Search Results (1,308)

This study examines the dynamic interactions between oil price shocks, monetary policy, and non-oil output in Saudi Arabia using Vector Autoregressive Model (VAR), and quarterly data spanning 2010: Q1–2025: Q3. The study aims to provide policy-relevant insights through which external oil price shocks and domestic monetary policy shocks affect inflation and non-oil economic activity in the context of Saudi Arabia’s structural transformation under Vision 2030. The results show that global oil prices behave largely as exogenous shocks, with limited feedback from domestic monetary conditions, implying that monetary policy effectiveness operates primarily through inflation and domestic demand channels rather than through oil prices directly. The findings underscore the importance of gradual and predictable monetary tightening, coordinated with fiscal and macroprudential policies, to mitigate the indirect spillovers of oil price volatility on the non-oil sector. While monetary policy plays a stabilizing role by containing inflation and supporting macroeconomic balance, sustaining diversification and non-oil growth under Vision 2030 requires complementary measures, including targeted credit support, financial market deepening, and structural reforms that enhance productivity and private-sector investment. Full article

Multi-task learning (MTL) is essential for satellite internet systems requiring simultaneous optimization of beam management, interference mitigation, resource allocation, and traffic prediction. However, existing evaluation methods rely predominantly on external performance metrics, neglecting internal dynamics governing task interactions. We propose TDS-Mamba (Task-Aware Decoupled State-Space Model), integrating selective state-space models with task-specific modulation for satellite networks. Our contributions include: (1) Task-Aware Decoupled S6 (TA-DS6) with hypernetwork-generated task-conditioned projection matrices; (2) Shared–Private State Decomposition disentangling cross-task representations from task-specific features; (3) Value-at-Risk (VaR) Gating for risk-sensitive optimization under varying orbital conditions; and (4) an internal diagnostic framework with Task-Specific Entropy and Interference Coefficient metrics. Experiments on LEO satellite constellation benchmarks show consistent improvements over the selected baselines and provide enhanced interpretability of multi-task dynamics via internal diagnostics. Full article

Missense single nucleotide variants (SNVs) represent one of the most common forms of genetic variation and account for a substantial proportion of variants of uncertain significance in clinical databases. Accurate computational classification of these variants remains an important challenge in precision medicine and genomic research. In this study, we present PathoPredictor, an interpretable machine-learning framework designed to distinguish pathogenic from benign missense variants using curated clinical variant data and functional annotations. High-confidence variants were obtained from the November 2023 ClinVar release and annotated using dbNSFP v5.1 (GRCh37). After data filtering, imputation, and normalization, 59,302 expert-reviewed missense variants were retained for model development. Six machine-learning algorithms were evaluated under identical cross-validation conditions applied to the training set. Among the evaluated models, LightGBM demonstrated the strongest overall performance and was selected as the final PathoPredictor classifier, achieving a mean ROC–AUC of 0.93 ± 0.004, accuracy of 0.90 ± 0.006, and Matthew’s correlation coefficient of 0.80 ± 0.008 across five cross-validation folds. Model interpretability was examined using SHAP (SHapley Additive exPlanations), enabling both global feature ranking and variant-level explanation of predictions. Temporal validation using ClinVar variants submitted after November 2023 showed consistent predictive performance on previously unseen submissions within the same database ecosystem (ROC–AUC = 0.91). While the framework demonstrates strong discrimination and structured interpretability, potential limitations include training data bias and partial circularity associated with the inclusion of existing meta-predictors. Overall, PathoPredictor provides a reproducible and interpretable computational framework for integrating functional annotations in missense variant prioritization, supporting research and genomic analysis workflows. Full article

Coastal seas around Aotearoa, New Zealand, are among the least observed parts of the global ocean, limiting our ability to monitor and forecast marine conditions. The Moana Project addresses this gap with a new observing system that includes temperature sensors mounted on commercial fishing gear—the Mangōpare fishing vessel network. This study presents the first evaluation of New Zealand’s operational ocean 4D-Var data assimilation system that incorporates these fishing vessel (FV) observations into a regional ROMS model. Using just over one year of operational forecasts, we show that FV temperature profiles significantly improve subsurface temperature representation, especially in coastal regions where satellite products have warm biases or miss key features such as upwelling and mesoscale variability. Assimilation of FV data reduces background temperature biases throughout the upper ocean and enhances forecast skill in areas influenced by major currents and dynamic coastal processes. We also identify sensitivity to periods of missing satellite sea surface temperature, which can lead to overfitting of the available observations. Overall, the results demonstrate that FV observations provide essential subsurface information and can substantially strengthen operational coastal ocean forecasting systems. Full article

Background: X-linked immunodeficiency with magnesium defect, Epstein–Barr virus (EBV) infection, and neoplasia (XMEN) disease is a rare inborn error of immunity caused by loss-of-function mutations in MAGT1, leading to impaired N-linked glycosylation. Although chronic EBV viremia is a hallmark of XMEN disease, the mechanisms underlying its marked clinical heterogeneity remain poorly understood. Methods: We performed an in-depth clinical, immunological, and genetic characterization of two siblings carrying a pathogenic MAGT1 variant (c.369_370insCC; p.Gly124fs), validated and deposited in ClinVar (SCV007293792). Assessments included whole-exome sequencing, multiparametric flow cytometry focusing on NKG2D expression, and longitudinal clinical follow-up. Results: Despite shared absence of NKG2D expression, the siblings exhibited strikingly divergent phenotypes. One sibling developed progressive neurodegeneration with central nervous system atrophy. The other presented with a complex immuno-hematologic phenotype, including EBV-positive Hodgkin lymphoma, recurrent autoimmune cytopenias, and lymphoma-associated thrombotic microangiopathy, representing a novel clinical association in XMEN disease. Comparative immunophenotyping revealed shared defects in B-cell maturation but distinct T-cell differentiation patterns. To contextualize neurological variability, we propose a descriptive, hypothesis-generating three-category conceptual classification comprising early-onset neurodevelopmental forms, adult-onset neurodegenerative manifestations, and secondary immune-mediated or vascular involvement of the nervous system. Conclusions: These findings demonstrate profound intrafamilial heterogeneity in XMEN disease and suggest a model in which modifier-sensitive factors influence organ-specific disease expression. The observation of lymphoma-associated thrombotic microangiopathy and the proposed descriptive neurological classification provide a conceptual framework that may help guide tailored, multidisciplinary surveillance beyond the primary genetic defect. Full article

This study investigates the forward discount puzzle by examining the dynamic relationships among excess returns arising from interest rate parity deviations, interest rate differentials, and the USD/JPY exchange rate. The empirical analysis employs correlation analysis, the Autoregressive Distributed Lag (ARDL) cointegration test, and variance decomposition together with impulse response functions derived from a Toda–Yamamoto augmented Vector Autoregressive (VAR) model, using data spanning January 2001 to September 2025. The correlation results indicate that the spot exchange rate is negatively related to both the swap rate and the interest rate differential. Impulse response analysis shows that the USD/JPY rate responds positively to swap rate shocks in the medium to long run, while responding negatively to interest rate differential shocks in the short run. Variance decomposition results are consistent with the impulse response analysis and underscore the dominant bilateral linkage between the exchange rate and the swap rate. The long-run ARDL estimates further reveal that the swap rate is positively associated with dollar appreciation, whereas both the interest rate differential and relative output are negatively related. Overall, although short-run arbitrage appears temporarily, the cointegration and dynamic results provide robust evidence that the forward discount puzzle persists for a substantial period rather than interest rate parity holding. Full article

Forest soil constitutes a critical reservoir within terrestrial carbon pools. Understanding the dynamics of soil organic carbon (SOC) in coniferous forests is crucial for enhancing ecosystem carbon sequestration capacity, yet systematic quantification of SOC characteristics and their driving factors remains limited across critical bioclimatic zones. This study examined SOC features in topsoil and driving factors across eight representative coniferous forest types in Longnan—an ecologically significant transition region of northwestern China. SOC concentrations ranged from 31.76 to 80.86 g·kg⁻¹, where Abies fargesii var. faxoniana exhibited significantly higher concentrations than other conifers. Fungal necromass dominated SOC formation (29%–45% contribution) versus minimal bacterial necromass inputs (3%–5%). Redundancy analysis identified that soil total nitrogen, C/N ratio, and tree evenness showed significant correlations with SOC concentrations and their fractions. Partial least squares path modeling revealed that tree species exerted a direct positive impact on soil total nitrogen, while having an adverse effect on soil pH. Lower soil pH and higher total nitrogen were associated with higher microbial-derived carbon and SOC concentrations. In contrast, plant-derived carbon exerted no direct influence on SOC concentrations, operating exclusively through microbial-derived carbon pathways. These results indicated that coniferous tree species-induced shifts in soil total nitrogen and pH facilitate the accumulation of microbial necromass carbon, rather than plant residues, and thus promote SOC sequestration. A. fargesii var. faxoniana can be regarded as a key strategic tree species for SOC sequestration and sustainable forest management, and its cultivation should be prioritized due to improvements in total nitrogen and microbial-derived carbon. Full article

Plants have a remarkable ability to regenerate tissues and organs from single cells, a property that underpins in vitro protoplast regeneration. Efficient protoplast-to-plant regeneration remains a major bottleneck for genome engineering in many crop species, including broccoli (Brassica oleracea var. italica). In this study, we established and optimized a regeneration system for broccoli cv. Claremont by evaluating enzyme composition, light quality, and culture media at successive stages of development. Among the tested enzyme mixtures, 1.5% Cellulase R-10 combined with 0.4% Macerozyme R-10 yielded the highest protoplast viability and recovery. Alginate-embedded protoplasts were cultured under control (dark), blue, and red + far-red LED illumination. Red + far-red treatment significantly enhanced microcolony formation, plating efficiency, and shoot regeneration compared with blue light, whereas blue illumination consistently reduced regenerative performance. The inclusion of activated charcoal in the regeneration medium further increased shoot production. The generalized linear model analyses identified light quality as a significant predictor of both shoot number and regeneration. To our knowledge, this study provides one of the first demonstrations of LED-assisted enhancement of protoplast regeneration in broccoli. The optimized protocol enables whole-plant recovery within approximately 5 months and offers a practical platform for CRISPR-based genome editing and advanced breeding applications in B. oleracea. Full article

This study examines the impact of the Russia–Ukraine war on crude oil tail risk using the Conditional Autoregressive Value at Risk (CAViaR) framework. We analyzed 2364 daily observations of West Texas Intermediate (WTI) crude oil futures spanning 1 January 2015 to 11 December 2023, thereby capturing both the pre-war period and the conflict regime. To operationalize the geopolitical shock, we identify four theoretically grounded event dates (21 February, 24 February, 11 May, and 15 June 2022) associated with military escalation and energy-supply disruptions, and incorporate them as exogenous dummy variables. Methodologically, we implement a two-step approach. First, we estimate 1-day Value at Risk (VaR) at the 5% and 1% levels using four alternative CAViaR specifications (Adaptive, Symmetric, Asymmetric, and Indirect GARCH(1,1)) within a rolling-window framework to capture the dynamic evolution of tail risk. Second, we regress the resulting VaR series on geopolitical-event indicators to quantify the marginal effect of war-related developments on downside risk. The empirical results show tail risk increases in oil-market after the most important geopolitical events in all the model specifications across the market characteristics. The Indirect GARCH(1,1) CAViaR model exhibited the highest sensitivity, producing event coefficients of 0.795 (5% VaR) and 0.710 (1% VaR), both significant at the 1% level. Our adaptive specification has magnitudes that are even higher at the extreme tail (2.002 at 1% VaR), further supporting increased vulnerability during periods of escalation in conflict. Evidence from the asymmetric model would also indicate stronger market response to unfavorable news, in line with loss-sensitive investor behavior. In sum, the outcomes indicate that the Russia–Ukraine war considerably elevated the downside risk of crude oil markets and that geopolitical events have economically and statistically significant effects on the tail dynamics. Incorporating event-based geopolitical indicators in the framework of CAViaR, contributes to the literature in energy-market risk modeling and applies practical information to investors, risk managers, and policymakers operating under a dynamic environment characterized by geopolitical uncertainty. Full article

Amid frequent external shocks and deepening market linkages, the information-transmission structure of financial markets is more prone to phase-specific abrupt changes, creating a need for real-time monitoring methods. This study develops an online framework to track financial information-flow networks and to provide early warnings of structural changes under exogenous shocks. Methodologically, information-flow networks are constructed from return spillovers using the Diebold–Yilmaz framework. An Exponential Random Graph Model is then employed to quantify how exogenous variables affect edge formation. Statistical process control methods, namely the Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA), are introduced to online monitoring of exogenous-effect coefficients. The simulation study uses simulated data to assess whether the two charts are properly calibrated and sensitive to alarms. The empirical study uses Shanghai Stock Exchange (SSE) 180 constituent stocks and exogenous variables—7-day Fixing Repo Rate (FR007), M2 growth rate (M2), the China Economic Policy Uncertainty Index (CEPU), and the Global Economic Policy Uncertainty Index (GEPU) over 2011–2025. The results indicate that both charts achieve the target in-control average run length, and detection accelerates with shock magnitude; FR007 is generally negative, M2 is positive, and uncertainty measures vary strongly over time; monitoring reveals shock clustering and long-term drift, implying both shock amplification and structural drift in the information-flow network. Practically, the framework provides an implementable warning tool for tracking shock amplification, supporting timely risk management. Full article

Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool. Full article

Indonesia’s financial system is bank-centric, with banks managing approximately 78% of the nation’s financial assets; therefore, the effectiveness of monetary policy transmission depends on banks’ responsiveness to the central bank’s interest rate policy (the BI Rate). However, a policy-relevant anomaly persists: deposit rate pricing is more strongly anchored to the Deposit Insurance benchmark (IDIC Rate) than to the BI Rate. This study argues that this research is significant because it identifies a “Dual Benchmark System” that traditional single-anchor models fail to address, representing a critical friction in emerging market transmission. This study examines this dual-benchmark paradigm and the associated asymmetric risks using a panel VAR with a Generalized Impulse Response Function (GIRF) on quarterly data for 63 commercial banks from 2010 to 2024. The results indicate that IDIC Rate shocks have a larger and more persistent effect on deposit rates than BI Rate shocks, generating asymmetric transmission risks. This dominance creates a structural “price ceiling” that keeps funding costs high, ultimately raising lending rates for borrowers and distorting deposit growth rates. Furthermore, this analysis reveals that external policy signals are far more influential than internal financial performance. This suggests that under the Basel III framework and prevailing financial regulations, banks prioritize liquidity compliance and safety net protection over internal operational efficiency. Macroeconomic shocks remain weaker than policy shocks and dissipate more quickly. This finding reveals a potential systemic coordination risk, implying an urgent need for tighter policy coordination between the Central Bank and the IDIC to reduce structural frictions, maintain transmission effectiveness, and protect long-term financial stability. Full article

This paper explores how climate risks affect the spillover between cryptocurrency and precious metals markets, given the increased interplay between climate-related threats and financial markets. The dynamic spillovers of the cryptocurrency and precious metals markets are analyzed initially by the TVP-VAR-DY model. Subsequently, it investigates how transition risk and physical risk affect these spillovers using quantile Granger causality (QGC), quantile–quantile regression (QQR), and wavelet quantile regression (WQR), with a particular focus on the differences in the results across the pre- and during-COVID-19 periods. The results show that climate risks significantly affect the spillovers in the cryptocurrency and precious metals markets, and these effects are heterogeneous in nature. Specifically, it is found that, under normal market conditions, both TRI and PRI have the effect of strengthening the spillovers. However, in extreme market states, their influences weaken because of investor distraction. In addition, at extremely low levels of climate risk, both TRI and PRI tend to intensify spillovers, and the impact of PRI is more pronounced. Moreover, during the COVID-19 crisis, climate risks seemed to have a limited effect in the short run, while they were more sustainable in the long run. These findings offer crucial implications for mitigating climate-related systemic risks and fostering a resilient, sustainable financial ecosystem amidst global decarbonization efforts. Full article

During the vacuum arc remelting (VAR) process, external convective cooling conditions exert a significant influence on both the heat transfer behavior and solidification microstructure of ingots. In this research, Φ 480 mm IN718 alloy VAR ingots were investigated. A heat transfer model for the VAR mold was established based on the equivalent thermal resistance method to analyze the effects of varying external convective cooling conditions on overall heat transfer performance. Industrial-scale VAR experiments were conducted at different cooling water flow velocities (0.48, 0.73 and 1.30 m/s) to assess how external cooling affects molten pool morphology and microstructure evolution. The results indicate that cooling water flow velocity is the primary factor affecting the heat transfer performance of the VAR mold. Increasing the flow velocity significantly enhances radial heat transfer capability while exerting a relatively limited effect on axial heat transfer. Furthermore, as the cooling water flow velocity increases, the molten pool depth decreases markedly, the pool morphology becomes shallower and more symmetric, and the ingot cooling rate is enhanced. Consequently, dendrite coarsening is effectively suppressed, resulting in a significant reduction in secondary dendrite arm spacing. Specifically, when the flow velocity increases from 0.48 to 1.30 m/s, SDAS decreases by 30.4% at the center, 31.0% at R/2, and 26.5% at the edge, and the SDAS-derived equivalent cooling rate (GR) increases from 6.53–18.25 K/min to 19.41–46.01 K/min across the three representative radial locations. A significant enhancement in the metallurgical quality of the VAR ingot is achieved. Full article

This paper examines the short-run transmission of monetary policy to bank credit granted to the non-financial corporate sector in Morocco, a bank-based emerging economy. Using monthly macro-financial data over the period of 2014–2024, the study estimates a reduced-form VAR model to analyze the dynamic interactions between the policy rate, bank credit, banks’ holdings of sovereign securities, credit risk indicators, and short-term market spreads. Impulse response functions and forecast error variance decompositions indicate that a one-standard-deviation monetary policy shock is associated with a small and short-lived response of non-financial corporations bank credit at a monthly horizon, accounting for only a limited share of its forecast error variance, while the same shock is more strongly reflected in market spreads and banks’ balance-sheet reallocations toward sovereign assets, alongside temporary movements in credit risk indicators. Overall, these results are consistent with a reduced-form transmission pattern in which monetary policy appears to affect bank credit primarily through indirect financial channels related to risk perception, portfolio reallocation, and balance-sheet management, rather than through immediate changes in aggregate credit volumes. This interpretation is conditional on the VAR specification and short-run horizon considered, and suggests an attenuation of the interest rate–credit channel in a bank-dominated emerging economy, rather than evidence of a structural breakdown of monetary transmission. Full article