Exploring sustainable energy consumption and social conflict risks in Turkey: Insights from a novel multiresolution ARDL approach.

Nonrenewable energy sources have been shown to be a cause of conflict and terrorism, highlighting the global conflict aspect, but little is known about the causal relationship between the energy system and terrorism in Turkey. This study aims to fill this gap by examining the causal links among renewable energy consumption, fossil fuels, terrorist attacks, education, trade opening, and geopolitical risks in Turkey from 1980 to 2016. Using the autoregressive distributed lag (ARDL) approach and Granger causality tests, the study analyzes the short and long-term relationships between the variables. Additionally, robustness tests are conducted using a powerful multiresolution ARDL approach to ensure the stability of the statistical findings. The results reveal the existence of long-term relationships between all the variables, particularly among terrorism, renewable energy, and education. In the short term, a one-way relationship exists between terrorism and education to renewable energies and from trade openness to terrorism. The study demonstrates that nonrenewable energy increases terrorism in the long term, whereas renewable energy and trade openness reduce terrorism, highlighting the potential impact of global conflicts on Turkey's sustainable development. Therefore, renewable energy is a powerful tool to fight against terrorism, and Turkey has encouraged its use and deployment of diplomatic efforts to resolve political and military conflicts, particularly in the Middle East. This study provides insights into the complex relationship among sustainable energy consumption, terrorism, education, and trade opening, contributing to the understanding of the geopolitical risks and economics in Turkey. It has implications for policymakers in the region, highlighting the importance of renewable energy and trade openness as tools for conflict resolution and sustainable development in the face of global conflicts.

Skewed multifractal scaling of stock markets during the COVID-19 pandemic.

This article proposes a new paradigm of asymmetric multifractality in financial time series, where the scaling feature varies over two adjacent intervals. The proposed approach first locates a change-point and then performs a multifractal detrended fluctuation analysis (MF-DFA) on each interval. The study investigates the impact of the COVID-19 pandemic on asymmetric multifractal scaling by analyzing financial indices of the G3+1 nations, including the world's four largest economies, from January 2018 to November 2021. The results show common periods of local scaling with increasing multifractality after a change-point at the beginning of 2020 for the US, Japanese, and Eurozone markets. The study also identifies a significant transition in the Chinese market from a turbulent multifractal state to a stable monofractal state. Overall, this new approach provides valuable insights into the characteristics of financial time series and their response to extreme events.

Multiscaled causality of infections on viral testing volumes: The case of COVID-19 in Tunisia.

OBJECTIVES: Coronavirus disease (COVID-19) is one of the most detrimental pandemics that affected the humanity throughout the ages. The irregular historical progression of the virus over the first year of the pandemic was accompanied with far-reaching health and social damages. To prepare logistically against this worsening disaster, many public authorities around the world had set up screening and forecasting studies. This article aims to analyse the time-frequency co-evolution of the number of confirmed cases (NCC) in Tunisia and the related number of performed polymerase chain reaction (PCR) tests over the COVID-19 first year. Accurately predicting such a relationship allows Tunisian authorities to set up an effective health prevention plan. STUDY DESIGN: In order to keep pace with the speed of evolution of the virus, we used uninterrupted daily time series from the Tunisian Ministry of Public Health (TMPH) recorded over the COVID-19 first year. The objective is to: (1) analyse the time-frequency progress of the NCC in relationship with the number of PCR tests, (2) identify a multi-scale two-factor stochastic model in order to develop a robust bivariate forecasting technique. METHODS: We assume a bivariate stochastic process which is projected onto a set of wavelet sub-spaces to investigate the scale-by-scale co-evolvement the NCC/PCR over the COVID-19 first year. A wavelet-based multiresolutional causality test is then performed. RESULTS: The main results recommend the rejection of the null hypothesis of no instantaneous causality in both directions, while the statistics of the Granger test suggest failing to reject the null hypothesis of non-causality. However, by proceeding scale-by-scale, the Granger causality is proven significant in both directions over varying frequency bands. CONCLUSIONS: It is important to include the NCC and PCR variables in any time series model intended to predict one of these variables. Such a bivariate and multi-scale model is supposed to better predict the needs of the public health sector in screening tests. On this basis, testing campaigns with multiple periodicities can be planned by the Tunisian authorities.

Maximal tachycardia and high cardiac strain during night shifts of emergency physicians.

PURPOSE: To compare tachycardia and cardiac strain between 24-hour shifts (24hS) and 14-hour night shifts (14hS) in emergency physicians (EPs), and to investigate key factors influencing tachycardia and cardiac strain. METHODS: We monitored heart rate (HR) with Holter-ECG in a shift-randomized trial comparing a 24hS, a 14hS, and a control day, within a potential for 19 EPs. We also measured 24-h HR the third day (D3) after both shifts. We measured perceived stress by visual analog scale and the number of life-and-death emergencies. RESULTS: The 17 EPs completing the whole protocol reached maximal HR (180.9 ± 6.9 bpm) during both shifts. Minutes of tachycardia >100 bpm were higher in 24hS (208.3 ± 63.8) than in any other days (14hS: 142.3 ± 36.9; D3/14hS: 64.8 ± 31.4; D3/24hS: 57.6 ± 19.1; control day: 39.2 ± 11.6 min, p < .05). Shifts induced a cardiac strain twice higher than in days not involving patients contact. Each life-and-death emergency enhanced 26 min of tachycardia ≥100 bpm (p < .001), 7 min ≥ 110 bpm (p < .001), 2 min ≥ 120 bpm (p < .001) and 19 min of cardiac strain ≥30% (p = .014). Stress was associated with greater duration of tachycardia ≥100, 110 and 120 bpm, and of cardiac strain ≥30% (p < .001). CONCLUSION: We demonstrated several incidences of maximal HR during shifts combined with a high cardiac strain. Duration of tachycardia were the highest in 24hS and lasted several hours. Such values are comparable to those of workers exposed to high physical demanding tasks or heat. Therefore, we suggest that EPs limit their exposure to 24hS. We, furthermore, demonstrated benefits of HR monitoring for identifying stressful events. ClinicalTrials.gov identifier: NCT01874704.

Perceived discrimination based on the symptoms of covid-19, mental health, and emotional responses-the international online COVISTRESS survey.

BACKGROUND: Despite the potential detrimental consequences for individuals' health and discrimination from covid-19 symptoms, the outcomes have received little attention. This study examines the relationships between having personally experienced discrimination based on the symptoms of covid-19 (during the first wave of the pandemic), mental health, and emotional responses (anger and sadness). It was predicted that covid-19 discrimination would be positively related to poor mental health and that this relationship would be mediated by the emotions of anger and sadness. METHODS: The study was conducted using an online questionnaire from January to June 2020 (the Covistress network; including 44 countries). Participants were extracted from the COVISTRESS database (Ntotal = 280) with about a half declaring having been discriminated due to covid-19 symptoms (N = 135). Discriminated participants were compared to non-discriminated participants using ANOVA. A mediation analysis was conducted to examine the indirect effect of emotional responses and the relationships between perceived discrimination and self-reported mental health. RESULTS: The results indicated that individuals who experienced discrimination based on the symptoms of covid-19 had poorer mental health and experienced more anger and sadness. The relationship between covid-19 personal discrimination and mental health disappeared when the emotions of anger and sadness were statistically controlled for. The indirect effects for both anger and sadness were statistically significant. DISCUSSION: This study suggests that the covid-19 pandemic may have generated discriminatory behaviors toward those suspected of having symptoms and that this is related to poorer mental health via anger and sadness.

Revisiting Islamic banking efficiency using multivariate adaptive regression splines.

Islamic banking is among rapidly-growing components in the world's financial system. Within its institutions, continuous criteria of efficiency facilitate the evaluation of the impact of the reforms and policies on the banks' performance. In this paper, we employ the Multivariate Adaptive Regression Splines (MARS) method for estimating the efficiency of Islamic banks in developed and developing countries. MARS is a well-known efficient method for the flexible modelling of high-dimensional data. Unlike previous work, using a nonparametric technique of such a robustness instead of parametric approaches contributes to the improvement of the various estimates, which provides investors with accurate and timely information they can immediately react upon for a better decision-making in turbulent times. On the one hand, the results of the experiments show that, in the emerging region, there is evidence of a strong linkage between Islamic banking efficiency and gross domestic product. On the other hand, in the developed region, the efficiency is rather based upon Sharia Supervisory Board and board committees. These outcomes confirm previous works showing that governance-related variables have a significant positive effect on Islamic banking efficiency. Furthermore, the overall MARS-based predictions reveal that Islamic banks operating in developed countries are relatively more efficient than their counterparts in emerging countries.

Assessment of the relationship between regional wall motion abnormality score revealed by parametric imaging and the extent of LGE with CMR.

The aim of this study was to assess the relationship between left ventricular (LV) regional myocardial wall motion abnormality (WMA), revealed by visual interpretation of cardiac magnetic resonance (CMR) cine images together with the computed wall motion parametric image, and the transmural scar extent, as assessed by Late gadolinium Enhancement (LGE), in 40 patients. Each cine CMR short-axis loop was processed to compute a parametric image where each pixel represents the amplitude of the Hilbert transform of videointensity over time. Two expert radiologists blindly interpreted the cine CMR images in combination with the corresponding parametric image to assign a WMA score for each of the 16 myocardial sectors in which the LV myocardium was subdivided. Such score was compared per sector to the level of transmural scar extent obtained by LGE images. A total of 592 myocardial segments were analyzed. A significant decrease in regional wall motion was observed in sectors with LGE transmural hyperenhancement > 75% of tissue, as well as a correlation between parametric image amplitude and peak radial and circumferential strain, computed by feature tracking. The results showed a reduction in prediction error Lambda of WMA from LGE of 65%, and of LGE from WMA of 63%. In particular, the estimated probability of correct prediction of WMA from LGE was 76%, while that of LGE from WMA was 75%. The interpretation of myocardial viability by LGE images combined with the WMA information, derived from cine CMR and parametric images, could improve the clinical decision making process.

The major worldwide stress of healthcare professionals during the first wave of the COVID-19 pandemic - the international COVISTRESS survey.

INTRODUCTION: The COVID-19 pandemic has initiated an upheaval in society and has been the cause of considerable stress during this period. Healthcare professionals have been on the front line during this health crisis, particularly paramedical staff. The aim of this study was to assess the high level of stress of healthcare workers during the first wave of the pandemic. MATERIALS AND METHODS: The COVISTRESS international study is a questionnaire disseminated online collecting demographic and stress-related data over the globe, during the pandemic. Stress levels were evaluated using non-calibrated visual analog scale, from 0 (no stress) to 100 (maximal stress). RESULTS: Among the 13,537 individuals from 44 countries who completed the survey from January to June 2020, we included 10,051 workers (including 1379 healthcare workers, 631 medical doctors and 748 paramedical staff). The stress levels during the first wave of the pandemic were 57.8 ± 33 in the whole cohort, 65.3 ± 29.1 in medical doctors, and 73.6 ± 27.7 in paramedical staff. Healthcare professionals and especially paramedical staff had the highest levels of stress (p < 0.001 vs non-healthcare workers). Across all occupational categories, women had systematically significantly higher levels of work-related stress than men (p < 0.001). There was a negative correlation between age and stress level (r = -0.098, p < 0.001). Healthcare professionals demonstrated an increased risk of very-high stress levels (>80) compared to other workers (OR = 2.13, 95% CI 1.87-2.41). Paramedical staff risk for very-high levels of stress was higher than doctors' (1.88, 1.50-2.34). The risk of high levels of stress also increased in women (1.83, 1.61-2.09; p < 0.001 vs. men) and in people aged <50 (1.45, 1.26-1.66; p < 0.001 vs. aged >50). CONCLUSIONS: The first wave of the pandemic was a major stressful event for healthcare workers, especially paramedical staff. Among individuals, women were the most at risk while age was a protective factor.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting.

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation.

BACKGROUND: Cardiac Magnetic Resonance Imaging (MRI) is the commonly used technique for the assessment of left ventricular (LV) function. Apart manually or semi-automatically contouring LV boundaries for quantification of By visual interpretation of cine images, assessment of regional wall motion is performed by visual interpretation of cine images, thus relying on an experience-dependent and subjective modality. OBJECTIVE: The aim of this work is to describe a novel algorithm based on the computation of the monogenic amplitude image to be utilized in conjunction with conventional cine-MRI visualization to assess LV motion abnormalities and to validate it against gold standard expert visual interpretation. METHODS: The proposed method uses a recent image processing tool called "monogenic signal" to decompose the MR images into features, which are relevant for motion estimation. Wall motion abnormalities are quantified locally by measuring the temporal variations of the monogenic signal amplitude. The new method was validated by two non-expert radiologists using a wall motion scoring without and with the computed image, and compared against the expert interpretation. The proposed approach was tested on a population of 40 patients, including 8 subjects with normal ventricular function and 32 pathological cases (20 with myocardial infarction, 9 with myocarditis, and 3 with dilated cardiomyopathy). RESULTS: The results show that, for both radiologists, sensitivity, specificity and accuracy of cine-MRI alone were similar and around 59%, 77%, and 71%, respectively. Adding the proposed amplitude image while visualizing the cine MRI images significantly increased both sensitivity, specificity and accuracy up to 75%, 89%, and 84%, respectively. CONCLUSION: Accuracy of wall motion interpretation adding amplitude image to conventional visualization was proven feasible and superior to standard image interpretation on the considered population, in inexperienced observers. Adding the amplitude images as a diagnostic tool in clinical routine is likely to improve the detection of myocardial segments presenting a cardiac dysfunction.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.