Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data.

BACKGROUND AND AIMS: Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets. DESIGN, SETTING AND PARTICIPANTS: Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part. MEASUREMENTS: Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks. FINDINGS: Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09). CONCLUSIONS: Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

Dynamics of postnatal depressive symptoms in early parenthood.

BACKGROUND: New mothers and fathers are at risk of developing postnatal depressive problems. To understand how postnatal depressive symptoms unfold over time, analyses at the within-person level are necessary. Inspecting postnatal depressive problems at the symptom level provides a novel perspective, ultimately offering insight into which symptoms contribute to the elevation of other symptoms over time. METHODS: Panel graphical vector-autoregression (GVAR) models were applied to analyze the within-person temporal and contemporaneous relations between depressive symptoms across the postnatal period in new mothers and fathers (at T1; Nmothers = 869, Nfathers = 579). Depressive symptoms were assessed at 6-, 12-, and 18-months postpartum, using the Edinburgh Postnatal Depression Scale. RESULTS: The results revealed that for mothers, sadness was a key symptom predicting symptom increases in multiple other depressive symptoms and itself (autoregressive effect) over time. Furthermore, anxiousness and feeling scared predicted each other across the postnatal period in mothers. For fathers, the most central predicting symptom in the overall network of symptoms was being anxious, while self-blame and being overwhelmed had strong self-maintaining roles in the fathers' symptomatology, indicating that these could be key features in fathers experiencing postnatal depressive problems. The pattern of symptoms that mothers and fathers experienced within the same time window (contemporaneous associations), shared many of the same characteristics compared to the temporal structure. CONCLUSIONS: This study suggests that across the postnatal period, from 6- to 18-months postpartum, depressive symptoms in mothers and fathers contribute differently to the pattern of depressive problems, highlighting sadness as a key feature in maternal symptomatology and anxiousness components in paternal symptomatology.

Temporal trend and driving effect of demographic transitions on embedded carbon emissions of Chinese households.

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input-output tables from 1997 to 2018 with the Mann-Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers.

Exploring the Chinese public's affective attitudes towards digital transformation in agriculture: A social media-based analysis.

This study utilizes natural language processing techniques and panel vector autoregression methodology, to delve into the perceived attitudes of social media users towards the digital transformation of agriculture, and to assess its impact on total agricultural output and agricultural science and technology inputs. Data related to agricultural digital transformation were collected from Sina Weibo using web crawlers. The SnowNLP model was employed to infer users' attitudes, encompassing both positive and negative aspects. Furthermore, the study delves into the specific themes capturing users' positive attitudes and explores regional variations in focus. The findings reveal a sustained increase in users' interest in agricultural digital transformation since 2013. Positive attitudes primarily center around green development, agricultural intelligence, and global cooperation and innovation. Moreover, the study establishes a significant positive impact of users' positive attitudes on both total agricultural output value and agricultural science and technology investment, highlighting the constructive influence of user support on the agricultural industry's development.

Comparative analyses of SARS-CoV-2 RNA concentrations in Detroit wastewater quantified with CDC N1, N2, and SC2 assays reveal optimal target for predicting COVID-19 cases.

To monitor COVID-19 through wastewater surveillance, global researchers dedicated significant endeavors and resources to develop and implement diverse RT-qPCR or RT-ddPCR assays targeting different genes of SARS-CoV-2. Effective wastewater surveillance hinges on the appropriate selection of the most suitable assay, especially for resource-constrained regions where scant technical and socioeconomic resources restrict the options for testing with multiple assays. Further research is imperative to evaluate the existing assays through comprehensive comparative analyses. Such analyses are crucial for health agencies and wastewater surveillance practitioners in the selection of appropriate methods for monitoring COVID-19. In this study, untreated wastewater samples were collected weekly from the Detroit wastewater treatment plant, Michigan, USA, between January and December 2023. Polyethylene glycol precipitation (PEG) was applied to concentrate the samples followed by RNA extraction and RT-ddPCR. Three assays including N1, N2 (US CDC Real-Time Reverse Transcription PCR Panel for Detection of SARS-CoV-2), and SC2 assay (US CDC Influenza SARS-CoV-2 Multiplex Assay) were implemented to detect SARS-CoV-2 in wastewater. The limit of blank and limit of detection for the three assays were experimentally determined. SARS-CoV-2 RNA concentrations were evaluated and compared through three statistical approaches, including Pearson and Spearman's rank correlations, Dynamic Time Warping, and vector autoregressive models. N1 and N2 demonstrated the highest correlation and most similar time series patterns. Conversely, N2 and SC2 assay demonstrated the lowest correlation and least similar time series patterns. N2 was identified as the optimal target to predict COVID-19 cases. This study presents a rigorous effort in evaluating and comparing SARS-CoV-2 RNA concentrations quantified with N1, N2, and SC2 assays and their interrelations and correlations with clinical cases. This study provides valuable insights into identifying the optimal target for monitoring COVID-19 through wastewater surveillance.

Short-term epileptic seizures prediction based on cepstrum analysis and signal morphology.

This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employing algorithms with high initial data volume and computations time to increase the accuracy is an important problem in prediction issues. Consequently, reduction of calculations is met by applying only two effective EEG signal channels without manual removal of artifacts by visual inspection as the algorithm's input. Autoregression (AR) modeling and Cepstrum detect changes due to IT period. We carry out the goal of higher accuracy by increasing sensitivity to interictal epileptiform discharges or artifacts and reduce errors caused by them, taking advantage of the discrete wavelet transform and the comparison of two channels epochs by applying the median filter. Averaging and positive envelope methods are introduced to patient-specific thresholds become more differentiated as soon as possible and can be lead to sooner prediction. We examined this method on a mathematical model of adult epilepsy as well as on 10 patients with EEG data. The results of our experiments confirm that performance of the proposed approach in accuracy and average false prediction rate is superior to other algorithms. Simulation results have been shown the robustness of our proposed method to artifacts and errors, which is a step towards the development of real-time alarm devices by non-invasive techniques.

Idiographic bidirectional associations of stressfulness of events and negative affect in daily life as indicators for mental health: An experience sampling study.

Evidence suggests that complex micro-dynamics occurring in daily life underly the development of mental distress. We aimed to (1) study the cross-lagged association between stressful events and negative affect (NA), (2) show that there is substantial between-person variability in idiographic associations and (3) show that idiographic associations are indicative of mental health. Experience sampling study assessing perceived stressfulness of events (PSE) and NA four times per day for 2 weeks in a non-clinical convenience sample (N = 70, mean age = 22.9, 61% female, 69% German). Bivariate vector autoregressive model implemented in dynamic structural equation modelling to model the associations between stressful events and NA and obtain idiographic associations. Stressfulness of events and NA were significantly reciprocally associated. Autocorrelations and cross-lagged associations from PSE to NA showed substantial variability and were significantly related with trait measures of depression, anxiety, well-being, and perceived stress. Contrary to expectations, cross-lagged associations from NA to stressfulness of events were not related to trait mental health. The approach outlined in this article is useful for studying idiographic dynamics in daily life. The findings increase our understanding of micro-dynamics underlying mental health and individual differences in these processes.

Effectiveness of hierarchical medical system and economic growth: based on China's urban vs. rural health perspectives.

Background: The hierarchical medical system is an important measure to promote equitable healthcare and sustain economic development. As the population's consumption level rises, the demand for healthcare services also increases. Based on urban and rural perspectives in China, this study aims to investigate the effectiveness of the hierarchical medical system and its relationship with economic development in China. Materials and methods: The study analyses panel data collected from Chinese government authorities, covering the period from 2009 to 2022. According to China's regional development policy, China is divided into the following regions: Eastern, Middle, Western, and Northeastern. Urban and rural component factors were downscaled using principal component analysis (PCA). The factor score formula combined with Urban-rural disparity rate (ΔD) were utilized to construct models for evaluating the effectiveness of the hierarchical medical system from an urban-rural perspective. A Vector Autoregression model is then constructed to analyze the dynamic relationship between the effects of the hierarchical medical system and economic growth, and to predict potential future changes. Results: Three principal factors were extracted. The contributions of the three principal factors were 38.132, 27.662, and 23.028%. In 2021, the hierarchical medical systems worked well in Henan (F = 47245.887), Shandong (F = 45999.640), and Guangdong (F = 42856.163). The Northeast (ΔDmax = 18.77%) and Eastern region (ΔDmax = 26.04%) had smaller disparities than the Middle (ΔDmax = 49.25%) and Western region (ΔDmax = 56.70%). Vector autoregression model reveals a long-term cointegration relationship between economic development and the healthcare burden for both urban and rural residents (ßurban = 3.09, ßrural = 3.66), as well as the number of individuals receiving health education (ß = -0.3492). Both the Granger causality test and impulse response analysis validate the existence of a substantial time lag between the impact of the hierarchical medical system and economic growth. Conclusion: Residents in urban areas are more affected by economic factors, while those in rural areas are more influenced by time considerations. The urban rural disparity in the hierarchical medical system is associated with the level of economic development of the region. When formulating policies for economically relevant hierarchical medical systems, it is important to consider the impact of longer lags.

Variable selection for quantile autoregressive model: Bayesian methods versus classical methods.

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.

The associations between daily reports of loneliness and psychotic experiences in the early risk stages for psychosis.

AIM: Bi-directional associations between loneliness and psychotic experiences (PEs) have been reported, but the mechanisms underlying these associations are unknown. This study aims to explore associations between daily reports of loneliness and PEs, and test differences in this association across young adult individuals at different levels of risk for psychosis. METHODS: We analysed 90-day diary data on loneliness and PEs from N = 96 participants (mean age 24.7, range 18-35, 77% female) divided into 4 subgroups, each indexing increased levels of risk for psychosis according to the clinical staging model: 'psychometric' (n = 25), 'low' (n = 27), 'mild' (n = 24), and 'ultra-high'(n = 20) risk. Multilevel vector autoregressive models examined within-day (contemporaneous) and between-day (temporal) associations between loneliness and PEs for the total sample. Next, these associations were compared across subgroups. RESULTS: Loneliness and PEs were significantly associated contemporaneously (partial correlation B = 0.14) but not temporally. Subgroup membership moderated both contemporaneous and temporal associations. The contemporaneous association between loneliness and PEs was stronger in the low-risk subgroup compared to the mild-risk (B = -0.35, p < .01) and ultra-high-risk (B = -0.36, p < .01) subgroups. The temporal association between loneliness on the previous day and PEs on the current day was stronger in mild-risk subgroup compared to the ultra-high-risk subgroup (B = -0.03, p = .03). After adjusting for multiple testing, only the contemporaneous-but not the temporal-associations remained statistically significant. CONCLUSIONS: Loneliness is associated with PEs in individuals at risk for psychosis, particularly in those with low to mild symptoms. Our findings tentatively suggest that especially individuals with low expressions of PEs may be more sensitive to social context, but future studies are needed to replicate and further unravel the potentially stage-specific interplay between social context and PEs.

Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics.

Objective.Traditional quantification of fluorescence signals, such asΔF/F, relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution.Approach.We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics.Main result.We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure (ΔF/F). Additionally, we demonstrate ARES's utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite.Significance.ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics-where traditional ratiometric measures falter-underscores its potential to revolutionize baseline-independent analyses in the field.

High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.

Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.

The expected sample allele frequencies from populations of changing size via orthogonal polynomials.

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for stepwise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder.

BACKGROUND: A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom. METHODS: A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network. RESULTS: In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality. CONCLUSIONS: This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

The dynamic relationship among green logistics, technological innovation and green economy: Evidence from China.

Under the background of "double carbon", exploring the growth path of green logistics and enhancing the driving force of technological innovation is the urgent need of our country to comply with the green transformation of its economy and realize high-quality economic growth. Taking the panel data of 30 Chinese provinces from 2010 to 2020 as the sample, the green logistics index evaluation system is constructed based on the driver-press-state-impact-response (DPSIR) theoretical framework, and the green economic efficiency of each province in the sample period is measured by using the non-expected output Super- Slacks-based measure (SBM) model, and by constructing the panel vector autoregressive (PVAR) model including technological innovation is used to systematically elaborate the dynamic influence paths among the three. The study found that: China's green economy, technological innovation, and green logistics all have their own mechanisms for growth, which will gradually diminish over time. In the near and long term, green logistics will promote technological innovations and the evolution of a green economy, but there is a lag in the long-term benefits of green logistics on technological progress. In the short term, technological innovation does not lend support to the growth of a green economy, but over time, the impact of technological innovation on the growth of that economy will shift from negative to positive. This shows that improving technological innovation capability is an important path for green logistics to promote green economic efficiency. The findings of the study provide a basis for decision making to achieve the emission reduction target and improve the efficiency of the green economy.

Coupling coordination and driving mechanisms of water resources carrying capacity under the dynamic interaction of the water-social-economic-ecological environment system.

The water resources carrying capacity (WRCC) is a complex and comprehensive system that is jointly influenced by water resources, society, the economy and the ecological environment. Previous WRCC studies have primarily focused on estimating the overall level of regional WRCC. Few studies have explored the interactions among the various elements in the WRCC system and their influence on the WRCC evolution. Therefore, the purpose of this paper is, on the one hand, to explore the dynamic interactive relationships within the WRCC system from the perspectives of water resources, society, the economy and the ecological environment using a coupling coordination degree model and a panel vector autoregressive (PVAR) model, and on the other hand, to determine the evolutionary driving mechanism of the WRCC using the geographically and temporally weighted regression (GTWR) model to improve the regional WRCC. Taking 21 cities in Guangdong Province as an example, the results show that (1) the coupling coordination degree among the four WRCC subsystems in Guangdong Province shows an overall upward trend from 2009 to 2020, and the coordination between water resources utilization and other subsystems needs to be further strengthened. (2) The economic subsystem is the core of the WRCC system with reinforcing effects on both water resources and social subsystems but significant inhibitory effects on the ecological environment subsystem. Notably, the development of the ecological environment plays a crucial role in promoting social and economic development. (3) From 2009 to 2020, the development of the WRCC in Guangdong Province is initially driven by social and economic development, followed by economic development and ecological environmental protection, and then mainly by ecological environmental protection, which gradually becomes the primary driving force. This study provides a new entry point for studying the regional WRCC and formulating targeted measures for enhancing the regional WRCC.

Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach.

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.

Who are the receivers and transmitters of volatility spillovers: oil, clean energy, and green and non-green cryptocurrency markets.

In the context of clean energy and green cryptocurrency development, the relationship between energy and cryptocurrency markets deserves further exploration. This study employs a quantile time-frequency connectedness approach to measure the dynamic connectedness and volatility propagation mechanisms between oil, clean energy, green cryptocurrency (GC), and non-green cryptocurrency (NGC) markets. Our findings suggest that, at median and low volatility levels, the oil and clean energy markets act as net receivers, taking on volatility spillovers from cryptocurrency markets. However, at high volatility levels, oil and clean energy markets transform into net transmitters. Most NGCs are volatility transmitters, while most GCs are volatility receivers in the median and extremely high volatility cases. We also observe that the total connectedness index (TCI) is heterogeneous over time and dependent on economic events. At median and low volatility levels, the short-run TCI makes the primary contribution. On the other hand, for high volatility levels, where short-term TCI does not have an absolute advantage, long-term TCI plays a greater role in many periods. Additionally, there is asymmetry in the TCI (including long-term and short-term TCI) at the quantile level. In the median and extreme scenarios, the COVID-19 has caused different levels of shock on oil, clean energy, GC, and NGC markets connectedness.