The Effects of Environmental Tax On Ecological Footprint and Carbon Dioxide Emissions: a Nonlinear Cointegration Analysis On Turkey

This article presents a nonlinear analysis in Turkey on the effect of an environmental tax (ET) on the ecological footprint (EF) and carbon dioxide (CO2) emissions. In the literature, most of the studies examining the effects of Environmental Taxes (ETs) on Environmental Degradation (ED) have used linear methods. The number of studies examining this relationship with nonlinear methods is few. However, there is no study examining the long-run effects of ETs on the EF, which is one of the most important indicators of ED, using nonlinear analysis. This study contributes to the literature by investigating the long-run effects of ETs on EF and CO2 emissions in Turkey by nonlinear analysis. Therefore, the model consisting of annual data for the period 1994–2019 was estimated by Dufrénot et al. (2006) nonlinear cointegration test. According to the estimation results obtained, ETs do not have any long-run effects on EF and CO2 emissions. Accordingly, it can be concluded that ETs in Turkey do not affect preventing ED.

College students’ innovation and entrepreneurship ability based on nonlinear model

The article analyzes why colleges and universities should strengthen innovation and entrepreneurship education based on “mass entrepreneurship and innovation”’ First, we conduct a questionnaire survey on the status of college students’ innovation and entrepreneurship attitude and use nonlinear methods to construct an evaluation model of innovation and entrepreneurship capabilities to evaluate students’ innovation and entrepreneurship capabilities quantitatively. Finally, we verify the effectiveness of the combined evaluation model through data on the innovation and entrepreneurship activities of college students. The research results can provide a new idea for appraisal of college students’ innovation and entrepreneurship ability.

Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann–Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60–100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.

Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models

Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework. Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021

Online social cohesion reflects real-world group action in Syria during the Arab Spring

In recent years, political activists have taken to social media platforms to rapidly reach broad audiences. Despite the prevalence of micro-blogging in these sociopolitical movements, the degree to which virtual mobilization reflects or drives real-world movements is unclear. Here, we explore the dynamics of real-world events and Twitter social cohesion in Syria during the Arab Spring. Using the nonlinear methods cross-recurrence quantification analysis and windowed cross-recurrence quantification analysis, we investigate if frequency of events of different intensities are coupled with social cohesion found in Syrian tweets. Results indicate that online social cohesion is coupled with the counts of all, positive, and negative events each day but shows a decreased connection to negative events when outwardly directed events (i.e., source events) were considered. We conclude with a discussion of implications and applications of nonlinear methods in political science research.

Influence of anxiety on the heart rate variability of patients in preoperative orthopedic surgery

Anxiety is a negative emotional response to situations that threaten the subject. Objective: The present study aims to verify the influence of anxiety on heart rate variability, considering two specific times: hospitalization and before surgery. In this analytical and cross-sectional study, the Hospital Anxiety and Depression Scale (HADS) was used to classify anxiety levels. Methodology: The time series of RR intervals were collected by Polar® monitor. Nonlinear methods and decision tree algorithm were combined with HADS scale to analyze the influence of the preoperative period on heart rate variability. The nonlinear methods used detrended fluctuation analysis (DFA), recurrence quantification analysis (RQA), and central tendency measure (CTM). Results: Among the 42 study participants, 13 (31%) were classified as anxious at hospital admission. The applied time domain methods found an increase in the heart rate variability (HRV) values in all features analyzed (p < 0.05). CTM method showed HRV reduction for the values considering radius between 6 and 20 milliseconds (p < 0.05). Conclusion: The anxiety identified at admission is directly related to the reduction in heart rate variability demonstrated by nonlinear methods, such as the central tendency measure.