Recursive Approach Research Articles

A fuzzy model predictive controller for stick‐slip type piezoelectric actuators

AbstractIn nano/micro‐positioning applications, stick‐slip type piezoelectric actuators (SSPEAs) are usually employed for a satisfactory performance in both the movement range and the positioning accuracy. Unfortunately, the controller design of SSPEAs is a challenging task, which is seldom studied in the literature. So this article presents an idea of using the fuzzy model predictive control approach for the high‐precision positioning. First, a prototype of the stick‐slip type piezoelectric actuator is designed. Then a Takagi‐Sugeno fuzzy model is established for the entire SSPEA, whose parameters can be further adapted by the recursive estimation approach based on the online measurement. Based on this model, a predictive controller can be designed for SSPEAs. Compared to some existing studies, the proposed fuzzy model directly describes the input–output relationship of the entire SSPEA rather than modeling separately. As a result, the controller can be simplified. Finally, some experiments are conducted on the SSPEA prototype, which demonstrates a satisfactory control performance

Developing flipped learning resources to support secondary school mathematics teaching during the COVID-19 pandemic

ABSTRACT Teachers and students have experienced an unusual year in 2020 because of the COVID-19 pandemic. To minimize the risk of the epidemic spreading, face-to-face lecturing hours have been reduced, resulting in a substantial need for online instruction. Our project thus aims to develop open access flipped learning resources (e.g. dynamic courseware and instructional videos) for secondary school mathematics teachers and students in Hong Kong. The overarching goal of this study is to make theoretically and empirically supported suggestions for developing the resources. Experiential learning theory was used as the theoretical foundation for developing the resources. A three-cycle recursive instructional design approach involving 34 mathematics educators was used to develop and improve materials that better meet the needs of frontline teachers. Both the qualitative (semi-structured interviews and written feedback) and quantitative (evaluative survey) data collection methods were conducted. The findings of this study reveal the concerns of and requests from our teacher participants, such as providing more detailed guidelines, advanced questions, and interactive quizzes. The findings also reflect the substantial need for open access flipped learning resources in secondary schools. Based on the lessons learned, suggestions are made to inform future development of resources (e.g. inviting retired teachers for assistance).

Who is at Risk of Loneliness? A Cross-sectional Recursive Partitioning Approach in a Population-based Cohort of Persons With Spinal Cord Injury

To develop a more thorough understanding of the risk factors for loneliness in persons with a physical impairment, using a population-based sample of persons with spinal cord injury (SCI), based on regression modeling and a recursive partitioning approach. Cross-sectional, observational cohort. Community, Switzerland. Community-dwelling persons with spinal cord injury (N=1283) 16 years or older. Not applicable. Loneliness was assessed using a modified version of the UCLA Three-Item Loneliness Scale. Those with the most disadvantaged socioeconomic characteristics in terms of education, income, subjective social position, employment status, and financial hardship demonstrated the highest risk for loneliness. Of the sociodemographic characteristics, only age had an association with loneliness, whereby persons aged 31-45 had the highest propensity for experiencing loneliness. We also saw that those with higher lesion levels and less functional independence were associated with higher levels of loneliness. In conditional inference tree analysis subjective social position, financial hardship, and functional independence had the highest discriminative power, with nationality and living arrangement having a less important role. Our findings highlight the vulnerability of persons with SCI with unfavorable socioeconomic status to loneliness. Furthermore, our findings show that persons who are more constrained because of functional limitations may face restrictions to social participation and therefore be at a higher risk of loneliness. This population-based evidence contributes to the better targeting of services aimed at alleviating loneliness for persons with a lower socioeconomic position and those with more functional limitations in everyday life.

A Control Theoretical Approach to Spectral Factorization is Unstable

Local stability analysis of a recently proposed recursive feedback-based approach to spectral factorization is performed. The method is found not to give stability guarantees. Interestingly enough, its global behavior often allows one to obtain reasonable approximations of spectral factorizations if a suitable stopping criterion is employed.

Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks.

Psychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called 'symptom network'. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk. To explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity (N = 7242). We sought to identify 'network phenotypes' by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol. Sex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse and domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation. Symptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

Fast Water-Assisted Lithium Ion Conduction in Restacked Lithium Tin Sulfide Nanosheets

While two-dimensional (2D) materials may preserve some intrinsic properties of the corresponding layered bulk material, new characteristics arise from their pronounced anisotropy or confinement effects. Recently, exceptionally high ionic conductivities were discovered in 2D materials such as graphene oxide and vermiculite. Here, we report on the water-assisted fast conduction of lithium ions in restacked lithium tin sulfide nanosheets. Li0.8Sn0.8S2 exfoliates spontaneously in water and can be restacked into homogeneous films in which the lithium content is decreased, and a partial substitution of sulfur with hydroxyl groups takes place. Using a recursive supercell refinement approach in reciprocal space along with real-space pair distribution function analysis, we describe restacked lithium tin sulfide as a partially turbostratically disordered material composed of lithium-containing and lithium-depleted layers. In humid air, the material takes up multiple layers of water that coordinate lithium ions in the space between the layers, increasing the stacking distance and screening the interaction between lithium ions and the anionic layers. This results in a 1000-fold increase in ionic conductivity up to 47 mS cm–1 at high humidities. Orientation-dependent impedance spectroscopy suggests a facile in-plane conduction and a hindered out-of-plane conduction. Pulsed field gradient nuclear magnetic resonance spectroscopy reveals a fast, simultaneous diffusion of a majority and a minority species for both 7Li and 1H, suggesting water-assisted lithium diffusion to be at play. This study enlarges the family of nanosheet-based ionic conductors and helps to rationalize the transport mechanism of lithium ions enabled by hydration in a nanoconfined 2D space.

A Recursive Least-Squares Approach with Memorizing Factor for Deriving Dynamic Equivalents of Power Systems

In this research, a two-stage identification-based approach is proposed to obtain a two-machine equivalent (TME) system of an interconnected power system for transient stability studies. To estimate the parameters of the equivalent system, a three-phase fault is applied near and/or at the bus of a local machine in the original multimachine system. The electrical parameters of the equivalent system are calculated in the first stage by equating the active and reactive powers of the local machine in both the original and the predefined equivalent systems. The mechanical parameters are estimated in the second stage by using a recursive least-squares estimation (RLSE) technique with a factor called “memorizing factor”. The approach is demonstrated on New England 10-machine 39-bus system, and its accuracy and efficiency are verified by computer simulation in MATLAB software. The results obtained from the TME system agree well with those obtained from the original multimachine system.

Combinatorial perspectives on Dollo-k characters in phylogenetics

Recently, the perfect phylogeny model with persistent characters has attracted great attention in the literature. It is based on the assumption that complex traits or characters can only be gained once and lost once in the course of evolution. Here, we consider a generalization of this model, namely Dollo parsimony, that allows for multiple character losses. More precisely, we take a combinatorial perspective on the notion of Dollo-k characters, i.e. traits that are gained at most once and lost precisely k times throughout evolution. We first introduce an algorithm based on the notion of spanning subtrees for finding a Dollo-k labelling for a given character and a given tree in linear time. We then compare persistent characters (consisting of the union of Dollo-0 and Dollo-1 characters) and general Dollo-k characters. While it is known that there is a strong connection between Fitch parsimony and persistent characters, we show that Dollo parsimony and Fitch parsimony are in general very different. Moreover, while it is known that there is a direct relationship between the number of persistent characters and the Sackin index of a tree, a popular index of tree balance, we show that this relationship does not generalize to Dollo-k characters. In fact, determining the number of Dollo-k characters for a given tree is much more involved than counting persistent characters, and we end this manuscript by introducing a recursive approach for the former. This approach leads to a polynomial time algorithm for counting the number of Dollo-k characters, and both this algorithm as well as the algorithm for computing Dollo-k labellings are publicly available in the Babel package for BEAST 2.

Two SAT solvers for solving quantified Boolean formulas with an arbitrary number of quantifier alternations

In recent years, expansion-based techniques have been shown to be very powerful in theory and practice for solving quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over Boolean variables. Such approaches partially expand one type of variable (either existential or universal) for obtaining a propositional abstraction of the QBF. If this formula is false, the truth value of the QBF is decided, otherwise further refinement steps are necessary. Classically, expansion-based solvers process the given formula quantifier-block wise and use one SAT solver per quantifier block. In this paper, we present a novel algorithm for expansion-based QBF solving that deals with the whole quantifier prefix at once. Hence recursive applications of the expansion principle are avoided and only two incremental SAT solvers are required. While our algorithm is naturally based on the forall Exp+Res calculus that is the formal foundation of expansion-based solving, it is conceptually simpler than present recursive approaches. Experiments indicate that the performance of our simple approach is comparable with the state of the art of QBF solving, especially in combination with other solving techniques.

Analysis of slip effects on slow viscoelastic flow of second order fluid through a small diameter permeable tube

There are various models which describe the hydro-dynamical aspects of the fluid flow and solute transport in small diameter permeable tubes without incorporating the slip effects, which play an important role to predict the flow behavior with higher accuracy. This study aims to investigate the slip effects on the slow, steady-state and axisymmetric flow of an incompressible second order Rivlin–Ericksen fluid in a small diameter cylindrical tube with constant wall permeability. The momentum equations have been derived in cylindrical coordinates and are converted in the form of a single compatibility equation which governs the stream function of the flow. Navier slip condition has been used to take slip phenomenon into account near the wall of the tube. Compatibility equation has been solved analytically subject to the associated boundary conditions by using Langlois recursive approach method. The detailed expressions have been obtained for velocity components, pressure, volume flow rate, pressure drop along longitudinal axis, wall shear stress, fractional reabsorption and leakage flux. Behavior of all the flow variables against the slip parameter has been examined graphically in detail. Overall, the solutions obtained her e show a great agreement with the work in literature. One of the key findings in this study is that the volume flow rate Q is found to be independent of the slip parameter ϕ.

Path Dependence in Sovereign Debt Modelling: System Dynamics Approach

The article offers a system dynamic modelling of sovereign debt using the path dependence concept. Using simulation modelling we are trying to find a fixed point in a motion of national sovereign debt towards its equilibrium and to change the existing mental model perception towards sovereign debt by changing the structure of the system.The research reveals the idea of the “debt snowball concept” using recursive dynamic approach. The dynamic linear and nonlinear recursive models of Ukrainian sovereign debt with the appropriate multi – order recursive equations are constructed.The fixed point as an equilibrium value for a country’s sovereign debt stock to GDP ratio with a linear dependence has been built. Finding the initial point for resolving sovereign debt issue is especially actual for national economy under strong fiscal pressure caused by COVID-19 and wartime on the East of the country. For this purpose, the sovereign debt cyclical behaviour was also reproduced using phase plot graphic.The fixed point as an equilibrium value for a country’s sovereign debt stock to GDP ratio with a nonlinear dependence was determined. The main purpose of this part of the analysis was to show the more complicated oscillated behaviour of the system and the multiplicity of possible equilibrium points.The stock and flows SD modelling and simulation analysis of sovereign debt in Ukrainian economy allows to make the conclusion of the inevitability of the sovereign debt existence even on the stable level and with the balanced national budget. JEL classіfіcatіon: E60, H63, H68

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study.

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning-based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics (P = 0.109-0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.

A novel recursive approach for online identification of continuous‐time switched nonlinear systems

AbstractThis article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete‐valued variable (switching signal). Specifically, we consider the continuous‐time switched nonlinear systems in the state‐space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state‐input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state‐input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.

An economic model of multi-level marketing.

This paper offers an economic model of the operation of multi-level marketing (MLM) firms in competitive and non-competitive markets. The model takes a recursive approach to analyse decision making at the distributor level in order to understand basic issues in the MLM market and firm structure. Specifically, it is shown that under reasonable assumptions MLM firms will have a limited structure. In cases where commissions increase with the number of levels, MLM firms will include no more than six to nine levels in equilibrium. In cases of fixed commissions, market conditions dictate a cap on the number of distributors. These conditions imply a limited “multi-level” structure. They also imply that the revenues of the median distributor are mainly a result of direct sales and not a result of commissions. The model also suggests that MLM firms will only arise where marketing costs are substantial, and that it is primarily individuals with small outside offers who choose to become distributors. Finally, the model provides a formula that calculates market prices for a monopoly MLM firm.

ADME prediction with KNIME: A retrospective contribution to the second "Solubility Challenge".

Computational models for predicting aqueous solubility from the molecular structure represent a promising strategy from the perspective of drug design and discovery. Since the first “Solubility Challenge”, these initiatives have marked the state-of-art of the modelling algorithms used to predict drug solubility. In this regard, the quality of the input experimental data and its influence on model performance has been frequently discussed. In our previous study, we developed a computational model for aqueous solubility based on recursive random forest approaches. The aim of the current commentary is to analyse the performance of this already trained predictive model on the molecules of the second “Solubility Challenge”. Even when our training set has inconsistencies related to the pH, solid form and temperature conditions of the solubility measurements, the model was able to predict the two sets from the second “Solubility Challenge” with statistics comparable to those of the top ranked models. Finally, we provided a KNIME automated workflow to predict aqueous solubility of new drug candidates, during the early stages of drug discovery and development, for ensuring the applicability and reproducibility of our model.

Simple and General Approach for Reversible Condensation Polymerization with Cyclization

We develop a simple recursive approach to treat reversible condensation polymerization with cyclization. Based upon a minimum set of balance equations, the law of mass action, Gaussian chain statistics, and the assumption of independent reactions, we derive exact analytical solutions for systems without cyclization, for systems containing only smallest loops, or systems that exclusively form loops. Exact numerical solutions are computed for the general case of a homopolymerization of flexible precursor polymers. All solutions were tested with Monte-Carlo simulations. A generalization for good solvent is discussed and it is shown that this generalization agrees with preceding work in the limit of low and high polymer volume fractions. The new aspect of our approach is its flexibility that allows for a rather simple generalization to more complex situations. These include different kinds of reversible linear polymerization, non-linear polymerization, first shell substitution effect, semiflexibility, or a low molecular weight cut-off for cyclization.

Efficient valuation of variable annuity portfolios with dynamic programming

AbstractThe valuation of variable annuity portfolios presents major challenges for US life insurers. Recent studies propose machine learning and metamodeling techniques based on selecting a few representative guarantees. However, these methods face a critical trade‐off between speed and accuracy. In contrast, I propose a recursive dynamic programming approach and demonstrate its ability to value a large and highly heterogeneous variable annuity portfolio with a high degree of accuracy and within a few seconds—even under stochastic interest rates and volatility—since the heavy computational burden can be fully front‐loaded (in a one‐time effort at the guarantee's pricing stage). This makes the dynamic programming approach ideally suited for all variable annuity applications, including the computation of reserves and capital requirements and to determine the insurer's hedging position. Moreover, dynamic programming can naturally incorporate optimal policyholder behavior into the insurer's valuation.

Modeling and Imaging of Ultrasonic Array Inspection of Side Drilled Holes in Layered Anisotropic Media.

There has been an increase in the use of ultrasonic arrays for the detection of defects in composite structures used in the aerospace industry. The response of a defect embedded in such a medium is influenced by the inherent anisotropy of the bounding medium and the layering of the bounding medium and hence poses challenges for the interpretation of the full matrix capture (FMC) results. Modeling techniques can be used to understand and simulate the effect of the structure and the defect on the received signals. Existing modeling techniques, such as finite element methods (FEM), finite difference time domain (FDTD), and analytical solutions, are computationally inefficient or are singularly used for structures with complex geometries. In this paper, we develop a novel model based on the Gaussian-based recursive stiffness matrix approach to model the scattering from a side-drilled hole embedded in an anisotropic layered medium. The paper provides a novel method to calculate the transmission and reflection coefficients of plane waves traveling from a layered anisotropic medium into a semi-infinite anisotropic medium by combining the transfer matrix and stiffness matrix methods. The novelty of the paper is the developed model using Gaussian beams to simulate the scattering from a Side Drilled Hole (SDH) embedded in a multilayered composite laminate, which can be used in both immersion and contact setups. We describe a method to combine the scattering from defects with the model to simulate the response of a layered structure and to simulate the full matrix capture (FMC) signals that are received from an SDH embedded in a layered medium. The model-assisted correction total focusing method (MAC-TFM) imaging is used to image both the simulated and experimental results. The proposed method has been validated for both isotropic and anisotropic media by a qualitative and quantitative comparison with experimentally determined signals. The method proposed in this paper is modular, computationally inexpensive, and is in good agreement with experimentally determined signals, and it enables us to understand the effects of various parameters on the scattering of a defect embedded in a layered anisotropic medium.

Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

SummaryThis article researches the filtering‐based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering‐based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering‐based multiinnovation recursive generalized extended least squares algorithm. The numerical example confirms that these two proposed algorithms are effective.

A recursive approach for aeroacoustic phased array measurements in wind tunnels.

Noise-interference suppression and data-processing acceleration are crucial to aeroacoustic measurements with phased array in wind tunnels. In this paper, we develop a "multi-window" beamforming algorithm that recursively processes data based on an array-acquisition model. This spatial filtering algorithm is derived from the Kalman filter theory for signal processing. The simulated results show that by using recursive operations, accurate signal estimation is acquired with incoherent and coherent background noise removed in the presence of both channel noise and phase noise. The convergence rate of this recursive algorithm is faster than the existing algorithms. As a result, considerable storage space and computational resources are saved, while testing defects in wind tunnel measurement are revealed and corrected immediately on-site. It has a great potential for real-time localization of sound sources in a noisy environment.