Search Results (3,697)

Background/Objectives: High sodium intake is a leading dietary risk factor for stomach cancer, particularly in East Asia. In Japan, traditional dietary patterns contribute to elevated sodium consumption and a high burden of stomach cancer. This study aims to forecast disability-adjusted life years (DALYs) for stomach cancer attributable to high sodium intake in Japan from 2022 to 2050, and to assess the impact of multiple sodium reduction policy scenarios. Methods: We conducted a longitudinal forecasting study using autoregressive integrated moving average with exogenous variables (ARIMAX) models based on Global Burden of Disease 2021 data (1990–2021). The Japanese population was stratified by sex and age groups (15–49, 50–69, and ≥70). Five future exposure scenarios were modelled: (1) reference (current trends), (2) best-case (50% reduction in sodium exposure by 2050), (3) optimal (30% reduction by 2032), (4) moderate (30% reduction by 2050), and (5) worst-case (highest exposure levels from recent years maintained). These scenarios were aligned with national and international sodium reduction targets, including the revised “Health Japan 21” (third term; 7 g/day by 2032) and the World Health Organisation (WHO) 5 g/day/30% reduction goals. Results: Under the reference scenario, age-standardised DALY rates are projected to decline by 31.4% (to 15.4 per 100,000) by 2050. The best-case scenario projects a 54.7% decline (to 10.1 per 100,000). Substantial demographic disparities persist: males and those aged ≥70 consistently show higher burdens. Notably, the 50–69 age group shows the greatest variation in 2050 projections across scenarios (17.1 to 73.5 per 100,000), indicating high policy sensitivity. Meanwhile, in the ≥70 group, DALY rates remain high regardless of scenario, especially among males (199.4 vs. 57.8 per 100,000 for females), reflecting cumulative lifetime exposure. Conclusions: Under modelled assumptions, sustained achievement of national sodium reduction targets could meaningfully reduce future stomach cancer DALYs in Japan, with the largest absolute gains in older adults but the largest relative gains in younger and middle-aged groups. Because stomach cancer aetiology is multifactorial and the projections rest on modelled associations and a continuity-of-trend assumption, these findings support strengthened, demographically targeted sodium reduction interventions as one complementary component of a broader, multi-risk factor approach to stomach cancer prevention. Full article

Standard Bayesian autoregressive changepoint models can become unstable near sample boundaries. As a candidate changepoint approaches either edge of the series, the local residual degrees of freedom shrink, producing a Gamma-function singularity in the marginal likelihood that can strongly bias the posterior toward spurious edge detections. To address this issue, we introduce a regularization framework driven by local degrees of freedom. By incorporating a centripetal prior of the form $\pi \left(k\right)\propto {\left({\nu }_1{\nu }_2\right)}^{\lambda }$—where ${\nu }_1=k-2p-1$ and ${\nu }_2=n-k-p-1$—the proposed method is designed to counteract this boundary effect. Theoretical analysis shows that a regularization intensity of $\lambda \ge 1$ is sufficient to offset this boundary effect asymptotically. Simulation results confirm that this approach substantially mitigates the U-shaped error profile typical of unregularized estimators, yielding a more favorable accuracy–robustness trade-off relative to the standard frequentist baselines considered in our study. Finally, empirical applications to several 2022 natural gas benchmarks, including TTF, SHPGX LNG, JKM, NBP, and NYMEX Henry Hub, demonstrate the framework’s ability to distinguish persistent structural transitions from transient market turbulence. These results suggest that degree-of-freedom-based centripetal prior regularization can improve the stability of Bayesian changepoint inference in nonstationary time series. Full article

This paper presents a Systematization of Knowledge (SoK) on the evaluation methodologies and capability boundaries of Large Language Models (LLMs) in cybersecurity. We propose a Three-Dimensional Taxonomy Matrix to systematize existing metrics across offensive domains, defensive applications, and inherent architectural flaws. Beyond categorization, this matrix functions as a predictive framework to expose structural evaluation blind spots. Specifically, by intersecting target domains with failure attributions, it identifies a critical, unresolved frontier: measuring cross-architecture semantic equivalence in low-level reverse engineering. Empirically, synthesizing 39 frontier benchmarks reveals a systemic evaluation gap: static metric success rarely translates into end-to-end adversarial efficacy. In offensive domains, high penetration rates correlate strongly with pre-training data contamination. When subjected to semantics-preserving code obfuscation as a stress test, zero-shot, tool-free exploit success rates collapse to near 0%. In defensive contexts, cross-procedural code auditing struggles, yielding a peak F1-score of only 23.83%. Furthermore, models suffer from over-alignment-induced functional degradation, with joint-testing frameworks recording up to a 77% functional loss in automated program repair. Our analysis strongly suggests that purely autoregressive mechanisms drive severe technical hallucinations, evidenced by a 19.7% package dependency fabrication rate. Evaluations also expose significant attack surfaces and a significant safety-utility tradeoff: models succumb to prompt leakage attacks at rates up to 86.2%, while heavily aligned versions simultaneously exhibit excessively high False Refusal Rates (FRR) for benign, borderline security queries. Finally, we delineate a theoretical neuro-symbolic roadmap—integrating LLM heuristics with deterministic formal methods—to structurally mitigate the limitations of the autoregressive paradigm. Full article

The finance–environment nexus in Sub-Saharan Africa remains complex, particularly in nations where institutional quality and fiscal policies are in an early stage. To address this, the study evaluates the impact of financial development on environmental sustainability in Sub-Saharan Africa, emphasising the moderating roles of environmental taxes and regulatory quality. Using a balanced panel methodology across 11 SSA nations from 2006 to 2023, the study employs a multi-estimation model (fixed effects (FE), Fully Modified Ordinary Least Squares (FMOLS) and Autoregressive Distributed Lag (ARDL)) to capture both short- and long-run relationships. From the analysis, the FE and FMOLS estimates indicate that financial development significantly increases ecological footprints, while foreign direct investment and government expenditure are associated with lower environmental footprints. However, the ARDL estimates reveal that environmental taxes and regulatory quality significantly reduce the ecological footprint, motivating a policy shift. Most importantly, the moderation estimation reveals that environmental taxes condition the finance–environment nexus in SSA. This depicts that while financial development worsens environmental outcomes, its adverse effects are nullified and reversed under a stronger environmental tax framework. These findings are relevant to the Environmental Kuznets Curve theory and draw insights from the institutional and financial intermediation theory. The study provides evidence that financial development, when integrated with effective environmental taxation and institutional quality, promotes environmental sustainability in SSA. Policymakers are therefore urged to strengthen environmental tax frameworks, integrate green financial intermediation and intensify regulatory institutions to achieve a sustainable finance–environment model and support SDG 13 in SSA. Full article

This study examines the long- and short-run determinants of green employment in Saudi Arabia over the period 1990–2024 using an Autoregressive Distributed Lag (ARDL) bounds testing framework within an error-correction model. Six macroeconomic and structural variables are analyzed: renewable energy capacity, GDP growth, domestic credit, urbanization, foreign direct investment, and the Vision 2030 policy regime shift. Supplementary analyses test the Environmental Kuznets Curve (EKC) hypothesis and map causal relationships using pairwise Granger causality tests. The bounds test indicates long-run cointegration among the variables (F = 8.45, exceeding the 5% I(1) critical bound of 3.61). The model explains 89% of the variation in log green employment (R² = 0.89) and passes standard diagnostic tests for serial correlation, heteroskedasticity, normality, and parameter stability. Three correlates of long-run green employment are identified. The post-2016 dummy used to capture the Vision 2030 regime shift is associated with the largest coefficient in the long-run equation (θ = 1.75, p = 0.008), although this estimate should be interpreted with caution because the dummy absorbs all post-2016 changes, including policy effects, the rapid expansion of renewable capacity, broader institutional reforms, and possibly changes in measurement practices. Renewable energy capacity is the primary continuously measurable driver (θ = 0.145, p = 0.018), with Toda–Yamamoto modified Wald tests indicating a bidirectional predictive relationship between investment and employment. Urbanization exerts a significant positive long-run effect (θ = 0.098, p = 0.001). The error correction term (δ = −0.520, p < 0.001) implies equilibrium reversion with a half-life of approximately one year. The EKC hypothesis is not supported in the Saudi context, suggesting that active decarbonization policy—rather than income-driven structural change alone—is needed for environmental improvement. The findings carry implications for Vision 2030 implementation and for other resource-dependent economies undertaking structural green transitions. Full article

Accurate tail-risk measurement in carbon markets is challenging because carbon allowance prices are shaped not only by heavy-tailed return dynamics, but also by policy changes that can alter the underlying risk dynamics. Models that ignore such structural shifts may perform reasonably well in normal periods while still understating downside risk when market conditions change. To address this issue, this paper proposes a break-regime generalized autoregressive score model with Student-t innovations, denoted as BR-GAS-t, for one-step-ahead forecasting of Value-at-Risk and Expected Shortfall. Using daily spot data from China’s carbon market, we compare BR-GAS-t with historical simulation, GARCH-N, GARCH-t, and regime-free GAS-t benchmarks. The results show that carbon returns are strongly heavy-tailed and that the post-break regime is characterized by stronger shock sensitivity, lower persistence, and a higher long-run conditional scale. Out-of-sample evidence further indicates that BR-GAS-t delivers the strongest overall VaR backtesting performance and the lowest average Fissler–Ziegel (FZ) loss in joint VaR–ES evaluation. Its advantage is most pronounced at the 2.5% and 1% tails, where downside risk is hardest to forecast. Robustness checks based on alternative break dates, window lengths, recursive schemes, and distributional assumptions confirm that the main conclusion is stable. The findings suggest that explicitly incorporating observed policy breaks improves tail-risk forecasting in policy-driven carbon markets. Full article

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. Full article

This paper examines how remittances-to-GDP are conditionally associated with GDP growth upswings and downturns in four lower-middle-income countries (LMICs) in mainland Southeast Asia—Cambodia, Laos, Myanmar, and Vietnam (CLMV)—over 2000–2021, conditional on other external inflows including foreign direct investment (FDI), official development assistance (ODA), and trade openness. Employing a nonlinear Autoregressive Distributed Lag (N-ARDL) model with a Dynamic Fixed Effects (DFE) estimator, this study estimates short- and long-run regime-differentiated associations between GDP growth regimes and remittances to GDP, controlling for foreign direct investment (FDI), official development assistance (ODA), and trade openness. GDP growth is decomposed into above- and below-median regimes, allowing the model to examine whether remittance dynamics differ across growth upswings and downturns. Panel estimates are complemented with dynamic multipliers that trace conditional adjustment paths over different horizons. The results reveal a high-growth-driven regime pattern rather than formal statistical evidence of unequal high- and low-growth coefficients. In the long run, above-median growth significantly reduces remittances to GDP (${\widehat{\theta }}_1=-0.130$, very strong evidence), consistent with the household insurance motive; below-median growth has no significant long-run association (${\widehat{\theta }}_2=-0.127$, no evidence). In the short run, above-median growth is positively associated with remittances (${\widehat{\tilde{\beta }}}_1^{+}=0.033$, very strong evidence), while below-median growth again shows no significant short-run response (${\widehat{\tilde{\beta }}}_1^{-}=0.051$, no evidence). Formal Wald tests do not reject equality between the high- and low-growth coefficients in either horizon; therefore, the findings should be interpreted as a regime-differentiated significance pattern within a nonlinear specification, not as formal proof of coefficient asymmetry. Taken together, these responses are consistent with a one-sided counter-cyclical interpretation of remittances: remittances to GDP decline when domestic growth is above the median, while no significant adjustment is observed during below-median growth episodes. The pattern documented here is therefore driven by the high-growth regime and should not be read as evidence of an active counter-cyclical surge during downturns. Trade openness and ODA exhibit significant positive short-run co-movement with remittances, whereas FDI shows a strong positive long-run association with remittances to GDP. The novelty of this study lies in providing new panel evidence on regime-differentiated remittance–growth associations for CLMV within a nonlinear N-ARDL and dynamic multiplier framework, while transparently reporting that formal Wald tests do not reject equality between high- and low-growth coefficients. Policy implications center on facilitating reliable remittance channels—reducing transfer costs and expanding financial inclusion—without assuming that remittance inflows automatically rise during downturns. Full article

Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover, mainstream prediction models struggle with long-term periodic patterns, while Reinforcement Learning (RL)-based planning often suffers from inefficient exploration in sparse topologies. To address these issues, this paper proposes a unified framework combining a spectral-temporal Graph Neural Network (GNN) and bi-directional decoding RL. Specifically, a time-frequency dual-stream adaptive learning module is introduced for prediction. Fast Fourier Transform (FFT) and Gated Recurrent Unit (GRU) are employed to capture global frequency periodicities and local temporal dynamics, respectively. Their adaptive fusion effectively mitigates the long-sequence information forgetting problem. For path planning, the task is formulated as sequence generation. A graph-aware attention encoder with adjacency masking is designed, and heuristic feature embeddings are incorporated to guide efficient exploration. Furthermore, a bi-directional autoregressive decoding strategy enhances robustness against topological bottlenecks. On PEMSD4 and PEMSD8, the proposed predictor achieves MAE/RMSE/MAPE values of 18.211/30.433/12.006 and 13.587/23.566/8.955, respectively. Path-planning simulations on the PEMSD4-derived sparse topology further demonstrate stable bi-directional RL optimization, faster convergence with heuristic guidance, and a sparsity-aware encoder that reduces redundant attention interactions in sparse road networks. These results validate the effectiveness of the proposed “predict-then-plan” paradigm. Full article

This research study compares alternative daily cryptocurrency volatility targets constructed from a common Binance intraday source under a unified and quality-controlled data pipeline. The analysis considers both realized-return-based and range-based measures, including intraday realized variance, calendar-boundary-augmented realized variance, and a realized-range proxy. Forecasting performance is evaluated using direct heterogeneous autoregressive (HAR) models at the 1-, 7-, and 30-day horizons on common out-of-sample support under two complementary loss functions: quasi-likelihood (QLIKE) and log-scale mean squared error. The results show that no universal winner emerges across these criteria. The calendar-boundary-augmented realized variance delivers the best average performance under QLIKE at all horizons, whereas the realized-range proxy performs best under log-scale mean squared error and exhibits greater month-by-month stability. By contrast, classical daily range estimators such as Garman–Klass and Parkinson are not competitive relative to the leading alternatives in this sample. A secondary Bitcoin-conditioned robustness analysis suggests that relative target rankings may vary across market conditions, with stronger contrasts during stress-like episodes. Overall, the findings indicate that the preferred daily volatility target depends primarily on the forecasting objective and should therefore be treated as a substantive empirical choice in cryptocurrency volatility forecasting rather than as a secondary implementation detail. Full article

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are becoming convenient modes of transportation; however, their safety remains a critical concern as crashes continue to occur. To reveal crash trends and temporal variations, this study develops time-series forecasting models to predict future crash counts of such vehicles. The crash dataset released by the National Highway Traffic Safety Administration (NHTSA) has been used here. Two univariate forecasting models—the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Facebook Prophet model—have been used here for different datasets. The models were trained on 30 months of data (July 2021 to December 2023) and validated on 6 months of data (January–June 2024). Validation metrics include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil’s U1 statistic. Results showed that Facebook Prophet significantly outperformed SARIMA for both datasets, achieving an RMSE of 2.71 and an MAPE of 6.9% for ADAS, and an RMSE of 2.24 and an MAPE of 8.85% for ADS. For both systems, the model revealed empirically observed cyclical patterns and consistent rising trends. ADAS crashes exhibit a bimodal temporal pattern, with recurring peaks in January and May–June, alongside notable troughs in February–March and August–September. ADS displays a trimodal pattern, with recurring peaks in April–May, August and October, alongside notable troughs in December and the early winter months. These patterns represent empirically identified temporal regularities rather than causally attributed seasonality. From the future forecasts for July to December 2024, the model showed that ADAS crashes are expected to range between 40 and 80 per month, while ADS crashes are projected to remain between 20 and 40 per month. These findings underscore the need for proactive safety measures and enhanced regulatory oversight during identified high-risk periods to mitigate the growing trend in AV crashes. Full article

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

Forecasting scrap steel prices is crucial for the economic sustainability of recycling operations, yet it remains challenging due to inherent volatility and non-stationary behavior. In this study, we develop and evaluate a delta-targeted Hybrid forecasting pipeline for short horizons of 1, 3, and 7 days. We benchmark classical baselines (Naive, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing (ETS)) against recurrent deep learning models (Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)) and recent neural forecasting baselines, including Decomposition-Linear (DLinear), Convolutional Kolmogorov–Arnold Network (C-KAN), and Neural Basis Expansion Analysis for Time Series (N-BEATS), using real-world daily scrap steel price data. The results indicate that delta-targeting generally yields more stable predictive performance than direct raw-price forecasting as the prediction horizon increases. For example, at the 7-day horizon, the predictive fit improves from approximately $R^2\approx 0.87$ for raw-price LSTM to around $R^2\approx 0.90$ for delta-trained recurrent models. At the same horizon, a delta-based RNN achieves the lowest Mean Absolute Percentage Error (MAPE) among the evaluated models (approximately $1.39\%$), while the proposed Hybrid model remains competitive across all tested horizons and maintains a goodness-of-fit of approximately $R^2\approx 0.90$ without uniformly minimizing point error relative to the best-performing recurrent baseline. Attention profiling and permutation-based feature importance analyses indicate that the model places relatively higher weight on calendar-related inputs, consistent with the presence of weekly patterns in the data; these results should be interpreted as sensitivity diagnostics rather than causal evidence. Overall, the findings suggest that delta-transformed targets provide a more suitable prediction space than raw-price targets for short-horizon scrap steel forecasting, while the Hybrid design offers a balanced combination of predictive performance and diagnostic interpretability for operational decision support. Full article

This study investigates the relationship between energy indicators and human development in South Africa over the period 1980–2023, employing a quantitative research design. Using secondary annual time-series data, the study examines the effects of electricity generation, per capita energy consumption, Oil-related fiscal revenue share as a share of total government revenue, and total energy consumption on the Human Development Index. The Autoregressive Distributed Lag (ARDL) bounds testing approach is employed to assess long-run and short-run relationships, complemented by Error Correction Models (ECM) to capture dynamic adjustments. Unit root and stability tests, including CUSUM and CUSUMSQ, ensure the robustness of the estimations, while Granger causality tests explore predictive linkages among variables. The findings reveal a positive long-run relationship between electricity generation and total energy consumption with human development, highlighting the importance of reliable and broad-based energy utilisation for enhancing welfare outcomes. In contrast, per capita energy consumption and Oil-related fiscal revenue share exhibit negative long-run effects, suggesting inefficiencies in energy use and the fiscal risks associated with reliance on oil-related government revenue. Short-run dynamics indicate that temporary adjustments, such as infrastructure expansion and transitional fiscal spending, can produce immediate but contrasting effects on human development. Granger causality analysis identifies unidirectional predictive relationships from electricity generation and Oil-related fiscal revenue share to human development, while total energy consumption exhibits weak bidirectional causality. Diagnostic tests confirm the model’s reliability and parameter stability over the study period. The results imply that energy policies in South Africa should prioritise efficient and inclusive energy use, ensure effective allocation of energy-related fiscal resources, and complement energy system improvements with broader socio-economic interventions. This study contributes to the understanding of the energy–development nexus in emerging economies, offering evidence-based insights for policymakers seeking sustainable human development. Future research could extend the analysis to provincial or sectoral levels, consider emerging energy technologies, and explore alternative development proxies to capture more nuanced socio-economic dynamics. Full article

Classical Algorithmic Information Theory (AIT) provides a rigorous foundation for information-based similarity measurement, but classical formulations and their compression-based approximations largely operate at the syntactic level, making them sensitive to surface-level variation and insufficient for semantic equivalence. To address this limitation, this paper introduces Semantic Algorithmic Information Theory. The contributions are organized around three core aspects. First, regarding algorithmic extension, we formalize the Semantic Turing Machine System (STMS) to decouple abstract concepts from their diverse syntactic realizations. Within this framework, Semantic Complexity is defined as the minimum program length required to generate some realization in a synonymous set, thereby characterizing compact meaning representation. Second, to enable approximate computation, we move from the ideal, uncomputable semantic information distance to a model-based direct estimator of the Normalized Semantic Information Distance (NSID), which uses neural autoregressive models as conditional probability estimators. Finally, through experimental validation and comparative analysis, we show that the NSID estimator suppresses syntactic variance while preserving semantic structure. Empirical results indicate that NSID provides a practical, computable surrogate for semantic distance and improves upon classical syntactic metrics in evaluating cross-representational equivalence. Full article