both simulation and real-world data, providing new insights and methodologies for studying PCS interactions.

Joint extented Kalman filter-based parameter estimation in networked SEIR models with stochastic variability: analysis using COVID-19 daily data from southern India

Graph Laplacian diffusion is utilised to augment the susceptible-infected-removed (SEIR) epidemic model by integrating a weighted network and introducing variability in the transmission rate parameter to simulate population movement across network nodes. Employing the Joint Extended Kalman filter (JEKF), our objective is to derive three critical parameters: the transmission rate (β), recovery rate (γ), and latent period rate (α). The JEKF's iterative approach continuously anticipates and updates the system state based on current parameter estimations, making it adaptable for parameter estimation in dynamic systems. Integration of model predictions with observed data further enhances accuracy. Subsequently, we offer visual insights and demonstrate our methodology through experiments conducted on a simple tree network and a small-world Watts-Strogatz graph. Our ultimate aim is to precisely determine optimal parameter values to formulate effective COVID-19 management plans tailored for South India.

Nonlinear Optimal and Multi-Loop Flatness-Basedcontrol of Omnidirectional 3-Wheel Mobile Robots

In this article, the control problem for omnidirectional 3-wheel autonomous mobile robots is solved with the use of (i) a nonlinear optimal control method (ii) a flatness-based control approach which is implemented in successive loops. To apply method (i) that is nonlinear optimal control, the dynamic model of the omnidirectional 3-wheel autonomous mobile robots undergoes approximate linearization at each sampling instant with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrix. The linearization point is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the nonlinear optimal control method are proven through Lyapunov analysis. To implement control method (ii), that is flatness-based control in successive loops, the state-space model of the omnidirectional 3-wheel autonomous mobile robot is separated into chained subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the preceding (i-th) subsystem become virtual control inputs for the subsequent (i+1-th) subsystem. In turn, exogenous control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The proposed method achieves trajectory tracking and autonomous navigation for the omnidirectional 3-wheel autonomous mobile robots without the need of diffeomorphisms and complicated state-space model transformations.

Review of current status and ongoing reforms of the mental health system in Uzbekistan

Uzbekistan's mental health system is undergoing significant reform aimed at improving care and service delivery. This article provides a comprehensive evaluation of the current state of Uzbekistan's mental health system using the WHO 2010 monitoring and evaluation framework. It also details ongoing reforms designed to address systemic issues, enhance mental healthcare and ensure better health outcomes for the population.

The possible role of coherence in highly diluted and succussed aqueous solutions

The historical problem of potentized (homeopathic, spagiric and other) remedies deals with their chemical-physical plausibility, i.e.: a consistent physical (molecular or electromagnetic) description have not yet been worked out and shared in scientific contexts [1]. I think that this difficulty pertains to the "generally accepted view of condensed matter" (to quote our mourned Giuliano Preparata [2]) and water condensed phases. Until we stay attached to a local, corpuscular and short-ranged description of matter (and of water, mainly) we won't be able to conceive the emergence of complex collective dynamics and their phenomenological outcomes. These are engendered by non-trivial ground states of the aqueous system in consequence both of its intrinsic nature and of the treatments to which it is subjected during dynamization. In this lecture I'll try, through a Quantum Electro-Dynamic (QED) theoretical framework [3,4,5], to sketch out the main features which let us envision liquid water as a two-phase system, where coherence is the basis both for its condensedness and for its ability to maintain long-lasting and far-from-equilibrium states able to non-randomly evolve in time through dissipative dynamics, and to understand the physical nature and properties of potentized remedies. Further comments about a likewise important question will be shared: how and why living matter is able to respond to such a kind of (possibly therapeutic) stimulations? [6,7]

BLESS Behavior Correctness Proof as ConvincingVerification Artifact

Safety-critical cyber-physical systems require evidence they are indeed safe. In practice, such evidence is results of system tests. Unfortunately, tests can only demonstrate the presence of software errors, not their absence, and can practically cover a tiny fraction of system state space. Valiant efforts to formally verify program correctness have either been excruciatingly difficult (theorem proving) or incomplete (static analysis, model checking, SAT or SMT solving). The BLESS Methodology was created specifically for engineers in industry to formally verify software controlling machines. The BLESS Methodology transforms programs that control machines, annotated with assertions to form proof outlines, into deductive proofs that every possible program execution will conform to its specification. To the extent that cyber-physical system specifications have been validated to express system safety and performance, a deductive proof can be a convincing argument to a person of program correctness. This paper uses a simple safety-critical system to argue that behavior correctness proof under the BLESS Methodology is a convincing verification artifact in addition to customary testing.

A Robust Control Strategy for Effective Field-Oriented Control of PMSMs

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.

Customizable Microfluidic Devices: Progress, Constraints, and Future Advances.

The field of microfluidics encompasses the study of fluid behavior within micro-channels and the development of miniature systems featuring internal compartments or passageways tailored for fluid control and manipulation. Microfluidic devices capitalize on the unique chemical and physical properties exhibited by fluids at the microscopic scale. In contrast to their larger counterparts, microfluidic systems offer a multitude of advantages. Their implementation facilitates the investigation and utilization of reduced sample, solvent, and reagent volumes, thus yielding decreased operational expenses. Owing to their compact dimensions, these devices allow for the concurrent execution of multiple procedures, leading to expedited experimental timelines. Over the past two decades, microfluidics has undergone remarkable advancements, evolving into a multifaceted discipline. Subfields such as organ-on-a-chip and paper-based microfluidics have matured into distinct fields of study. Nonetheless, while scientific progress within the microfluidics realm has been notable, its translation into autonomous end-user applications remains a frontier to be fully explored. This paper sets forth the central objective of scrutinizing the present research paradigm, prevailing limitations, and potential prospects of customizable microfluidic devices. Our inquiry revolves around the latest strides achieved, prevailing constraints, and conceivable trajectories for adaptable microfluidic technologies. We meticulously delineate existing iterations of microfluidic systems, elucidate their operational principles, deliberate upon encountered limitations, and provide a visionary outlook toward the future trajectory of microfluidic advancements. In summation, this work endeavors to shed light on the current state of microfluidic systems, underscore their operative intricacies, address incumbent challenges, and unveil promising pathways that chart the course toward the next frontier of microfluidic innovation.

Comparison of recent advances in set-membership techniques: Application to state estimation, fault detection and collision avoidance

There has been a vast body of research on set-membership techniques in recent years. These algorithms compute convex sets that contain the state of a dynamical system given bounds on disturbance and noise signals. Recently, a thorough comparison of zonotopes-based methods against interval arithmetic and ellipsoids has been presented in the literature. However, two main issues were left unexplored: (i) added conservatism in the presence of bounds of different kinds such as ℓ2-norm for the disturbance and an ℓ∞-norm bound on the noise: (ii) set-membership methods can be used in different settings apart from guaranteed state estimation, such as fault detection and isolation and collision avoidance of autonomous vehicles. In this paper, we extend this comparison by considering state estimation, fault detection and isolation, and collision avoidance for interval arithmetic, ellipsoids, zonotopes, constrained zonotopes, polytopes and constrained convex generators in the presence of various combination of bounds for the exogenous signals. The main objective is to compare accuracy, computation time and the scalability of the growth of the data structures required by each set representation. The results indicate that intervals, ellipsoids and zonotopes have a much worse accuracy. The recently introduced Constrained Convex Generators have a negligible increase in computation time in comparison with constrained zonotopes but have a better accuracy when bounds for disturbances, noise and initial conditions are heterogeneous or at least not polytopic.

Study on the change of the oil-paper insulation’s AC conductivity characteristics based on dielectric response in frequency domain

Oil-immersed power equipment (transformers and high voltage bushings) are key components of power systems. Oil-paper insulation has endured complex and harsh operating conditions when used as a primary insulation system, so it is essential to determine the insulation state of oil-paper insulation system to ensure a stable operation for the power grid. The oil-paper insulation’s conductivity behavior can effectively characterize the degree of insulation deterioration. However, the relationship between the alternating current (AC) and direct current (DC) conductivity behavior for oil-paper insulation systems is still unclear in current research, resulting in limitations in the traditional evaluation model’s application. Therefore, this paper uses oil-paper insulation materials under different aging to carry out relevant research about the changing features of the complex AC conductivity under different test temperature environments. The influence of oil-paper insulation aging, test temperature, and excitation frequency on the complex AC conductivity is verified. Considering the correlation between low-frequency AC conductivity and DC conductivity, the frequency range of AC conductivity is extended by means of frequency temperature and conductance double translation. The temperature dependence of the AC conductivity behavior is defined over a wide frequency test range. The relaxation polarization process dominated the AC conductivity variation law is obtained. The impact of experimental temperature and insulation aging on the cumulative characteristic parameters of AC ionic mobility is clarified. This study theoretically corrects the traditional calculation model for AC conductivity of oil-paper insulation. The bias in conductivity calculations caused by only considering a single relaxation polarization process is eliminated. The conductivity model that can characterize multiple relaxation polarization processes in the dielectric is established. This model provides theoretical support for the later assessment of oil-paper insulation state depending on conductance behavior.

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

A scaled derivative-based DMDc method for modelling multiple-input multiple-output mechanical systems

Dynamic mode decomposition with control (DMDc) is a powerful data-driven method for modelling dynamical systems with inputs and outputs. However, the inability to identify oscillations and the high sensitivity to noise limit the application of standard DMDc in structural dynamics. To address this challenge, this paper proposes a novel method called scaled derivative-based DMDc (sd-DMDc) to construct a discrete state-space model for mechanical systems using noisy data. This method requires only the displacement and excitation data, computes the scaled derivatives of displacement to extend the system states, and integrates the extended forward-backward strategy. Two crucial parameters are defined and optimized by minimizing the response error. The sd-DMDc algorithm is first verified through numerical simulations of a multiple-input multiple-output (MIMO) cantilever plate. The results indicate that sd-DMDc exhibits superior modelling accuracy across various noise levels and measurement points compared to the delay-DMDc method. It has also been proved that sd-DMDc can accurately identify dynamical systems from partial observations. Subsequently, vibration experiments are conducted on a MIMO cantilever plate. The sd-DMDc method is further verified using experimental data, and the conclusions align with those drawn from simulation studies. The sd-DMDc algorithm demonstrates great modelling accuracy, surpassing that of delay-DMDc, especially for partial observations. In addition, the model given by sd-DMDc can extract the modal parameters of the plate, which are consistent with the modal test results.

Variational approach to atom-membrane dynamics

Using the Dirac–Frenkel variational principle, a time-dependent description of the dynamics of a two-level system coupled to a bosonic bath is formulated. The method is applied to the case of a gas of cold atoms adsorbing to an elastic membrane at a finite temperature via phonon creation. The time-dependence of the system state is analytically calculated using Laplace transform methods, and a closed-form expression for the transition rate is obtained. Atoms in the gas transition to the adsorbed state through a resonance that has contributions from a distribution of vibrational modes of the membrane. The resonance can decay with the creation of a phonon to complete the adsorption process. The adsorption rate at low membrane temperatures agrees with the golden rule estimate to the lowest order in the coupling constant for values greater than a critical coupling strength. Below this critical coupling strength, the adsorption rate is exponentially suppressed by a phonon reduction factor whose exponent diverges with increasing adsorbent size. The rate changes discontinuously with coupling strength for low temperature membranes, and the magnitude of the discontinuity decreases with increasing temperature. These variational results suggest the quantum adsorption model may contain a first-order quantum phase transition.

Observer-based self-triggered adaptive decentralized control for uncertain interconnected nonlinear systems

In this paper, an observer-based self-triggered adaptive decentralized control method is presented for uncertain interconnected nonlinear systems (UINS) with unmeasured states. First, an adaptive state observer is constructed to estimate unmeasured system states. Then, the observer-based self-triggered controller is developed by applying the famous backstepping technique, in which the controller signal is updated based on the well-designed self-triggered protocol. Moreover, parameter adaptive laws are designed to address unknown parameters of UINS. Lyapunov theory is employed to prove that all signals of the UINS are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno behavior will not appear. Simulation results manifest the efficacy of the derived self-triggered control solution.

Control Design of Fractional Multivariable Grey Model-Based Fast Terminal Attractor for High Efficiency Pure Sine Wave Inverters in Electric Vehicles

In this paper, a fast and efficient control method is proposed for a pure sine wave inverter used in an electric vehicle system, which can provide better performance under transient and steady-state conditions. The proposed control technique consists of a fast terminal attractor (FTA) and a fractional multivariable grey model (FMGM). The FTA with finite time convergence offers a faster convergence rate of the system state and a singularity-free solution. However, if the uncertain system boundaries are overestimated or underestimated, chatter/steady-state errors can occur during the FTA, which can lead to significant harmonic distortion at the output of the pure sine wave inverter. A computationally efficient FMGM is incorporated into the FTA to solve the chatter/steady-state error problem when an uncertain estimate of the system boundary cannot be satisfied. Simulation results show that the proposed control technique exhibits low total harmonic distortion. Experimental results of a prototype pure sine wave inverter are presented to support the results of the simulation and mathematical analysis. Since the proposed pure sine wave inverter outperforms the classical TA (terminal attractor)-controlled pure sine wave inverter in terms of convergence speed, computational efficiency, and harmonic distortion elimination, this paper will serve as a useful reference for electric vehicle systems.