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Site response analyses are crucial for estimating local effects on ground shaking during earthquakes. However, recent investigations utilizing KiK-net borehole array data have revealed a consistent underprediction of high-frequency ground motion by both equivalent linear and fully nonlinear methods, contrary to expectations. This study reassess the accuracy of 1D site response analysis methods, including equivalent linear, frequency-dependent equivalent linear model, and nonlinear analysis, by integrating depth-dependent stiffness and adjusted shear wave velocity. Nine instrumented vertical arrays, featuring a total of 132 recorded ground motions, are subjected to analysis. For the sites and ground motions considered, the results indicate that the nonlinear method performs without significant bias, whereas the equivalent linear and frequency-dependent equivalent linear methods tend to respectively underestimate and overestimate high-frequency results. To enhance accuracy, it is recommended to incorporate depth-dependent stiffness and adjusted shear wave velocity when predicting high-frequency ground motion in site response analyses. [ABSTRACT FROM AUTHOR]

The existing research investigates the impact of global and national variables on local food prices. This study adds to the current research by examining such impact at various quantiles, frequencies, and time periods. Utilizing data from January 2004 to June 2021, our research incorporates six global factors and three domestic factors as explanatory variables. Furthermore, this study employs cutting-edge econometric techniques such as Quantile-on-Quantile Regression (QQR), Wavelet Coherence (WC), and Granger Causality in Quantiles (GCQ). Similarly, this research utilizes the Quantile Regression (QR) method to assess the robustness of the results. Results based on the GCQ technique indicate that the association between the variables exists at most quantiles, except for the lowest, and middle quantiles for all variables. Moreover, the WC findings demonstrate a strong correlation between the determinants of local food prices and their occurrences at various moments and intervals. Likewise, QQR results indicate that the impact of independent variables on local food prices varies across quantiles. The QR technique also supports these results. Finally, our research suggests policy recommendations based on the findings of this study. [ABSTRACT FROM AUTHOR]

Abstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative. We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.

Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems.Here, we develop a management scheme that is based on predictive models driven by the observed dynamics.We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative.We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species. [ABSTRACT FROM AUTHOR]

Due to increasing geopolitical tensions and disruption of gas supplies, European countries have been looking for alternatives for electricity generation (EG). As part of this process, one of the most important goals for long-term sustainability is to ensure carbon neutrality. Therefore, this study analyzes time, frequency, and quantile-based impacts of EG from different electricity generation sources (i.e., renewable, nuclear, and fossil fuels) on carbon neutrality, focusing on four leading European countries. The study applies the wavelet transform coherence (WC), quantile-on-quantile regression (QQ), and Granger causality in quantiles (GQ) to high-frequency daily data between January 2, 2019 and March 10, 2023. Results show that (i) there is a strong time and frequency dependence between EG and CO2 emissions across countries, while results vary by EG sources and countries; (ii) renewable EG dampens CO2 emissions. At higher quantiles, a higher share of renewable EG lowers CO2 emissions in Germany and Spain, while they increase in France; (iii) nuclear EG is beneficial only for the United Kingdom. (iv) Fossil EG increases CO2 emissions in all countries. Excessive fossil EG leads to more CO2 emissions at higher quantiles; (v) the impacts of EG on CO2 emissions have a time-, frequency-, quantile-, country-, and EG source-dependent structure. The outcomes of the study demonstrates that the ideal EG source for countries is mainly renewable EG, while in the case of the United Kingdom, nuclear EG could be an alternative for improving the environment while reducing fossil fuels. [ABSTRACT FROM AUTHOR]

AbstractThe current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study.

Over the past three decades, Van Gelder's dynamical hypothesis has been instrumental in reconceptualizing the ways in which perception‐action‐cognition unfolds over time and in context. Here, I examine how the dynamical approach has enriched the theoretical understanding of social dynamics within cognitive science, with a particular focus on interpersonal coordination. I frame this review around seven principles in dynamical systems: three that are well‐represented in interpersonal coordination research to date (emergent behavior, context‐sensitive behavior, and attractors) and four that could be useful opportunities for future growth (hysteresis, sensitivity to initial conditions, equifinality, and reciprocal compensation). In addition to identifying specific promising lines of theoretical inquiry, I focus on the significant potential afforded by computationally intensive science—especially in naturally occurring data or trace data. Building on the foundation laid over the past three decades, I argue that looking to increasingly situated and naturalistic settings (and data) is not only necessary to realize the full commitment to the dynamical hypothesis but is also critical to building parsimonious and principled theories of social phenomena. [ABSTRACT FROM AUTHOR]

The role of the autonomic nervous system in the onset of supraventricular and ventricular arrhythmias is well established. It can be analysed by the spontaneous behaviour of the heart rate with ambulatory ECG recordings, through heart rate variability measurements. Input of heart rate variability parameters into artificial intelligence models to make predictions regarding the detection or forecast of rhythm disorders is becoming routine and neuromodulation techniques are now increasingly used for their treatment. All this warrants a reappraisal of the use of heart rate variability for autonomic nervous system assessment. Measurements performed over long periods such as 24H-variance, total power, deceleration capacity, and turbulence are suitable for estimating the individual basal autonomic status. Spectral measurements performed over short periods provide information on the dynamics of systems that disrupt this basal balance and may be part of the triggers of arrhythmias, as well as premature atrial or ventricular beats. All heart rate variability measurements essentially reflect the modulations of the parasympathetic nervous system which are superimposed on the impulses of the adrenergic system. Although heart rate variability parameters have been shown to be useful for risk stratification in patients with myocardial infarction and patients with heart failure, they are not part of the criteria for prophylactic implantation of an intracardiac defibrillator, because of their high variability and the improved treatment of myocardial infarction. Graphical methods such as Poincaré plots allow quick screening of atrial fibrillation and are set to play an important role in the e-cardiology networks. Although mathematical and computational techniques allow manipulation of the ECG signal to extract information and permit their use in predictive models for individual cardiac risk stratification, their explicability remains difficult and making inferences about the activity of the ANS from these models must remain cautious. [ABSTRACT FROM AUTHOR]

The dynamics of cardiac signals can be studied using methods for nonlinear analysis of heart rate variability (HRV). The methods that are used in the article to investigate the fractal, multifractal and informational characteristics of the intervals between heartbeats (RR time intervals) are: Rescaled Range, Detrended Fluctuation Analysis, Multifractal Detrended Fluctuation Analysis, Poincaré plot, Approximate Entropy and Sample Entropy. Two groups of people were studied: 25 healthy subjects (15 men, 10 women, mean age: 56.3 years) and 25 patients with arrhythmia (13 men, 12 women, mean age: 58.7 years). The results of the application of the methods for nonlinear analysis of HRV in the two groups of people studied are shown as mean ± std. The effectiveness of the methods was evaluated by t-test and the parameter Area Under the Curve (AUC) from the Receiver Operator Curve (ROC) characteristics. The studied 11 parameters have statistical significance (p < 0.05); therefore, they can be used to distinguish between healthy and unhealthy subjects. It was established by applying the ROC analysis that the parameters Hq=2(MFDFA), F(α)(MFDFA) and SD2(Poincaré plot) have a good diagnostic value; H(R/S), α1(DFA), SD1/SD2(Poincaré plot), ApEn and SampEn have a very good score; α2(DFA), αall(DFA) and SD1(Poincaré plot) have an excellent diagnostic score. In conclusion, the methods used for nonlinear analysis of HRV have been evaluated as effective, and with their help, new perspectives are opened in the diagnosis of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC. Methods: This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn). Results: Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05). Conclusion: There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods. [ABSTRACT FROM AUTHOR]

We discuss the numerical solution of the nonlinear integro-differential equation for the probability of a divergent neutron chain in a stationary system (i.e., the probability of initiation (POI)). We follow the development described in Bell's classic paper on the stochastic theory of neutron transport. As noted by Bell, the linearized form of this equation resembles the linear adjoint neutron transport equation. A matrix formalism for the discretized steady state (or forward) neutron equation in slab geometry is first developed, and is then used to derive the discrete adjoint equation. A main advantage of this discrete development is that the resulting discrete adjoint equation does not depend upon how the multigroup cross sections for the forward problem are obtained. That is, we derive the discrete adjoint directly from the discrete forward equations rather than discretizing directly the adjoint equation. This also guarantees that the discrete adjoint operator is consistent with the inner product used to define the adjoint operator. We discuss three approaches for the numerical solution of the POI equations, and present numerical results on several test problems. The three solution methods are a simple fixed point iteration, a second approach that is akin to a nonlinear Power iteration, and a third approach which uses a Newton-Krylov nonlinear solver. We also give sufficient conditions to guarantee the existence and uniqueness of nontrivial solutions to our discrete POI equations when the discrete system is supercritical, and that only the trivial solution exists when the discrete system is subcritical. Our approach is modeled after the analysis presented for the continuous POI equations by Mokhtar-Kharroubi and Jarmouni-Idrissi, and by Pazy and Rabinowitz. [ABSTRACT FROM AUTHOR]

Nowadays, nonlinear vibration methods are increasingly used for the detection of damage mechanisms in polymer matrix composite (PMC) materials, which are anisotropic and heterogeneous. The originality of this study was the use of two nonlinear vibration methods to detect different types of damage within PMC through an in situ embedded polyvinylidene fluoride (PVDF) piezoelectric sensor. The two used methods are nonlinear resonance (NLR) and single frequency excitation (SFE). They were first tested on damage introduced during the manufacturing of the smart PMC plates, and second, on the damage that occurred after the manufacturing. The results show that both techniques are interesting, and probably a combination of them will be the best choice for SHM purposes. During the experimentation, an accelerometer was used, in order to validate the effectiveness of the integrated PVDF sensor. [ABSTRACT FROM AUTHOR]

The current series of studies examine how local food prices are affected by domestic and international factors. This research advances the existing body of knowledge by examining this effect at different quantiles, frequencies, and times. We use research data from January 1999 to August 2022 using three local and six global variables as independent variables. Additionally, our study uses recent econometric methods, including Wavelet Coherence, Quantile-on-Quantile Regression (QQR), and Granger Causality in Quantiles (GCQ). Moreover, this research uses the Quantile Regression (QR) approach to determine how reliable the findings are. Based on the GCQ approach, the results demonstrate that the correlation persists at most of the quantiles. Moreover, the WC results demonstrate a substantial association between local prices of food and the independent factors across various frequencies and times. Additionally, QQR estimates demonstrate that the impact of exogenous variables on food prices vary among quantiles. These findings are also supported by the QR method. Last but not least, our study offers policy suggestions obtained based on the results of this study. [ABSTRACT FROM AUTHOR]

Introduction: The variability of successive RR intervals has been pointed out as an indicator of systemic homeostasis. The entropy of successive RR intervals is associated with a greater adaptive capacity, which is essential after childbirth, characterized by a change from an intrauterine environment that constantly adapts to the fetal demands, to an extrauterine environment that requires constant biological adaptations by the neonate.Objectives: To analyze the association between gestational age (GA) and the entropy of RR intervals in term infants with adequate birth weight in the first hours of extrauterine life.Methods: In a cross-sectional study design maternal, labor and neonatal characteristics were collected from the obstetric records. Successive RR intervals were recorded from neonates up to 72 hours (i.e. 3 days) of birth.Subjects: Fifty term infants, healthy and with adequate birth weight. Outcome measures: the variability of RR intervals was analyzed obtaining the entropy of 1000 successive RR intervals. Pearson's correlation was used to evaluate the association between GA and the entropy of successive RR intervals, while linear regression was used to obtain the coefficient of determination (r2) as well as a prediction model. The adequacy of the prediction model was evaluated using the Komolgorov-Smirnov test to evaluate the residuals distribution.Results: There was a positive and significant association between the studied variables (r = 0.437; p = .002). The coefficient of determination allowed us to infer that approximately 19.3% of the RR interval entropy from the studied sample can be explained by the GA (r2 = 0.193; p = .002). The analysis of the residuals distribution confirmed that the regression model obtained here was adequate.Conclusion: Our results indicate that, even within a normal range of GA (≥37 a < 42 weeks) and birth weight, a longer intrauterine life allows a greater entropy of successive RR intervals, indicating a greater maturation of biological systems and adaptive capacity. [ABSTRACT FROM AUTHOR]

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The analysis and design of pipe networks is usually a very complex task and depending of many factors. The paper here presented explores issues related to the accuracy and robustness of three iterative nonlinear methods used for designing pipe networks: Hardy Cross method, Gradient method and Newton-Raphson method. The last two methods are implemented in EPANET and Matlab, through the fsolve function. In order to evaluate the performance of each numerical method, three pipe networks, were assessed. No differences in the results were found by using fsolve (of MatLab) and Hardy-Cross methods. However, the EPANET results show differences up to 115% regarding other two methods. This behavior can be explained by the magnitude of head loss and the flow rate in some tubes of the network with errors close to the errors set up and by the Nodal Newton-Raphson method used at EPANET's code. The Hardy Cross method proved to be robust on three evaluated cases. [ABSTRACT FROM AUTHOR]

The ability to monitor the physiological effect of the analgesic agent is of interest in clinical practice. Nonstationary changes would appear in photoplethysmography (PPG) during the analgesics-driven transition to analgesia. The present work studied the properties of nonlinear methods including approximate entropy (ApEn) and sample entropy (SampEn) derived from PPG responding to a nociceptive stimulus under various opioid concentrations. Forty patients with ASA I or II were randomized to receive one of the four possible remifentanil effect-compartment target concentrations (Ceremi) of 0, 1, 3, and 5 ng·ml−1 and a propofol effect-compartment target-controlled infusion to maintain the state entropy (SE) at 50 ± 10. Laryngeal mask airway (LMA) insertion was applied as a standard noxious stimulation. To optimize the performance of ApEn and SampEn, different coefficients were carefully evaluated. The monotonicity of ApEn and SampEn changing from low Ceremi to high Ceremi was assessed with prediction probabilities (PK). The result showed that low Ceremi (0 and 1 ng·ml−1) could be differentiated from high Ceremi (3 and 5 ng·ml−1) by ApEn and SampEn. Depending on the coefficient employed in algorithm: ApEn with k = 0.15 yielded the largest PK value (0.875) whereas SampEn gained its largest PK of 0.867 with k = 0.2. Thus, PPG-based ApEn and SampEn with appropriate k values have the potential to offer good quantification of analgesia depth under general anesthesia. [ABSTRACT FROM AUTHOR]

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.

AimIn many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be “phase transitions” of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors.Methods and ProceduresSeven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system’s variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment.ResultsThe applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected.ConclusionChanges can occur in many different ways in the psychotherapeutic process. For instance, there can be very slow and small transitions or very high and sudden ones. The results show the validity and stability of different measures indicating pattern transitions and/or early warning signals of those transitions. This has profound implications for real-time monitoring in psychotherapy, especially in cases where a transition is not obvious to the eye. Reliably identifying points of change is mandatory also for research on precursors, which in turn can help improving treatment.

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one. [ABSTRACT FROM AUTHOR]

Current anomaly detection (AD) algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed–Xiaoli (KRX) method for AD by approximating the kernel function with either the data-independent random Fourier features or the data-dependent basis with the Nyström approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost, and they perform similar to (or outperform) the standard KRX algorithm thanks to their implicit regularization effect. Last but not least, the Nyström approach has an improved power of detection. [ABSTRACT FROM AUTHOR]

Aim: In many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be "phase transitions" of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors. Methods and Procedures: Seven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system's variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment. Results: The applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected. Conclusion: Changes can occur in many different ways in the psychotherapeutic process. For instance, there can be very slow and small transitions or very high and sudden ones. The results show the validity and stability of different measures indicating pattern transitions and/or early warning signals of those transitions. This has profound implications for real-time monitoring in psychotherapy, especially in cases where a transition is not obvious to the eye. Reliably identifying points of change is mandatory also for research on precursors, which in turn can help improving treatment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple approach to the construction of a general class of L -stable explicit second-order one-step methods for solving stiff problems. These methods are nonlinear and derive from a novel approximation for the right-hand side functions of differential equations inspired by the nonstandard finite difference methodology introduced by Mickens. Through rigorous mathematical analysis, it is proved that the proposed numerical methods are not only explicit and L -stable, but also convergent of order two. Therefore, they are suitable and efficient to solve stiff problems. The proposed numerical methods generalize and improve a nonstandard explicit integration scheme for initial value problems formulated by Ramos (2007). Moreover, the present approach can be extended to construct A -stable and L -stable high-order explicit one-step methods for differential equations. Finally, the theoretical findings and advantages of the developed numerical methods are supported and illustrated by a series of numerical experiments in which stiff problems are considered. [ABSTRACT FROM AUTHOR]

Reactions involved in photocatalytic hydrogen production through water splitting combined with a hole scavenger involve several stages that make kinetic analysis quite complicated. In the present work, a kinetic model for the photocatalytic production of hydrogen has been developed based on competitive adsorption, considering the photocatalytic decomposition of an organic molecule coupled to hydrolysis through photocatalytic water splitting. Parameter estimation is performed by using the Levenberg‐Marquardt method. Model validation results give an estimation of the strength of adsorption of acetol and the acetaldehyde. As a fuel, hydrogen has a high heating value and is environmentally friendly, and thus, a hydrogen production process must be sustainable and inexpensive. Photocatalytic processes meet these requirements. An improved multi‐response kinetic model based on experimental observations is presented for the photocatalytic production of hydrogen. [ABSTRACT FROM AUTHOR]

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

A challenge for importers in the automobile industry is adjusting to rapidly changing market demands. In this work, we describe a practical study of car import planning based on the monthly car registrations in Austria. We model the task as a data driven forecasting problem and we implement four different prediction approaches. One utilizes a seasonal ARIMA model, while the other is based on LSTM-RNN and both compared to a linear and seasonal baselines. In our experiments, we evaluate the 33 different brands by predicting the number of registrations for the next month and for the year to come. [ABSTRACT FROM AUTHOR]

Service research tends to operationalize word of mouth (WOM) behavior as one of the many responses to service satisfaction. In this sense, little is known about its antecedents or moderators. The objective of this study was to investigate the role of customers’ emotions during service experiences on WOM, applying nonlinear techniques and exploring the moderating role of customers’ propensity for emotional contagion. Using the critical incidents technique, 122 customers recalled significant service experiences and the emotions they aroused, and reported if they shared said experiences with other individuals. We found that, whereas linear methods presented non-significant results in the emotions-WOM relationship, nonlinear ones (artificial neural networks) explained 46% of variance. Negative emotions were stronger predictors of WOM and the importance of emotions for WOM was significantly higher for individuals with high propensity for emotional contagion (R² = .79) than for those with lower levels (R² = .48). Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Background and Objectives: When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model., Methods: We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020., Results: In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%)., Conclusion: Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model., Competing Interests: Declaration of Competing Interest The authors of this manuscript have no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The article discusses a calculation technique for a reinforced concrete building with a braced frame on an earthquake. It allows to produce an adequate assessment of a of seismic stability level. A direct simulation of concrete, reinforcement and their binding with the help of volumetric and core finite elements is fulfilled. The paper presents the concrete work chart and considers mathematical models for the material. The authors propose calculation technique of reinforced concrete buildings with the actual reinforcement taking into account. The results of the numerical calculations of structural elements (crossbar) are compared to the field experiments conducted by US Department of Transportation Federal Highway Administration. The article examines the level of seismic stability of buildings with a braced frame, the actual reinforcement taking into account when calculating on a seismic action. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the impact of rebate policies in various US states on the sales share of ENERGY STAR household appliances. We use annual ENERGY STAR sales data for clothes washers, dishwashers, refrigerators and air conditioners from 2001 to 2006 and the variation in the coverage of rebates across US states and over time to identify the impact of rebate policies. We use, at first, a difference-in-differences approach to estimate this impact. Then, we take into account the possibility of rebate policies being endogenous and use instrumental variables approaches in fixed effects panel data regression models. Results suggest that rebate policies increase the sales share of ENERGY STAR household appliances by 3.3 to 6.6 percentage points, and this represents an impact of 9 to 18 % on the mean level of the sales share of ENERGY STAR household appliances in the US between 2001 and 2006. We conclude that rebates on ENERGY STAR appliances increase the uptake of energy-efficient appliances. [ABSTRACT FROM AUTHOR]

In our previous work, we have developed a sudden cardiac death index (SCDI) using electrocardiogram (ECG) signals that could effectively predict the occurrence of SCD four minutes before the onset. Thus, the prediction of SCD before its onset by using heart rate variability (HRV) signals is a worthwhile task for further investigation. Therefore, in this paper, a new novel methodology to automatically classify the HRV signals of normal and subjects at risk of SCD by using nonlinear techniques has been presented. In this study, we have predicted SCD by analyzing four-minutes of HRV signals (separately for each one-minute interval) prior to SCD occurrence by using nonlinear features such as Renyi entropy (REnt), fuzzy entropy (FE), Hjorth's parameters (activity, mobility and complexity), Tsallis entropy (TEnt), and energy features of discrete wavelet transform (DWT) coefficients. All the clinically significant features obtained are ranked using their t -value and fed to classifiers such as K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). In this work, we have achieved an accuracy of 97.3%, 89.4%, 89.4%, and 94.7% for prediction of SCD one, two, three, and four minutes prior to the SCD onset respectively using SVM classifier. Furthermore, we have also developed a novel SCD Index (SCDI) by using nonlinear HRV signal features to classify the normal and SCD prone HRV signals. Our proposed technique is able to identify the person at risk of developing SCD four minutes earlier, thereby providing sufficient time for the clinicians to respond with treatment in Intensive Care Units (ICU). Thus, this proposed technique can thus serve as a valuable tool for increasing the survival rate of many cardiac patients. [ABSTRACT FROM AUTHOR]

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant’s vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant’s limbs and an audio recorder was worn on the child’s chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant’s behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior.

In this study, the fi rst and last names of people (FN and LN), enterprises (EN) (with plants' species roots in their names) and phytotoponyms (PT) in fi ve Croatian regions are analyzed, in their relationships. The goals of the study were: to determine the correlations between FN, LN, EN and PT; to determine the latent structure of these variables; to forecast number of PT (criterion) on the base of predictors (FN, LN, EN); to determine grouping of the places (within certain regions) as cases by two plants' categorizations; to determine grouping of the plants as cases by regions. We have analyzed 15 places, grouped in fi ve regions, with 39 different plant species. The results revealed that the only principal component highly positively correlated with the variables last name and office name, while the projections for the variables fi rst name (moderate high) and phytotoponyms (low size) were negative. Prediction of the criteria phytotoponyms is satisfactorily good, using three predictors: last name, fi rst name and the offi ce name. First cluster analysis revealed that phytotoponyms are mostly related with trees and deciduous plants, while names are related with trees, deciduous and herbaceous plants. Second cluster analysis obtained clear distinction between regions in dominant PTs, based on certain plants' names. The results indicate clear association between phytotoponyms and names of people. [ABSTRACT FROM AUTHOR]

Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPACMP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD. [ABSTRACT FROM AUTHOR]

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Patients with non-specific low back pain (LBP) may use postural control strategies that differ from healthy subjects. To study these possible differences, we measured the amount and structure of postural sway, and the response to muscle vibration in a working cohort of 215 subjects. Subjects were standing on a force plate in bipedal stance. In the first trial the eyes were open, no perturbation applied. In the following 6 trials, vision was occluded and subjects stood under various conditions of vibration/no vibration of the lumbar spine or m. Triceps Surae (TSM) on firm surface and on foam surface. We performed a factor analysis to reduce the large amount of variables that are available to quantify all effects. Subjects with LBP showed the same amount of sway as subjects without LBP, but the structure of their sway pattern was less regular with higher frequency content. Subjects with LBP also showed a smaller response to TSM vibration, and a slower balance recovery after cessation of vibration when standing on a solid surface. There was a weak but significant association between smaller responses to TSM vibration and an irregular, high frequency sway pattern, independent from LBP. A model for control of postural sway is proposed. This model suggests that subjects with LBP use more co-contraction and less cognitive control, to maintain a standing balance when compared to subjects without LBP. In addition, a reduced weighting of proprioceptive signals in subjects with LBP is suggested as an explanation for the findings in this study. [ABSTRACT FROM AUTHOR]

Rational and sophisticated structural solutions are essential requirements for structural designers, as a consequence of architectural design evolution and new building management concepts. Thus, waffle slabs turn out to be an interesting alternative, despite their laborious numerical modeling. It is necessary to increase knowledge about the structural behavior of and improve the theoretical models used for the simulation of these slabs. To better understand the behavior of RC waffle slabs, and more realistically quantify stresses and displacements of this kind of element under actual work conditions, a real-scale ribbed slab was tested and the results are presented in this paper. The chosen structure, designed to serve as a parking floor, was instrumented with strain and deflection gauges to assess the deformations and deflections developed under some induced loading conditions. A grillage model and a three-dimensional finite-element model were used for the numerical study. The data collected indicates that both strategies can be used to obtain adequate estimates of deflections and bending moments. [ABSTRACT FROM AUTHOR]

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior. [ABSTRACT FROM AUTHOR]

The time domain measures and power spectral analysis of heart rate variability (HRV) are classic conventional methods to assess the complex regulatory system between autonomic nervous system and heart rate and are most widely used. There are abundant scientific data about the prognostic significance of the conventional measurements of HRV in patients with various conditions, particularly with myocardial infarction. Some studies have suggested that some newer measures describing nonlinear dynamics of heart rate, such as fractal measures, may reaveal prognostic information beyond that obtained by the conventional measures of HRV. An ideal risk indicator could specifically predict sudden arrhythmic death as the implantable cardioverter-defibrillator (ICD) therapy can prevent such events. In postinfarction patients, numerically the highest number of sudden deaths occur in patients with better preserved left ventricular function than in those with severe left ventricular dysfunction. Recent data support the concept that HRV measurements, when analyzed several weeks after acute myocardial infarction, predict life-threatening ventricular tachyarrhythmias in patients with moderately depressed left ventricular function. However, well-designed prospective randomized studies are needed to evaluate whether the ICD therapy based on the assessment of HRV alone or with other risk indicators improves the patients´ prognosis. Several issues, such as the optimal target population, optimal timing of HRV measurements, optimal methods of HRV analysis and optimal cutpoints for different HRV parameters, need clarification before the HRV analysis can be a widespread clinical tool in risk stratification.