Anatomical connectivity reconstruction of biological neuronal networks using Granger causality.

The poultry industry has become one of the fastest-growing agricultural sectors in Asia, driven by rising incomes, and shifting food preferences. Therefore, this study aims to examine the relationship between chicken meat production and key determinants, including chicken meat consumption, gross domestic product, and trade openness, over 30 years (1993-2022) across 28 Asian countries. This study's foundation was based on the theories of consumer demand and international trade. Wavelet coherence and Granger causality analysis were utilised to identify the direction of causality of the variables. The Wavelet results reveal that chicken consumption and GDP become most significant with the production in the Asian continent, while Granger results reveal that most Asian countries showed unidirectional causal flows from trade openness to chicken meat production and from chicken meat production to gross domestic product and consumption. Furthermore, this study provides novel insights that inform policy considerations for policymakers, international and domestic organisations, and governments, aligning with the Sustainable Development Goals established by the United Nations.

Traditional predictive maintenance approaches in assembly systems often overlook the complex interplay between operational modes and their distinct failure propagation mechanisms. This study contributes to predictive maintenance within the Industry 5.0 paradigm by introducing an unsupervised operational-mode learning and causal forecasting framework designed to jointly enhance operational reliability and environmental sustainability. The proposed methodology integrates unsupervised operational-mode discovery, supervised classification for real-time mode identification, and mode-conditioned Granger causal analysis to uncover context-dependent causal dependencies among system variables, forming the causal foundation for mode-specific forecasting models. A three-station automatic assembly line with shared resources serves as the validation case study, using over 190 days of multi-sensor operational data. The mode-specific analysis reveals three fundamentally distinct causal topologies: a sustainability-driven mode, where carbon-footprint and power metrics act as dominant causal drivers; an operator-interaction-driven mode, where human–machine delays generate multi-path cascades across subsystems; and an emergency-propagation mode, where failures propagate bidirectionally between stations, forming tightly coupled cascading loops. By analyzing mode-specific causal structures, the proposed framework demonstrates improved interpretability of failure propagation mechanisms across operational contexts and provides a principled basis for developing mode-specific causal forecasting models. Moreover, the integration of environmental indicators into the causal layer enables a dual-objective analysis that links reliability degradation with sustainability impact. The findings highlight how mode-aware causal analysis can support context-adaptive and sustainability-oriented predictive maintenance, while positioning mode-specific multivariate regression forecasting as a natural extension of the proposed framework. • Introduces an operational mode discovery and causal analysis framework for Industry 5.0 assembly systems. • Integrates unsupervised and supervised learning to identify context-dependent failure propagation regimes. • Uses Granger causality to link sustainability indicators with reliability via mode-specific topologies. • Empirically validates the framework on assembly lines, proving stability over unstable global baselines. • Identifies dominant cascading failure pathways through spectral analysis and propagation intensity metrics.

• Surveyed 1,000 rural households in Mahendragarh (Haryana) and Jhunjhunu (Rajasthan) to assess health impacts of biomass fuel use. • Identified high prevalence of respiratory symptoms, especially among women and children exposed to smoke from wood, dung cakes, and crop residues. • Developed a comprehensive analytical system integrating PM monitoring, weather data, and health surveys to model exposure-risk relationships. • Applied machine learning (Random Forest, XGBoost, LSTM) and causal inference methods (Granger causality, Causal Impact) to predict pollution and health outcomes. • Designed real-time API and dashboard tools for public health alerts and policy support in rural air quality management. This study investigates the spatiotemporal dynamics of ambient air pollution and its health impacts across two semi-urban districts in India- Jhunjhunu and Mahendragarh, using a multidisciplinary approach combining statistical analysis, machine learning, and causal inference. A one-year high-resolution monitoring dataset of PM₁, PM₂.₅, PM₄, and PM₁₀ was integrated with structured household health surveys covering over 1,000 households. High-resolution monitoring of PM₁, PM 2.5 , PM₄, and PM₁₀, along with survey-based health data, was analyzed to explore pollutant behavior, exposure-response relationships, and symptom prevalence. Linear regression models effectively predicted PM 2.5 trends in Jhunjhunu, while advanced models such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) captured complex variability in Mahendragarh. Models were trained using a 70:30 train–test split with k-fold cross-validation and evaluated using RMSE, MAE, and R² metrics. LSTM and XGBoost achieved the best performance (R² up to 0.87; RMSE reduced by approximately 30% compared to linear regression). SHAP analysis highlighted PM₁ and PM₄ as critical predictors, underscoring the need to expand national air quality standards beyond PM 2.5 and PM₁₀. Explainable machine learning using SHAP identified PM₁ and PM₄ as influential predictors of health-related outcomes, underscoring the need to expand national air quality standards beyond PM2.5 and PM₁₀. Granger-causal links, residual diagnostics, and health symptom anomalies revealed significant associations between particulate pollution and respiratory, cardiovascular, and visual symptoms, particularly in Mahendragarh. Policy insights emphasize cleaner fuel adoption, improved ventilation, and awareness campaigns to mitigate risk among vulnerable, low-income households. By integrating machine learning with epidemiological modeling, this study provides robust, location-specific evidence to support targeted environmental health interventions in under-monitored regions. A key innovation of this study lies in the joint monitoring and modeling of PM₁ and PM₄ alongside conventional PM₂.₅ and PM₁₀ using explainable ML and causal inference. This framework captures nonlinear exposure–response patterns and improves predictive accuracy while providing mechanistic insight into particle-size-specific health risks. The results offer actionable evidence for clean fuel transition, household ventilation improvements, and community-level air quality management in semi-urban and rural settings. Integration of high-resolution particulate monitoring, machine learning, and causal inference reveals strong links between PM₁-PM₁₀ exposure and cardiopulmonary and ocular symptoms in semi-urban India, highlighting PM₁ and PM₄ as key predictors for targeted interventions.

MSDG: A multiscale dynamic graph Neural network for inferring dynamic granger causality in multivariate time series

Economic development strongly influences both the level and scope of the tax burden, while the level and structure of taxation can, in turn, decisively influence economic development. Similarly, the convergence processes of both phenomena might be interrelated. This paper analyses the convergence of both the tax burden and economic development, as measured by the tax level ratio and GDP per capita, respectively, in OECD countries over the period 1995–2022. To analyse the possible interdependence between the tax burden and economic development, we use a recursive panel analysis and control for heterogeneity with fixed effects to estimate the time evolution of the convergence rates of both variables. Our findings show that the speed of convergence of the tax burden is much higher than that of GDP per capita. During the recessionary phase of the economic cycle, the speed of economic convergence increases because better positioned countries experience a larger decline in growth than those initially in a worse position; whereas during the expansionary phase of the cycle, the opposite holds. The Granger causality test confirms that convergence in GDP per capita influences convergence in the tax burden, but not vice versa. • Economic development affects a country’s tax capacity, while taxation, in turn, influences economic growth. • The speed of convergence of the tax burden is much higher than that of GDP per capita. • In recessionary periods, both the speed of economic convergence and that of tax burden convergence tend to accelerate, while in expansionary periods they tend to slow down. • An analysis of the long-term evolution of taxation in OECD countries shows that the observed trends towards convergence in overall ratios and structures are not necessarily irreversible. • GDP per capita convergence drives tax burden convergence, but not vice versa.

Comprehending the dynamic between globalisation and economic growth in low-income nations is vital to understanding how they navigate growth trajectories whilst addressing global concerns. This study examined the nexus between globalisation and its financial, social, and political facets in relation to growth in fourteen low-income nations. The analysis spanned over five decades and the Wavelet Coherence and Granger Causality methodologies. The findings revealed a bidirectional causal relationship between globalisation and growth in Rwanda, unidirectional causal flows in Burundi, Sierra Leone, Sudan, and Uganda. A bidirectional relationship between economic integration and growth was identified in Burkina Faso. Possible policy actions aligned with the United Nations Sustainable Development Goals have been developed, focusing on the country-specific dynamics of each nation. These policy recommendations comprise introducing incentives for foreign investments in Rwanda and liberalising trade in Burkina Faso to reinforce economic globalisation. The study also recommends the expansion of digital infrastructure and global educational avenues in Burundi and Uganda to strengthen social integration, and the reinforcement of governance mechanisms in Chad and Togo to encourage political integration. This study contributes to the globalisation-growth literature by offering time-sensitive insights into the growth trajectories of low-income economies.

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO2 intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO2 intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO2 and energy intensity, while short-run effects are negative and significant only for CO2 intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development.

The role of Indoor Environmental Quality (IEQ) factors in shaping student behavior and emotional states in the classroom, which have been observed as potentially diminishing performance, necessitates objective and continuous assessment to overcome the limitations of subjective methods. This study addressed this need by utilizing a case study approach. We deployed an AI-powered behavioral observation system to anonymously estimate aggregate student behavior metrics (Engagement, Attention, Interaction) in real-time, synchronized with data collected from a custom-built multi-sensor device monitoring IEQ factors, including temperature, humidity, equivalent carbon dioxide (eCO[Formula: see text]), total volatile organic compounds (TVOCs), air quality index (AQI), light variations, and oxygen volume (O[Formula: see text]). Comprehensive statistical and causality analyses included nonparametric correlations, Cross-Correlation Function (CCF) analyses to assess lagged effects, Time-Varying Granger Causality (TV-GC) tests, and categorical analysis with Chi-squared tests. The results revealed that thermal and humidity extremes correlate with increased behavioral volatility. Temperature is the most consistent predictor of student attention; Chi-squared and violin plot analyses demonstrated that attention levels are significantly higher at slightly lower temperatures, specifically below 30.9[Formula: see text]C, within the reported range [29.62 - 31.35[Formula: see text]C]. Besides, a significant association between humidity and attention was observed, although it was significant at the [Formula: see text] level rather than the more stringent [Formula: see text] threshold applied to core findings. Additionally, the study identified a critical TV-GC relationship between O[Formula: see text] volume and engagement, pinpointing specific causal bursts that global correlation measures failed to capture. Standard CCF analyses suggested that lower light levels may be associated with higher interaction levels; however, this pattern was not statistically significant after pre-whitening and bootstrapping the CCF, nor was it supported by the TV-GC analyses. These findings advocate for responsive, automated classroom systems that dynamically adjust IEQ parameters to synchronize with the temporal demands of the learning process.

Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high-throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems. Granger causality (GC) and convergent cross mapping (CCM), two prominent dynamical causal discovery methods, have gained attention in ecological studies for uncovering causal relationships in nonlinear systems. GC traces its roots to economics and was later extended through the information-theoretic framework of transfer entropy (TE). On the other hand, CCM was developed from studies of chaotic time series. Both methods have provided critical insights into the dynamics of complex systems. In this review, we synthesize foundational concepts and recent developments in GC and CCM, exploring their respective strengths and limitations, while clarifying their interrelationship. We also review recent advances in temporal causal discovery methods, originally developed within the framework of statistical causal inference for non-temporal data, and highlight their applicability to ecological data sets. Despite these advances, such approaches remain largely unfamiliar to ecologists. We argue that a rigorous framework of time-series-based causal inference, together with an appreciation of their diverse methodological developments to date, will not only raise awareness of unresolved challenges but also create new research opportunities in ecology. By offering an integrated perspective, we encourage the application and development of cutting-edge methods in ecology to help foster a deeper understanding of ecosystem dynamics.

Suicide exhibits a consistent seasonal pattern worldwide, peaking in late spring and early summer in both hemispheres. Yet, the biological mechanisms and environmental triggers underlying this temporal pattern remain poorly understood. Most studies have focused on temperate regions, while few have examined tropical or equatorial zones or conducted detailed, latitude-stratified analyses. Additionally, methodological inconsistencies limit cross-regional comparisons and contribute to conflicting findings. We analyzed monthly suicide data (2010-2019) from 5259 Brazilian municipalities, integrating sociodemographic, geographic, and climatic variables. Seasonal patterns and long-term temporal dynamics across variables were evaluated using Cosinor models, ARIMA forecasting, Granger causality, machine learning, and robust regressions. We confirmed a significant seasonal rhythm in suicide rates, with peaks near the summer solstice (December) and troughs in winter (June), particularly below -20° latitude, closely tracking photoperiod variation. Suicide rates and seasonal amplitude increased toward southern latitudes, indicating a latitudinal cline. Suicide rates were significantly associated with social-solar time misalignment (dGMT), with higher rates observed in western time zones. Regression models identified photoperiod, dGMT, urbanization, and Human Development Index as significant predictors, with urbanization inversely related to suicide. Although socioeconomic development correlated with suicide rates at the national level, this association did not persist within the seasonal region, likely reflecting underlying socioeconomic disparities between Brazil's geographic regions. Together, these findings highlight a latitudinal gradient in suicide seasonality in Brazil and suggest that circadian misalignment driven by photoperiod and social time structures may play a key role in shaping suicidal behavior.

Classification of autism spectrum disorder using a directional graph attention network on brain effective connectivity.

Asymmetric effects of climate change adaptation on energy transition in top clean and dirty energy-consuming countries.

Objective.The brain functions as a complex network of billions of interconnected neurons, coordinating processes from basic reflexes to high-level cognition. Dysfunction in these networks contribute to neurological and psychiatric disorders, including epilepsy, depression, and Parkinson's disease. Understanding these network alterations is essential for developing effective therapies. However, reconstructing network topology from human electrophysiology data is challenging due to sparse spatial sampling, measurement noise, and variable time delays in interregional communication. Effective connectivity (EC) metrics have been developed to infer directed neural interactions, but their accuracy under real-world data constraints remain unclear. This study empirically compares the ability of common EC metrics to reconstruct relationships between simulated time series with known temporal relationships and network topologies in the presence of data limitations common to human electrophysiology data. By utilizing networks and temporal relationships that are mathematically simple, this framework provides broad conceptual backing to understand the reliability of EC metrics and establishes groundwork upon which more complex spatial and temporal relationships between time series can be evaluated.Approach.We generated Erdős-Rényi networks and simulated time series using a time-delayed vector autoregressive model. We systematically varied network size, data length, measurement noise, and network coverage. Variations of four commonly used EC metrics, cross-correlation, Granger causality (GC), mutual information (MI), and transfer entropy, were evaluated for reconstruction accuracy using cosine distance, as well as receiver operating characteristic (ROC) curves, to compare estimated and true coupling matrices.Main Results.Multivariate transfer entropy demonstrated the highest accuracy across various conditions but required significantly longer computation times. For small networks (<30 nodes), MI and GC rapidly and accurately reconstructed networks. For larger networks, partial cross-correlation performed well with good computational efficiency. Notably, zero-lag metrics perform no better than chance for time-lagged time series relationships in nearly all conditions.Significance.The choice of an EC metric should consider specific data constraints. While multivariate transfer entropy is the most reliable across conditions, its long runtime limits its practical application. For large networks, partial cross-correlation offers a faster and reasonably accurate alternative. GC and MI are effective for small networks. Critically, time-lagged metrics are essential for accurate network reconstructions, as failing to account for time delays leads to reconstructions no more accurate than random network models.

Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand.

ABSTRACT In an increasingly globalized and intricate global supply chain, understanding the interaction between geopolitical risks (GPR) and global supply chain is critical to ensuring efficiency and viability. We examine the causality and transmission mechanism between GPR and global supply chain pressure (GSCP) in Taiwan. By identifying the time-varying transmission mechanism through the bootstrap rolling-window Granger causality test, we find that GPR has positive and negative effects on GSCP. The positive one suggests that GPR leads to supply chain disruptions and increases transport costs, thus increasing the pressure on global supply chains; the negative one indicates that GPR promotes restructuring and innovation in industries, hence improving supply chain resilience. In turn, GPR is positively influenced by GSCP, which suggests that the restructuring of supply chains leads to pressure on Taiwan to shift the industrial chain and thus at risk of marginalization. The results suggest that Taiwan’s deep integration into the global supply chain makes the latter highly vulnerable to geopolitical risk shocks originating from Taiwan. With tensions in Sino-U.S. and cross-Strait relations, these findings provide valuable implications for responding to GSCP and enhancing its resilience by preventing and mitigating potential risks arising from geopolitical events.

This study empirically investigates the price discovery mechanism within the Taiwanese Exchange-Traded Fund (ETF) market by examining the dynamic lead-lag relationships between ETF futures, spot prices, and Net Asset Value (NAV). Focusing on three major ETFs (Yuanta Taiwan 50, Yuanta High Dividend, and Cathay Sustainable High Dividend) from August 2023 to August 2024, multi-frequency datasets are analyzed using Granger causality tests. Results reveal heterogeneous price discovery patterns across ETFs. For the Yuanta High Dividend ETF (0056) and Cathay Sustainable High Dividend ETF (00878), futures prices exhibit a dominant informational role, significantly leading spot prices and NAV, particularly in high-frequency (5-minute) data. In contrast, for the Yuanta Taiwan 50 ETF (0050), the price discovery process is more complex, with spot prices and NAV exerting a stronger causal influence on futures prices rather than the reverse. These findings suggest that the direction of information transmission varies with ETF characteristics and data frequency, highlighting the importance of multi-frequency analysis in understanding ETF market dynamics. JEL classification: C01, C32, G00, G14. Keywords: Exchange-Traded Funds (ETFs); Price Discovery; Futures; Granger Causality.

ABSTRACT Forced displacement has become an increasingly pressing global challenge, particularly in Africa where refugee movements interact with fragile development structures and growing environmental pressures. Despite the rapid rise in refugee flows across the continent, the literature largely examined refugee dynamics in the context of humanitarian, social and economic perspectives by leaving the relationship between refugee movements, ecological footprint and sustainable development largely unexplored. To fill this gap, the paper empirically investigated the long-run relationship and Granger causality among refugees population, ecological footprint, sustainable development and economic growth in Uganda, Kenya, Congo, and the Democratic Republic of Congo over the period 1990–2023 by the Fourier-based Autoregressive Distributed Lag and the Fourier-based Granger causality test. Before model-based policy recommendations, the robustness of the results is thoroughly evaluated through ARDL methods, residual diagnostics (Jarque–Bera and kurtosis tests), lag-variant Granger causality tests, Fourier F-tests and forecast performance evaluations. The results revealed the existence of cointegration among the variables. Granger causality results indicated a unidirectional causality running from refugee movements to both ecological footprint and sustainable development, and economic growth, and unidirectional causality from EF to SDI except Uganda. Overall, the results demonstrated that refugee movements significantly influence both ecological footprint and sustainable development in the selected countries by suggesting that refugee policies should be integrated with environmental management and sustainable development strategies rather than addressed solely within a humanitarian framework.

Purpose This study investigates the association between artificial intelligence (AI) investments and nuclear energy consumption in Europe. As digital transformation accelerates and clean energy transitions intensify, understanding how AI-driven technological progress shapes nuclear energy demand becomes increasingly important. By focusing on AI venture capital investments alongside economic, institutional and structural factors, the study aims to provide empirical evidence on whether AI development contributes to the expansion and efficiency of nuclear energy consumption within European countries. Design/methodology/approach The analysis employs an unbalanced panel dataset covering 12 European countries over the period 2006–2024. Nuclear energy consumption is modeled as a function of AI venture capital investments, high-technology exports, industrial value added, rule of law and population size. The baseline estimations are conducted using Feasible Generalized Least Squares (FGLS). Robustness is assessed through Prais–Winsten regression, Panel-Corrected Standard Errors (PCSE) and Driscoll–Kraay standard errors. Panel Granger causality tests are applied to explore dynamic causal relationships among the variables. Findings The empirical results reveal that AI investments exhibit a statistically significant and positive association on nuclear energy consumption. A 1% increase in AI investments is associated with an approximate 0.004–0.06% rise in nuclear energy consumption across alternative specifications. High-technology exports also exert a consistently positive influence, while industrial value added emerges as a key driver of AI investments and technological exports. Granger causality results indicate that nuclear energy consumption precedes population growth, highlighting the strategic role of nuclear energy in supporting long-term economic and demographic dynamics. Originality/value This study contributes to the literature by providing one of the first macro-level econometric analyses linking AI investments directly to nuclear energy consumption. Unlike existing studies that focus primarily on renewable energy, it extends the AI–energy nexus to nuclear power within the EU context. The findings offer empirical insights relevant to ongoing policy debates by documenting associations between digital innovation ecosystems and nuclear energy utilization. The study informs, but does not prescribe, policy approaches to energy security, technological advancement, and sustainable energy transitions, leaving normative judgments to policymakers and stakeholders.

The neural basis of fake news accuracy judgments and sharing decisions.