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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.

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.

This paper aims to focus on the contribution of segregated renewable energy (RE) sources such as solar, wind, bagasse, biomass, small hydropower (SHP) and waste to heat in driving sustainable industrial production in India.

This study uses non-linear modelling techniques such as quantile regression and the non-linear Granger causality test to explore the interplay between segregated RE generation and industrial production in India.

The study findings support the role of segregated RE sources generation, especially SHP and bagasse, on industrial production in India. This paper finds unidirectional non-linear Granger causality running from segregated RE sources to industrial production. Bidirectional non-linear Granger causality has been established from biomass, waste-heat to index of industrial production and vice versa, supporting an asymmetric feedback hypothesis.

The study findings will aid the energy policymaker in framing policies for RE sources, especially bagasse-based and SHP generation for the sustainable industrial growth of India.

To the best of the authors’ knowledge, this is one of the first studies to explore the role of segregated RE sources generation to drive sustainable industrial growth in India using non-linear techniques.

Tourism systems have been considered more and more in the light of complexity and chaos theories. Most of the work done in this area has highlighted the reasons for and the issues regarding this approach. A steadily growing strand of the recent literature uses the theories to overcome the problems of a reductionist and mechanistic view that is considered unable to provide a full understanding of the structural and dynamic characteristics of tourism systems, and specifically of tourism destinations. This paper seeks to continue this approach and to provide a series of quantitative methods to assess the dynamics of non‐linear complex tourism systems.

The time series used in the paper contains data collected from a sample of 23 large (four‐star) hotels located in Milan, Italy. For each structure daily data of occupancy, average room rate and RevPAR (revenue per available room) were recorded for the period 2006‐2009. The daily distributions of these observations are highly skewed, and therefore the median of the daily values were considered. This results in three series of 1,461 points per type (occupancy, room rate and RevPAR).

The data confirm the complex nature of the destination system and its tendency towards a chaotic state. Additionally, high stability and long memory effects are detected. The outcomes and the implications of this analysis are examined.

A comparison of the values obtained leads to the conclusion that the series under study has a detectable level of non‐linearity, even if it does not reach the pure chaoticity of the Lorenz attractor. A first conclusion is that, as qualitatively assessed in many similar studies, the tourism destination is a complex system with a tendency to become chaotic.

The picture obtained with the analyses conducted can be summarised by saying that the system under study exhibits an unequivocally complex nature. It tends towards a chaotic stage but does so at a slow pace. The stability of the system is quite high: it might be able to resist transient shocks well but, once led in one direction, its long memory characteristics tend to keep it on the resulting path.

The aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.

Initially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.

The findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.

This paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.

This study aims at characterizing and identifying the existing research on tourism business networks.

The authors conducted a systematic mapping study to identify and analyze related literature. They identified 225 primary studies, dated from 1997-2016, and classified them with respect to research focus, types of research and contribution.

Seventy four studies were identified and mapped, synthesizing the available evidence on tourism business networks. “Business networks” with 27 articles is the dominant research focus and “Network configuration” with 22 articles is the next dominant one. Regarding the research type, “Solution proposal” is the most frequently used research type. “Interview” and “Case study”, respectively, were the most used research methods. However, “Agent-based modeling”, “Delphi study” and “Non-linear time series analysis” were used less often. “Philosophical paper” was the most common research type between 1997 and 2002, and after that “Solution proposal” was the dominant research paper type. Further, the number of publications has declined between 2012 and 2014.

This mapping study provides the first systematic exploration of the state-of-art on tourism business networks research. The existing body of knowledge is limited to a few high quality studies.

The social responsibility of a tourism destination results from the combined efforts of the single stakeholders. This needs coordination and harmonization that cannot be achieved without a deep understanding of the structural and dynamic characteristics of the destination. A tourism destination is a complex dynamic system and requires specific methods to be analyzed and understood in order to better tailor governance actions for steering it along an evolutionary growth path, respectful of the social responsibility towards the community. Many methodological recommendations exist that allow to assess these features, and some have been successfully applied to tourism destinations. This paper aims to explore a new proposal: the visibility graph algorithm (VGA), which is able to provide the required level of information in a fast and simple way.

VGA is a technique to map a time series into a network. The method and its implementation are relatively simple and straightforward. The mapping allows examining the system's properties by using network analytic methods. An example is worked out using data from two destinations: Italy as a country and the island of Elba, one of its most popular areas.

The complexity properties of the two destinations are examined and found in agreement with those obtained by using more complicated approaches, thus strengthening the reliability of the method.

This paper provides a new method to examine a tourism destination using a readily available set of data and a simple algorithm. The contribution of this work is mainly methodological. The technique provides insights into the complex structure and dynamics of a tourism destination. This has important implications for those interested in enriching the toolsets used to study a destination from a complex system perspective.

Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

The purpose of this paper is to examine the effect of crude oil prices on the Nigerian exchange rate with emphasis on discriminating between the effects of positive and negative changes in oil price on exchange rate.

The authors used monthly time series data from 1996:1 to 2019:6 and adopted two oil price measures, namely, Brent crude and West Texas Intermediary prices. For analysis, the authors used stepwise least squares to estimate a non-linear ARDL (NARDL) model and Wald tests to determine cointegration and the presence of asymmetric effects.

The findings showed that positive and negative Brent crude price changes significantly affect exchange rates differently in nominal terms, both in the long-run and short-run. However, the differences were purely in terms of effect size because the exchange rate decreased for both negative and positive oil price changes.

Whilst empirical research on asymmetries in the effect of oil price on exchange rate abounds, little evidence exists in Nigeria’s case. Although some studies previously tested for asymmetric oil price effects on the Nigerian currency, the approach used did not estimate long and short-run effects or test of long-run and short-run asymmetries. This paper fills this methodological gap using monthly using the NARDL approach. The NARDL approach provided the advantage of estimating effects for the long-run and short-run and testing for asymmetries in both time spans.