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Principles of counseling and psychotherapy : learning the essential domains and nonlinear thinking of master practitioners
Research has shown that the most effective way to prepare students for practice with real clients is to learn to think in a new way rather than simply learning and using a set of steps. While there is much to be learned from what master practitioners do in their sessions, there is even more knowledge to gain from learning how they think. The second edition of Principles of Counseling and Psychotherapy offers students and practitioners a way to understand the processes behind effective outcomes with a wide variety of clients. The second edition is infused with real-world clinical case examples and opportunities for readers to apply the material to the cases being presented. New \"thought-exercise\" sections are specifically designed to engage the reader's natural non-linear thinking, and transcript material both from cases and from master therapists themselves are interwoven in the text. -- !c From publisher's description.
Book
Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review
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.
Journal Article
Computer-Aided Diagnosis of Depression Using EEG Signals
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.
Journal Article
Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
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.
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.
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%).
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.
Journal Article
Applying Linear Forms of Pseudo-Second-Order Kinetic Model for Feasibly Identifying Errors in the Initial Periods of Time-Dependent Adsorption Datasets
Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of an adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. Results indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing these points, the experimental data were adequately fitted with the PSO model. Statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 value (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points are not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of these models indicated that the fault experimental points from the initial periods were not detected in this study.
Journal Article
Structural Health Monitoring (SHM) Study of Polymer Matrix Composite (PMC) Materials Using Nonlinear Vibration Methods Based on Embedded Piezoelectric Transducers
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.
Journal Article
Evaluation of the Methods for Nonlinear Analysis of Heart Rate Variability
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.
Journal Article
Re-evaluation of One-Dimensional Site Response Methods Using Vs Adjusted Borehole Arrays
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.
Journal Article
Nonlinear approximation method of vehicle velocity Vt and statistical population of experimental cases
•A nonlinear approach to finding velocity before car crash is proposed.•Approach relies on a second order polynomial, dependence between precrash velocity, deformation coefficient and vehicle mass.•Approach shows great improvement in comparison to linear methods.
In car crash analysis three calculations methods can be distinguished: analytical (Campbell, McHenry, Strother, Prasad, Crash3 etc.), comparative and graphical (Lindquist et al., 2003; Prasad, 1990; Sharma et al., 2007; Wach and Unarski, 2006; Żuchowski, 2015) [15,23,25,29,33]. The number and reliability of these methods, in reference to modern vehicles and their structure, may lead to unclear conclusions. This issue proves to be significant, especially due to slight modifications of both input parameters and input data for the analysis of the method. This may give substantially different answers to the questions asked in court. After a thorough analysis of this problem, a new analytical method was devised, based on a new input database — NHTSA, which shows great improvement in the accuracy of obtained results. The aim of this paper is to prove that the nonlinear method is a very effective tool for processing experimental data in a large enough number of cases.
Journal Article
Empirical dynamic programming for model‐free ecosystem‐based management
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.
Journal Article