Recurrence Quantification Analysis Research Articles

Influences of Attentional Focus on Across- and Within-Sentence Variability in Adults Who Do and Do Not Stutter.

Research has found an advantage to maintaining an external attentional focus while speaking as an increase in accuracy and a decrease in across-sentence variability has been found when producing oral-motor and speech tasks. What is not clear is how attention affects articulatory variability both across and within sentences, or how attention affects articulatory control in speakers who stutter. The purpose of this study was to investigate the effects of an internal versus external attention focus on articulatory variability at the sentence level. This study used linear (spatial-temporal index [STI]) and nonlinear (recurrence quantification analysis [RQA]) indices to measure lip aperture variability in 10 adults who stutter (AWS) and 15 adults who do not stutter (ANS) while they repeated sentences under an internal versus external attentional focus, virtual reality task (withVR.app; retrieved December 2023 from https://therapy.withvr.app). Four RQA measures were used to calculate within sentence variability including percent recurrence, percent determinism (%DET), stability (MAXLINE), and stationarity (TREND). Sentence duration measures were also obtained. AWS' movement durations were significantly longer than those of the ANS across conditions, and the AWS were more affected by the attentional focus shifts as their speech rate significantly increased when speaking with an external focus. AWS' speech patterns were also significantly more deterministic (%DET) and stable (MAXLINE) across attentional focus conditions compared to those of the ANS. Both groups showed an effect from attentional shifts as they exhibited less variability (i.e., more consistent) across sentences (STI) and less determinism (%DET) and stability (MAXLINE) within sentences when repeating sentences under an external attentional focus. STI values were not significantly different between the AWS and ANS for the internal or external attentional focus tasks. There were no significant main effects for group or condition for TREND; however, a main effect for sentence type was found. Results suggest that AWS use a more restrictive and less flexible approach to movement and that an external focus fosters more flexibility and thus responsiveness to external factors.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Personalized Model-Driven Interventions for Decisions From Experience.

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.

Recurrence Quantification Analysis Based Methodology in Automatic Aerobic Threshold Detection: Applicability and Accuracy across Age Groups, Exercise Protocols and Health Conditions

Recurrence Quantification Analysis Based Methodology in Automatic Aerobic Threshold Detection: Applicability and Accuracy across Age Groups, Exercise Protocols and Health Conditions

Lagged multidimensional recurrence quantification analysis for determining leader-follower relationships within multidimensional time series.

The current article introduces lagged multidimensional recurrence quantification analysis. The method is an extension of multidimensional recurrence quantification analysis and allows to quantify the joint dynamics of multivariate time series and to investigate leader-follower relationships in behavioral and physiological data. Moreover, the method enables the quantification of the joint dynamics of a group, when such leader-follower relationships are taken into account. We first provide a formal presentation of the method, and then apply it to synthetic data, as well as data sets from joint action research, investigating the shared dynamics of facial expression and beats-per-minute recordings within different groups. A wrapper function is included, for applying the method together with the "crqa" package in R. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ratcheting damage evaluation based on critically refracted longitudinal wave combined with recurrence quantification analysis

Excessive accumulation of irreversible plastic strain in ratcheting poses a highly urgent need in early-stage damage evaluation. A strategy integrating critically refracted longitudinal (LCR) wave with recurrence quantification analysis (RQA) is proposed for iron. The signals at different damage stages are reconstructed in multidimensional phase space, and three features, recurrence rate RR, determinism DET, and entropy ENTR, are extracted. With increasing ratcheting cycle, the normalized ENTR difference, ENTRdif, exhibits an obvious increase within ∼1 % life followed by a stabilization trend, while the normalized RR difference, RRdif, fluctuates after an initial rapid rise. Both features show higher sensitivity to the amplitude-based indexes in the LCR wave and pulse-echo methods. The ultrasonic response mechanism is discussed with the rearrangement and mobility of dislocations confirmed by microstructural observation.

Data-driven system identification and model predictive control of pneumatic conveying using nonlinear dynamics analysis for optimised energy consumption

Pneumatic conveying systems provide secure transportation of particulate material and a dust-free environment. These systems face high energy consumption, material degradation, and pipeline blockages. This research presents an innovative solution by integrating nonlinear dynamics analysis of electrostatic sensor data, including chaos and recurrence quantification analysis, sparse identification of nonlinear dynamics with control (SINDYc) and model predictive control (MPC). The Lyapunov exponent, approximate entropy and recurrence rate of electrostatic sensor data reveal the chaotic nature of gas-solid flows. MPC framework was tailored for real-time optimisation of a pneumatic conveying system. SINDYc system models were developed using data collected from an open-loop control pneumatic conveying process to conduct MPC simulations and select an appropriate model for real-time control. This research illustrates the potential of integrating nonlinear dynamics analysis, SINDYc integrated and MPC for enhanced system performance, showcased a significant reduction in energy consumption without compromising the system's efficiency or reliability.

Using nonlinear dynamics analysis to evaluate time response of cupping therapy with different intervention timings on reducing muscle fatigue.

Cupping therapy has been indicated effective in reducing muscle fatigue after 24h based on the spectral analyses of surface electromyography (sEMG). However, there is no sufficient evidence showing changes of sEMG nonlinear indexes at more time points after cupping therapy. Furthermore, it is unclear whether the intervention timings of cupping therapy affect the recovery from muscle fatigue. The purpose of this study was to use the sEMG nonlinear analysis to assess the difference of time response of cupping therapy between different intervention timings after muscle fatigue. This randomized controlled trial recruited 26 healthy volunteers. Cupping therapy (-300mmHg pressure for 5min by the 45mm-diameter cup) was applied before (i.e., pre-condition) or after (i.e., post-condition) muscle fatigue induced by performing repeated biceps curls at 75% of the 10 repetitions of maximum (RM) on the non-dominant upper extremity. Subjects were randomly allocated to the pre-condition group or the post-condition group. The sEMG signals during the maximal voluntary isometric contractions (MVC) of the biceps were recorded at four time points (i.e., baseline; post 1: immediate after cupping-fatigue/fatigue-cupping interventions; post 2: 3h after cupping-fatigue/fatigue-cupping interventions; post 3: 6h after cupping-fatigue/fatigue-cupping interventions). Two nonlinear sEMG indexes (sample entropy, SampEn; and percent determinism based on recurrence quantification analysis, %DET) were used to evaluate the recovery from exercise-introduced muscle fatigue. The Friedman test followed by the Nemenyi test and the Mann-Whitney U test were applied in statistics. The SampEn and %DET change rate did not show any significant differences at four time points in the pre-condition group. However, there were significant delayed effects instead of immediate effects on improving muscle fatigue in the post-condition group (SampEn change rate: baseline 0.0000 ± 0.0000 vs. post 2 0.1105 ± 0.2253, p < 0.05; baseline 0.0000 ± 0.0000 vs. post 3 0.0627 ± 0.4665, p < 0.05; post 1-0.0321 ± 0.2668 vs. post 3 0.0627 ± 0.4665, p < 0.05; and %DET change rate: baseline 0.0000 ± 0.0000 vs. post 2-0.1240 ± 0.1357, p < 0.01; baseline 0.0000 ± 0.0000 vs. post 3 0.0704 ± 0.6495, p < 0.05; post 1 0.0700 ± 0.3819 vs. post 3 0.0704 ± 0.6495, p < 0.05). Moreover, the SampEn change rate of the post-condition group (0.1105 ± 0.2253) was significantly higher than that of the pre-condition group (0.0006 ± 0.0634, p < 0.05) at the post 2 time point. No more significant between-groups difference was found in this study. This is the first study demonstrating that both the pre-condition and post-condition of cupping therapy are useful for reducing muscle fatigue. The post-condition cupping therapy can e ffectively alleviate exercise-induced muscle fatigue and there is a significant delayed effect, especially 3h after the interventions. Although the pre-condition cupping therapy can not significantly enhance muscle manifestations, it can recover muscles into a non-fatigued state.

The complexity of interpersonal physiology during rupture and repair episodes in the treatment of borderline personality disorder: a proof-of-concept multimethod single case study of verbal and non-verbal interactional dynamics.

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. 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. 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. 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.

Recurrence quantification analysis of uterine vectormyometriogram reveals differences between normal-weight and overweight parturient women

Objective.This study aims to use recurrence quantification analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal orX, vertical orY) in normal-weight (NW) and overweight (OW) women during the first stage of labor.Approach. The study involved 41 parturient women (NW = 21 and OW = 20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands.Main results. Significant differences in SampEn and Dom were observed in the SW band between NW and OW in bothXandYdirections, indicating more regular dynamics of electrical uterine activity and a higher Dom in NW parturient women compared to OW women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women.Significance. The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in NW women compared to OW women. This highlights the importance of tailored clinical strategies for managing labor in OW women to improve maternal and infant outcomes.

Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography

Application of Recurrence Quantification Analysis in the Detection of Osteoarthritis of the Knee with the Use of Vibroarthrography

Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

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.

Recurrence quantification analysis of rs-fMRI data: A method to detect subtle changes in the TgF344-AD rat model

Background and objectiveAlzheimer's disease (AD) is one of the leading causes of dementia, affecting the world's population at a growing rate. The preclinical stage of AD lasts over a decade, hence understanding AD-related early neuropathological effects on brain function at this stage facilitates early detection of the disease. MethodsResting-state functional magnetic resonance imaging (rs-fMRI) has been a powerful tool for understanding brain function, and it has been widely used in AD research. In this study, we apply Recurrence Quantification Analysis (RQA) on rs-fMRI images of 4-months (4 m) and 6-months-old (6 m) TgF344-AD rats and WT littermates to identify changes related to the AD phenotype and aging. RQA has been focused on areas of the default mode-like network (DMLN) and was performed based on Recurrence Plots (RP). RP is a mathematical representation of any dynamical system that evolves over time as a set of its state recurrences. In this paper, RPs were extracted in order to identify the affected regions of the DMLN at very early stages of AD. ResultsUsing the RQA approach, we identified significant changes related to the AD phenotype at 4 m and/or 6 m in several areas of the rat DMLN including the BFB, Hippocampal fields CA1 and CA3, CG1, CG2, PrL, PtA, RSC, TeA, V1, V2. In addition, with age, brain activity of WT rats showed less predictability, while the AD rats presented reduced decline of predictability. ConclusionsThe results of this study demonstrate that RQA of rs-fMRI data is a potent approach that can detect subtle changes which might be missed by other methodologies due to the brain's non-linear dynamics. Moreover, this study provides helpful information about specific areas involved in AD pathology at very early stages of the disease in a very promising rat model of AD. Our results provide valuable information for the development of early detection methods and novel diagnosis tools for AD.

The effect of cutting parameters on defect detection based on recurrence analysis of cutting force signals obtained from GFRP composite milling

This article presents the results of a recurrence analysis conducted on the milling of GFRP material. The novelty lies in applying variable milling parameters to assess the detectability of defects. It appears that cutting parameters play a significant role in detecting defects. The research aimed to determine the sensitivity of recurrence analysis in detecting defects of various sizes in composites machined with different milling parameters. Furthermore, the study identified the most appropriate quantifications for defects detection. The values of some quantifications derived from the cutting force showed significant changes compared to the indicators obtained during machining without defects. It was demonstrated that recurrence quantification analysis could identify defects as small as 0.25 mm, while recurrence diagrams alone could detect defect sizes of 0.75 mm or larger. The most effective indicators were RR, T1, and T2. Additionally, it was demonstrated that certain indicators depend on cutting parameters, complicating their use for defect detection.

The relationship between students' self‐regulated learning behaviours and problem‐solving efficiency in technology‐rich learning environments

AbstractBackgroundScholars 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).ObjectivesThis study examined the relationship between students' SRL behaviours and problem‐solving efficiency in the context of TREs.MethodsEighty‐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 ConclusionsEfficient 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 &gt; Add Test &gt; Add Hypotheses &gt; Categorise Evidence) as well as longer sequences of repeated SRL behaviours (e.g., Add Test &gt; Add Test &gt; Add Test &gt; Add Test). Moreover, efficient students exhibited more sequential transitions between different SRL behaviours than less efficient.TakeawaysOverall, 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.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

Noncooperative Spectrum Sensing Strategy Based on Recurrence Quantification Analysis in the Context of the Cognitive Radio

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.

Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing Artificial Intelligence Diagnostic Precision.

In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 1.27 ± 0.15 and a Fréchet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess the quality of prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist's evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions.

Identification of climatic tipping points and transitions in Chinese loess grain-size records utilizing nonlinear time series analysis

As one of the most important terrestrial sediments, Chinese loess provides valuable information on regional and global climatic and environmental changes and holds great potential for studying on nonlinear behaviors of the East Asian monsoon system. Utilizing objective and quantitative methods to identify tipping points and climate transitions in paleoclimatic records can help us understand the climatic change in the Chinese Loess Plateau (CLP). This study explores critical tipping points and nonlinear climate transitions within the CLP using the Chiloparts record, a comprehensive 2600-ka paleoclimate dataset. We pinpointed potential tipping points using recurrence quantification analysis and the augmented Kolmogorov-Smirnov test, ultimately leading to 15 critical tipping points. We argued that these 15 tipping points represent some of the most significant climatic changes recorded in the Chinese loess paleoclimate record. Employing recurrence quantification analysis, recurrence networks, and visibility graphs, we also identified several climate transitions and provided some nonlinear information, including the Mid-Pleistocene Transition (MPT) as well as the Mid-Brunhes Transition (MBT). We particularly highlight a significant climatic regime transition around 500 ka that may reflect a nonlinear response to variations in the Atlantic Meridional Overturning Circulation (AMOC). Our research also contributes to the understanding of the complex interplay between loess deposition, environmental change, and tectonic activity, emphasizing the need for further investigations to elucidate the mechanisms driving these transitions.

Understanding Electric Current Effects on Tribological Behaviors of Instantaneous Current-Carrying Pair with Recurrence Plot

Abstract Armature-rail instantaneous current-carrying friction in electromagnetic launchers refers to a sliding electric-mechanical impact friction and transition-induced arc erosion on a millisecond time scale. To reveal the electric current (50 to 300 A) effects on friction behavior and wear mechanism, the instantaneous current-carrying friction tests were performed with Al 1060 and Brass H62. Given the short nonlinear friction-induced signals, the friction behavior, including the time-domain information and system state, was comprehensively analyzed via frictional sound pressure (FSP), recurrence plot (RP), and recurrence quantification analysis (RQA). The wear topography was observed, and characterized by the multifractal spectrum. Recurrence analyses demonstrate that as the current increases, the nonstationarity of the system state weakens, and the complexity and unpredictability enhance. Higher currents reduce the FSP amplitude, i.e., enhance the interfacial lubrication effect, but intensify electrical wear and surface roughness. This signifies a wear mechanism transition from abrasive wear and slight adhesive wear to arc ablation, fatigue wear, and severe adhesive wear. The widening spectrum width implies that the irregularity and fluctuation of the topography are enhanced with the current. RP patterns and RQA quantifiers correlate with the wear damage state. The results provide a reference for anti-wear design and online degradation tracking of the rail.