Correction Term Research Articles (Page 1)

This study investigates the dynamic relationship between carbon dioxide (CO 2 ) emissions, financial development, and economic growth in Oman from 1998 to 2022 within the framework of the Environmental Kuznets Curve (EKC) hypothesis. Using time series data from the World Bank, IMF, and Oman’s National Centre for Statistics and Information, the autoregressive distributed lag (ARDL) model is applied to evaluate both long-run and short-run interactions. Diagnostic tests, including Breusch–Godfrey, Breusch–Pagan–Godfrey, and Ramsey’s RESET, confirm the robustness of the model. The results reveal a cointegrating relationship, with economic growth and financial development exerting significant long-term impacts on CO 2 emissions. The error correction term indicates a 70% annual adjustment towards equilibrium, underscoring the dynamic nature of the linkage. Bidirectional causality is observed between CO 2 emissions, economic growth, and financial development. While economic diversification has contributed to short-term emission reductions, long-term sustainability requires enhanced green investment and renewable energy development. These research data provide fundamental knowledge that Gulf Cooperation Council region governments, along with national decision-makers in Oman, can use to handle economic progress without sacrificing environmental preservation.

Abstract The clustering of galaxies and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys, such as Euclid and LSST. Unfortunately, the complicated relationship between galaxies and matter greatly limits the exploitation of this data. Sophisticated theoretical galaxy bias models – such as the hybrid Lagrangian bias expansion – allow describing galaxy clustering down to scales as small as k = 0.7 h Mpc−1. However, the galaxy-matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy-galaxy lensing. In this work, we propose a way to extend state-of-the-art models of the galaxy-matter cross-power spectrum Pgm(k) (currently only accounting for dark matter) by including a baryonic correction term inferred from the matter component (the suppression Smm(k) = Pmm, full physics/Pmm, gravity only), so that $P_{\rm gm, full \, physics} (k) = \sqrt{S_{\rm mm}} P_{\rm gm, gravity \, only}$. We use the FLAMINGO hydrodynamical simulations to measure the effect of baryons on the galaxy-matter cross-power spectrum and to assess the performance of our model. Specifically, we perform a Bayesian analysis of synthetic data, implementing a model based on BACCO’s hybrid Lagrangian bias expansion (for the nonlinear galaxy bias) and Baryon Correction Model (for the baryon suppression of the matter power spectrum). Ignoring the effect of baryons on the galaxy-matter cross-power spectrum leads to a biased inference of the galaxy bias parameters, while ignoring baryons in both the galaxy-matter and matter-matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1% accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models (k < 0.7 h Mpc−1). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1σ with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.

Background Tourism is a vital component of economic development, particularly in emerging economies like India, where international tourist arrivals contribute significantly to foreign exchange earnings, employment generation, and regional growth. While prior research has explored various determinants of tourism demand, limited empirical studies have assessed the macroeconomic underpinnings of ITA using time series models capable of handling mixed integration orders. This study investigates the short-run and long-run effects of international tourist arrivals on GDP, FDI, and inflation in India. Methods This study uses the Autoregressive Distributed Lag bounds testing approach on annual data spanning from 1995 to 2022. The Augmented Dickey-Fuller (ADF) test was conducted to determine the stationarity properties of the time series data, after which the optimal lag structure for the ARDL model was identified using the Akaike Information Criterion (AIC). The model was built on a time series dataset spanning 1995 to 2022, and the empirical results provide both statistically significant findings and interpretive depth that are relevant to policy and theory. Results The findings of this study confirm a statistically significant and positive relationship between GDP and international tourist arrivals in both the short and long run. The ARDL (1,2,0,2) model demonstrated strong explanatory power (Adjusted R² = 0.9565), and the bounds test confirmed the presence of cointegration among the variables. However, FDI and inflation were found to be statistically insignificant in influencing ITA. The error correction term was negative and empirically significant, indicating that approximately 51% of the disequilibrium adjusts each year toward long-run equilibrium. Conclusion This study highlights GDP as the primary macroeconomic driver of international tourism demand in India, with implications for economic planning and tourism policy. While FDI and inflation were not significant in this model, their potential indirect effects needs further investigation.

This study evaluates the effectiveness of tax reforms on revenue mobilization in Nigeria over the period 1999–2023. Using annual revenue data from the Federal Inland Revenue Service (FIRS), the analysis integrates multiple econometric approaches, including structural break tests, unit root diagnostics, an error correction model (ECM), and ARIMA forecasting. The findings reveal significant structural breaks in 2004, 2011, and 2014, which correspond to major reform episodes and external shocks, indicating that policy changes have had measurable impacts on revenue performance. Stationarity tests confirmed that revenue is integrated of order one, justifying the use of ECM to capture both short-run dynamics and long-run equilibrium relationships. The ECM results highlight a significant and negative error correction term, suggesting that deviations from the long-run path are corrected over time, thereby strengthening the resilience of Nigeria’s fiscal system. Forecast results project sustained revenue growth, with collections expected to surpass ₦15 trillion by 2026, assuming no major disruptions. Collectively, these results provide robust evidence that tax reforms have enhanced Nigeria’s revenue mobilization capacity, though the system remains sensitive to external shocks, particularly oil price fluctuations. The study underscores the importance of sustaining reforms and diversifying the revenue base to ensure long-term fiscal stability.

Engineers and scientists adopt numerical integration to achieve an approximate solution for definite integrals that have no analytic solution. This research focuses on developing some new derivative based quadrature schemes for numerically integrating the integral of Riemann-Stieltjes (Rs-integral) by proposing new schemes which are based on trapezoid-type quadrature. The process of undetermined coefficients has been used for the derivation of proposed schemes. The theoretical derivation and numerical verification of orders of accuracy have been addressed in line with the degrees of precision, and a sufficient improvement has been demonstrated over the existing schemes. The theorems regarding single and mul-tiple use of the suggested schemes in a finite interval have been proved along with the theoretical results on residual terms, both locally and globally. All suggested schemes have been verified to reduce to corresponding variants for the Riemann integral in the case when integrator g(t) = t. Numerical experiments have been performed on all the discussed schemes with the help of MATLAB coding. The experimental results assure the smaller numerical errors by the proposed schemes in the comparison of existing schemes. The obtained results show the efficiency of proposed schemes in light of computational burden and CPU time (in seconds) with reference to the existing quadrature. The proposed work substantially advances the existing knowledge with an addition of derivative-based correction terms in the usual quadrature in a way that the consequent computational costs and execution times are also minimized.

Abstract This study investigates conditional income convergence among 26 European Union member states from 1995 to 2022, focusing on GDP per capita growth and complemented by an analysis of unemployment dynamics. Three advanced heterogeneous panel estimators are employed: the pooled mean group autoregressive distributed lag (PMG-ARDL) and dynamic common correlated effects ARDL (CS-ARDL) to capture linear relationships, and the pooled mean group nonlinear ARDL (PMG-NARDL) to account for potential asymmetric adjustments. These methods address slope heterogeneity, cross-sectional dependence, and nonstationarity. Panel unit-root and cointegration tests confirm long-run relationships, and Dumitrescu–Hurlin tests assess causality. In the linear PMG and CS-ARDL models for GDP per capita growth, inflation and gross capital formation hinder long-run growth, while government spending, labor force participation, and trade openness have positive effects. In the unemployment models, GDP growth, trade openness, and government spending reduce unemployment, whereas inflation and gross capital formation increase it. The nonlinear PMG-NARDL results reveal that the direction of change in variables such as government debt, capital formation, inflation, and trade openness matters, with positive and negative shocks often having different long-run effects. Error correction terms across models indicate stable adjustment, in some cases with damped oscillations. Policy implications show the need to maintain price stability, improve the efficiency of public investment, sustain trade openness, and promote labor force participation alongside prudent debt management. Convergence patterns remain heterogeneous across member states, reflecting differences in structural and policy characteristics.

Reverse docking is a pivotal computational strategy for drug repurposing and polypharmacology studies, yet existing tools often suffer from limitations in throughput, accuracy, and reliance on centralized servers. To overcome these challenges, we present AutoRevDock, an open-source Python toolkit designed to streamline and enhance the reverse docking workflow. Key features include: (1) support for two established docking engines (AutoDock Vina and idock) with a hybrid scoring scheme (Vina_SFCT, combining the Vina score with a scoring function correction term (SFCT)); (2) pre-processed target libraries covering the human proteome and DrugBank pharmacologically active targets; (3) support for custom target libraries and fully automated local execution. Benchmark evaluations demonstrate that idock operates over 40 times faster than AutoDock Vina. For multiple-target drugs, Vina_SFCT outperforms the default scoring function in identifying biologically relevant targets. Furthermore, incorporating protein family information leads to increased hit rates, suggesting enhanced predictive power for real-world applications. By combining robust methodology with user-centric design, AutoRevDock offers a scalable solution for high-throughput target fishing in drug discovery. The toolkit is freely available at https://github.com/AI4Bio-GuoLAB/AutoRevDock.git.

Analytical wake models play a crucial role in assessing wake effects, optimizing wind farm layouts, and enabling active wake control. However, conventional models primarily focus on the far-wake region and show limited accuracy in the near wake, restricting their applicability in modern compact wind farms. To address this, this study proposes a pressure-corrected double-Gaussian analytical wake model to improve prediction accuracy across the entire wake. Time-averaged wakes under different thrust coefficients and yaw angles are simulated using large-eddy simulation coupled with an actuator disk model with rotation. Based on the near-wake pressure distribution obtained from large-eddy simulation (LES), a pressure correction term is incorporated into the momentum conservation equation to construct the pressure-corrected double-Gaussian wake model. An analytical expression for the position of the minimum velocity in the wake is derived from mass conservation. The model requires only the wake expansion coefficient as an adjustable parameter. Comparison with LES data indicates that, relative to the uncorrected double-Gaussian model, the normalized root mean square error of wake velocity in the near-wake region, particularly near the rotor, is reduced from approximately 40% to below 13% of the inflow wind speed. The model successfully captures wake velocity distributions across different turbulence intensities, yaw angles, and tip speed ratios, demonstrating strong robustness and general applicability.

SSR1M: A stochastic SR1 method with momentum acceleration for non-Convex optimization.

Abstract The initial-to-final-state inverse problem consists in determining a quantum Hamiltonian assuming the knowledge of the state of the system at some fixed time, for every initial state. This problem was formulated by Caro and Ruiz and motivated by the data-driven 
prediction problem in quantum mechanics. Caro and Ruiz analysed the question of uniqueness for Hamiltonians of the form -Δ + V with an electric potential V = V(t, x) that depends on the time and space variables. In this context, they proved that uniqueness holds in dimension n > 1 whenever the potentials are bounded and have super-exponential decay at infinity. Although their result does not seem to be optimal, one would expect at least some degree of exponential decay to be necessary for the potentials. However, in this paper, we show that by restricting the analysis to Hamiltonians with time-independent electric potentials, namely V = V(x), uniqueness can be established for bounded integrable potentials exhibiting only super-linear decay at infinity, in any dimension n > 1. This surprising improvement is possible because, unlike Caro and Ruiz's approach, our argument avoids the use of complex geometrical optics (CGO). Instead, we rely on the construction of stationary states at different energies---this is possible because the 
potential does not depend on time. These states will have an explicit leading term, given by a Herglotz wave, plus a correction term that will vanish as the energy grows. Besides the significant relaxation of decay assumptions on the potential, the avoidance of CGO solutions is important in its own right, since such solutions are not readily available in more complicated geometric settings.

Shallow gas is an unconventional natural gas resource with great potential and has received growing attention recently. Accurate estimation of gas saturation is crucial for reserves assessments and for development program formulations. However, such reservoirs are characterized by weak diagenesis, a high clay content, and low resistivity. These properties pose significant challenges for saturation evaluations. To address the challenge of insufficient accuracy in evaluating the saturation of gas-bearing reservoirs, we propose an acoustic-based saturation evaluation method. In this study, a shallow unconsolidated rock physics model is first constructed to investigate the effect of variations in the gas saturation on elastic wave velocities. The model especially considers the patchy distribution of fluids within pores. In addition, we propose an iterative algorithm based on the updated relationship between porosity and gas saturation by introducing a correction term for the saturation to the density porosity, and successfully apply it to the logging data collected from the shallow gas reservoirs in the Pearl River Mouth Basin of the South China Sea. It is evident from the results that the saturation derived from the array acoustic logs is comparable to that obtained from the resistivity logs, with a mean absolute error of less than 6%. Additionally, it is also consistent with the drill stem test (DST) data, which further verifies the validity and reliability of this method. This study provides a novel non-electrical method for estimating the saturation of shallow gas reservoirs, which is essential to promote the evaluation of unconsolidated sandstone gas reservoirs.

The random-phase approximation (RPA) formulated within the adiabatic connection fluctuation-dissipation framework is a powerful approach to compute the ground-state energies and properties of molecules and materials. Its overall underbinding behavior can be effectively mitigated by a simple correction term, called renormalized single excitation (rSE) correction. Analytical gradient calculations of the RPA energy have become increasingly available, enabling structural relaxations and even molecular dynamics at the RPA level. However, such calculations at the RPA+rSE level have not been reported, due to the lack of the rSE analytical gradient. Here, we present the first formulation and implementation of the analytical gradients of the rSE energy with respect to the nuclear coordinates within an atomic-orbital basis set framework, which allows us to assess the performance of RPA+rSE in determining the molecular geometries and energetics. It is found that the slight overestimation behavior of RPA for small covalently bonded molecules is strengthened by rSE, while such behavior for molecules bonded with purely dispersion interactions is corrected. We further applied the approach to the water clusters and found that the energy difference between the low-energy isomers of water hexamers is almost unchanged when going from RPA to RPA+rSE geometries. For the bigger WATER27 test set, using the RPA+rSE geometries instead of the RPA ones leads to a slight reduction of the mean absolute error of RPA+rSE from 0.91 to 0.70 kcal/mol, at the complete basis set.

A vast number of research articles are published as a result of the enormous expansion in scientific study. An effective search from this collection of articles is made possible by using the correct search terms. Novice researchers find it challenging to search for articles that are pertinent to their areas of interest and to select the relevant keywords. Numerous studies suggest that optimization algorithms are effective in improving topic identification and detection. This provides researchers a valuable tool to help them traverse the deluge of research publications and keep abreast of the most recent developments in their field. By combining a TV-Tree (Telescopic Vector Tree) and Hybrid BERT (Bidirectional Encoder Representations from Transformers) with EPO (Emperor penguin optimization) enhanced NSGA - II (Nondominated Sorting Genetic Algorithm-II), this paper suggests a novel method for identifying research topics. The Hybrid BERT model combines the strengths of BERT and other machine learning algorithms to improve topic detection accuracy, while the TV-Tree based system provides an efficient and scalable framework for organizing and retrieving research topics. This method seeks to attain state-of-the-art performance in research topic detection by utilizing the advantages of both approaches, allowing for more effective and efficient categorization and retrieval of research papers. In order to determine the most important research subjects across a range of disciplines, this study suggests a revolutionary topic identification technique. This system uses sophisticated algorithms to examine enormous volumes of data in order to identify areas of interest and trending themes. Experimental results demonstrate the effectiveness of this approach in detecting prominent research topics, leading to potential accuracy improvements and significantly outperforming baseline models. This provides valuable insights for researchers, academics, and industry professionals seeking to stay updated on the latest developments in their fields.

Adhatoda vasica, an herbal bronchodilator used for treating respiratory ailments, requires accurate nutrient profiling to support health benefits. In this work, the optical emission spectra from laser-generated plasma of Adhatoda vasica leaves confirmed the presence of Ca, K, Mg, Na, Fe, Al, Si, Sr, Zn, Rb, and Mn. The application of an external electric field across the laser-produced plasma not only significantly enhanced the intensities of the detected lines but also resulted in the appearance of additional lines of trace elements with a good signal-to-noise ratio. To improve the accuracy of elemental quantification in the sample, an integrated approach was employed by coupling the multi-element Saha-Boltzmann plot (ME-SBP) with calibration-free laser-induced breakdown spectroscopy (CF-LIBS). The key aspect of this approach is that the electron temperature calculated from the ME-SBP method can be effectively used for slope fixation of the Boltzmann plot in CF-LIBS, ensuring correct intercept estimation. The intercept is highly sensitive to the slope of the Boltzmann plot, as even minor changes in the spectral intensity can alter the slope, causing a shift in the intercept. This shift can result in temperature error and inaccuracy in the calculation of elemental composition. Incorporating ME-SBP, which involves multi-element correction terms and also reduces the reliance on intensity error, resulted in a more reliable determination of the electron temperature with a mean relative error of 3% and improved R2 of 0.992, leading to a more precise elemental quantitative analysis.

The dynamic interaction between fiscal and monetary policies plays a crucial role, especially in economies characterized by Fiscal Dominance and Monetary Policy Constraints, such as Saudi Arabia’s fixed exchange rate regime. Fiscal policy, encompassing public revenues and spending, alongside monetary policy, which manages the money supply, are the primary macroeconomic tools aimed at achieving price stability. Given the limitations of linear models in capturing complex economic behaviors, this study investigates the asymmetric effects of these policy instruments on inflation dynamics under structural constraints. Utilizing the Nonlinear Autoregressive Distributed Lag (NARDL) model on data ranging from 1980 to 2021 obtained from the Saudi Central Bank (SAMA), the salient findings are as follows: Confirmation of Asymmetry: The model reveals significant asymmetric effects of policy shocks on inflation, where the impact of policy increases differs markedly from decreases, confirming the need for a nonlinear framework. Fiscal Dominance and Nonlinearity: Although the currency peg attenuates the statistical significance of some long-run individual policy shocks—consistent with the monetary constraint—the short-run impacts are strong and highly nonlinear. Fiscal policy variables show mixed, time-dependent asymmetric influences. Severe Monetary Policy Constraints: Monetary policy exhibits complex short-run asymmetry. Notably, a liquidity reduction (negative money supply shock) leads to an unexpected sharp rise in inflation, highlighting the significant limitations and potentially destabilizing effects of monetary policy actions under the fixed exchange rate system. Equilibrium and Stability: A significant Error Correction Term (ECM) confirms a stable long-run equilibrium at the 1% level, with a moderate adjustment speed back to equilibrium estimated at approximately two years. This study emphasizes the critical importance of employing nonlinear models and considering the timing and direction of policy shocks to better understand and manage inflation within structurally constrained, fiscally dominant economies like Saudi Arabia.

To address the problem of the degraded positioning accuracy of the long-baseline undifferenced network RTK (URTK) under extreme space weather conditions, herein, we propose a user-side atmospheric delay estimation strategy based on the undifferenced network RTK concept to enhance positioning performance in geomagnetic storm environments. First, an ambiguity-resolution model that jointly estimates atmospheric error parameters is used to fix the carrier-phase integer ambiguities for long-baseline reference stations. The accurately fixed inter-station ambiguities are then linearly transformed to recover station-specific undifferenced integer ambiguities; undifferenced observation errors at each reference station are computed to generate corresponding undifferenced correction terms. Lastly, recognizing that ionospheric delays vary sharply during geomagnetic storms and can severely compromise the availability of regional undifferenced correction models, we estimate the residual atmospheric parameters on the user side after applying regional corrections. Experimental results show that the server side is not significantly impacted during geomagnetic storms and can continue operating normally. On the user side, augmenting the solution with atmospheric parameter estimation effectively improves positioning availability. Under strong geomagnetic storms, the proposed mode improves user-station positioning accuracy by 63.7%, 60.7%, and 64.4% in the east (E), north (N), and up (U) components, respectively, relative to the conventional user-side solution; under moderate storm conditions, the corresponding improvements are 16.7%, 10.0%, and 11.1%.

In this paper, we intend to define complexity related to the self-gravitating composition in a new way by assuming the formalism of [Formula: see text] theory. The irrotational spherically static space-time associated with a spatially anisotropic fluid is contemplated in this regard along with the orthogonal decomposition of Riemann tensor by assuming the formalism related to modified field equations and the conservation law. We perceive [Formula: see text] as a complexity factor out of all the determined structural scalars, which includes the features of anisotropic pressure and the compelling appearance of the energy density. The correction terms related to [Formula: see text] theory are essential for deriving some particular results for the Tolman mass, Weyl scalar, and complexity factor. Furthermore, the scalars determined in our case study are employed to derive the result for the complexity factor and the restriction of the diminishing complexity is considered to calculate the solutions related to certain models. A self-gravitational fluid with non-uniform energy density and anisotropic pressure exhibits ultimate complexity. However, these fluids might have no complexity if the impacts of anisotropic pressure and non-uniformity in energy density are canceled due to the appearance of correction values associated with [Formula: see text] theory.

We continue our work [ arXiv:2403.07628 ] on asymptotic expansions at the soft edge for the classical n-dimensional Gaussian and Laguerre random matrix ensembles. By revisiting the construction of the associated skew-orthogonal polynomials in terms of wave functions, we obtain concise expressions for the level densities that are well suited for proving asymptotic expansions in powers of a certain parameter [Formula: see text]. In the unitary case, the expansion for the level density can be used to reconstruct the first correction term in an established asymptotic expansion of the associated generating function. In the orthogonal and symplectic cases, we can even reconstruct the conjectured first and second correction terms.

This paper analyzes the impact of exchange rate fluctuations on Nigeria’s export performance between 2012 and 2024. Annual time series data on exchange rates were obtained from the Central Bank of Nigeria (CBN), while data on exports were sourced from the National Bureau of Statistics. To test both short-run and long-run relationships between the variables, the study employed the Vector Error Correction Model (VECM). Evidence from the analysis shows that exchange rate fluctuations have mixed effects on exports in the short run and are statistically insignificant. However, in the long run, there is a negative and statistically significant relationship between the two variables (0.0020 & 0.039 < 0.05) with p-values of the error correction term and cointegrating equation coefficient both being less than 0.05. The model showed an R² of 0.269, with post-estimation diagnostics indicating no presence of heteroskedasticity or serial autocorrelation. The study concludes that persistent fluctuations in the exchange rate can have severe implications for the Nigerian economy, given the significantly negative impact on exports in the long run. In light of these findings, the research recommends that the Nigerian government intensify efforts aimed at diversifying the economy to boost non-oil exports and adopt long-term policies to stabilize the exchange rate. These interventions are critical for achieving exchange rate stability and growth in the country’s export sector.

Liver steatosis is now the politically correct term for excessive fat in the liver (fatty liver). Its incidence and prevalence, in lockstep with diabetes and obesity, continue to climb to over 2 billion persons worldwide. It is the most common cause of chronic liver disease and the leading cause of liver-related morbidity and mortality. This treatise, resulting from a selection of PubMed literature of relevant steatosis studies since the nomenclature change in 2023, will include the change in the names for the steatotic liver entities, documenting the contribution of metabolic dysfunctions (obesity, diabetes, hypertension, and dyslipidemias) to the pathology of insulin resistance and alcohol in the evolution of these liver diseases. The various modalities for measuring the degree of fat, fibrosis, and cirrhotic scarring of the liver will be discussed, followed by a review of the mortality implications of the subcategories of liver steatosis, including intrahepatic cirrhosis and malignancy, extrahepatic malignancies, and cardiovascular disease. Finally, a review of treatments to address these entities will be briefly reviewed.