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The purpose of this paper is to study the dynamic features of friction coefficient during running-in state based on recurrence analysis, so as to recognize the running-in state of crankshaft journal bearings.

The friction coefficient was measured in the friction experiments and the dynamic features are analyzed by recurrence plots (RPs), unthreshold recurrence plots (URPs) and recurrence quantification analysis.

During the running-in process, RPs have gone through disrupted patterns, drift patterns and homogeneous patterns successively. URP shows that the phase trajectory spirals in the disrupted pattern gradually converge in the drift pattern and remain stable in the homogeneous pattern. Three independent measures, recurrence rate, entropy and laminarity, are chosen to characterize friction coefficient from the perspective of point, diagonal line and vertical line structures of the RPs.

The results provide a feasible way to monitor the running-in process and recognize the running-in state.

It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

AISI 52100-AISI 1045 specimens were used as the ring-on-disc tribopairs in the experiments to investigate the stability of friction process.

The coefficient of friction (COF) signals were measured throughout the friction process and the recurrence plots (RPs) and recurrence quantification analysis (RQA) are adapted to analyze the stability of the tribosystem.

The results show that the COF time-series acquired from different tests possess the same dynamic evolution laws. The evolution of RPs follows the rules of “disrupted-homogeneous-disrupted,” which corresponds to the “running-in, steady-state and increasing stages” of friction process. Additionally, the evolution laws of RQA measures LAM, Vmax and TT accord with the “bathtub curve.” Therefore, both RPs and RQA measures can inform quantitative interpretations of tribological behaviors and friction process identification.

The both RPs and RQA are capable of characterizing the tribological behaviors and can depict the various stages of friction process.

The purpose of this research is to investigate the time structure characteristics of collaborative knowledge production behaviors in Q&A (question-and-answer) communities for explicit and tacit knowledge, and systematically investigate the supply side and the demand side of knowledge production.

Taking Zhihu as the research object, using the methods of recurrence plot and recurrence quantification analysis, this paper analyzes the recursive characteristics of the motion trajectories of the three behavioral sequences of questioning, answering, and discussion, qualitatively and quantitatively analyzing the generation and evolution mechanism of explicit and tacit knowledge.

The results show that compared with the demand-side behavior sequence, the supply-side behavior sequence exhibits higher stability, complexity and periodicity. Compared with the tacit knowledge topics, the demand-side behavior sequence of the explicit knowledge topics shows stronger nonlinearity, and the supply-side behavior sequence shows lower complexity.

The research conclusions provide preliminary evidence for the effectiveness of the recurrence plot method in distinguishing different types of knowledge production behaviors and have important application value for the “crowdsourcing” knowledge generation and identification under the knowledge economy and the sustainable development of the socialized question-and-answer community.

Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.

The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.

It was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.

Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

The purpose of this paper is to first, test for nonlinearity in Local Indian Exchange Traded Funds (ETFs) listed at NSE, India – NIFTYBEES, JUNIORBEES, BANKBEES, PSUBANKBEES, and INFRABEES – using a battery of nonlinearity tests; second, to ascertain, using both metric and topological approaches, the adequacy of appropriate AR-GARCH models when it comes to capturing all of the nonlinearity in Indian ETFs; and third, to test for chaos in Indian ETFs.

To start with, a battery of tests such as and limited to McLeod Li test, Engle's LM test, Tsay F-test, Hinich Bispectrum Test and Hinich Bicorrelation test were employed to test for nonlinearity in Indian ETFs. Subsequently, the nature of nonlinearity in all the ETFs was systematically investigated by subjecting the ETF data sets to a metric (BDS test) and a topological test (close returns tests) at different stages of the model-building process. Finally, Lyapunov Exponent test was employed to test for chaos in Indian ETFs.

Test outcomes pertaining to a battery of nonlinearity tests indicate prevalence of nonlinearity amidst all ETFs except for INFRABEES. BDS test outcomes at the different stages of the model-building process indicated high sensitivity of the test outcomes to choice of embedding dimension, threshold value and residual transformations. Close returns test outcomes indicated that, but for BANKBEES, all of the nonlinearity in Indian ETFs could be captured by appropriate GARCH models. Finally, chaos was found to be absent in any of the ETFs considered for this study.

The collective take-way from this study is threefold in nature. First, in light of the many limitations of the BDS test, topological approaches such as close-returns test offer a better avenue to test for adequacy of AR-GARCH models in explaining the nature of nonlinearity in asset price movements. Second, adequacy of AR-GARCH models in capturing all of the nonlinearity in NIFTYBEES, JUNIORBEES, PSUBANKBEES, and INFRABEES, as indicated by close-returns test findings, is a reflection of multiplicative nature of nonlinearity in these five ETFs. Third, persistence of nonlinearity in AR-GARCH filtered standardized residuals of BANKBEES, coupled with the absence of chaos in any of the ETFs considered for this study, brings to light the possibility of existence of additive nonlinearity in conjunction with multiplicative nonlinearity.

This is possibly the first study that systematically investigates the nature of nonlinearity in Indian ETFs and ascertains the adequacy of AR-GARCH models when it comes to capturing all of the nonlinearity in Indian ETFs using a topological approach.

Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.

This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.

The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.

Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.

This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

The purpose of this paper is to examine the possibility of biotic patterns. In economics, markets are thought to tend to equilibrium with random and unpredictable deviations. However, an explosion of empirical work searching for possible chaos show an enormous amount of unexplained nonlinear structure. These observations led the authors to examine the possibility of biotic patterns in economics.

Bios is defined as a causally generated creative process. It is the causal counterpart to random walk, just as chaos is the causal equivalent to randomness. Economic data consisting of time series from several categories, including banking, employment and population, and gross domestic product and components, were studied for diversification, recurrence, and predictability patterns characteristic of bios. Diversification was quantified as increased variance with embedding, recurrences were measured using newly developed computer programs, and predictability was measured with a nonlinear prediction method.

Dynamic analyses of the data show: episodic patterning and asymmetric statistical distribution, typical of bios; increase in variance with embedding (diversification), less recurrence than shuffled copies of the data (novelty), demonstrating creativity; consecutive recurrence; and patterning in the series of differences, indicating non‐random causation.

The demonstration of bios in empirical data indicates that the economy is non‐stationary, causal, and creative. This contradicts the notion that markets regulate themselves and tend to equilibrium, and the characterization of market variation as random or chaotic. Further economic crises may be avoided by acknowledging that financial markets are not bound within limits and can be modified into new forms by human action.

The paper seeks to propose a specific approach based on Dynamic Analysis and Chaos Theory aiming to emphasize the differences into the eddy current signals obtained by related non‐destructive tests, when the inspected specimens have flaws with different shapes.

Non‐linear eddy current analysis is very useful for flaw detection in many in‐service inspections. State‐of‐the‐art technologies allow one to define position and depth of defects, but the shape identification is still an open problem. In this paper, experimental data have been subjected to a dynamical analysis in order to relate the trend of eddy current signals to the shape of analyzed defect.

In particular, a dynamical reconstruction by means of recurrence plots (RPs) has been carried out in order to detect analogies and differentiations between different eddy current signals. Moreover, cross‐correlation between RPs of a reference benchmark and testing eddy current signals has been applied in order to emphasize a different dynamical behaviour and to detect a particular flaw's shape. In this way, a real‐time algorithm for defect shape classification has been performed.

Proposed approach is very interesting, and it is an innovation in non‐destructive testing procedures. In fact, the shape identification of a flaw is still an open challenge. The proposed approach, based on dynamic analysis, gives the key to solve this particular ill‐posed problem, by introducing a relation between the eddy current measurements and the shape of defect existing in the inspected specimen. Very interesting preliminary results have been obtained.

The purpose of this paper is to report recent advances on a collaborative project that aims to develop content‐based methods for music information retrieval (MIR) as an alternative to standard text‐based modes of access to digital music libraries.

The paper describes current practices and ongoing research, and it discusses potential applications for future use.

Content‐based MIR approaches can extend and enhance the capabilities of traditional text‐based discovery and delivery systems and thus support the work of expert users such as musicians and musicologists. Examples of technologies developed in the context of the project include novel methods for automatic chord identification, motif finding, the visualization of musical structure, and retrieval of musical variations using harmonic and structural information.

The paper looks at new, non‐verbal modes of interaction with digital music archives based on musically substantive features such as chords, motifs, rhythms, etc. By building more sophisticated dimensions of interactivity into a discovery‐and‐delivery system, these tools could give the end‐user a more meaningful and rewarding experience. The tools potentially would be less costly and more scalable than textual annotation and markup, and their applicability extends beyond digital libraries to other music services.

This article discusses the advantages and challenges posed by audio‐based MIR and shows, via project‐specific examples, its relevance to supporting the needs of digital music library users.