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Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Sports injuries usually have severe consequences for the concerned athletes as well as for trainers and teams. The question is if accidents can be predicted in specific cases. Can early-warning signals be detected in psychological time series? Methods: An App-based method of process-monitoring was applied for data collection of psychological parameters. Daily self-assessments using a Sports Process Questionnaire were realized by a professional soccer player during the after-care period of a psychiatric treatment. Methods for the prediction of critical events were applied (Dynamic Complexity, Recurrence Plots, dynamic inter-item correlations). Injuries may demarcate pattern transitions in the mental functioning of athletes, which could be identified by the Pattern Transition Detection Algorithm (PTDA). Results: Early-warning signals of the accident could be identified in the time series. Dynamic Complexity revealed a critical instability, Recurrence Plots a transient period, and the dynamic inter-item correlations a period of increased system coherence just before the accident. The PTDA revealed a phase transition at the occurring injury. Conclusions: Even if the analysis is based on a single case, the results are promising. Psychological self-reports allow a short-term prediction of bio-mechanical injuries and by this, can help to prevent them. Nonlinear measures can be applied to time series data collected by digital process monitoring.

Climate change is the reason behind most contemporary economic problems. The rising inflationary pressures in the food sector are one of these problems, and stable food prices are a necessity for economic development and social cohesion in societies. Therefore, this study analyzes the relationship between food prices and climate change in Nigeria by using various non-linear and quantile-based methods and data from 2008m5 to 2020m12. The empirical findings indicate that (i) there is a time- and frequency-based dependence between food prices and some explanatory variables, including climate change (i.e., temperature). (ii) At higher quantiles, temperature, oil prices, food exports, monetary expansion, global food prices, agricultural prices, and fertilizer prices stimulate food prices. (iii) The increase in food prices due to the rise in temperature and the difficulties in agriculture indicate that the heatflation phenomenon is present in Nigeria. The evidence outlines that Nigerian decisionmakers should adopt a national food security policy that considers environmental, agricultural, and monetary factors to stabilize food prices. [ABSTRACT FROM AUTHOR]

Abstract:Objectives: Sports injuries usually have severe consequences for the concerned athletes as well as for trainers and teams. The question is if accidents can be predicted in specific cases. Can early-warning signals be detected in psychological time series? Methods: An App-based method of process-monitoring was applied for data collection of psychological parameters. Daily self-assessments using a Sports Process Questionnaire were realized by a professional soccer player during the after-care period of a psychiatric treatment. Methods for the prediction of critical events were applied (Dynamic Complexity, Recurrence Plots, dynamic inter-item correlations). Injuries may demarcate pattern transitions in the mental functioning of athletes, which could be identified by the Pattern Transition Detection Algorithm (PTDA). Results: Early-warning signals of the accident could be identified in the time series. Dynamic Complexity revealed a critical instability, Recurrence Plots a transient period, and the dynamic inter-item correlations a period of increased system coherence just before the accident. The PTDA revealed a phase transition at the occurring injury. Conclusions: Even if the analysis is based on a single case, the results are promising. Psychological self-reports allow a short-term prediction of bio-mechanical injuries and by this, can help to prevent them. Nonlinear measures can be applied to time series data collected by digital process monitoring. [ABSTRACT FROM AUTHOR]

Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use.

The development of the unique, hierarchical, and endless combinatorial capacity in a human language requires neural maturation and learning through childhood. Compared with most non‐human primates, where combinatorial capacity seems limited, chimpanzees present a complex vocal system comprising hundreds of vocal sequences. We investigated how such a complex vocal system develops and the processes involved. We recorded 10,929 vocal utterances of 98 wild chimpanzees aged 0–55 years, from Taï National Park, Ivory Coast. We developed customized Generalized non‐Linear Models to estimate the ontogenetic trajectory of four structural components of vocal complexity: utterance length, diversity, probability of panting (requiring phonation across inhalation and exhalation), and probability of producing two adjacent panted units. We found chimpanzees need 10 years to reach adult levels of vocal complexity. In three variables, the steepest increase coincided with the age of first non‐kin social interactions (2–5 years), and plateaued in sub‐adults (8–10 years), as individuals integrate into adult social life. Producing two adjacent panted units may require more neuromuscular coordination of the articulators, as its emergence and steepest increase appear later in development. These results suggest prolonged maturational processes beyond those hitherto thought likely in species that do not learn their vocal repertoire. Our results suggest that multifaceted ontogenetic processes drive increases in vocal structural complexity in chimpanzees, particularly increases in social complexity and neuro‐muscular maturation. As humans live in a complex social world, empirical support for the "social complexity hypothesis" may have relevance for theories of language evolution. Research Highlights: Chimpanzees need around 10 years to develop the vocal structural complexity present in the adult repertoire, way beyond the age of emergence of every single vocal unit.Multifaceted ontogenetic processes may drive increases in vocal structural complexity in chimpanzees, particularly increases in social complexity and neuro‐muscular maturation.Non‐linear increases in vocal complexity coincide with social developmental milestones.Vocal sequences requiring rapid articulatory change emerge later than other vocal sequences, suggesting neuro‐muscular maturational processes continue through the juvenile years. [ABSTRACT FROM AUTHOR]

Summary: The recent crisis caused by COVID‐19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non‐linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price‐dividend ratio. Long‐term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry. [ABSTRACT FROM AUTHOR]

Climate change is the reason behind most contemporary economic problems. The rising inflationary pressures in the food sector are one of these problems, and stable food prices are a necessity for economic development and social cohesion in societies. Therefore, this study analyzes the relationship between food prices and climate change in Nigeria by using various non-linear and quantile-based methods and data from 2008m5 to 2020m12. The empirical findings indicate that (i) there is a time- and frequency-based dependence between food prices and some explanatory variables, including climate change (i.e., temperature). (ii) At higher quantiles, temperature, oil prices, food exports, monetary expansion, global food prices, agricultural prices, and fertilizer prices stimulate food prices. (iii) The increase in food prices due to the rise in temperature and the difficulties in agriculture indicate that the heatflation phenomenon is present in Nigeria. The evidence outlines that Nigerian decisionmakers should adopt a national food security policy that considers environmental, agricultural, and monetary factors to stabilize food prices.

IntroductionPerinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model.ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.MethodsSingle gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models.ResultsThe data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%].ConclusionBoth BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Accurate inversion of seismic fault parameters has been a challenge in the studies of geophysical non-linear inversion problems. Many non-linear methods such as Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Multipeaks Particle Swarm Optimization (MPSO), have usually been applied to inverse the fault parameters from geodetic observation data. However, their accuracy and availability can vary from different-type fault earthquakes (pure strike-slip, pure dip-slip fault, oblique-slip fault earthquakes). In order to evaluate the accuracy and availability of these non-linear methods on inversion for fault parameters of different-type fault earthquakes, we applied the SA, GA, PSO, MPSO methods and a new non-linear method—Black Hole Particle Swarm Optimization (BHPSO), to inverse fault parameters of different-type earthquakes from synthetic and observed GPS and InSAR data. We found that the MPSO and BHPSO performed better than SA, GA, and PSO for inversion from both the synthetic and observed data. The synthetic data simulation results showed that the Root-Mean-Square Errors (RMSEs) of MPSO and BHPSO methods were 0.01–0.06 m, smaller than those of SA, GA and PSO. We then applied these five methods to inverse fault parameters of two real earthquakes—the 2020 Nevada Mw 6.4 earthquake and 2021 Maduo Mw 7.4 earthquake, from observed GPS and InSAR data. We found that the RMSEs of MPSO and BHPSO were 0.005–0.195 m, also smaller than those of SA, GA, and PSO, and the MPSO and BHPSO performed better than SA, GA, and PSO. The results in this study demonstrated that the MPSO and BHPSO, can hold high accuracy and availability for inversion of fault parameters of different-type fault earthquakes.

Buckling is a complex phenomenon, influenced by the non‐linear material properties, as yielding of steel, cracking of concrete as well as, slenderness, geometrical imperfection and residual stresses. The design codes provide essential rules for mechanical resistance, stability and safety aspects. However, the current Eurocodes essentially stipulates engineering models that can easily be transferred to classical structural analysis, but often do not guide sufficiently the application and evaluation of advanced numerical analysis. The present study investigates the buckling analysis of steel and partial encased composite columns. Different definitions for geometrical imperfections and residual stresses are considered. The geometrical imperfections are taken into account in accordance with prEN1993‐1‐14 [2]and EN1090‐2 alternatively. The residual stresses are based on prEN1993‐1‐14 [2] and ECCS – Pub. No.33. In addition, analysis without the consideration of residual stresses are investigated. The scope is to assess the non‐linear finite element analysis (GMNIA), comparing the behavior of hot‐rolled and welded steel cross‐section as well as the impact of additional reinforced concrete encasement. Guidance for the numerical analysis for steel and composite columns is developed. [ABSTRACT FROM AUTHOR]

Finite element analysis (FEA) is no longer a new technique in the fields of palaeontology, anthropology, and evolutionary biology. It is nowadays a wellestablished technique within the virtual functional-morphology toolkit. However, almost all the works published in these fields have only applied the most basic FEA tools i.e., linear materials in static structural problems. Linear and static approximations are commonly used because they are computationally less expensive, and the error associated with these assumptions can be accepted. Nonetheless, nonlinearities are natural to be used in biomechanical models especially when modelling soft tissues, establish contacts between separated bones or the inclusion of buckling results. The aim of this review is to, firstly, highlight the usefulness of non-linearities and secondly, showcase these FEA tool to researchers that work in functional morphology and biomechanics, as non-linearities can improve their FEA models by widening the possible applications and topics that currently are not used in palaeontology and anthropology. [ABSTRACT FROM AUTHOR]

Finite element analysis (FEA) is no longer a new technique in the fields of palaeontology, anthropology, and evolutionary biology. It is nowadays a well-established technique within the virtual functional-morphology toolkit. However, almost all the works published in these fields have only applied the most basic FEA tools i.e., linear materials in static structural problems. Linear and static approximations are commonly used because they are computationally less expensive, and the error associated with these assumptions can be accepted. Nonetheless, nonlinearities are natural to be used in biomechanical models especially when modelling soft tissues, establish contacts between separated bones or the inclusion of buckling results. The aim of this review is to, firstly, highlight the usefulness of non-linearities and secondly, showcase these FEA tool to researchers that work in functional morphology and biomechanics, as non-linearities can improve their FEA models by widening the possible applications and topics that currently are not used in palaeontology and anthropology.

This brief presents a tutorial on multifaceted techniques for high efficiency piezoelectric energy harvesting. For the purpose of helping design piezoelectric energy harvesting system according to different application scenarios, we summarize and discuss the recent design trends and challenges. We divide the design focus into the following three categories, namely, (1) AC-DC rectifiers, (2) CP compensation circuits, (3) maximum power point tracking (MPPT) circuits. The features, problems encountered, and suitable systems of various AC-DC rectifier topologies are introduced and compared. The important role of non-linear methods for piezoelectric energy harvesting is illustrated from the perspective of impedance matching. Energy extraction techniques and voltage flipping techniques based on inductors, capacitors, and hybrid structures are analyzed. MPPT techniques with different features and targets are discussed. [ABSTRACT FROM AUTHOR]

The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

Our work examines the relationship between socially responsible funds and the traditional energy market over daily returns data ranging from December 2015 to April 2019. We apply quantile cross-spectral analysis to measure returns correlation under different market conditions in the short, medium, and long run to measure the connectedness between both markets. Our results highlight that correlation based on different quantile distributions yields different investment opportunities. In the short run, investors can benefit from diversifying assets under extreme market conditions. No significant diversification opportunities are available in the medium- and long-run periods. Our findings provide implications for individual investors making investments under different horizons and dynamic market conditions.

Cluster resolution (CR) is a useful metric for guiding automated feature selection of classification models. CR is a measure of class separation in a linear subspace for variable subsets via the determination of maximal, non‐intersecting confidence ellipses. Feature selection by cluster resolution (FS‐CR) is most commonly used to extract panels of useful, discriminating features from sparsely populated chromatographic peak tables, optimizing models from raw signals, or when working with datasets with many more variables than samples. The absence of a numerical method for calculating CR necessitates a great deal of dynamic programming and algorithmic complexity. In this work, we present a numerical determination of the CR metric, which reduces computation time by about 65 times when compared with the dynamic programming approach and simplifies the operating principles of FS‐CR algorithm. Cluster resolution is a measure of class separation in a linear subspace, which has been used to guide feature selection algorithms. In this contribution, a numerical method for calculating cluster resolution is presented. An algorithm implementing the numerical determination of cluster resolution is 65× faster than the current implementation, which relies on dynamic programming. [ABSTRACT FROM AUTHOR]

Feed efficiency traits are important for reducing feeding costs while potentially decreasing the environmental impact of broiler production. This study aimed to compare the predictive ability of different genome-wide prediction models for residual feed intake (RFI) calculated with linear and non-linear regression techniques. We also evaluated the heritability of such RFI as well as their associations with body weight. Genotypes and phenotypes used in this study were recorded from 32,747 purebred broiler chickens of both sexes. The individual feed intake was monitored daily in floor-raised birds using a radio-frequency antenna system. RFI was computed as the residual term of total feed intake (FI) adjusted for the average metabolic weight (AMW), body weight gain (BWG), and sex. The following regression methods were used to compute RFI: multiple linear regression (MLR), generalized additive model using splines (GAM), support vector regression using a radial basis kernel (SVR), and two-hidden layer neural network (NN). The different RFI assessments were fitted jointly with body weight measured at the end of the feeding trial (FBW) using a two-trait mixed model. The effects of contemporary groups were fitted as fixed for both traits, whereas sex was also considered for FBW. RFI (computed with different regression techniques) was predicted using different genomic prediction models (BayesA, BayesB, BayesCp, GBLUP, RKHS, and SVR). Genomic prediction accuracy (ACC) was assessed using a random forward validation scheme, in which data from previous generations were used for training the models while later generations were used to test predictions. The effect of the RFI computation method on ACC was tested using an unpaired Wilcoxon test. Sample variance observed for RFI ranged between 57,207.04 and 59,254.76, depending on the regression method used to compute it. Overall, the RFI sample variance was smaller for non-linear methods (NN, SVR, and GAM) compared with the linear model (MLR). The regression methods did not impact the genetic parameter estimates for RFI, which presented a heritability value around 0.32 ± 0.015 and genetic correlation with FBW ranging between 0.22 ± 0.04 and 0.25 ± 0.04. Prediction accuracy across genomic regression methods was slightly higher (P-value = 0.038) when RFI was computed with SVR (0.336 ± 0.023) compared with the MLR (0.329 ± 0.025), although this small difference would have little practical implications. No differences in terms of ACC were observed among the other regression methods used to compute RFI. Our preliminary results indicate that the regression technique used to compute RFI does not have major implications for this trait. Therefore, the MLR method is the preferred choice as it is simpler to implement, and it provides easy-to-interpret parameters. [ABSTRACT FROM AUTHOR]

This brief presents a tutorial on multifaceted techniques for high efficiency piezoelectric energy harvesting. For the purpose of helping design piezoelectric energy harvesting system according to different application scenarios, we summarize and discuss the recent design trends and challenges. We divide the design focus into the following three categories, namely, (1) AC-DC rectifiers, (2) CP compensation circuits, (3) maximum power point tracking (MPPT) circuits. The features, problems encountered, and suitable systems of various AC-DC rectifier topologies are introduced and compared. The important role of non-linear methods for piezoelectric energy harvesting is illustrated from the perspective of impedance matching. Energy extraction techniques and voltage flipping techniques based on inductors, capacitors, and hybrid structures are analyzed. MPPT techniques with different features and targets are discussed.

New approach for calculation of electroencephalogram Sample Entropy, experimental data and peculiarities of its interpretation are presented. Main aim of the research to formulate rules that allow to use Sample Entropy in human state monitoring by electroencephalogram more efficiently and give new information about electroencephalogram signal that can be useful for different researchers. This method is suitable for any non-linear, non-stationary signals with a chaotic nature. Moreover, it is recommended for descrying as additional technique for other entropy-based methods.

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this paper, a plaintext related image hybrid encryption scheme is proposed based on Lagrange interpolation, generalized Henon map and nonlinear operations of matrices. The proposed scheme consists of three parts. In the first part, a generalized chaotic map is constructed on the basis of Henon map. Using the novel map, a chaotic sequence is built. And then, both the chaotic sequence and the plaintext pixels are used to implement the first nonlinear operation for generating the first cipher matrix associated with the plaintext. By performing an exclusive XOR operation between the original pixels matrix and the first cipher matrix, the diffusion encryption is carried out. In the second part, Lagrange interpolation is used to create the second cipher matrix related to the diffused image; the second nonlinear transformation is developed between the diffused image and the second cipher matrix; and sequence rearrangement is adopted to scramble the diffused image. In the third part, the third nonlinear transformation of matrices based on point operation and rounding operation is implemented on the scrambled image to complete the image encryption. Accordingly, the decryption process is executed by the inverse operations in the opposite order. The proposed algorithm has some distinctive features: a variety of nonlinear tools such as nonlinear polynomial interpolation, nonlinear chaotic map, and nonlinear operations were involved in the scheme. The cryptosystem is designed with the plaintext to enhance the algorithm security. Due to the combination of multiple nonlinear methods and random factors, the scheme is one time pad, which can withstand multiple types of attacks. The algorithm has a clear structure and a simple calculation, so it is easy to program. In addition, encryption simulation and performance analysis are carried out. The feasibility and effectiveness of the algorithm are verified by the simulated results. The security of the algorithm are proved by the objective indicators such as the running time, key space, statistical properties, key sensitivity, and differential analysis, etc. [ABSTRACT FROM AUTHOR]

This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (ropt) corresponding to ApEnmax has been used for RQA calculation. The experimental data length (N) of RR interval series (RRi) is optimized by taking ropt as key parameter. ropt is found to be lying within the recommended range of 0.1 to 0.25 times the standard deviation of the RRi, when N ≥ 300. Consequently, RQA is applied to the age stratified RRi and indices such as percentage recurrence (%REC), percentage laminarity (%LAM), and percentage determinism (%DET) are calculated along with ApEnmax, [Formula: see text], [Formula: see text], and an index namely the radius differential (RD). Certain standard HRV statistical indices such as mean RR, standard deviation of RR (or NN) interval (SDNN), and the square root of the mean squared differences of successive RR intervals (RMSSD) (Eur Hear J 17:354-381, 1996) are also found for comparison. It is observed that (i) RD can discriminate between the elderly and young subjects with a value of 0.1151 ± 0.0236 (mean ± SD) and 0.0533 ± 0.0133 (mean ± SD) respectively for the elderly and young subjects and is found to be statistically significant with p < 0.05. (ii) Similar significant discrimination was obtained using [Formula: see text] with a value of 0.1827 ± 0.0382 (mean ± SD) and 0.2248 ± 0.0320 (mean ± SD) (iii) other significant indices were found to be %REC, %DET, %LAM, SDNN, and RMSSD; however, ApEnmax was found to be insignificant with p > 0.05. The above features of RRi time series were tested for classification using support vector machine (SVM) and multilayer perceptron neural network (MLPNN). Higher classification accuracy was achieved using SVM with a maximum value of 99.71%. Graphical abstract. [ABSTRACT FROM AUTHOR]

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

In this paper, Emotional recognition in Parkinson's disease (PD) has been analyzed in both time and frequency domain using Entropy, Energy-Entropy and Teager Energy-Entropy features. Classification results were observed using three classifiers namely Probabilistic Neural Network, K-Nearest Neighbors Algorithm and Support Vector Machine. Emotional EEG stimuli such as happiness, sadness, fear, anger, surprise, and disgust were used to categorize the PD patients and healthy controls (HC). For each EEG signal, the alpha, beta and gamma band frequency features are obtained for six different feature extraction methods (Entropy, Energy-Entropy, Teager Energy-Entropy, Spectral Entropy, Spectral Energy-Entropy and Spectral Teager Energy-Entropy). The proposed Energy-Entropy feature performs evenly for all six emotions with accuracy of above 80% when compared to other features, whereas different features with classifier gives variant results for few emotions with highest accuracy of above 95%. [ABSTRACT FROM AUTHOR]

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta (), theta (), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) (), Approximate Entropy (), Energy (), Fractal Dimension (FD) (), Permutation Entropy (), Detrended Fluctuation Analysis (), Hurst Exponent (), Largest Lyapunov Exponent (), Sample Entropy (), Shannon's Entropy (), Renyi's entropy (), Tsalli's entropy (), Fuzzy entropy (), Wavelet entropy (), Kolmogorov-Sinai entropy (), Modified Multiscale Entropy (), Hjorth's parameters (activity (), mobility (), and complexity ()) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), -test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index ( ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain. [ABSTRACT FROM AUTHOR]

Removal of phenolic pollutants from wastewaters has become mandatory due to stringent environmental regulations and adverse effects on aquatic marine environment. Adsorption process which is most effective process to remove pollutants, hinders its applications in diverse field due to high cost of adsorbents. In this study, a low cost and easily available agricultural waste of coconut shells based activated carbon were utilized as adsorbent to study its viability and efficiency for phenol removal from wastewater. The efficacy of the phenol removal by adsorption process in a batch reactor is evaluated with respect to contact time for effective adsorption and influence of initial phenol concentration on percentage phenol removal. The experimental adsorption data were examined with conventional isotherms models to describe the equilibrium characteristics of adsorption of phenol. The vigorousity and non-linearity inherent in the isotherm models were validated using various traditional linear and non-linear estimation methods namely gradient method, non-linear least square method and hybrid evolutionary optimization. A novel inverse modeling technique based on differential evolution (DE) optimization which is first of this kind for adsorption applications was implemented to estimate the isotherm parameters in their non-linear form. The model predictions from the DE based optimized parameters provided better predictions and closer to experimental values. The percentage removal of phenol from four different adsorbent dosages with constant initial feed concentrations of phenol found to be varying from 63% to 96% as increase in carbon loading at constant liquid flow rate. These experimental results also revealed that coconut shell based activated carbon is a viable cheaper adsorbent for phenols removal from effluent wastewater. [ABSTRACT FROM AUTHOR]

This study presents the evaluation of the original diatomite (DO) and manganese oxide modified (DMn) diatomite used as adsorbents for Pb (II) removal from aqueous solution. The experimental data were fitted to the non-linear twoparameter equation of Langmuir, Freundlich, Dubinin-Radushkevich, and non-linear threeparameter equation of Redlich-Peterson, Sips and Toth Isotherms. The correlation coefficient (R2), standard error (S.E) and Chi-square (x²) values were used to select the best theoretical isotherm. Adsorption mechanism of Pb(II) ion onto the DO was explained by heterogeneous adsorption model while homogenous adsorption model was used to explain the adsorption on DMn. Also, both adsorbents were observed to comply better with the three-parameter isotherms. Pseudo first-order, pseudo second-order, Elovich and intraparticle diffusion equations were used to describe the adsorption rate of Pb(II). The experimental data were well fitted by the pseudosecond-order kinetic model. Thermodynamic parameters such as free energy (AG°), enthalpy (ΔH°) and entropy (ΔS°) showed spontaneous and endothermic in nature of adsorption for both of adsorbents. [ABSTRACT FROM AUTHOR]

Recent studies show right hemisphere has a unique contribution to emotion processing. The present study investigated EEG using non-linear measures during emotional processing in PD patients with respect to motor symptom asymmetry (i.e., most affected body side). We recorded 14-channel wireless EEGs from 20 PD patients and 10 healthy age-matched controls (HC) by eliciting emotions such as happiness, sadness, fear, anger, surprise and disgust. PD patients were divided into two groups, based on most affected body side and unilateral motor symptom severity: left side-affected (LPD, n = 10) or right side-affected PD patients (RPD, n = 10). Nonlinear analysis of these emotional EEGs were performed by using approximate entropy, correlation dimension, detrended fluctuation analysis, fractal dimension, higher order spectra, hurst exponent (HE), largest Lyapunov exponent and sample entropy. The extracted features were ranked using analysis of variance based on F value. The ranked features were then fed into classifiers namely fuzzy K-nearest neighbor and support vector machine to obtain optimal performance using minimum number of features. From the experimental results, we found that (a) classification performance across all frequency bands performed well in recognizing emotional states of LPD, RPD, and HC; (b) the emotion-specific features were mainly related to higher frequency bands; and (c) predominantly LPD patients (inferred right-hemisphere pathology) were more impaired in emotion processing compared to RPD, as showed by a poorer classification performance. The results suggest that asymmetric neuronal degeneration in PD patients may contribute to the impairment of emotional communication. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) analysis has become an important tool in cardiology, these noninvasive measurements are relatively easy to perform, have good reproducibility and also provide prognostic information on patients with cardiac diseases. There are various methods in use to analyze the HRV; these methods usually can help in the early detection of some cardiac diseases. HRV analysis (meaning the study of hearts inter-beat time intervals) is useful for understanding the status of the Autonomic Nervous System (ANS). HRV reflects the cardiac system's ability to adapt to the changing external or internal circumstances by detecting and fast responding to the unexpected and unpredictable stimuli. HRV analysis has the ability to assess overall cardiac health and the state of the ANS responsible for regulating cardiac activity. This paper presents a detrended fluctuation analysis of RR time intervals and of their discrete wavelet transforms, comparing longer and shorter time series in order to find long term significant variations in the studied signals. Signals are taken from MIT-BIH Long Term ECG database, the analysis is performed under MATLAB environment. [ABSTRACT FROM AUTHOR]

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The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately. [ABSTRACT FROM AUTHOR]

The piezoelectric transformers reach densities of power more significant than their magnetic counterparts. However, one of the principal factors limiting the density of power is the acceptable maximum deformation by material constituting the transformer. The heating of the piezoelectric transformers is mainly of mechanical origin. This heating generates a degradation of the characteristics which in its turn generates an additional heating being able to lead to a phenomenon of thermal avalanche. In this work, two nonlinear methods [synchronized switch harvesting on inductor (SSHI) and SSHI-max] have been explored to improve the performance of the Rosen transformer basing on the tension generated by the secondary so as to increase the capacity of mechanic-electric conversion. The simulation results show that SSHI and SSHI-max techniques significantly increase the capacity of mechanic-electric conversion of inserts stuck on a vibrating structure and consequently, the power recovered in electric form. The comparative results of voltage gain, efficiency and the transmitted power of the transformer, before and after SSHI-max and SSHI control are given. These ones indicated that the two nonlinear techniques are promising as applications to improve the performances of the piezo-transformers. [ABSTRACT FROM AUTHOR]