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Rapid launching is a crucial technology for the digitalization and regularization of space transportation systems. This paper focuses on the fast trajectory prediction of the launch-vehicle boost phase. A high-precision and high-efficiency analytical solution of the whole boost phase dynamics model of the rocket is obtained by adopting one of the regular perturbation methods called the state-space perturbation method in the launch rotated coordinate system. An analytical framework known as the perturbation expansion framework is proposed by first figuring out the correlation with the state variables/independent variables and then performing the approximate expansion. This framework can successfully address the issue of analytically solving the transfer matrix and integral term caused by the coupling and time-varying properties among state variables of the affine equations of the boost phase. The analytical solution developed in this paper has great fidelity and takes into account the combined effects of various factors, including thrust, aerodynamic force, Earth's gravity, oblateness, and rotation. It can serve as a theoretical guide for the rapid launch requirements and iterative design processes for early rocket demonstrations in engineering. [ABSTRACT FROM AUTHOR]

Purpose: Multilayer composite liner structures are the primary structural form of hydraulic tunnels. However, the bearing mechanism of multilayer composite liners has not been investigated thoroughly. Many existing design schemes do not properly achieve a balance between structural safety, anti-seepage capacity, and cost effectiveness. Thus, a new composite liner structure type and its theoretical model was proposed. Design/methodology/approach: A novel hydraulic tunnel composite liner structure with a polyurea spray coating interlayer was proposed in this study. A theoretical model based on the state-space method was developed and verified using FEM models and existing theoretical models. Parametric analysis was conducted based on the theoretical model to investigate the influence of various variables, including interfacial shear stiffness, inner liner thickness, and outer liner elastic modulus. Findings: It was concluded that the proposed theoretical model can be used successfully to calculate multilayer composite liner structures with high calculation efficiency. The overall deformation stiffness of the composite liner system increased with the interfacial shear stiffness. The sprayed coating interlayer significantly affects the residual force distribution between the outer and inner liners, which can also be affected by the adjustment of the thickness of the outer and inner liners. Thus, attention should be paid to these factors in the rational design of the proposed composite liner system. Originality/value: With the development of China's water conservancy projects, complex geological conditions, high surrounding rock stress, high internal and external water pressures, and other unique application scenarios have gradually increased. This places higher requirements on the bearing performance and impermeability of hydraulic tunnel lining structures. On the other hand, conventional hydraulic tunnel lining structures can hardly achieve a satisfactory balance between economy, structural safety, and impermeability. Thus, the proposed structure has the potential to be used in a wide range of applications. [ABSTRACT FROM AUTHOR]

• Semi-analytical solution of damping fluid-conveying pipe systems are obtained. • The concepts of the original system and the adjoint system are introduced for research. • The eigenvalues and eigenfunctions of the original system and the adjoint system are obtained. • The orthogonality condition of damped gyroscopic system is obtained in state space. • It is proved that the original system and the adjoint system of the system with free boundary are not orthogonal. In this paper, the response solutions of the damped fluid-conveying pipe system with elastic torsion constraints at both ends are analyzed. The pipe system considering gyroscopic effect induced by internal flow and damping effect is a typical damped gyroscopic system. This system cannot be decoupled in the modal space by the traditional modal analysis, and then the semi-analytical response solutions cannot be obtained by the classical modal superposition method. In order to remedy this problem, this paper proposes a new method based on complex modal superposition method and state-space method to give the semi-analytical response solutions of the damped fluid-conveying pipe system. The adjoint system is introduced through the concept of adjoint operator, and the orthogonality conditions of the damped fluid-conveying pipe system are derived in the state-space by using the eigenvalue relationship between the original system and the adjoint system. The Laplace transformation method is used to solve the eigenvalue problems of the original system and the adjoint system, and the implicit function equations about the eigenvalues and analytical eigenfunctions are obtained. According to the obtained orthogonality conditions, the semi-analytical response solutions of the system under arbitrary initial conditions and excitations are given, and the obtained solutions include transient and steady-state response solutions. In the numerical discussion part, the reliability and accuracy of the present method are verified. This research has positive significance for the dynamic analysis of fluid-conveying pipe systems, and the obtained orthogonality conditions have potential value for the nonlinear dynamics research. [ABSTRACT FROM AUTHOR]

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Abstract In biological systems, impulses are often used to model mutations in gene expression. In order to describe the instantaneity of the impulse, this paper employs a hybrid‐index model of impulsive Boolean networks with state‐triggered impulses. Based on this model, the time‐domain output controllability is investigated. Using the quotient mapping method, the hybrid‐domain solutions of this model are mapped to the time‐domain solutions, mathematically. Then a necessary and sufficient condition for output controllability in the time domain is presented. Finally, an example is given to demonstrate the correctness and effectiveness of the obtained results.

This paper presents a high-performance, multilevel inverter with symmetry and simplification. This inverter is a single-phase, seven-level symmetric switched-capacitor inverter based on the concept of the double voltage clamping circuit connected to the half-bridge circuit. Above all, only a single DC power supply is used to achieve a single-phase inverter with seven levels and a voltage gain of three. In addition to analyzing the operating principle of the proposed switched-capacitor multilevel inverter in detail, the stability analysis and controller design are carried out by the state-space averaging method. The feasibility and effectiveness of the proposed structure are validated by some simulated results based on the PSIM simulation tool and by some experiments using FPGA as a control kernel, respectively. However, in this study, the switches were implemented by MOSFETs to meet a high switching frequency. These MOSFETs can be replaced by IGBTs in the motor drive applications so that the used switching frequency can be reduced. [ABSTRACT FROM AUTHOR]

In this paper we present a framework for the construction and implementation of general virtual element spaces based on projections built from constrained least squares problems. Building on the triples used for finite element spaces, we introduce the concept of a virtual element method (VEM) tuple which encodes the necessary building blocks to construct these projections. Using this approach, a wide range of virtual element spaces can be defined. We discuss Hk-conforming spaces for k = 1, 2 as well as divergence and curl free spaces. This general framework has the advantage of being easily integrated into any existing finite element package, and we demonstrate this within the open source software package Dune. [ABSTRACT FROM AUTHOR]

In biological systems, impulses are often used to model mutations in gene expression. In order to describe the instantaneity of the impulse, this paper employs a hybrid‐index model of impulsive Boolean networks with state‐triggered impulses. Based on this model, the time‐domain output controllability is investigated. Using the quotient mapping method, the hybrid‐domain solutions of this model are mapped to the time‐domain solutions, mathematically. Then a necessary and sufficient condition for output controllability in the time domain is presented. Finally, an example is given to demonstrate the correctness and effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Goal: Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. Methods: We employ a Bayesian filtering approach within an expectation-maximization framework to track the hidden states during the $n$-back task in the presence of calming and exciting music. We decode the arousal and performance states from the skin conductance and behavioral signals, respectively. We derive an arousal-performance model based on the Yerkes—Dodson law. We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation. Results: The quantified arousal and performance are presented. The existence of Yerkes—Dodson law can be interpreted from the arousal-performance relationship. Findings display higher matrices of performance within the exciting music. Conclusions: The performance-based arousal decoder has a better agreement with the Yerkes—Dodson law. Our study can be implemented in designing non-invasive closed-loop systems.

Modern transducers and actuators may have functional layers with multi-field coupling and some elastic layers. This paper considers a tubular bilayer system consisting of a thin dielectric tube coated with a thick elastic layer. We study the nonlinear electromechanical response and the linear axisymmetric vibration of the system subject to different applied voltages and inner/outer pressures within the framework of the general nonlinear theory of electro-elasticity, the related linear incremental theory, and by considering the continuity conditions at the interface. We investigate instability behaviour using the same basic formulae. The state-space method provides efficient and accurate free vibration analysis, considering the dynamic response at the lowest frequencies, so we can neglect the viscous and damping effects, which is well suited to this problem. New results indicate that the bilayer system improves its frequency capability and stability compared to the monolayer dielectric tube. The thick outer elastic layer stiffens the bilayer system against axisymmetric bifurcation, bulging and necking instabilities. It also performs better in front of axisymmetric instability, increasing the system's capability to receive or produce higher voltages, especially for long waves.This work thoroughly explains bilayer functional systems' behaviour when exposed to extreme environments such as high voltage or pressure. [ABSTRACT FROM AUTHOR]

Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has been extended with the proposal of alternative estimation methods to the maximum likelihood. However, maximum likelihood estimation assumes, as a rule, that the errors are normal. This paper suggests implementing the bootstrap methodology, utilizing the model's innovation representation, to derive distribution-free estimates—both point and interval—of the parameters in the time-varying state-space model. Additionally, it aims to estimate the standard errors of these parameters through the bootstrap methodology. The simulation study demonstrated that the distribution-free estimation, coupled with the bootstrap methodology, yields point forecasts with a lower mean-squared error, particularly for small time series or when dealing with smaller values of the autoregressive parameter in the state equation of state-space models. In this context, distribution-free estimation with the bootstrap methodology serves as an alternative to maximum likelihood estimation, eliminating the need for distributional assumptions. The application of this methodology to real data showed that it performed well when compared to the usual maximum likelihood estimation and even produced prediction intervals with a similar amplitude for the same level of confidence without any distributional assumptions about the errors. [ABSTRACT FROM AUTHOR]

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introduced to calculate the extended observation matrix, and finally the on-line model parameters are extracted. Compared with the traditional subspace schemes, smaller computation complexity and higher identification precision are anticipated in the proposed method. A case study on the modeling of the ethylene polymerization verifies the effectiveness of the developed approach, in which the related statistical indexes of the obtained identification model are better. [ABSTRACT FROM AUTHOR]

This research proposes the effect of micropolar-Cosserat (MC) parameters (length-scale parameters and Cosserat shear modulus) on the dispersion characteristics of propagating wave modes in periodic composite panels (PCPs). These inbuilt parameters are due to the assumption of the length-scale boundary conditions that allow for capturing the micro-rotational (MR) wave mode along with the flexural ones. A significant contribution of this study is the transformation of the two-dimensional (2-D) periodic composite problem into a series of one-dimensional (1-D) ones using the MC continuum theory. The analysis employs the transfer matrix method in the framework of the state-space approach to investigate periodic systems in the eigenvalue domain. Additionally, Bloch–Floquet's periodic boundary conditions (PBCs) are applied to the unit cell to ensure the periodicity of the system. The main innovation lies in observing veering, locking, and coupling phenomena, which occur due to alterations in lamina orientation and MC parameters. Moreover, the presence of inbuilt parameters renders the dispersion characteristics highly sensitive to even minor coefficient variations, with a mere 1% change significantly impacting eigenmode fluctuations. The sudden bandgap (BG) disappearing nature could be used to identify the accurate value of the coefficient for designing and analyzing PCPs. [ABSTRACT FROM AUTHOR]

Summary: It is well known that all practical control systems are subject to external disturbances. Therefore, a feedback controller should be designed so that the closed‐loop system has some desired properties such as stability, good tracking performance, at the same time, the impact of external disturbances on the performance of the closed‐loop system is attenuated to some degree. On the other hand, almost all models for real fully actuated systems, which are derived based on the physical laws, are described by a set of high order differential equations. The traditional method for controlling these fully actuated systems is to convert the high order differential equations to first order differential equations, which are called the state equations, and then to design feedback controllers by using the state‐space design methods. Such conversion causes some inconveniences. To overcome such inconveniences, a high‐order fully actuated (HOFA) design method has been proposed to control HOFA systems. The main goal of this paper is to design a weak disturbance decoupling controller for HOFA nonlinear systems to guarantee the stability and desired output tracking performance of the closed‐loop system and to attenuate the impact of disturbances. [ABSTRACT FROM AUTHOR]

To design and optimize the thermal management of metal hydride (MH) tank, high-fidelity model (HFM) composed of numerous partial differential equations, is often adopted to capture the spatial evolution of key parameters accurately. However, computational difficulties raise in dealing with such nonlinear interconnections for system-level simulation. Therefore, the model reduction of MH system has drawn much attention aiming at the trade-off between accuracy and computational cost. In this paper, a hybrid modeling method which integrates advantages of the state-space model (SSM) and HFM, is proposed and applied for the model reduction of MH tank. In particular, four data-driven matrices are introduced to improve the model accuracy. The results show that the root mean square errors of the average temperature between hybrid reduced model (HRM) and HFM are 0.0399 K and 0.1989 K respectively for the cases without and with thermal management. In addition, the HRM is able to capture dynamic characteristic under perturbed input variables with a relative error of temperature within 0.4%, indicating great potential for system-level simulation. What's more, the analysis of predominant pole derived from the state matrices of different thermal managements, reveals that the expanded graphite has superior effect on the adjustment time extension compared with fins, while the fins have distinct advantages in heat transfer enhancement at the cost of volumetric hydrogen storage density. • A hybrid modeling method combining state-space model and data-driven correction is proposed. • The reduced model of MH tank shows great agreement with HFM under various operation parameters. • The accuracy of reduced model has also been verified for the MH tank with thermal management and perturbed variables. • The effect of thermal management on the dynamic characteristic of MH tank is summarized. [ABSTRACT FROM AUTHOR]

With this paper, we purpose to introduce and characterized some generalized classes of completely monotone functions on some types of white noise spaces [ABSTRACT FROM AUTHOR]

Abstract The voltage source converter (VSC) interlinking AC and DC power networks is a core equipment in AC/DC hybrid power systems. To analyze the stability of this system based on the impedance method, it is necessary to establish the hybrid AC/DC admittance model of interlinking VSC. This paper presents a unified and systematic AC/DC hybrid admittance modelling framework which is widely applicable to different control methods of interlinking VSCs. The hybrid admittance models for interlinking VSCs under PQ control, VdcIq control, and Vf control are explicitly formulated, with their elements individually reflecting the dynamics of the AC side, DC side, and the interaction between them. Subsequently, the impedance network model of interlinking VSC systems is formulated based on the hybrid admittance, and furthermore the frequency‐domain modal analysis is performed to analyze the system oscillation stability. Finally, frequency scanning and time‐domain simulation results in MATLAB/Simulink are provided to validate the effectiveness of the modelling and oscillation stability analysis method.

The voltage source converter (VSC) interlinking AC and DC power networks is a core equipment in AC/DC hybrid power systems. To analyze the stability of this system based on the impedance method, it is necessary to establish the hybrid AC/DC admittance model of interlinking VSC. This paper presents a unified and systematic AC/DC hybrid admittance modelling framework which is widely applicable to different control methods of interlinking VSCs. The hybrid admittance models for interlinking VSCs under PQ control, VdcIq control, and Vf control are explicitly formulated, with their elements individually reflecting the dynamics of the AC side, DC side, and the interaction between them. Subsequently, the impedance network model of interlinking VSC systems is formulated based on the hybrid admittance, and furthermore the frequency‐domain modal analysis is performed to analyze the system oscillation stability. Finally, frequency scanning and time‐domain simulation results in MATLAB/Simulink are provided to validate the effectiveness of the modelling and oscillation stability analysis method. [ABSTRACT FROM AUTHOR]

With the expansion of electronics in recent decades, it is notorious to observe that embedded systems are increasingly necessary to improve people's quality of life and to facilitate the diagnosis of systems in general, ranging from pacemakers to control systems. The increased use of electronic components for technological support, such as telemetry systems, electronic injection, and automotive diagnostic scanners, enhances the perspective of data analysis through an embedded system aimed at vehicular systems. Thus, this work aims to design and implement an embedded data acquisition system for the analysis of vehicle vertical dynamics. The methodology for this study was structured into several stages: mathematical modeling of a motorcycle's mass-spring-damper system, coding for the Arduino microcontroller, computational data analysis supported by MATLAB software version 9.6, electronic prototyping of the embedded system, implementation on the vehicle, and the analysis of motorcycle vertical dynamics parameters. In addition, a mathematical modeling of the mass-spring-damper system was performed using the state-space method. The system was implemented on the Arduino microcontroller platform, enabling real-time data transfer from a motorcycle. The experimental results have successfully validated the proposed data acquisition system. [ABSTRACT FROM AUTHOR]

We consider general nonlinear function‐on‐scalar (FOS) regression models, where the functional response depends on multiple scalar predictors in a general unknown nonlinear form. Existing methods either assume specific model forms (e.g., additive models) or directly estimate the nonlinear function in a space with dimension equal to the number of scalar predictors, which can only be applied to models with a few scalar predictors. To overcome these shortcomings, motivated by the classic universal approximation theorem used in neural networks, we develop a functional universal approximation theorem which can be used to approximate general nonlinear FOS maps and can be easily adopted into the framework of functional data analysis. With this theorem and utilizing smoothness regularity, we develop a novel method to fit the general nonlinear FOS regression model and make predictions. Our new method does not make any specific assumption on the model forms, and it avoids the direct estimation of nonlinear functions in a space with dimension equal to the number of scalar predictors. By estimating a sequence of bivariate functions, our method can be applied to models with a relatively large number of scalar predictors. The good performance of the proposed method is demonstrated by empirical studies on various simulated and real datasets. [ABSTRACT FROM AUTHOR]

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect. Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data. [ABSTRACT FROM AUTHOR]

Thin-walled open section composite beams (TWOSCB) used extensively in aerospace structures exhibit several non-classical effects such as torsional warping, warping shear, material coupling, and shear deformation. Arbitrary cross-section and material coupling make the governing equations highly coupled and difficult to solve analytically. In this paper, a unified coupled-field formulation is developed to analyze TWOSCB accommodating all non-classical effects. Using the state-space method to derive analytical expressions for displacements and forces, Vlasov solutions of isotropic beams are recovered. Results obtained for arbitrarily laminated mono-symmetric composite channel and I-section beams subjected to bending and torsion loads show excellent agreement with available results in the literature. [ABSTRACT FROM AUTHOR]

• State-space method improves solving efficiency of lumped-parametric models of blisks • Traveling wake excitation is derived by aerodynamic load fitting of blade surface • The modular expression of three-blade blisk model can be extended to the multi-blade blisk model. • Dimensions of state space is two times of degrees of freedom for the blisk models • Stiffness mistuning is independent of the steady-state response of the tuned blade In order to improve the solution efficiency of vibration characteristics for the mistuned blisk, a modular method combined with state-space method based on lumped-parametric model is developed for the first attempt, which provides a general modeling framework which can be employed to conveniently construct the lumped-parametric models of bladed disk systems with multiple mistuning and variable blade number. It is noteworthy that a pressure fit in the axial-span-pressure space is performed to obtain the continuous equations of aerodynamic forces according to the aerodynamic loads acting on the blade surface. The function of traveling wake excitation is established by considering the rotational velocity. Subsequently, 1N-degree-of-freedom (1N-DOF) and 2N-DOF lumped-parametirc models are respectively used to construct the mistuned blisk. A modular solution method of multiple mistuned lumped-parametirc model is derived from the controllable canonical form and minimum realisation, which reveals that the minimum dimension of the state space is two times of its DOF. The correctness of the present method is varified by several comparisions. Vibration localization is found in the transient response and steady-state response of the blisk. The effects of the mistuning on the blades and the disk are discussed, respectively. [ABSTRACT FROM AUTHOR]

• A continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. • Combination of modal truncation order and sensor layout optimization by modal strain energy. • The proposed algorithm has better accuracy and noise immunity than SSM and GKFM. • For continuous systems, the proposed algorithm has a solution time of 0.38s and is insensitive to the time step. • The proposed algorithm has good accuracy in experimental validation. Dynamic load identification based on structural responses is an important problem in the field of engineering and plays an important role in the condition assessment of mechanical structures. Current popular load identification methods such as the state-space method (SSM) and the Green's kernel function method (GKFM) are implemented on discrete systems with outstanding performance, but when dealing with complex continuous systems, there are some limitations such as low efficiency and inaccuracy. In this paper, a continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. Using modal coordinate transformation and modal truncation methods, the number of infinite vibration differential equations of a continuous system in physical space are converted to a finite number of vibration differential equations in modal space, where the modal truncation order and the optimal layout of the sensors are combined by modal strain energy. The Newmark-β method in modal space is derived and thus combined with SSM to obtain a load identification model Y =HF called SSM-Newmark-β method, which reduces the size of the transfer matrix H. The solution time of the proposed algorithm is 0.38 s for continuous systems, which is rather shorter than that for discrete systems. Furthermore, the simulations show that it has better accuracy and noise immunity than SSM and GKFM in the identification of sinusoidal load, impact load, and random load. The effect of time step is also discussed which reveals that the larger time step has less effect on the proposed algorithm. An experimental study is carried out on a cantilever beam system. The result verifies that the SSM-Newmark-β algorithm has better accuracy in load identification for the continuous system. This research provides a new sight for the real-time identification of dynamic loads for complex structures. [ABSTRACT FROM AUTHOR]

The aim of this work is the design of a current control by means of state feedback, based on linear matrix inequalities (LMI), and it is applied in a DC-DC coupled inductors Buck-Boost modular converter, which has been widely used in distributed generation systems with renewable energy sources. This method considers constraints on the location of poles defined in the complex plane called d-stability. The closed-loop control system is implemented in Matlab® Simulink and validated under different test scenarios, where better dynamic performance is obtained, its operating range is extended, with shorter settling times and a better time-response, in contrast with the classical PI control technique. [ABSTRACT FROM AUTHOR]

Purpose: The main aim of this work is to investigate the free vibration of functionally graded rotating nanotubes using the Euler–Bernoulli beam theory under the assumptions of Eringen's nonlocal elasticity theory, which gets close to the nanostructure's behaviors. The material properties are assumed to be graded in the nanotube thickness direction according to the power-law distribution. Methods: Hamilton's principle is used to derive equations of motion and boundary conditions, which are uncoupled and solved analytically using the state-space method. The gyroscopic effect is considered in the equations of motion and, unprecedentedly, in the boundary conditions. Results: The effects of rotating speed, mode number, material power-law index, and geometrical and nonlocal parameters on both critical speeds and natural frequencies under various boundary conditions for functionally graded rotating nanotubes are plotted and investigated for the first time. Conclusion: The current formulation of the rotating FG-NT is expected to serve as a standard for assessing the accuracy of designing various nanomotors, whether experimental, numerical, or analytical. [ABSTRACT FROM AUTHOR]

Piezoelectric quasicrystals have attracted extensive attention due to their unique physical and mechanical properties. This paper studies the dynamic response of multilayered two-dimensional decagonal piezoelectric quasicrystal sector plates with imperfect interfaces. Based on the quasicrystal linear elasticity, partial differential state equations along the thickness direction are derived by using the state-space method. Then, by virtue of the differential quadrature method and the Fourier series expansions, this boundary-value problem with mixed boundary conditions and imperfect interfaces is solved. In addition, via the joint coupling matrix, the field quantities in the interior of the structure are connected to those on the external surfaces with numerical instability. Finally, parameter studies on the effects of angular spans, imperfect interfaces, and mixed boundary conditions are numerically investigated where the dimensionless frequencies and modes are exhibited. [ABSTRACT FROM AUTHOR]

Two state–space representations, also known as state–space models (SSMs), are proposed to estimate dynamic spatial relationships from time series data. At each time step, the weight matrix, which captures the latent state, is updated in the context of a spatial autoregressive process. Specifically, two types of SSMs are considered: the first one identifies the spatial effects in the form of multivariate regression, where the higher orders of the spatial matrix are introduced to the regression coefficient, while the second one updates the spatial matrix taking advantage of the likelihood function of a spatial autoregressive (SAR) model. Different filtering algorithms are proposed to estimate the latent state. The simulation results show that the first state–space representation performs better in both lower-dimensional and higher-dimensional cases, while the performance of the second representation is sensitive to the state dimension. In a real-world case study, the time-varying weight matrices are estimated with weekly credit default swap (CDS) data for 16 banks, and the results show that the methods can identify communities that are consistent with the country-driven partition. [ABSTRACT FROM AUTHOR]

This study aims to analyze the physical-mechanical properties and dynamic models of tissue-simulating hydrogels, specifically the photoacoustic (PA) response signals, by varying the concentrations of polyvinyl alcohol (PVA) and molecular weight (MW). A state-space model (SSM) is proposed to study the PVA hydrogels to retrieve the PA-related signal's damping ratio and natural frequency. Nine box-shaped PVA hydrogels containing saline solution were used, with five concentrations of PVA (7, 9, 12, 15, 20%) for MW1 and four for MW2. The results indicated that the concentration of PVA and MW played an important role in the PA wave's amplitude, arrival time, and speed of sound over the hydrogels. The SSM parameters showed that increasing PVA and MW concentrations improved the hydrogels' ability to absorb and transfer energy under the PA effect. These parameters were also found to be correlated with density and modulus of elasticity. Additionally, the concentrations of PVA and MW affected the absorption and optical scattering coefficients. The physical-mechanical properties, including porosity, density, and modulus of elasticity, improved as the concentration of PVA and MW increased. The ultimate goal of this study is to develop hydrogels as phantoms that can be used for tissue simulation and imaging. [ABSTRACT FROM AUTHOR]

Approximate analytical solutions are presented for the transient thermoelastic problem of rectangular plates with time-dependent convection and radiation boundaries. To include the nonlinear radiation boundary, the whole heating process is divided into several time steps, and a linearized approximation is used to simplify the radiation term for each step. The one-dimensional transient temperature along the thickness direction is solved using the technique of the separation of variables. The displacement and stress solutions are obtained by applying the state-space method to the three-dimensional thermoelasticity equations. The accuracy of the present solutions is demonstrated by comparison with the reported results in the open literature and the finite element solutions. In the numerical examples, two kinds of thermal boundaries, namely, time-independent convection boundaries and time-dependent convection and radiation boundaries, are considered to show the availability of the present solutions. [ABSTRACT FROM AUTHOR]

Due to the nonlinear behavior of grid-connected cascaded multilevel inverters (GCCMI), the use of nonlinear controllers can guarantee system stability over a wide range of operation. Therefore, state-space modeling is required to design nonlinear controllers. In this manuscript, a comprehensive method is proposed for the general state-space modeling of an n-level GCCMI with LCL coupling. To validate the accuracy of obtained state-space model, an experimental setup of a cascaded multilevel inverter including two H-bridges has been implemented. The outputs of the state-space model are compared with the simulation and experimental results of the GCCMI. This shows that the proposed model is compatible with a real closed-loop system. The simulations were performed using EMTDC/PSCAD software. In the following, the designed general model is used to develop a nonlinear controller based on the Lyapunov stability criteria for a multilevel shunt active power filter (SAPF). Results show that the designed controller is stable and robust in a wide range of operating point changes. [ABSTRACT FROM AUTHOR]

This paper proposes an effective VAR source established as a cascaded H-bridge (CHB) static compensator (STATCOM), which is based on the flux cancellation method. The conventional CHB-STATCOM uses a low-frequency large capacitor as a source. The required capacitance value of the capacitor increases as a function of the amount of VAR produced to compensate the power system. The proposed topology is based on flux cancellation. Therefore, the source of VAR in this case is not limited by the capacitors or the power decoupling transformer size. The sub-module (SM) of the proposed topology comprises of a CHB module with power decoupling circuits. The double-frequency ripple powers on the sub-module (SM) capacitors, which have a phase shift of 120° with respect to each other. The ripple powers are derived from each of the three phases toward a common magnetic core to cancel each other out. An isolated bidirectional triple port dual half-bridge converter is utilized for the flux cancellation process. In this converter, the main challenges are leakage inductances and the high voltage insulation among the three windings of the high-frequency transformer. An αβ-frame-based model is proposed, using the generalized state-space averaging method, for the flux cancellation circuit. Furthermore, the size is significantly reduced by the proposed method, since a small sub-module capacitance of a few microfarads is sufficient to operate the CHB-STATCOM. The analysis and the controller design process are presented, followed by simulation and hardware validations. [ABSTRACT FROM AUTHOR]

Unconsciousness maintained by GABAergic anesthetics, such as propofol and sevoflurane, is characterized by slow-delta oscillations (0.3 to 4 Hz) and alpha oscillations (8 to 14 Hz) that are readily visible in the electroencephalogram. At higher doses, these slow-delta–alpha (SDA) oscillations transition into burst suppression. This is a marker of a state of profound brain inactivation during which isoelectric (flatline) periods alternate with periods of the SDA patterns present at lower doses. While the SDA and burst suppression patterns have been analyzed separately, the transition from one to the other has not. Using state–space methods, we characterize the dynamic evolution of brain activity from SDA to burst suppression and back during unconsciousness maintained with propofol or sevoflurane in volunteer subjects and surgical patients. We uncover two dynamical processes that continuously modulate the SDA oscillations: alpha-wave amplitude and slow-wave frequency modulation. We present an alpha modulation index and a slow modulation index which characterize how these processes track the transition from SDA oscillations to burst suppression and back to SDA oscillations as a function of increasing and decreasing anesthetic doses, respectively. Our biophysical model reveals that these dynamics track the combined evolution of the neurophysiological and metabolic effects of a GABAergic anesthetic on brain circuits. Our characterization of the modulatory dynamics mediated by GABAergic anesthetics offers insights into the mechanisms of these agents and strategies for monitoring and precisely controlling the level of unconsciousness in patients under general anesthesia. [ABSTRACT FROM AUTHOR]

The pitch control system has a profound impact on the development of wind energy, and yet a delay or non-minimum phase can weaken its performance. Thus, there is a strong incentive to enhance pitch control technology in order to counteract the negative effects of unidentified delays and non-minimum phase characteristics. To reduce the complexity of the parameter-tuning process and improve the performance of the system, in this paper, we propose a novel control method for wind turbine pitch angle with time delays. Specifically, the proposed control method is state-space PIDD2, which is based on internal model control (IMC) and the open-loop system step response. Then, considering the tracking, disturbance rejection and measurement noise, the proposed controller is verified through simulations. The simulation results demonstrate that the state-space PIDD2 (SS-PIDD2) can provide a trade-off between robustness, time domain performance and measurement noise attenuation and effectively improve pitch control performance in contrast to series PID and PI control methods. [ABSTRACT FROM AUTHOR]

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition. [ABSTRACT FROM AUTHOR]

There are few mathematical models that describe how human perceptual and affective responses evolve with time. We developed a state-space model of the interrelationships based on time-evolving perceptual and affective responses acquired by the temporal dominance (TD) method. We defined and computed the state variables that endow the system with memory using canonical variate analysis. Furthermore, we determined the model parameters on the basis of a cross-validation approach such that the observed and estimated changes in the affective responses are highly correlated. We applied this method to the TD curves of responses to eating strawberries and plum pickles, reported in our previous work. The model describes what happens during food intake in terms of a few state variables that summarize a large number of observable responses reductively, thus helping to make the perceptual and affective dynamics understandable. [ABSTRACT FROM AUTHOR]

Multiple state variables governed by internal processes within the human body remain unobserved. On a number of occasions, these states are linked to point process bioelectric and biochemical observations coupled together with continuous-valued variables. These observations provide a means to estimate the latent states of interest. We develop a state-space method to estimate unobserved sympathetic arousal and energy production states from skin conductance and cortisol data respectively, comprising of a marked point process and a continuous-valued observation. The method involves Bayesian filtering applied within an expectation-maximization (EM) framework for state estimation and model parameter recovery. Results are evaluated on both simulated and experimental data. On experimental skin conductance data, high arousal levels are generally detected during cognitive stress periods and lower values are detected during relaxation. Results are also in conformity with general physiology for cortisol data. On separate experimental data, skin conductance-based estimates are validated/cross-checked with functional Near Infrared Spectroscopy features. Estimation is also performed on heart rate and skin conductance data to illustrate the method’s wider applicability. We also compare the method with earlier approaches. We show how it outperforms a previous method for cortisol-based energy estimation, and its superiority to earlier methods for estimating sympathetic arousal. The EM approach is thus able to estimate latent physiological states within the body from point process bioelectric and biochemical phenomena. The method could be applied in wearable monitoring and automated closed-loop therapy delivery for patients diagnosed with certain types of neuropsychiatric or hormone disorders.

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context. [ABSTRACT FROM AUTHOR]

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly. [ABSTRACT FROM AUTHOR]

A high gain two-phase interleaved coupled inductor-based DC-DC converter step-up low input voltage sources like PV array and fuel cell in distribution generation system (DGS). The converter operates for a wide range of load changes when it is part of DGS. At light load, the converter operates in discontinuous conduction mode (DCM). A small-signal model (SSM) is required in both continuous conduction mode (CCM) and DCM to know the complete dynamic behavior of the converter. Steady-state and small-signal analysis of the two-phase interleaved coupled inductor boost converter (ICIBC) is quite complex when the converter operates in DCM. This paper presents the complete derivation of SSM of a two-phase ICIBC-based high step-up gain converter in DCM. The conditions of the converter operating in DCM are discussed. The small-signal model of two-phase ICIBC is derived using the state-space averaging method. The control-to-output voltage transfer function and steady-state voltage gain of the two-phase ICIBC are derived. When the converter operates in DCM, it eliminates the right half plane zero from the control-to-output voltage transfer function. Thus, it improves bandwidth and stability margin. Thus, single loop voltage control is sufficient to achieve regulated output voltage with faster response for all operating ranges of DCM. The laboratory prototype of two-phase ICIBC is implemented to step-up 24 V to 100 V at a power level of 300 W. The experimental results of two-phase ICIBC are obtained in DCM and CCM with step-change in load to validate the derived small-signal model. [ABSTRACT FROM AUTHOR]

The high-order rational function provides an accurate and effective model of self-excited forces for flutter analysis of bridges. This study proposes a direct identification method for the rational function of a bridge deck, which enables high-order modeling. In this method, the single degree of freedom (SDOF) forced vibration test is first carried out on the sectional model of the bridge deck. The tested results are then processed by a harmonic function-based filter, yielding force time histories with high signal-to-noise ratios. Formulas are derived to identify parameters of the high-order rational function from these time histories. To deal with the numerical difficulties of the formulas, two practical optimal nonlinear fitting procedures were proposed, which simplify the identification but at the same time maintain a reasonable accuracy. A thin flat plate and a streamlined box section under various wind angles of attack were used as examples to elucidate the effectiveness and feasibility of the proposed identification method. Results showed that the identified high-order rational function could accurately replicate the self-excited force. In contrast, a significant deviation was observed if the first-order rational function was utilized as an alternative. Finally, the high-order rational function model identified was applied in the flutter analysis by a state-space method. The yielded critical flutter wind speed matched satisfactorily with the free-vibration wind tunnel test results, confirming the advantage of the proposed model. [ABSTRACT FROM AUTHOR]

With the increasing integration of new energy generation into the power system and the massive withdrawal of traditional fossil fuel generation, the power system is faced with a large number of stability problems. The phenomenon of low-frequency oscillation caused by lack of damping and moment of inertia is worth studying. In recent years, virtual synchronous generator (VSG) technique has been developed rapidly because it can provide considerable damping and moment of inertia. While improving the stability of the system, it also inevitably causes the problem of active power oscillation, especially the low mutual damping between the VSG and the power grid will make the oscillation more severe. The traditional time-domain state-space method cannot reflect the interaction among state variables and study the interaction between different nodes and branches of the power grid. In this paper, a frequency-domain method for analyzing low-frequency oscillations considering VSG parameter coupling is proposed. First, based on the rotor motion equation of the synchronous generator (SG), a second order VSG model and linearized power-frequency control loop model are established. Then, the differences and connections between the coupling of key VSG parameters and low-frequency oscillation characteristics are studied through frequency domain analysis. The path and influence mechanism of a VSG during low-frequency power grid oscillations are illustrated. Finally, the correctness of the theoretical analysis model is verified by simulation. [ABSTRACT FROM AUTHOR]

The state-space model is a powerful statistical tool to estimate linear or non-linear discrete-time dynamic systems. This model naturally leads to the estimation problem of the time-varying parameters of the discovery-time demographic version of the susceptible-infected-recovered (SIR) model that we consider. In this paper, we consider computational methods to perform Bayesian inference on state-space models for analysing time-series data. We compare the three popular Bayesian computational methods for state-space models: the adaptive Metropolis-within-Gibbs algorithm, Liu and West's algorithm and variational approximation method based on Gaussian distributions. The performances of the three methods are compared based on synthetic datasets. Furthermore, we analyse the trend of the spread of COVID-19 in South Korea to point out the limitations of existing methods and derive meaningful results. [ABSTRACT FROM AUTHOR]

Population and community ecology traditionally has a very strong theoretical foundation with well‐known dynamical models, such as the logistic and its variations, and many modifications of the classical Lotka–Volterra predator–prey and interspecific competition models. More and more, these classical models are being confronted with data via fitting to empirical time series for purposes of projections or for estimating model parameters of interest. However, using statistical models to fit theoretical models to data is far from trivial, especially for time series data where subsequent measurements are not independent. This raises the question of whether statistical inferences using pure observation error models, such as simple (non‐)linear regressions, are biased, and whether more elaborate process error models or state‐space models have to be used to address this complexity. In order to help empiricists, especially researchers working with experimental laboratory populations in micro‐ and mesocosms, make informed decisions about the statistical formalism to use, we here compare different error structures one could use when fitting classical deterministic ordinary differential equation (ODE) models to empirical data. We consider a large range of biological scenarios and theoretical models, from single species to community dynamics and trophic interactions. In order to compare the performance of different error structure models, we use both realistically simulated data and empirical data from microcosms in a Bayesian framework. We find that many model parameters can be estimated precisely with an appropriate choice of error structure using pure observation error or state‐space models, if observation errors are not too high. However, Allee effect models are typically hard to identify and state‐space models should be preferred when model complexity increases. Our work shows that, at least in the context of low environmental stochasticity and high quality observations, deterministic models can be used to describe stochastic population dynamics that include process variability and observation error. We discuss when more complex state‐space model formulations may be required for obtaining accurate parameter estimates. Finally, we provide a comprehensive tutorial for fitting these models in R. [ABSTRACT FROM AUTHOR]

Bias accuracy of micromachined gyroscope is deteriorated severely when suffering from unknown environmental disturbances (UD), such as temperature, vibration, and shock. In this article, gyros array using a two-stages UD-decoupled fusion strategy is for the first time proposed to maintain high-accuracy bias under unknown disturbances. In detail, the evolvement model of the individual gyroscope among four-gyros array is first deduced to decouple UD. Then, local estimator is devised to simultaneously derive the estimates of the state, the bias, and the UD of each gyro among the array in the first stage, respectively. Further, due to the nonoptimal local state estimates caused by the inherent response difference between each gyro, the global state fusion estimator weighted by dynamic coefficients is devised to achieve the uniformly optimal state estimate of gyros array in the second stage. The experimental test shows that RMSE of the estimate based on the proposed method is evaluated as 0.13°/s, which is reduced by 92.4%, 87%, 82.4%, and 51.9% compared with kalman filter (KF), internal model approach (IMA), distributed optimal linear fusion estimator (DOLFE), and minimal learning parameter-neural network (MLP-NN), respectively. The tested scale factor of the array is calibrated as 5.9 mv/°/s and the corresponding bias stability is calculated as 0.019°/s, which is improved by 68.8%, 54.8%, 34.5%, and 17.4% compared with KF, IMA, DOLFE, and MLP-NN, respectively. Not only is bias accuracy under UD not deteriorated, on the contrary, the accuracy is improved significantly by 3.7 times compared with that without UD. The proposed array-based UD-decoupled method paves an effective path to apply microgyro in actual environment engineering. [ABSTRACT FROM AUTHOR]

Uncertainties and disturbances in traffic flows will result in performance degradation and even the critical instability of automated freeway systems. To handle these issues, a multivariable model predictive controller is proposed in this article, taking the speed and density of traffic flows as the control objectives and accounting for the uncertainty caused by ramps. Specifically, a state-space macroscopic traffic flow model is established based on a macroscopic traffic flow model. Then, a multivariable model predictive controller is developed based on the state-space model, and a sequence quadratic program algorithm is designed to find the optimal inputs. Finally, extensive simulations and comparisons are conducted. The results from the simulations verify that the proposed controller can make the density and speed of traffic flows approach a desired value more quickly and without chattering. In addition, the controller can effectively avoid traffic congestion and has better stability in the presence of disturbances. [ABSTRACT FROM AUTHOR]

This article proposes a cyberattack-resilient secondary frequency control (SFC) scheme for a stand-alone microgrid (MG) system. The proposed scheme is based on a hybrid system that employs an unknown input observer for state estimation, cyberattack detection and reconstruction, and a Type-2 fuzzy logic system for minimization of frequency deviation that may be caused by cyberattacks on the MG system. A cyberattack on the MG’s frequency measurement disrupts the operation of the SFC system and eventually compromises the frequency stability of the MG. The proposed approach shows an accurate estimation of the MG states and maintains the frequency of the MG within operating limits during various cyberattack scenarios. The practicability of the proposed hybrid scheme is validated using the Speedgoat real-time digital system simulator. [ABSTRACT FROM AUTHOR]

In the context of "TO CHAIR" project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/ website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts. [ABSTRACT FROM AUTHOR]

Since the coupling relationship of excitation sources is complicated, meantime the motor torque changes quickly under the acceleration condition, the problem of torsional vibration is prominent. This paper studies an active damping control (ADC) strategy for a parallel hybrid electric vehicle (HEV) under acceleration condition. Primarily, a full-order dynamic model is built, and the corresponding motion equations are derived. Moreover, a controller design–oriented model is established based on model reduction algorithm. Furthermore, a method that considers time delay characteristics of actuator based on model predictive control (MPC) theory is proposed to solve the torsional vibration problem. The controller handles delay characteristics of an actuator by state-space reorganization method, and the optimal control sequence is obtained by solving the objective function. Finally, the controller is tested using Simulink simulation and hardware-in-loop simulation platform, which mainly includes the verification of model reduction and vibration damping effect. The results show that simplified third-order model has a good consistency with the original full-order model in time and frequency domain. Meanwhile, the designed controller has a considerable damping effect and ensures the comfort performance of the vehicle. This study provides an important reference for vibration control of the hybrid powertrain. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug‐resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure‐related morbidity. Here, we evaluated the performance of a state‐space method that uses prior seizure count data to predict future counts. Methods: A Bayesian negative‐binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1–7 days ahead. Results: One‐day‐ahead prediction of the number of electrographic seizures using a negative‐binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error =.99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer‐valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. Significance: This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions. [ABSTRACT FROM AUTHOR]