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Introduction: The aim of this proof-of-concept multimethod exploratory single case study is to increase knowledge of the underlying mechanisms of alliance ruptures and repairs in Borderline Personality Disorder treatment across and within the psychotherapeutic treatment process. Method: The multimethod includes outcome assessment of patient self-reporting questionnaires (the Affect Integration Inventory, the Hopkins Symptom Checklist), observation-based ratings of sessions with the Rupture Resolution Rating System, quantitative analysis of heart rate variability using recurrence quantification analysis, and a qualitative multimodal interaction analysis of within-session dynamics. Result: Results reveal how patterns of heart rate synchrony between patient and therapist reflect periodical patterns of emotional interaction corresponding to key therapeutic alliance processes throughout the treatment process. Particularly, heart rate synchronization and desynchronization correspond with increasing rupture resolution ratings and positive outcome measures in the last part of the therapy process, indicating increased productivity, affectivity, and positive change. The qualitative microanalysis highlights context sensitivity to alliance management within sessions. Physiological arousal is found to underlie important alliance processes, including emotion regulation, relatedness, security, empathic responding, sense-making, and validation in correspondence with different therapist verbal and non-verbal markerbs. Discussion: Clinical implications and study limitations are discussed. Recommendations are made for future directions in relation to applying multimethod approaches when studying rupture and repair processes in psychotherapy. [ABSTRACT FROM AUTHOR]

Background/Objectives: Understanding how dual-tasking and Parkinson's disease medication affect gait and balance regularity can provide valuable insights to patients, caregivers, and clinicians regarding frailty and fall risk. However, dual-task gait and balance studies in PD most often only employ linear measures to describe movement regularity. Some have used nonlinear techniques to analyze PD performances, but only in the on-medication state. Thus, it is unclear how the nonlinear aspects of gait or standing balance are affected by PD medication. This study aimed to assess how dopaminergic medication influenced the regularity and determinism of joint angle and anterior–posterior (AP) and medial–lateral (ML) center of pressure (COP) path time-series data while single- and dual-tasking in PD. Methods: Sixteen subjects with PD completed single- and dual-task gait and standing balance trials for 3 min off and on dopaminergic medication. Sample entropy and percent determinism were calculated for bilateral hip, knee, and shoulder joints, and the AP and ML COP path. Results: There were no relevant medication X task interactions for either the joint angles series or the balance series. Instead, the results supported independent effects of medication, dual-tasking, or standing with eyes closed. Balance task difficulty (i.e., eyes open vs. eyes closed) was detected by the nonlinear analyses, but the nonlinear measures yielded opposing results such that standing with eyes closed simultaneously yielded less regular and more deterministic signals. Conclusions: When juxtaposed with previous findings, these results suggest that medication-induced functional improvements in people with PD might be accompanied by a shift from lesser to greater signal consistency, and the effects of dual-tasking and standing with eyes closed were mixed. Future studies would benefit from including both linear and nonlinear measures to better describe gait and balance performance and signal complexity in people with PD. [ABSTRACT FROM AUTHOR]

Previous literature has associated math literacy with linguistic factors such as verbal ability and phonological skills. However, few studies have investigated linguistic synchrony, shown in mathematical discussions. This study modelled math literacy and examined the relationship of math literacy with linguistic synchrony between students and facilitators. We retrieved data from 20,776 online mathematical discussion threads at a secondary school level. First, we assessed students' math literacy based on their discussions and classified them into high‐ and low‐math literacy groups. Then, we conducted Cross‐Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The result implies that students with high math literacy are more likely to share common words (eg, mathematical terms) with facilitators. At the same time, they would paraphrase the facilitators' words rather than blindly mimic them as the exact sentences or phrases. On the other hand, students with low math literacy tend to use overlapping words with facilitators less frequently and are more likely to repeat the exact same phrases from the facilitators. The findings provide an empirical data analysis and insights into mathematical discussions and linguistic synchrony. In addition, this paper implies the directions to improve online mathematical discussions and foster math literacy. Practitioner notesWhat is already known about this topic Mathematical discussions are known to be an effective way to promote math literacy.Math literacy and linguistic skills have a strong link.Linguistic synchrony is related to better collaboration and common knowledge building.What this paper adds Reveals the relationship between math literacy and linguistic synchrony and deepens the understanding of digital communication in online learning environments.Provides empirical analysis of natural language data in group discussions using CRQA.Conceptualizes linguistic synchrony with three sub‐concepts: linguistic concurrence, predictability, and complexity.Implications for practice and/or policy Educators and practitioners could utilize the automatic formative assessment of math literacy based on the student's language use in mathematical discussions.Educational technology researchers and designers could include CRQA indices and recurrence plots in the dashboard design to provide information to support teachers and learners.Teachers would be able to provide real‐time interventions to promote effective mathematical communication and foster math literacy throughout mathematical discussions. [ABSTRACT FROM AUTHOR]

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs. [ABSTRACT FROM AUTHOR]

Background: Scholars have confirmed the vital roles of self‐regulated learning (SRL) behaviours in predicting task performance, especially within non‐linear technology‐rich learning environments (TREs). However, few studies focused on the learning costs (e.g., study effort and time‐on‐task) related to SRL and the efficiency outcome of SRL (i.e., the relative relationship between learning costs and performance). Objectives: This study examined the relationship between students' SRL behaviours and problem‐solving efficiency in the context of TREs. Methods: Eighty‐two medical students accomplished a diagnostic task in a computer‐simulated environment, and they were classified into the efficient or less efficient group according to diagnostic performance and time‐on‐task. Then we coded students' SRL behaviours from trace data and counted the frequency of each SRL behaviour. The recurrence quantification and lag sequential analyses were performed to extract the dynamic characteristics of SRL behaviours, including recurrent patterns and sequential transitions. Results and Conclusions: Efficient students conducted more frequent Self‐reflection behaviours than the less efficient. For the recurrent patterns, efficient students tended to exhibit longer SRL behaviour sequences comprising a variety of different SRL behaviours (e.g., Task Analysis > Add Test > Add Hypotheses > Categorise Evidence) as well as longer sequences of repeated SRL behaviours (e.g., Add Test > Add Test > Add Test > Add Test). Moreover, efficient students exhibited more sequential transitions between different SRL behaviours than less efficient. Takeaways: Overall, this study revealed the effects of SRL on problem‐solving efficiency, which inspired researchers to incorporate problem‐solving efficiency as an evaluation criterion of SRL processes. Lay Description: What is already known about this topic?: Previous studies have highlighted the importance of self‐regulated learning (SRL) behaviours in facilitating students' performance.SRL behaviours also affect learning costs, such as cognitive effort and study time.SRL behaviours influence both learning costs and outcomes and thus their relative relationship, that is, problem‐solving efficiency.Efficient problem‐solvers can achieve high performance with low effort and vice versa. What this paper adds?: Efficient problem‐solvers conducted more self‐reflection behaviours than less efficient.Efficient problem‐solvers repeated longer SRL behaviour sequences than less efficient.Efficient problem‐solvers repeated the same SRL behaviour before moving forward to a different behaviour.Efficient problem‐solvers transitioned between different SRL behaviours more frequently than less efficient. Implications for practice: Instructors should design appropriate metacognitive prompts to facilitate students' self‐reflection and thus problem‐solving efficiency.Intelligent tutoring systems can generate real‐time feedback based on students' SRL features to improve problem‐solving efficiency. [ABSTRACT FROM AUTHOR]

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

This paper addresses the problem of noncooperative spectrum sensing in very low signal-to-noise ratio (SNR) conditions. In our approach, detecting an unoccupied bandwidth consists of detecting the presence or absence of a communication signal on this bandwidth. Digital communication signals may contain hidden periodicities, so we use Recurrence Quantification Analysis (RQA) to reveal the hidden periodicities. RQA is very sensitive and offers reliable estimation of the phase space dimension m or the time delay τ. In view of the limitations of the algorithms proposed in the literature, we have proposed a new algorithm to simultaneously estimate the optimal values of m and τ. The new proposed optimal values allow the state reconstruction of the observed signal and then the estimation of the distance matrix. This distance matrix has particular properties that we have exploited to propose a Recurrence-Analysis-based Detector (RAD). The RAD can detect a communication signal in a very low SNR condition. Using Receiver Operating Characteristic curves, our experimental results corroborate the robustness of our proposed algorithm compared with classic widely used algorithms.

The main goal of this work is summed up in a univariate chaotic analysis of the runoff series using a topological approach. As such, nineteen series of daily runoff from eight large watersheds in northern Algeria were analyzed. Firstly, preliminary analyses of two traditional dynamic and metric approaches have been tested. In the dynamic approach, three algorithms have shown that the largest Lyapunov exponent for the series is positive, which supports the hypothesis of the existence of chaos. In the metric approach, the Grassberger and Procaccia algorithm clearly shows the saturation of the correlation dimension, which indicates the existence of deterministic dynamics for all studied stations. Secondly, the application of the topological approach in this study constitutes in itself a new contribution to the demonstration of chaos in hydrology by using the recurrence plot (RP) and the recurrence quantification analysis (RQA). As such, the RP structures of the runoff series seem to be more comparable to chaotic systems. In addition, RQA parameters give high values of determinism and laminarity, which supports the hypothesis of the existence of deterministic chaos. The presence of chaos in the runoff series can be identified by the existence of a strong probability of recurrence, indicating a fairly low level of complexity and fairly high predictability. Ultimately, the comparison of these approaches together made it possible to confirm the hypothesis according to which the process generating the runoff series is deterministic and suggests low-dimensional chaotic dynamics. [ABSTRACT FROM AUTHOR]

Objectives Some research conceptualizes routineness of daily life as an indicator of cognitive vulnerability that would lead to lower well-being in older age, whereas other research expects routineness to give rise to more meaning and stability in life and thus to higher well-being. Further research is needed to understand routineness in older adults in relation to cognitive abilities and well-being. This study examined routineness of social interactions. Methods We examined data from an event-contingent experience sampling study with 103 Swiss community-dwelling older adults (aged 65 to 84 years). Participants completed in-lab cognitive assessments (reasoning, episodic memory, speed, and vocabulary) and reported their well-being (positive affect, negative affect, and life satisfaction). For more than 21 days, participants reported the time and context of their social interactions (including modality, partner type, and location). Routineness of social interactions was defined as social interactions that occurred at the same time of day over the study period. It was calculated using recurrence quantification analysis. Results Linear regressions showed that higher routineness of social interaction in general, of social interaction through the same modality, and of social interaction with the same partner type were associated with higher positive affect. Higher routineness of social interaction in general was associated with lower negative affect. Routineness of social interactions was not associated with life satisfaction or cognitive abilities. Discussion A routine social life may increase older adults' affective well-being. Results are discussed in the context of activity engagement and time use in older age. [ABSTRACT FROM AUTHOR]

IntroductionThe aim of this proof-of-concept multimethod exploratory single case study is to increase knowledge of the underlying mechanisms of alliance ruptures and repairs in Borderline Personality Disorder treatment across and within the psychotherapeutic treatment process.MethodThe multimethod includes outcome assessment of patient self-reporting questionnaires (the Affect Integration Inventory, the Hopkins Symptom Checklist), observation-based ratings of sessions with the Rupture Resolution Rating System, quantitative analysis of heart rate variability using recurrence quantification analysis, and a qualitative multimodal interaction analysis of within-session dynamics.ResultResults reveal how patterns of heart rate synchrony between patient and therapist reflect periodical patterns of emotional interaction corresponding to key therapeutic alliance processes throughout the treatment process. Particularly, heart rate synchronization and desynchronization correspond with increasing rupture resolution ratings and positive outcome measures in the last part of the therapy process, indicating increased productivity, affectivity, and positive change. The qualitative microanalysis highlights context sensitivity to alliance management within sessions. Physiological arousal is found to underlie important alliance processes, including emotion regulation, relatedness, security, empathic responding, sense-making, and validation in correspondence with different therapist verbal and non-verbal markerbs.DiscussionClinical implications and study limitations are discussed. Recommendations are made for future directions in relation to applying multimethod approaches when studying rupture and repair processes in psychotherapy.

Social interactions, particularly parent-child conversations, play a critical role in children’s early learning and pre-academic skill development. While these interactions are bidirectional, complex, and dynamic, much of the research in this area tends to separate speakers’ talk and capture the frequency of words or utterances. Beyond the aggregation of talk exists rich information about conversational structures and processes, such as the extent to which speakers are aligned or reciprocate each other’s talk. These measures can be derived using categorical cross-recurrence quantification analysis (CRQA), a method that quantifies the temporal structure and co-visitation of individual and sequential events, e.g., utterances between speakers. In this paper, we present an application of CRQA, following the protocol described in our tutorial paper (Duong et al., 2024, this issue), to describe alignment in parent-child conversations about numbers and math (i.e., number talk). We used the ‘crqa’ package in R and the code used in this application is available in the Supplemental Materials. Further, the CRQA measures derived from this application were compared to traditional frequency measures of talk, i.e., counts of utterances, in the prediction of children’s math skills. Overall, we showed that (1) CRQA can be applied to existing transcription data to uncover theoretically-driven patterns of parent-child talk that are not captured by common frequency measures and (2) these CRQA measures offer additional, rich information about interactions beyond frequencies of talk and can be used to predict individual differences in children’s math skills.

Introduction: Tai Chi has proved to be an effective therapy for balance performance and cognition. However, non-consistency exists in the results of the effect of Tai Chi training on standing balance control in older adults. This study aimed to use traditional and non-traditional methods to investigate the effect of Tai Chi on standing balance in older adults. Methods: Thirty-six Tai Chi practitioners (TC group) and thirty-six older adults with no Tai Chi practice (control group) were recruited in this study. A Nintendo Wii Balance Board was used to record the center of pressure (COP) during standing balance over 20 s in the condition of eyes closed with three repetitions. The wavelet analysis, multiscale entropy, recurrence quantification analysis, and traditional methods were used to evaluate the standing balance control in the anterior-posterior (AP) and mediolateral (ML) directions. Results: (1) Greater sway mean velocity in the AP direction and sway Path length were found in the TC group compared with the control group; (2) lower Very-low frequency band (0.10-0.39Hz) and higher Moderate frequency band (1.56-6.25Hz) in the AP andML directions were found in the TC group compared with the control group; (3) greater complexity index (CI) and lower determinism (DET) in the AP and ML directions were observed in the TC group compared with control group; (4) greater path length linked with smaller Very-low frequency band in the AP and ML directions and higher Moderate frequency band in the AP direction in both groups; (5) greater path length linked with lower DET and higher CI in the AP direction only in the TC group. Conclusion: Long-term Tai Chi practice improved sensory reweighting (more reliance on the proprioception systemand less reliance on the vestibular system) and complexity of standing balance control in older adults. In addition, greater sway velocity may be as an exploratory role in standing balance control of TC older adults, which correlated with greater complexity, but no such significant relationship in the control group. Therefore, the effects of Tai Chi practice on standing balance control in older adults may be attributed to the improvement of sensory reweighting and complexity rather than reduced sway velocity or amplitude. [ABSTRACT FROM AUTHOR]

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli. [ABSTRACT FROM AUTHOR]

In airborne data or model outputs, clouds are often defined using information about Liquid Water Content (LWC). Unfortunately LWC is not enough to retrieve information about the dynamical boundary of the cloud, that is, volume of turbulent air around the cloud. In this work, we propose an algorithmic approach to this problem based on a method used in time series analysis of dynamical systems, namely Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA). We construct RPs using time series of turbulence kinetic energy, vertical velocity and temperature fluctuations as variables important for cloud dynamics. Then, by studying time series of laminarity (LAM), a variable which is calculated using RPs, we distinguish between turbulent and non‐turbulent segments along a horizontal flight leg. By selecting a single threshold of this quantity, we are able to reduce the number of subjective variables and their thresholds used in the definition of the dynamical cloud boundary. Plain Language Summary: Cloud is defined by the presence of liquid (or solid) water in the atmosphere. By agreeing on a certain threshold of liquid water content, one can define a cloud boundary. Cloud processes result in disturbances in the airflow around the cloud (turbulence). Its presence should allow the cloud dynamical boundary to be defined in a similar manner. However, a single simple variable, which could be used in an analogous way is not well defined. In this study we propose a method to define such a variable to distinguish between turbulent and non‐turbulent volumes around the cloud (i.e. determining dynamic cloud interface), adopting a method used in the study of dynamical systems. Based on recorded temperature and air velocity fluctuations characteristic for turbulent transport and mixing, a threshold of a variable named "laminarity" is used to define the dynamic cloud boundary on the aircraft trajectory. Key Points: A vector characterizing turbulence along the aircraft track based on velocity and temperature fluctuations is constructedA time series of this vector allows the construction of a Recurrence Plot, a tool used in studies of dynamical systemsA quantity named laminarity, derived from the Recurrence Plot, is used to detect turbulent and non‐turbulent regions [ABSTRACT FROM AUTHOR]

Objectives: This study explored the potential of non‐parametric and complexity analysis metrics to detect changes in activity post‐ketamine and their association with depressive symptomatology. Methods: Individuals with treatment‐resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double‐blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18–65 years old, medication‐free, and had a MADRS score ≥20. Non‐parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non‐parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting. Results: In HVs, lower intradaily variability was observed within daily rest/activity patterns post‐ketamine versus post‐placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug‐by‐time or correlations between depressive symptomatology and activity were detected. Conclusions: Weak associations between non‐parametric variables and ketamine were found but were not consistent across actigraphy measures. Clinical Trial registration: ClinicalTrials.gov, NCT00088699. [ABSTRACT FROM AUTHOR]

Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states. [ABSTRACT FROM AUTHOR]

Objective: This research aimed to investigate the relationship between gaze behaviour dynamics and operator performance. Background: Individuals differ in their approach when learning a new task often resulting in performance disparity. During training some individuals learn the structure and dynamics of the task and develop a systematic approach, whereas others may achieve the same result albeit with increased perceived workload, or indeed some may fail to achieve superior performance levels. Previous research has shown that comparing gaze of experts with novices can provide unique insights into cognitive functioning of superior performers. Methods: Twenty-five individuals participated in a computer-based simulation task. The concept of coefficient of variation (CoV) of task scores was used to compute the participants' consistency of performance. Based on CoV, the cohort was split into two performance categories. The temporal patterns in participants gaze data were transformed using autocorrelation, and recurrence quantification analysis (RQA) was employed to analyse and quantify the patterns. Results: A Mann–Whitney U analysis demonstrated significantly (p <.01) higher determinism, entropy and laminarity in the superior group compared to the moderate group. Pearson's correlation revealed a significant (p <.01) negative correlation between the consistency of task performance (CoV) and the RQA measures. Conclusion: The results demonstrated that eye gaze dynamics can be used as an objective measure of performance. Participants classified as superior performers consistently demonstrated a systematic gaze activity which were in line with the task structure. Application: The methods presented here are applicable to observe and evaluate operators' strategic distribution of gaze. Specifically, for tactical monitoring and decision making in task environments where spatial locations of elements-of-interest vary continuously. [ABSTRACT FROM AUTHOR]

Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production. [ABSTRACT FROM AUTHOR]

This study investigates the linear and nonlinear dynamics of precipitation and temperature extremes. Monthly daily temperature range (DTR) and maximum 5-day precipitation (RX5) data for sixty three years were obtained from the archives of the Expert Team on Climate Change Detection and Indices (ETCCDI). Spatial maps were constructed for the descriptive statistics (mean, standard deviation, skewness, trend and variance) of both DTR and RX5, as well as nonlinear analysis (entropy, Hurst exponent, determinism and laminarity) for Africa and South America. The research focuses on identifying the unique signature of climate extremes using linear and nonlinear approaches. The deserts of Africa and South America were found to exhibit lower complexity (determinism greater than 0.7) and higher persistence in RX5 values compared to the rest of the continent. The high complexity observed on the African continent were attributed to dynamic atmospheric circulation patterns in the region. The dynamics of equatorial West Africa and Congo River basin were found to be less complex (determinism greater than 0.3) than the rest of the continent when DTR values were analyzed. Using determinism and laminarity (greater than 0.5), the complexity of the Brazilian highlands were observed to be the highest in both DTR and RX5 analysis. Quantitative analysis of climate extremes using nonlinear approaches have been found to be detailed and useful. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a crucial non-invasive medical tool for diagnosing neurological disorder called encephalopathy. There is a requirement for powerful signal processing algorithms as EEG patterns in encephalopathies are not specific to a particular etiology. As visual examination and linear methods of EEG analysis are not sufficient to get the subtle information regarding various neuro pathologies, non-linear analysis methods can be employed for exploring the dynamic, complex and chaotic nature of EEG signals. This work aims identifying and differentiating the patterns specific to cerebral dysfunctions associated with Encephalopathy using Recurrence Quantification Analysis and Fractal Dimension algorithms. This study analysed six RQA features, namely, recurrence rate, determinism, laminarity, diagonal length, diagonal entropy and trapping time and comparing them with fractal dimensions, namely, Higuchi's and Katz's fractal dimension. Fractal dimensions were found to be lower for encephalopathy cases showing decreased complexity when compared to that of normal healthy subjects. On the other hand, RQA features were found to be higher for encephalopathy cases indicating higher recurrence and more periodic patterns in EEGs of encephalopathy compared to that of normal healthy controls. The feature reduction was then performed using Principal Component Analysis and fed to three promising classifiers: SVM, Random Forest and Multi-layer Perceptron. The resultant system provides a practically realizable pipeline for the diagnosis of encephalopathy. [ABSTRACT FROM AUTHOR]

Industrial policy and globalization have made the industrial environment competitive, making maintenance of machines crucial. Condition monitoring (CM) is a process that monitors machinery parameters to predict and measure potential failures. This allows for early maintenance and prevents costly downtime. Vibration analysis is used to measure machine condition, but it requires high skills and experience. Recurrence Quantification Analysis (RQA) is a nonlinear data analysis method that quantifies the number and duration of recurrences in a dynamic system. A test setup is set up to produce flaws in the rotary system, such as unbalance, misalignment, and looseness. The mathematically generated simulated defect signals are examined, and they are contrasted with the experimental values obtained from the test setup using RQA. It has been found that RQA can accurately predict issues like unbalance, misalignment, and looseness, and it has been recommended that RQA can be used as an advanced tool for online/offline condition monitoring of machines. [ABSTRACT FROM AUTHOR]

Abstract In airborne data or model outputs, clouds are often defined using information about Liquid Water Content (LWC). Unfortunately LWC is not enough to retrieve information about the dynamical boundary of the cloud, that is, volume of turbulent air around the cloud. In this work, we propose an algorithmic approach to this problem based on a method used in time series analysis of dynamical systems, namely Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA). We construct RPs using time series of turbulence kinetic energy, vertical velocity and temperature fluctuations as variables important for cloud dynamics. Then, by studying time series of laminarity (LAM), a variable which is calculated using RPs, we distinguish between turbulent and non‐turbulent segments along a horizontal flight leg. By selecting a single threshold of this quantity, we are able to reduce the number of subjective variables and their thresholds used in the definition of the dynamical cloud boundary.

Abstract Objectives This study explored the potential of non‐parametric and complexity analysis metrics to detect changes in activity post‐ketamine and their association with depressive symptomatology. Methods Individuals with treatment‐resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double‐blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18–65 years old, medication‐free, and had a MADRS score ≥20. Non‐parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non‐parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting. Results In HVs, lower intradaily variability was observed within daily rest/activity patterns post‐ketamine versus post‐placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug‐by‐time or correlations between depressive symptomatology and activity were detected. Conclusions Weak associations between non‐parametric variables and ketamine were found but were not consistent across actigraphy measures. Clinical Trial registration ClinicalTrials.gov, NCT00088699.

A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility. If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily euro/dollar exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and euro/dollar exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the euro/dollar exchange rate index volatility.

The ‘social brain’, consisting of areas sensitive to social information, supposedly gates the mechanisms involved in human language learning. Early preverbal interactions are guided by ostensive signals, such as gaze patterns, which are coordinated across body, brain, and environment. However, little is known about how the infant brain processes social gaze in naturalistic interactions and how this relates to infant language development. During free-play of 9-month-olds with their mothers, we recorded hemodynamic cortical activity of ´social brain` areas (prefrontal cortex, temporo-parietal junctions) via fNIRS, and micro-coded mother’s and infant’s social gaze. Infants’ speech processing was assessed with a word segmentation task. Using joint recurrence quantification analysis, we examined the connection between infants’ ´social brain` activity and the temporal dynamics of social gaze at intrapersonal (i.e., infant’s coordination, maternal coordination) and interpersonal (i.e., dyadic coupling) levels. Regression modeling revealed that intrapersonal dynamics in maternal social gaze (but not infant’s coordination or dyadic coupling) coordinated significantly with infant’s cortical activity. Moreover, recurrence quantification analysis revealed that intrapersonal maternal social gaze dynamics (in terms of entropy) were the best predictor of infants’ word segmentation. The findings support the importance of social interaction in language development, particularly highlighting maternal social gaze dynamics.

A mere co-presence of an unfamiliar person may modulate an individual's attentive engagement with specific events or situations to a significant degree. To understand better how such social presence affects experiences, we recorded a set of parallel multimodal facial and psychophysiological data with subjects (N = 36) who listened to dramatic audio scenes alone or when facing an unfamiliar person. Both a selection of 6 s affective sound clips (IADS-2) followed by a 27 min soundtrack extracted from a Finnish episode film depicted familiar and often intense social situations familiar from the everyday world. Considering the systemic complexity of both the chosen naturalistic stimuli and expected variations in the experimental social situation, we applied a novel combination of signal analysis methods using inter-subject correlation (ISC) analysis, Representational Similarity Analysis (RSA) and Recurrence Quantification Analysis (RQA) followed by gradient boosting classification. We report our findings concerning three facial signals, gaze, eyebrow and smile that can be linked to socially motivated facial movements. We found that ISC values of pairs, whether calculated on true pairs or any two individuals who had a partner, were lower than the group with single individuals. Thus, audio stimuli induced more unique responses in those subjects who were listening to it in the presence of another person, while individual listeners tended to yield a more uniform response as it was driven by dramatized audio stimulus alone. Furthermore, our classifiers models trained using recurrence properties of gaze, eyebrows and smile signals demonstrated distinctive differences in the recurrence dynamics of signals from paired subjects and revealed the impact of individual differences on the latter. We showed that the presence of an unfamiliar co-listener that modifies social dynamics of dyadic listening tasks can be detected reliably from visible facial modalities. By applying our analysis framework to a broader range of psycho-physiological data, together with annotations of the content, and subjective reports of participants, we expected more detailed dyadic dependencies to be revealed. Our work contributes towards modeling and predicting human social behaviors to specific types of audio-visually mediated, virtual, and live social situations. [ABSTRACT FROM AUTHOR]

When multiple individuals interact in a conversation or as part of a large crowd, emergent structures and dynamics arise that are behavioral properties of the interacting group rather than of any individual member of that group. Recent work using traditional signal processing techniques and machine learning has demonstrated that global acoustic data recorded from a crowd at a basketball game can be used to classify emergent crowd behavior in terms of the crowd's purported emotional state. We propose that the description of crowd behavior from such global acoustic data could benefit from nonlinear analysis methods derived from dynamical systems theory. Such methods have been used in recent research applying nonlinear methods to audio data extracted from music and group musical interactions. In this work, we used nonlinear analyses to extract features that are relevant to the behavioral interactions that underlie acoustic signals produced by a crowd attending a sporting event. We propose that recurrence dynamics measured from these audio signals via recurrence quantification analysis (RQA) reflect information about the behavioral dynamics of the crowd itself. We analyze these dynamics from acoustic signals recorded from crowds attending basketball games, and that were manually labeled according to the crowds' emotional state across six categories: angry noise, applause, cheer, distraction noise, positive chant, and negative chant. We show that RQA measures are useful to differentiate the emergent acoustic behavioral dynamics between these categories, and can provide insight into the recurrence patterns that underlie crowd interactions. [ABSTRACT FROM AUTHOR]

The dynamical hypothesis claims that cognitive systems, such as teams, are dynamical systems (i.e., an interdependent collection of individuals and their technology that change together over time). Following this hypothesis, team researchers have adopted dynamical approaches to better understand the team cognitive processes and states that form team cognition, as well as how they emerge over time. One approach focuses on team coordination dynamics, which examines the coupling of signals between interacting individuals in various modalities, and has been shown to reflect aspects of team functioning including team cognition. However, how changes in team coordination relate to high‐level team cognitive processes and states, as well as important events, are not yet fully understood. To this end, we advance a methodological framework for researching team cognition under the dynamical hypothesis. Subsequently, we provided an empirical case‐study application of this framework. Thereby, this work contributes methodologically and empirically to a deeper understanding of team cognition, the dynamical hypothesis, and the synergy between them. [ABSTRACT FROM AUTHOR]

Dynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems. [ABSTRACT FROM AUTHOR]

Tactile perception is processed in the somatosensory cortices of the brain. To study the electrical activity of the brain evoked by the tactile perceptions of fabric textures, electroencephalograph signals during tactile perception were evaluated using event-related potentials and recurrence quantification analysis methods. A support vector machine was used to realize the identification of electroencephalograph signals. The results showed that fabric surface with large surface roughness, critical buckling force, and fiber diameter, and low warp-weft density was preferentially perceived, need large attentional resources, and evoke strong nonstationary state of the electroencephalographic system during the tactile perception. The δ rhythm was the main rhythm of event-related potential signals evoked by tactile perception of fabrics and the main energy of the electroencephalograph rhythm was concentrated in the low frequency band. The electroencephalographic system was in a strong nonstationary and fluctuating state during the production stage of P200 and P300 components of the event-related potential wave. The combination of P200, P300, mean diagonal line length L, and trapping time TT can significantly improve the classification accuracy of the electroencephalograph signal of tactile perception to about 72%. [ABSTRACT FROM AUTHOR]

Brain–body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task. [ABSTRACT FROM AUTHOR]

A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility. If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily €/$ exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and €/$ exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the €/$ exchange rate index volatility. [ABSTRACT FROM AUTHOR]

Changes in affect over time have been associated with health outcomes. However, previously utilized measurement methods focus on variability of affect (e.g., standard deviation, root mean squared successive difference) and ignore the more complex temporal patterns of affect over time. These patterns may be an important feature in understanding how the dynamics of affect relate to health. Recurrence quantification analysis (RQA) may help alleviate this problem by assessing temporal characteristics unassessed by past methods. RQA metrics, such as determinism and recurrence, can provide a measure of the predictability of affect over time, indexing how often patterns within affective experiences repeat. In Study 1, we first contrasted RQA metrics with commonly used measures of variability to demonstrate that RQA can further differentiate among patterns of affect. In Study 2, we analyzed the associations between these new metrics and health, namely, depressive and somatic symptoms. We found that RQA metrics predicted health above and beyond mean levels and variability of affect over time. The most desirable health outcomes were observed in people who had high mean positive affect, low mean negative affect, low affect variability, and high affect predictability. These studies are the first to demonstrate the utility of RQA for determining how temporal patterns in affective experiences are important for health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions. Participants were retrospectively classified as young adults (age< 60, n=85), non-fallers (age≥60, falls=0, n=56), and fallers (age≥60, falls≥1, n=18). Mixed ANOVA and post hoc analyzes were performed to test for differences between groups. For CoP fluctuations in the anterior-posterior direction, all RQA measures showed significantly higher values for young than older adults when standing on a compliant surface, indicating less predictable and stable balance control among seniors under testing conditions where sensory information is restricted or altered. However, no significant differences between non-fallers and fallers were observed. These results support the use of RQA to characterize balance control in young and old adults, but not to discriminate between different fall risk groups.

Nondestructive evaluation on fatigue damage of metallic materials with ultrasound is important to ensure the load-bearing capability and in-service reliability of high-performance components.The fatigue damage of industrial pure iron was studied, and a nondestructive testing method which combined the critically refracted longitudinal (LCR) wave and recurrence quantification analysis (RQA) was proposed. Results show that with the increasing loading cycles and hardening during the tensile-compressive cyclic loading until 1000 cycles, the amplitude of LCR wave and the corresponding normalized amplitude difference Adifboth evolve monotonically. The sensitivity of 5 MHz testing frequency is significantly higher than that of 2.25 MHz. A further analysis of LCR wave with RQA presents that the resulting recurrence plot possesses higher sensitivity. The normalized recurrence rate difference RRdif is extracted. The sensitivity indicates an obvious increase up to 44% compared with the maximum amplitude indexes in time domain, frequency domain, and time-frequency domain. It proves that the proposed method would be a new solution to the nondestructive evaluation on early-stage fatigue damage of metallic materials.

Examining the sequential patterns of self-regulated learning (SRL) behaviors is gaining popularity to understand students’ performance differences. However, few studies have looked at the transition probabilities among different SRL behaviors. Moreover, there is a lack of research investigating the temporal structures of students’ SRL behaviors (e.g., repetitiveness and predictability) and how they related to students’ performance. In this study, 75 students from a top North American university were tasked to diagnose a virtual patient in an intelligent tutoring system. We used recurrence quantification analysis and sequential analysis to analyze the temporal structures and sequential patterns of students’ SRL behaviors. We compared the differences between low and high performers. We found that low performers had more single, isolated recurrent behaviors in problem-solving, whereas the recurrent behaviors of high performers were more likely to be part of a behavioral sequence. High performers also demonstrated a higher transition probability across the three phases of SRL than low performers. In addition, high performers were unique in that their behavioral state transitions were cyclically sustained. This study provided researchers with theoretical insights regarding the cyclical nature of SRL. This study has also methodological contributions to the analysis of the temporal structures of SRL behaviors.

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study. [ABSTRACT FROM AUTHOR]

Human postural control is commonly assessed by center of pressure (CoP) displacement analysis. However, traditional linear parameters do not provide a complete picture of postural control, so a number of nonlinear analyses have been introduced. One of these is recurrence quantification analysis (RQA), which is used to determine the number and duration of repeated states in a dynamic system. This review aimed to show how the RQA measures look in different groups of subjects when assessing postural stability during quiet standing and how the authors interpret them. Therefore, a systematic review was conducted of papers published from 2000 to February 2023. Searched databases were PubMed, ScienceDirect, and EBSCO. Twenty-eight publications were included in this review. The RQA parameters most commonly found in papers are %DET (determinism), ENT (Shannon entropy), %REC (recurrence), and %LAM (laminarity). %LAM is the most sensitive factor in determining differences in CoP position between different age groups, as it describes motion fluidity. The vision affects the deterministic structure of CoP motions. When the sensory organization test conditions became difficult, CoP variability increased, while the %DET decreased. It was concluded that traditional and nonlinear methods provide complementary and not redundant information for assessing age- and health-related changes in standing balance. [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]

The Recurrence Quantification Analysis (RQA), a pattern recognition-based time series analysis method, can be successfully utilized for short, nonstationary, nonlinear, and chaotic time series. These RQA measures quantify several properties of time series, including predictability, regularity, stability, randomness, and complexity. In this regard, first, we analyzed the intraday seasonality with RQA and demonstrated how RQA measures change among the intraday periods by using 160 million row matched orders of 100 shares from Borsa Istanbul Equity Market between 2019M10 and 2020M02. We selected 50 stocks from the BIST50 Index group and 50 stocks from outside of the BIST100 Index group. Since these two share groups exhibit similar intraday RQA seasonality, our results are robust. Second, we explained intraday volatility with RQA measures and found a relationship between RQA measures and intraday volatility using a regression model. [ABSTRACT FROM AUTHOR]

Thermoacoustic oscillation is indeed a phenomenon characterized by the symmetric coupling of thermal and acoustic waves. This paper introduces a novel approach for monitoring and predicting thermoacoustic combustion instability using a combination of recurrence quantification analysis (RQA) and an optimized deep belief network (DBN). Six samples of combustion state data were collected using two distinct types of burners to facilitate the training and validation of GA-DBN. The proposed methodology leverages RQA to extract intricate patterns and dynamic features from time series data representing combustion behavior. By quantifying the recurrence plot of specific patterns, the analysis provides valuable insights into the underlying thermoacoustic dynamics. Among three different feature extraction methods, RQA stands out remarkably in performance. These RQA-derived features serve as input to a carefully tuned DBN, which is trained to learn the complex relationships within the combustion process. The classification accuracy of deep belief network optimized by genetic algorithm (GA-DBN) reached an impressive 99.8%. Subsequent multiple comparisons were conducted between GA-DBN, DBN, and support vector machine (SVM), revealing that GA-DBN consistently demonstrated satisfactory classification results. This method holds significant importance in monitoring intricate combustion states.

We investigated the application of non-linear analysistechniques for capturing stability of neural oscillatory activitywithin and across brains. Recurrence Quantification Analysis(RQA), a technique that has been applied to detect stabilityand flexibility of motor performance, was extended to observeand quantify changes in patterns of non-linear neural activity.Participants synchronized their finger-tapping with aconfederate partner who tapped at two different rhythmswhile neural activity was recorded from both partners usingelectroencephalography (EEG). Auto-recurrence (intra-brain)and cross-recurrence (inter-brain) of EEG activity were ableto distinguish differences across tapping rhythms in stabilityof neural oscillatory activity. We also demonstrated theefficacy of RQA to capture how both period and phasechanges in neural dynamics evolve over time.

Cryptocurrencies are new kinds of electronic currencies based on communication technologies. These currencies have attracted the attention of investors. However, cryptocurrencies are very volatile and unpredictable. For investors, it is very difficult to make investment decisions in cryptocurrency market. Therefore, revealing changes in the dynamics of cryptocurrencies are valuable for investors. Bitcoin is the most popular and representative cryptocurrency in cryptocurrency market. In this study how dynamical properties of Bitcoin changed through time is analyzed with recurrence quantification analysis (RQA). RQA is a pattern recognition-based time series analysis method that reveals dynamics of the time series by calculating some metrics called RQA measures. This method has been successfully applied to nonlinear, nonstationary, short and chaotic time series and does not assume a statistical model. RQA can reveal important properties of time series data such as determinism, laminarity, stability, randomness, regularity and complexity. By using sliding window RQA we show that in 2021 RQA measures for Bitcoin prices collapse and Bitcoin becomes more unpredictable, more random, more unstable, more irregular and less complex. Therefore, dynamics and stability of the Bitcoin prices significantly changed in 2021.

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1-2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for 'language-free, culturally fair' low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders.MethodsRQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0-18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0-6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2-6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach.ResultsIn the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of training and test segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge.ConclusionsRQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting.

The purpose of this study was to examine if prosodic patterns in oral reading derived from Recurrence Quantification Analysis (RQA) could distinguish between struggling and skilled German readers in Grades 2 (n = 67) and 4 (n = 69). Furthermore, we investigated whether models estimated with RQA measures outperformed models estimated with prosodic features derived from prosodic transcription. According to the findings, struggling second graders appear to have a slower reading rate, longer intervals between pauses, and more repetitions of recurrent amplitudes and pauses, whereas struggling fourth graders appear to have less stable pause patterns over time, more pitch repetitions, more similar amplitude patterns over time, and more repetitions of pauses. Additionally, the models with prosodic patterns outperformed models with prosodic features. These findings suggest that the RQA approach provides additional information about prosody that complements an established approach.

Metal futures markets are complex systems which generally show nonstationary and complex behaviours. However, the characteristics behind these complex processes can be uncovered by exploring their prices fluctuations. This paper identified the deterministic states and measured the complexity of these six metal futures prices based on recurrence plots. The results show that the prices fluctuations characteristics of the five metals, except for Copper, were significantly different around April 2008. The average time for prices to maintain similar fluctuations for each metal is roughly within 15 days. We further used entropy indicator to quantify the complexity of metal prices systems and the sliding window method to identify time-varying features of complexity. After 2013, all these metal futures systems show higher complexity behaviours than before. Moreover, when the entropy indicator is at a high value, these metal prices are likely to change significantly. [ABSTRACT FROM AUTHOR]