Recursive System Research Articles

Competing demand-side explanations and populism: cross-national variation in a recursive ideational system

Competing demand-side explanations and populism: cross-national variation in a recursive ideational system

Firm ownership and ESG performance in European agri‐food companies: The mediating effect of risk‐taking and time horizon

AbstractThe environmental, social, and corporate governance (ESG) performance of European agri‐food companies is crucial amidst sustainability challenges. Employing property rights and agency theory, we investigate the influence of firm ownership structure on ESG performance, and the mediating role of risk‐taking and time horizon. A recursive system of equations is employed to test the model using data from 936 European firms. The findings indicate that investor‐owned firms (IOFs) outperform family firms and cooperatives in terms of ESG performance. Family firms demonstrate a longer time horizon, while IOFs exhibit greater risk‐taking. Risk‐taking and time horizon are positively and negatively associated with ESG performance, respectively. However, we find no evidence of a mediation effect. This paper contributes to the agency and property rights literature by exploring the implications of ownership structure for other firm characteristics and ESG performance, and outlines implications for policymakers and managers in the development of focused interventions towards sustainability.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

On the importance of internet access for children’s health and subjective well-being: the case of China

ABSTRACT Digital technology is transforming children’s daily routines and can potentially have long-lasting effects on their lives. This paper studies the impact of internet access on the health and subjective wellbeing of Chinese children. To conduct such analysis, we employ data from the China Education Panel Survey (CEPS), a longitudinal dataset of Chinese seventh-year students representative of the whole country. This database allows us to control for unobserved individual heterogeneity and socio-economic characteristics. We use two alternative methods to address endogeneity concerns. The first one employs a recursive system of equations that allows unobserved individual heterogeneity to simultaneously affect both health outcomes and digital access. The second approach exploits the fact that Chinese students cannot choose which school to attend and employ the proportion of students owning a computer in the same school as an instrumental variable. Regardless of the method, we find that internet access improves health. We also find some positive impact of digital access on subjective wellbeing, albeit the evidence is weaker. This stands in stark contrast to prior research, which has found a negative effect of internet use on mental health in Western countries.

High-order allpass cascade-connected phase-system design utilizing fractional polynomial errors

ABSTRACT This paper proposes a nonlinear optimization method for designing a high-order recursive allpass digital phase system that comprises several cascade-connected second-order (SO) allpass sections. This method first splits a given phase design specification (approximation target) into a set of equal sub-specifications, and then the equal sub-specification acts as the ideal phase for each of the SO sections. Since an SO section can be designed via employing a linear programming to minimize a linear fractional error function, this splitting process generates an excellent starting point (good initial values for the coefficients of the cascaded SO sections) for further minimizing the overall approximation error. This paper first derives a wide variety of fractional polynomial error functions for designing cascade-connected allpass phase systems of different high orders, and then shows that a high-order cascade-connected allpass phase system can be designed by utilizing a nonlinear solver to minimize the peak deviation of such a fractional polynomial error. Using the derived fractional polynomial error functions in the system design enables the system designer to find an excellent starting point for further minimizing the approximation error and thus reach a convergent design solution. This paper first derives a set of fractional polynomial errors, and then adopts the eighth-order cascade-connected phase system design to exemplify the effectiveness of the fractional-polynomial-error-based design tactic. Furthermore, the stability of the cascade-connected high-order allpass system can be readily checked via confirming that all of the cascaded SO sections have their poles inside the unit circle in the complex plane.

ESTIMATING PROBABILITIES,MARGINAL EFFECTS AND TREATMENT EFFECTS IN RECURSIVE BIVARIATE PROBIT MODELS

This article addresses the interpreting results of recursive system of binary equations estimation in the case when the system does not satisfy exclusion restriction conditions. It means that the equation defining the endogenous variable does not contain a unique covariate. This article extends the analysis of previous studies on the identifiability of parameters of recursive binary systems by analyzing the conditions for the identifiability of probabilities, marginal effects and treatment effects. We provide a reasonable consideration suggesting that even if parameters of the model are unidentifiable, it is still possible to estimate accurately the conditional probabilities and marginal effects, but not the treatment effects. The problem of identifiability discussed in the paper is also considered on real data. We estimate the probability of purchasing medicine depending on individuals’ characteristics and the fact of visiting a doctor. An important practical contribution of the work is the recommendation for researchers to interpret the result recursive binary system estimation via marginal effects in the case when it is not possible to include at least one unique variable in the equation for the binary covariate.

A hybrid recursive direct system for multi-step mortality rate forecasting

A hybrid recursive direct system for multi-step mortality rate forecasting

Chip-scale all-optical complex-valued matrix inverter

Matrix inversion is a fundamental and widely utilized linear algebraic operation but computationally expensive in digital-clock-based platforms. Optical computing is a new computing paradigm with high speed and energy efficiency, and the computation can be realized through light propagation. However, there is a scarcity of experimentally implemented matrix inverters that exhibit both high integration density and the capability to perform complex-valued operations in existing optical systems. For the first time, we experimentally demonstrated an iterative all-optical chip-scale processor to perform the computation of complex-valued matrix inversion using the Richardson method. Our chip-scale processor achieves an iteration speed of 10 GHz, which can facilitate ultra-fast matrix inversion with the assistance of high-speed Mach–Zehnder interferometer modulators. The convergence can be attained within 20 iterations, yielding an accuracy of 90%. The proposed chip-scale all-optical complex-valued matrix inverter represents a distinctive innovation in the field of all-optical recursive systems, offering significant potential for solving computationally intensive mathematical problems.

Kernel-algorithms in frame-approximations

With view to applications, we present here general classes of non-orthogonal expansions in Hilbert space. The main purpose of our paper is a new approach to design of algorithms of Kaczmarz type in the framework of operators in Hilbert space. Our work includes a diverse list of optimization problems, new Karhunen–Loève transforms, and Principal Component Analysis (PCA) for digital images. A key feature of our algorithms is our use of recursive systems of projection operators. For this we also make use of specific reproducing kernel Hilbert spaces, kernel factorizations, and finite-dimensional approximations. Our projection algorithms are designed with view to maximum likelihood solutions, minimization of “cost” problems, identification of principal components, and data-dimension reduction.

Sufficient condition for (α,β) Somos 4 Hankel determinants

By using Sulanke-Xin continued fractions method, Xin proposed a recursion system to solve the Somos 4 Hankel determinant conjecture. We find Xin's recursion system indeed give a sufficient condition for (α,β) Somos 4 sequences. This allows us to prove 4 conjectures of Barry on (α,β) Somos 4 sequences in a unified way.

Adaptive recursive system identification using optimally tuned Kalman filter by the metaheuristic algorithm

Adaptive recursive system identification using optimally tuned Kalman filter by the metaheuristic algorithm

CONSTRUCTION OF A WEIGHTED FRACTAL INTERPOLATION SURFACE BASED ON MATKOWSKI CONTRACTIONS

In this paper, we construct a new kind of weighted recursive iteration function system (IFS) and prove the existence of the unique attractor for the kind of IFS based on the Matkowski fixed point theorem. We confirm that the attractor is a bivariate fractal interpolation surface (FIS), which interpolates a given set of data. In addition, we also provide an upper error estimate of such FISs caused by changes of weights. Finally, we give their box dimension estimates for a specific type of the FISs.

Emergent scalar symmetry in discrete dynamical systems

Discrete quadratic dynamical systems are frequently used to define models of reality. To understand these recursive systems is to know the behavior of their complex orbits. The Mandelbrot Set plays an important role in this: the behavior of the orbits of its points can be extrapolated to all quadratic polynomials. In calculating the map of periods for the orbits of the points belonging to the Mandelbrot Set by means of discrete (and finite) encoding, some singular points called symmetrical points appear on the map. Such points configure, in a surprisingly harmonic and regular manner, the values of the rest of points in the map of periods. Thus, knowing these points and their properties is extremely helpful to better understand the behavior of quadratic and discrete dynamical systems. Important properties of such points are described in this article. We emphasize the emergent scalar symmetry property, which appears as a consequence of the finiteness of discrete values with which we are inevitably limited to represent the continuum. Thanks to this property, the images created around these points are not altered when removing or selecting one of their several pixel rows and columns. The image can be sampled in its vicinity, at any scale, without losing information regarding the vicinity. We propose a justification on why the map of periods is highly hypersensitive to small changes in the parameters defining the calculation mode of the map.

Recursive system identification for Havriliak-Negami functions by using modified LMRPEM method

Initially thought of as a mere mathematical object, fractional calculus has proven over the latest years to be useful to well model complex dynamics in diffusion or propagation. Fractional-order models allow to include a vast coexistence of time-constants without having a huge increase in the number of parameters for the model. Two original functions were once used to analyze dielectric phenomena: Cole-Cole and Davidson-Cole functions. A third one generalizes the two previous ones: the Havriliak-Negami function. By using the concept of recursive poles and zeros (see Oustaloup (1995)) and a decomposition in a Davidson-Cole and a complementary function (Sommacal et al. (2008b)), it is possible to approximate and simulate the response of the Havriliak-Negami function in the time domain. This paper deals with the recursive identification in the continuous-time domain for a system whose model is described as a Havriliak-Negami function. The LMRPEM method is then adapted and tested in simulation.

Spreading dynamics of an impulsive reaction-diffusion model with shifting environments

This paper focuses on the effects of environmental improvement and worsening on the spread and invasion of populations with birth pulse. We propose an impulsive reaction-diffusion model with a shifting environment to describe the dynamics of species with distinct reproduction stage and dispersal stage. First, the impulsive reaction-diffusion model is reduced to a discrete-time recursive system defined by a discrete map. Next, with the aid of the appropriate test function and comparison principle, we obtain some sufficient conditions on the nonexistence and uniqueness of nontrivial fixed points of the discrete map. This, combined with the abstract theory of spatially non-translation dynamical systems, enables us to establish the existence of traveling wave solutions and the asymptotic propagation properties of the model.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia.

The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

Socioeconomic status and access to care in a universal health care system: The case of acute myocardial infarction in Australia

This paper examines the role of socioeconomic status (SES) in affecting access to care and the survival of acute myocardial infarction (AMI) patients in Australia's universal health care system. We jointly model the probabilities of patients being admitted to a catheterisation-capable hospital, receiving invasive coronary angiography (ICA), and surviving 30 days post discharge as a recursive system of probit equations. We further investigate the role of capacity and whether the access gap between SES groups widens when capacity becomes limited.Our study shows that SES plays a significant role in affecting the survival of AMI patients, both directly and indirectly through healthcare access. We find that socioeconomically disadvantaged patients are less likely to gain access to crucial services like catheterisation hospitals and ICA, which indirectly affects their survival probability in addition to the adverse direct impact of SES. While healthcare capacity showed no overall effect on access, its interplay with SES exacerbates the access disparity in situations of limited capacity. Our findings suggest that, to reduce inequality in health outcomes, public health strategy needs to focus not only on enhancing access but also addressing the direct consequences of SES.

A recursive system identification inertia estimator for traditional and converter-interfaced generators

This paper proposes a method to estimate the inertia constant of synchronous generators (SGs) and the virtual inertia provided by converter-interfaced generators (CIGs). It can be applied when the system is under normal operating conditions or after power imbalance events. The algorithm is based on a recursive system identification approach and uses the active power and bus frequency measurements proceeding from phasor measurement units. The measurements are preprocessed to improve the estimation accuracy. Subsequently, a model-independent rotor speed estimator is used to compute rotor speed variations (or internal frequency variations, in the case of CIGs). Afterwards, a recursive least-square equation error formulation is implemented to compute the SG and CIG parameters in the z-domain, which are later converted into their respective counterparts in the s-domain. The method is applied to several generation devices in the IEEE 39-bus and IEEE 118-bus test systems, including traditional SGs and CIGs providing virtual inertia. Numerical results show the accuracy and the low computational burden of the proposed method.

The Aesthetics of Musical Complex Systems

This article introduces the history and aesthetics of feedback-based music, from early practitioners to more advanced methods and state-of-the-art works based on adaptation. Some of the most relevant techniques developed over almost six decades of investigations in the area of recursive systems for electronic music are discussed to show the variety and richness that a single specialised domain can have, providing examples of how scientific and philosophical principles can be translated into music. The historical context is key to understanding the evolution of the field: feedback-based music arose during the same years in which cybernetics, together with other disciplines, were experiencing a profound transformation. I will provide an overview of how such disciplines changed, highlighting the connections between seemingly distant areas such as philosophy, biology and engineering, and the fact that the development of feedback-based music appears to have followed somewhat closely the evolution of systems thinking. Finally, the article explores questions of musical aesthetics and music theory related to the use of complex autonomous systems in live performance through observations on the author’s creative practice.

Optimal risk sharing and dividend strategies under default contagion: A semi-analytical approach

We investigate the risk control and dividend optimization problem of an insurance group in a general setting and propose an innovative semi-analytical approach to the problem. The group consists of multiple subsidiaries and is subject to exogenous default risk. The default intensity is subject to the contagious effect. The contagious effect refers to the increase in default intensities of surviving subsidiaries within the group when a default event occurs. The recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs) is derived together with the verification theorem. We propose a semi-analytical approach that first finds the analytical solution in the continuation region and then the numerical solution in the risk exposure region. We further present a numerical example of a three-subsidiary insurance group to demonstrate the semi-analytical method and illustrate the recursive computation procedures that are extendible to cases with more subsidiaries.